FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Qiu, PY
   Gao, JL
   Yu, L
   Lu, F
AF Qiu, Peiyuan
   Gao, Jianliang
   Yu, Li
   Lu, Feng
TI Knowledge Embedding with Geospatial Distance Restriction for Geographic
   Knowledge Graph Completion
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE geographic knowledge graph; geographic knowledge embedding; knowledge
   graph completion; geographic relation triplet
AB A Geographic Knowledge Graph (GeoKG) links geographic relation triplets into a large-scale semantic network utilizing the semantic of geo-entities and geo-relations. Unfortunately, the sparsity of geo-related information distribution on the web leads to a situation where information extraction systems can hardly detect enough references of geographic information in the massive web resource to be able to build relatively complete GeoKGs. This incompleteness, due to missing geo-entities or geo-relations in GeoKG fact triplets, seriously impacts the performance of GeoKG applications. In this paper, a method with geospatial distance restriction is presented to optimize knowledge embedding for GeoKG completion. This method aims to encode both the semantic information and geospatial distance restriction of geo-entities and geo-relations into a continuous, low-dimensional vector space. Then, the missing facts of the GeoKG can be supplemented through vector operations. Specifically, the geospatial distance restriction is realized as the weights of the objective functions of current translation knowledge embedding models. These optimized models output the optimized representations of geo-entities and geo-relations for the GeoKG's completion. The effects of the presented method are validated with a real GeoKG. Compared with the results of the original models, the presented method improves the metric Hits@10(Filter) by an average of 6.41% for geo-entity prediction, and the Hits@1(Filter) by an average of 31.92%, for geo-relation prediction. Furthermore, the capacity of the proposed method to predict the locations of unknown entities is validated. The results show the geospatial distance restriction reduced the average error distance of prediction by between 54.43% and 57.24%. All the results support the geospatial distance restriction hiding in the GeoKG contributing to refining the embedding representations of geo-entities and geo-relations, which plays a crucial role in improving the quality of GeoKG completion.
C1 [Qiu, Peiyuan; Gao, Jianliang; Lu, Feng] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.
   [Gao, Jianliang; Lu, Feng] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Yu, Li] Chinese Acad Sci, Natl Sci Lib, Beijing 100190, Peoples R China.
   [Lu, Feng] Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Fujian, Peoples R China.
   [Lu, Feng] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China.
RP Lu, F (通讯作者)，Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.; Lu, F (通讯作者)，Univ Chinese Acad Sci, Beijing 100049, Peoples R China.; Lu, F (通讯作者)，Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Fujian, Peoples R China.; Lu, F (通讯作者)，Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China.
EM qiupy@lreis.ac.cn; gaojl@lreis.ac.cn; yul@mail.las.ac.cn;
   luf@lreis.ac.cn
OI Yu, Li/0000-0002-4374-8743
FU National Natural Science Foundation of China [41631177, 41801320]
FX This research was supported by the National Natural Science Foundation
   of China (Grant No. 41631177, Grant No. 41801320). Their supports are
   gratefully acknowledged. We also thank the anonymous referees for their
   helpful comments and suggestions.
CR Bengio Y, 2001, ADV NEUR IN, V13, P932
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chi Y, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10040995
   Diestel R., 2018, GRAPH THEORY
   Dong L, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P260
   Feng J., 2016, COLING, P641
   Galarraga L.A., 2013, P 22 INT C WORLD WID, P413, DOI DOI 10.1145/2488388.2488425
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Jiang T., 2016, P 2016 C EMP METH NA, P2350, DOI DOI 10.18653/V1/D16-1260
   Khan A., 2013, P 1 ACM SIGSPATIAL I
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin YC, 2015, ADV SOC SCI EDUC HUM, V39, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Liu K, 2017, ISPRS INT J GEO-INF, V6, DOI 10.3390/ijgi6110321
   Mikolov T., 2013, ADV NEURAL INFORM PR, V2, P3111, DOI DOI 10.1162/JMLR.2003.3.4-5.951
   ROBBINS H, 1951, ANN MATH STAT, V22, P400, DOI 10.1214/aoms/1177729586
   Shi J, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2299, DOI 10.1145/3132847.3133119
   Uyar A, 2015, ONLINE INFORM REV, V39, P197, DOI 10.1108/OIR-10-2014-0257
   Wang Q., 2015, P 24 INT JOINT C ART
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   West R, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P515, DOI 10.1145/2566486.2568032
   Wu YR, 2018, REPRESENTATION LEARNING FOR NLP, P132
   Xiao H., 2015, COMPUTER SCI
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Xiong CY, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1271, DOI 10.1145/3038912.3052558
   Yu L, 2018, INT J DIGIT EARTH, V11, P845, DOI 10.1080/17538947.2017.1359688
NR 30
TC 5
Z9 7
U1 10
U2 26
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2220-9964
J9 ISPRS INT J GEO-INF
JI ISPRS Int. J. Geo-Inf.
PD JUN
PY 2019
VL 8
IS 6
DI 10.3390/ijgi8060254
PG 23
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA II6MC
UT WOS:000475307000012
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Montalvan-Burbano, N
   Velastegui-Montoya, A
   Gurumendi-Noriega, M
   Morante-Carballo, F
   Adami, M
AF Montalvan-Burbano, Nestor
   Velastegui-Montoya, Andres
   Gurumendi-Noriega, Miguel
   Morante-Carballo, Fernando
   Adami, Marcos
TI Worldwide Research on Land Use and Land Cover in the Amazon Region
SO SUSTAINABILITY
LA English
DT Article
DE land cover; land use; Amazon; bibliometric analysis; knowledge mapping;
   cocitation; co-occurrence
ID TIME-SERIES; BRAZILIAN AMAZON; BIBLIOMETRIC ANALYSIS; AGRICULTURAL
   INTENSIFICATION; SUSTAINABLE DEVELOPMENT; ENVIRONMENTAL SERVICES;
   MULTITEMPORAL ANALYSIS; SCIENTIFIC LITERATURE; SPATIAL-DISTRIBUTION;
   FOREST RESILIENCE
AB Land cover is an important descriptor of the earth's terrestrial surface. It is also crucial to determine the biophysical processes in global environmental change. Land-use change showcases the management of the land while revealing what motivated the alteration of the land cover. The type of land use can represent local economic and social benefits, framed towards regional sustainable development. The Amazon stands out for being the largest tropical forest globally, with the most extraordinary biodiversity, and plays an essential role in climate regulation. The present work proposes to carry out a bibliometric analysis of 1590 articles indexed in the Scopus database. It uses both Microsoft Excel and VOSviewer software for the evaluation of author keywords, authors, and countries. The method encompasses (i) search criteria, (ii) search and document compilation, (iii) software selection and data extraction, and (iv) data analysis. The results classify the main research fields into nine main topics with increasing relevance: 'Amazon', 'deforestation', 'remote sensing', 'land use and land cover change', and 'land use'. In conclusion, the cocitation authors' network reveals the development of such areas and the interest they present due to their worldwide importance.
C1 [Montalvan-Burbano, Nestor] Univ Almeria, Dept Business & Econ, Almeria 04120, Spain.
   [Montalvan-Burbano, Nestor; Velastegui-Montoya, Andres; Gurumendi-Noriega, Miguel; Morante-Carballo, Fernando] ESPOL Polytech Univ, Ctr Invest & Proyectos Aplicados Ciencias Tierra, POB 09-01-5863, Guayaquil, Ecuador.
   [Velastegui-Montoya, Andres; Gurumendi-Noriega, Miguel] ESPOL Polytech Univ, Fac Ingn Ciencias Tierra, POB 09-01-5863, Guayaquil, Ecuador.
   [Velastegui-Montoya, Andres] Fed Univ Para, Geosci Inst, BR-66075110 Belem, Para, Brazil.
   [Gurumendi-Noriega, Miguel] Novasci Res Associates, Edif Terra 2, Guayaquil 90902, Ecuador.
   [Morante-Carballo, Fernando] ESPOL Polytech Univ, Fac Ciencias Nat & Matemat FCNM, POB 09-01-5863, Guayaquil, Ecuador.
   [Morante-Carballo, Fernando] ESPOL Polytech Univ, Georecursos & Aplicac GIGA, POB 09-01-5863, Guayaquil, Ecuador.
   [Adami, Marcos] Natl Inst Space Res INPE, Amazon Spatial Coordinat, BR-66077830 Belem, Para, Brazil.
RP Montalvan-Burbano, N (通讯作者)，Univ Almeria, Dept Business & Econ, Almeria 04120, Spain.; Montalvan-Burbano, N; Gurumendi-Noriega, M (通讯作者)，ESPOL Polytech Univ, Ctr Invest & Proyectos Aplicados Ciencias Tierra, POB 09-01-5863, Guayaquil, Ecuador.; Gurumendi-Noriega, M (通讯作者)，ESPOL Polytech Univ, Fac Ingn Ciencias Tierra, POB 09-01-5863, Guayaquil, Ecuador.; Gurumendi-Noriega, M (通讯作者)，Novasci Res Associates, Edif Terra 2, Guayaquil 90902, Ecuador.
EM nmb218@inlumine.ual.es; dvelaste@espol.edu.ec; nmb218@inlumine.ual.es;
   fmorante@espol.edu.ec; marcos.adami@inpe.br
RI Velástegui-Montoya, Andrés/ABG-5943-2020; Montalvan-Burbano,
   Nestor/AAR-1617-2020; Adami, Marcos/AAK-8358-2020
OI Velástegui-Montoya, Andrés/0000-0002-6926-3314; Montalvan-Burbano,
   Nestor/0000-0002-6024-1629; Adami, Marcos/0000-0003-4247-4477; Morante,
   Fernando/0000-0003-0374-0306
FU research project of the ESPOL University (Escuela Superior Politecnica
   del Litoral): "Estudios de impacto ambiental de grandes obras de
   ingenieria en la Amazonia ecuatoriana" (Studies of the environmental
   impact of major engineering works in the Ecuadorian [FICT-53-2020];
   ESPOL University [CIPAT-01-2018]; Brazilian National Council for
   Scientific and Technological Development (CNPq) [306334/2020-8]
FX This study was supported by the research project of the ESPOL University
   (Escuela Superior Politecnica del Litoral): "Estudios de impacto
   ambiental de grandes obras de ingenieria en la Amazonia ecuatoriana"
   (Studies of the environmental impact of major engineering works in the
   Ecuadorian Amazon) with code no. FICT-53-2020. The financial support
   belongs to ESPOL University with code no. CIPAT-01-2018 "Registro del
   Patrimonio Geologico y Minero y su incidencia en la defensa y
   preservacion de la geodiversidad en Ecuador" (Registry of Geological and
   Mining Heritage and its impact on the defense and preservation of
   geodiversity in Ecuador)Y. Marcos Adami acknowledges the Brazilian
   National Council for Scientific and Technological Development (CNPq) for
   the fellowship [306334/2020-8]. The authors appreciate the anonymous
   reviewers for their suggestions.
CR Abad-Segura E, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12145792
   Abe CA, 2018, WATER-SUI, V10, DOI 10.3390/w10070932
   Adami M., 2018, ANU I GEOCIENCIAS, V41, P553, DOI [10.11137/2018_2_553_567, DOI 10.11137/2018_2_553_567]
   ADAMS JB, 1995, REMOTE SENS ENVIRON, V52, P137, DOI 10.1016/0034-4257(94)00098-8
   Alvarez-Berrios Nora L., 2015, Environmental Research Letters, V10, DOI 10.1088/1748-9326/10/1/014006
   Arantes CC, 2018, J APPL ECOL, V55, P386, DOI 10.1111/1365-2664.12967
   do Nascimento CW, 2018, ENVIRON MONIT ASSESS, V190, DOI 10.1007/s10661-018-6989-4
   Picoli MCA, 2018, ISPRS J PHOTOGRAMM, V145, P328, DOI 10.1016/j.isprsjprs.2018.08.007
   Arima EY, 2014, LAND USE POLICY, V41, P465, DOI 10.1016/j.landusepol.2014.06.026
   Arima EY, 2013, ANN ASSOC AM GEOGR, V103, P1485, DOI 10.1080/00045608.2013.834231
   Armenteras D, 2006, ECOL INDIC, V6, P353, DOI 10.1016/j.ecolind.2005.03.014
   Asner GP, 2004, P NATL ACAD SCI USA, V101, P6039, DOI 10.1073/pnas.0400168101
   Asner GP, 2001, INT J REMOTE SENS, V22, P3855, DOI 10.1080/01431160010006926
   Asner GP, 2010, P NATL ACAD SCI USA, V107, P16738, DOI 10.1073/pnas.1004875107
   Baier-Fuentes H, 2019, INT ENTREP MANAG J, V15, P385, DOI 10.1007/s11365-017-0487-y
   Barlow J, 2007, P NATL ACAD SCI USA, V104, P18555, DOI 10.1073/pnas.0703333104
   Bartolacci F, 2020, BUS STRATEG ENVIRON, V29, P1297, DOI 10.1002/bse.2434
   Batjes NH, 1999, GEODERMA, V89, P273, DOI 10.1016/S0016-7061(98)00086-X
   Battisti C, 2016, INTRO DISTURBANCE EC
   Bell AR, 2015, J LAND USE SCI, V10, P19, DOI 10.1080/1747423X.2013.832427
   Beuchle R, 2015, APPL GEOGR, V58, P116, DOI 10.1016/j.apgeog.2015.01.017
   Bielecka E, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12132088
   Bogdanovic Z., 2021, BIBLIOMETRICS SCI MA, P95
   Bonilla-Bedoya S, 2018, SCI TOTAL ENVIRON, V644, P1044, DOI 10.1016/j.scitotenv.2018.07.028
   Bonilla-Bedoya S, 2017, SOIL SCI, V182, P128, DOI 10.1097/SS.0000000000000203
   Bonilla-Bedoya S, 2014, J FORESTRY RES, V25, P301, DOI 10.1007/s11676-013-0419-9
   Bovolo CI, 2018, ENVIRON RES LETT, V13, DOI 10.1088/1748-9326/aacf60
   Boyle SA, 2010, BIOL CONSERV, V143, P1134, DOI 10.1016/j.biocon.2010.02.008
   Brando PM, 2014, P NATL ACAD SCI USA, V111, P6347, DOI 10.1073/pnas.1305499111
   Brinkmann W. L. F., 1985, GeoJournal, V11, P89, DOI 10.1007/BF00572942
   Briones-Bitar J, 2020, GEOSCIENCES, V10, DOI 10.3390/geosciences10100403
   BRONDIZIO ES, 1994, HUM ECOL, V22, P249, DOI 10.1007/BF02168853
   Brown KS, 1997, J INSECT CONSERV, V1, P25, DOI 10.1023/A:1018422807610
   Buermann W, 2008, J BIOGEOGR, V35, P1160, DOI 10.1111/j.1365-2699.2007.01858.x
   Bustamante MMC, 2015, ENVIRON RES LETT, V10, DOI 10.1088/1748-9326/10/6/065007
   de Carvalho MAC, 2018, SOIL TILL RES, V184, P207, DOI 10.1016/j.still.2018.08.004
   Cardille JA, 2003, REMOTE SENS ENVIRON, V87, P551, DOI 10.1016/j.rse.2002.09.001
   Cardozo FD, 2014, REMOTE SENS-BASEL, V6, P8002, DOI 10.3390/rs6098002
   Carrion-Mero P., 2021, International Journal of Design and Nature and Ecodynamics, V16, P191, DOI 10.18280/ijdne.160209
   Carrion-Mero P, 2020, GEOSCIENCES, V10, DOI 10.3390/geosciences10090347
   Carvalho R, 2020, REG ENVIRON CHANGE, V20, DOI 10.1007/s10113-020-01626-5
   Castello L, 2018, FISH FISH, V19, P431, DOI 10.1111/faf.12261
   Chernysh Y, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su122410384
   Cobo MJ, 2011, J INFORMETR, V5, P146, DOI 10.1016/j.joi.2010.10.002
   Coe MT, 2013, PHILOS T R SOC B, V368, DOI 10.1098/rstb.2012.0155
   Costa MH, 2010, INT J CLIMATOL, V30, P1970, DOI 10.1002/joc.2048
   Davidson EA, 2000, BIOGEOCHEMISTRY, V48, P53, DOI 10.1023/A:1006204113917
   de Almeida CA, 2016, ACTA AMAZON, V46, P291, DOI 10.1590/1809-4392201505504
   De Bellis N, 2009, BIBLIOMETRICS CITATI
   de Carvalho WD, 2017, NAT ECOL EVOL, V1, DOI 10.1038/s41559-017-0100
   del Rio-Rama MD, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12020724
   de la Cruz-Lovera C, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9081294
   De Moya-Anegon F, 2007, SCIENTOMETRICS, V73, P53, DOI 10.1007/s11192-007-1681-4
   Decaens T, 2004, AGR ECOSYST ENVIRON, V103, P301, DOI 10.1016/j.agee.2003.12.005
   do Prado JW, 2016, SCIENTOMETRICS, V106, P1007, DOI 10.1007/s11192-015-1829-6
   Dong DH, 2015, SCIENTOMETRICS, V105, P1111, DOI 10.1007/s11192-015-1769-1
   Duque-Acevedo M, 2020, AGRONOMY-BASEL, V10, DOI 10.3390/agronomy10091261
   Duran-Sanchez A, 2019, J ENTERP COMMUNITIES, V13, P84, DOI 10.1108/JEC-10-2018-0072
   Chaves MED, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12183062
   Ertz M, 2018, J CLEAN PROD, V196, P1073, DOI 10.1016/j.jclepro.2018.06.095
   Espejo JC, 2018, REMOTE SENS-BASEL, V10, DOI 10.3390/rs10121903
   Etter A, 2006, AGR ECOSYST ENVIRON, V114, P369, DOI 10.1016/j.agee.2005.11.013
   Fahimnia B, 2015, INT J PROD ECON, V162, P101, DOI 10.1016/j.ijpe.2015.01.003
   Fearnside P.M., 1988, ENVIRONMENTALIST, V8, P281, DOI [10.1007/BF02243602, DOI 10.1007/BF02243602]
   Fearnside PM, 2008, ECOL SOC, V13
   Fearnside PM, 1997, ECOL ECON, V20, P53, DOI 10.1016/S0921-8009(96)00066-3
   Fearnside PM, 1997, CLIMATIC CHANGE, V35, P321, DOI 10.1023/A:1005336724350
   Fearnside PM, 2002, WATER AIR SOIL POLL, V133, P69, DOI 10.1023/A:1012971715668
   Feddema JJ, 2005, SCIENCE, V310, P1674, DOI 10.1126/science.1118160
   Finer M, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0002932
   Foley JA, 2007, FRONT ECOL ENVIRON, V5, P25, DOI 10.1890/1540-9295(2007)5[25:ARFDAL]2.0.CO;2
   Fujisaka S, 1996, AGR ECOSYST ENVIRON, V59, P115, DOI 10.1016/0167-8809(96)01015-8
   Fujisaka S, 1998, AGROFOREST SYST, V42, P45, DOI 10.1023/A:1006195810077
   Galford GL, 2008, REMOTE SENS ENVIRON, V112, P576, DOI 10.1016/j.rse.2007.05.017
   Gallice GR, 2019, ORYX, V53, P284, DOI 10.1017/S0030605317000412
   Gao H, 2020, J CLEAN PROD, V275, DOI 10.1016/j.jclepro.2020.122580
   Gao YS, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0173192
   Garcia-Garcia P, 2008, PHYTOMEDICINE, V15, P566, DOI 10.1016/j.phymed.2008.04.014
   Gardner TA, 2013, PHILOS T R SOC B, V368, DOI 10.1098/rstb.2013.0307
   Garrett RD, 2018, GLOBAL ENVIRON CHANG, V53, P233, DOI 10.1016/j.gloenvcha.2018.09.011
   Gaui TD, 2019, FOREST ECOL MANAG, V440, P258, DOI 10.1016/j.foreco.2019.02.033
   Geist, 2006, LAND CHANGE FORCING, V1
   Gizzi FT, 2015, ARAB J GEOSCI, V8, P10893, DOI 10.1007/s12517-015-1878-4
   Glinskis EA, 2019, LAND USE POLICY, V80, P95, DOI 10.1016/j.landusepol.2018.09.032
   Gomez C, 2016, ISPRS J PHOTOGRAMM, V116, P55, DOI 10.1016/j.isprsjprs.2016.03.008
   Grieg-Gran M, 2005, WORLD DEV, V33, P1511, DOI 10.1016/j.worlddev.2005.05.002
   Hagensieker R, 2018, REMOTE SENS-BASEL, V10, DOI 10.3390/rs10020257
   HENDERSON-SELLERS A, 1984, CLIMATIC CHANGE, V6, P231, DOI 10.1007/BF00142475
   Herrera-Franco G, 2021, WATER-SUI, V13, DOI 10.3390/w13091283
   Herrera-Franco G, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13031175
   Herrera-Franco G, 2020, GEOSCIENCES, V10, DOI 10.3390/geosciences10100379
   Hilker T, 2014, P NATL ACAD SCI USA, V111, P16041, DOI 10.1073/pnas.1404870111
   Homrich AS, 2018, J CLEAN PROD, V175, P525, DOI 10.1016/j.jclepro.2017.11.064
   Houghton RA, 2001, GLOBAL CHANGE BIOL, V7, P731, DOI 10.1046/j.1365-2486.2001.00426.x
   Houghton RA, 2000, NATURE, V403, P301, DOI 10.1038/35002062
   Jakimow B, 2018, REMOTE SENS ENVIRON, V205, P453, DOI 10.1016/j.rse.2017.10.009
   Jakovac CC, 2015, J ECOL, V103, P67, DOI 10.1111/1365-2745.12298
   Jesus ED, 2009, ISME J, V3, P1004, DOI 10.1038/ismej.2009.47
   Joppa LN, 2008, P NATL ACAD SCI USA, V105, P6673, DOI 10.1073/pnas.0802471105
   Kalamandeen M, 2020, J APPL ECOL, V57, P1730, DOI 10.1111/1365-2664.13669
   Keathley-Herring H, 2016, SCIENTOMETRICS, V109, P927, DOI 10.1007/s11192-016-2096-x
   Keller M, 2004, ECOL APPL, V14, pS3, DOI 10.1890/03-6003
   LAL R, 1986, ADV AGRON, V39, P173
   Lambin EF, 2001, GLOBAL ENVIRON CHANG, V11, P261, DOI 10.1016/S0959-3780(01)00007-3
   Lambin EF., 2006, LAND USE LAND COVER
   Lambin Eric F., 2003, PEOPLE ENV APPROACHE
   Laurance WF, 2002, J BIOGEOGR, V29, P737, DOI 10.1046/j.1365-2699.2002.00721.x
   Laurance WF, 2001, SCIENCE, V291, P438, DOI 10.1126/science.291.5503.438
   Lessmann J, 2014, ECOL EVOL, V4, P2410, DOI 10.1002/ece3.1102
   Lima A, 2012, APPL GEOGR, V34, P239, DOI 10.1016/j.apgeog.2011.10.013
   Lima HN, 2002, GEODERMA, V110, P1, DOI 10.1016/S0016-7061(02)00141-6
   Lima JMT, 2017, PHILOS T R SOC B, V372, DOI 10.1098/rstb.2016.0125
   Llerena-Montoya S, 2021, ISPRS INT J GEO-INF, V10, DOI 10.3390/ijgi10030191
   Lopez-Munoz F, 2006, J AFFECT DISORDERS, V92, P161, DOI 10.1016/j.jad.2006.02.006
   Lv PH, 2011, SCIENTOMETRICS, V88, P399, DOI 10.1007/s11192-011-0386-x
   Macedo Marcia N, 2013, Philos Trans R Soc Lond B Biol Sci, V368, P20120153, DOI 10.1098/rstb.2012.0153
   Macedo MN, 2012, P NATL ACAD SCI USA, V109, P1341, DOI 10.1073/pnas.1111374109
   Magnusson WE, 2018, FRONT ECOL EVOL, V6, DOI 10.3389/fevo.2018.00163
   Maldonado-Erazo CP, 2021, LAND-BASEL, V10, DOI 10.3390/land10010076
   Martinelli LA, 2008, ECOL APPL, V18, P885, DOI 10.1890/07-1813.1
   Martinez JM, 2007, REMOTE SENS ENVIRON, V108, P209, DOI 10.1016/j.rse.2006.11.012
   Mena CF, 2011, APPL GEOGR, V31, P210, DOI 10.1016/j.apgeog.2010.04.005
   Mendes LW, 2014, ISME J, V8, P1577, DOI 10.1038/ismej.2014.17
   Mercier A, 2019, REMOTE SENS-BASEL, V11, DOI 10.3390/rs11080979
   Mitchard ETA, 2014, GLOBAL ECOL BIOGEOGR, V23, P935, DOI 10.1111/geb.12168
   Montalvan-Burbano N, 2020, COGENT BUS MANAG, V7, DOI 10.1080/23311975.2020.1745043
   MORAN EF, 1993, HUM ECOL, V21, P1, DOI 10.1007/BF00890069
   Moran EF, 2000, FOREST ECOL MANAG, V139, P93, DOI 10.1016/S0378-1127(99)00337-0
   Morton DC, 2008, GLOBAL CHANGE BIOL, V14, P2262, DOI 10.1111/j.1365-2486.2008.01652.x
   Morton DC, 2006, P NATL ACAD SCI USA, V103, P14637, DOI 10.1073/pnas.0606377103
   Mu Y, 2020, INT J REMOTE SENS, V41, P8682, DOI 10.1080/2150704X.2020.1802529
   Muller R, 2013, LAND USE POLICY, V30, P895, DOI 10.1016/j.landusepol.2012.06.019
   MURA C, 2003, REMOTE SENS ENVIRON, V87, P482, DOI DOI 10.1016/J.RSE.2002.12.001
   Myers N, 2000, NATURE, V403, P853, DOI 10.1038/35002501
   Najmi A, 2017, SCIENTOMETRICS, V110, P843, DOI 10.1007/s11192-016-2171-3
   Neill C, 1997, ECOL APPL, V7, P1216, DOI 10.1890/1051-0761(1997)007[1216:SCANSF]2.0.CO;2
   Nepstad D, 2006, CONSERV BIOL, V20, P65, DOI 10.1111/j.1523-1739.2006.00351.x
   Nepstad D, 2004, GLOBAL CHANGE BIOL, V10, P704, DOI 10.1111/j.1529-8817.2003.00772.x
   Nobre CA, 2016, P NATL ACAD SCI USA, V113, P10759, DOI 10.1073/pnas.1605516113
   Noyons ECM, 1999, SCIENTOMETRICS, V46, P591, DOI 10.1007/BF02459614
   O'Connell CS, 2018, ENVIRON RES LETT, V13, DOI 10.1088/1748-9326/aaafd8
   Oliveira PJC, 2007, SCIENCE, V317, P1233, DOI 10.1126/science.1146324
   Ometto JP, 2011, CARBON MANAG, V2, P575, DOI 10.4155/cmt.11.48
   PARODI JL, 1990, FOREST ECOL MANAG, V33-4, P157, DOI 10.1016/0378-1127(90)90190-M
   Payan-Sanchez B, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13031136
   Perz SG, 2001, POPUL RES POLICY REV, V20, P159, DOI 10.1023/A:1010658719768
   Perz SG, 2013, LAND USE POLICY, V34, P27, DOI 10.1016/j.landusepol.2013.01.009
   Pfaff ASP, 1999, J ENVIRON ECON MANAG, V37, P26, DOI 10.1006/jeem.1998.1056
   Pichon FJ, 1997, WORLD DEV, V25, P67, DOI 10.1016/S0305-750X(96)00091-5
   Pico-Saltos R, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13094625
   Pitman NCA, 2001, ECOLOGY, V82, P2101, DOI 10.1890/0012-9658(2001)082[2101:DADOTS]2.0.CO;2
   Pizzi S, 2020, J CLEAN PROD, V276, DOI 10.1016/j.jclepro.2020.124033
   Pongratz J, 2006, EARTH INTERACT, V10
   Pontes PRM, 2019, J ENVIRON MANAGE, V235, P489, DOI 10.1016/j.jenvman.2019.01.090
   PRITCHARD A, 1969, J DOC, V25, P348
   Randerson JT, 2009, GLOBAL CHANGE BIOL, V15, P2462, DOI 10.1111/j.1365-2486.2009.01912.x
   Reno VF, 2011, REMOTE SENS ENVIRON, V115, P3446, DOI 10.1016/j.rse.2011.08.008
   Rodrigues JLM, 2013, P NATL ACAD SCI USA, V110, P988, DOI 10.1073/pnas.1220608110
   Roux E, 2010, HYDROLOG SCI J, V55, P104, DOI 10.1080/02626660903529023
   Saatchi SS, 2007, GLOBAL CHANGE BIOL, V13, P816, DOI 10.1111/j.1365-2486.2007.01323.x
   Saatchi SS, 1997, REMOTE SENS ENVIRON, V59, P191, DOI 10.1016/S0034-4257(96)00153-8
   Salafsky N, 2008, CONSERV BIOL, V22, P897, DOI 10.1111/j.1523-1739.2008.00937.x
   SANCHEZ PA, 1982, SCIENCE, V216, P821, DOI 10.1126/science.216.4548.821
   Sangermano F, 2012, LANDSCAPE ECOL, V27, P571, DOI 10.1007/s10980-012-9710-y
   Santana NC, 2018, REMOTE SENS-BASEL, V10, DOI 10.3390/rs10121904
   Silva RD, 2018, AGR SYST, V161, P102, DOI 10.1016/j.agsy.2018.01.003
   Siqueira-Gay J, 2020, RESOUR POLICY, V67, DOI 10.1016/j.resourpol.2020.101662
   Smith RC, 2003, HUM ORGAN, V62, P357, DOI 10.17730/humo.62.4.3d82x31cywm8wl25
   Soltangheisi A, 2019, SOIL TILL RES, V194, DOI 10.1016/j.still.2019.104330
   Zaiatz APSR, 2018, ACTA AMAZON, V48, P168, DOI 10.1590/1809-4392201701930
   Spera SA, 2016, GLOBAL CHANGE BIOL, V22, P3405, DOI 10.1111/gcb.13298
   STONE TA, 1988, INT J REMOTE SENS, V9, P95, DOI 10.1080/01431168808954839
   Sturmer SL, 2011, MYCORRHIZA, V21, P255, DOI 10.1007/s00572-010-0330-6
   Sweileh WM, 2017, BMC INT HEALTH HUM R, V17, DOI 10.1186/s12914-017-0116-4
   Tancredi, 1989, GEOJOURNAL, V19, P391, DOI [10.1007/BF00176908, DOI 10.1007/BF00176908]
   THIELE G, 1993, AGR SYST, V42, P179, DOI 10.1016/0308-521X(93)90053-5
   Tian HQ, 1998, NATURE, V396, P664, DOI 10.1038/25328
   Tranfield D, 2003, BRIT J MANAGE, V14, P207, DOI 10.1111/1467-8551.00375
   Trenberth KE, 1999, J CLIMATE, V12, P1368, DOI 10.1175/1520-0442(1999)012<1368:AMRROA>2.0.CO;2
   TRUMBORE SE, 1993, GLOBAL BIOGEOCHEM CY, V7, P275, DOI 10.1029/93GB00468
   van der Ent RJ, 2010, WATER RESOUR RES, V46, DOI 10.1029/2010WR009127
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Velastegui-Montoya A, 2020, ISPRS INT J GEO-INF, V9, DOI 10.3390/ijgi9100583
   Veldman JW, 2009, FOREST ECOL MANAG, V258, P1643, DOI 10.1016/j.foreco.2009.07.024
   Veldman JW, 2011, BIOL CONSERV, V144, P1419, DOI 10.1016/j.biocon.2011.01.011
   Vieira ICG, 2003, REMOTE SENS ENVIRON, V87, P470, DOI 10.1016/j.rse.2002.09.002
   Villa PM, 2018, ECOL INDIC, V95, P687, DOI 10.1016/j.ecolind.2018.08.005
   Villa PM, 2018, FOREST ECOL MANAG, V430, P312, DOI 10.1016/j.foreco.2018.08.014
   Vogel B, 2021, LEADERSHIP QUART, V32, DOI 10.1016/j.leaqua.2020.101381
   Vorosmarty CJ, 1989, GLOBAL BIOGEOCHEM CY, V3, P241, DOI 10.1029/GB003i003p00241
   Walker R, 1996, ECOL ECON, V18, P67, DOI 10.1016/0921-8009(96)00033-X
   Wulder MA, 2018, INT J REMOTE SENS, V39, P4254, DOI 10.1080/01431161.2018.1452075
   Xie HL, 2020, LAND-BASEL, V9, DOI 10.3390/land9010028
   Yang YY, 2014, J GEOGR SCI, V24, P746, DOI 10.1007/s11442-014-1117-z
   Yu DW, 1997, ENVIRON CONSERV, V24, P130, DOI 10.1017/S0376892997000192
   Zhang YY, 2019, REMOTE SENS-BASEL, V11, DOI 10.3390/rs11010091
   Zinn YL, 2005, SOIL TILL RES, V84, P28, DOI 10.1016/j.still.2004.08.007
   Zupic I, 2015, ORGAN RES METHODS, V18, P429, DOI 10.1177/1094428114562629
NR 198
TC 10
Z9 10
U1 5
U2 21
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUN
PY 2021
VL 13
IS 11
AR 6039
DI 10.3390/su13116039
PG 24
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA SR0OX
UT WOS:000660745900001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Sanguri, K
   Bhuyan, A
   Patra, S
AF Sanguri, Kamal
   Bhuyan, Atanu
   Patra, Sabyasachi
TI A semantic similarity adjusted document co-citation analysis: a case of
   tourism supply chain
SO SCIENTOMETRICS
LA English
DT Article
DE Document co-citation analysis; Network analysis; Cluster analysis;
   Tourism supply chain; Latent semantic analysis
ID OPTIMAL PRICING STRATEGY; PARTIAL LEAST-SQUARES; SUSTAINABLE TOURISM;
   SCIENTIFIC LITERATURE; WORD ANALYSIS; R PACKAGE; MANAGEMENT;
   INFORMATION; OPERATORS; FOOD
AB Document co-citation analysis (DCA) is employed across various academic disciplines and contexts to characterise the structure of knowledge. Since the introduction of the method for DCA by Small (J Am Soc Inf Sci 24(4):265-269, 1973) a variety of modifications towards optimising its results have been proposed by several researchers. We recommend a new approach to improve the results of DCA by integrating the concept of the document similarity measure into it. Our proposed method modifies DCA by incorporating the semantic similarity using latent semantic analysis for the abstracts of the top-cited documents. The interaction of these two measures results in a new measure that we call as the semantic similarity adjusted co-citation index. The effectiveness of the proposed method is evaluated through an empirical study of the tourism supply chain (TSC), where we employ the techniques of the network and cluster analyses. The study also comprehensively explores the resulting knowledge structures from both the methods. The results of our case study suggest that the clustering quality and knowledge map of the domain can be improved by considering the document similarity along with their co-citation strength.
C1 [Sanguri, Kamal; Bhuyan, Atanu; Patra, Sabyasachi] Indian Inst Management Kashipur, Kashipur 244713, Uttarakhand, India.
RP Sanguri, K (通讯作者)，Indian Inst Management Kashipur, Kashipur 244713, Uttarakhand, India.
EM kamal.fpm1807@iimkashipur.ac.in
RI Patra, Sabyasachi/AAU-4473-2020
OI Sanguri, Kamal/0000-0003-2190-5418; Patra,
   Sabyasachi/0000-0003-4119-7420; Bhuyan, Atanu/0000-0001-9374-4911
CR Alamdari F, 2002, J AIR TRANSP MANAG, V8, P339, DOI 10.1016/S0969-6997(02)00014-5
   Alcazar P, 2019, AEROBIOLOGIA, V35, P269, DOI 10.1007/s10453-019-09558-2
   Aljaber B, 2010, INFORM RETRIEVAL, V13, P101, DOI 10.1007/s10791-009-9108-x
   Penichet LA, 2018, COMPUT SIST, V22, P93, DOI [10.13053/CyS-22-1-2763, 10.13053/cys-22-1-2763]
   BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
   Barratt M, 2004, SUPPLY CHAIN MANAG, V9, P30, DOI 10.1108/13598540410517566
   Bastakis C, 2004, TOURISM MANAGE, V25, P151, DOI 10.1016/S0261-5177(03)00098-0
   Bastian M, 2009, INT AAAI C WEBL SOC
   BELISLE FJ, 1983, ANN TOURISM RES, V10, P497, DOI 10.1016/0160-7383(83)90005-1
   Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008
   Borgatti S.P., 1997, MULTIDIMENSIONAL SCA
   Boyack KW, 2013, J AM SOC INF SCI TEC, V64, P1759, DOI 10.1002/asi.22896
   BRAAM RR, 1991, J AM SOC INFORM SCI, V42, P233, DOI 10.1002/(SICI)1097-4571(199105)42:4<233::AID-ASI1>3.0.CO;2-I
   Brock G, 2011, J STAT SOFTW, V45, P50
   Brohman J, 1996, ANN TOURISM RES, V23, P48, DOI 10.1016/0160-7383(95)00043-7
   Bu Y, 2016, SCIENTOMETRICS, V108, P143, DOI 10.1007/s11192-016-1959-5
   Budeanu A, 2005, J CLEAN PROD, V13, P89, DOI 10.1016/j.jclepro.2003.12.024
   Burnham Judy F, 2006, Biomed Digit Libr, V3, P1
   Cachon GP, 2005, MANAGE SCI, V51, P30, DOI 10.1287/mnsc.1040.0215
   Carey S, 1997, TOURISM MANAGE, V18, P425, DOI 10.1016/S0261-5177(97)00044-7
   Charrad M, 2014, J STAT SOFTW, V61, P1
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen H, 2013, FRONT PHYSIOL, V4, DOI 10.3389/fphys.2013.00008
   Chin WW, 1998, QUANT METH SER, P295
   Colavizza G, 2018, J ASSOC INF SCI TECH, V69, P600, DOI 10.1002/asi.23981
   Cox A., 1999, SUPPLY CHAIN MANAG, V4, P167, DOI DOI 10.1108/13598549910284480
   DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
   Ding Y, 2014, J ASSOC INF SCI TECH, V65, P1820, DOI 10.1002/asi.23256
   Dumais S., 1988, P SIGCHI C HUM FACT, V88, P281, DOI DOI 10.1145/57167.57214
   Dumais ST, 2004, ANNU REV INFORM SCI, V38, P189
   EISENHARDT KM, 1989, ACAD MANAGE REV, V14, P532, DOI 10.2307/258557
   Elkiss A, 2008, J AM SOC INF SCI TEC, V59, P51, DOI 10.1002/asi.20707
   Evangelopoulos NE, 2013, WIRES COGN SCI, V4, P683, DOI 10.1002/wcs.1254
   Everett S, 2008, J SUSTAIN TOUR, V16, P150, DOI 10.2167/jost696.0
   Feng J, 2017, SCIENTOMETRICS, V111, P1521, DOI 10.1007/s11192-017-2286-1
   Font X., 2008, BUSINESS STRATEGY EN, V17, P260, DOI DOI 10.1002/BSE.527
   Froud H., 2012, ARXIV12123634
   Garcia D, 2006, ANN TOURISM RES, V33, P456, DOI 10.1016/j.annals.2005.12.010
   Gipp B, 2009, PRO INT CONF SCI INF, V2, P571
   Gmur M, 2003, SCIENTOMETRICS, V57, P27, DOI 10.1023/A:1023619503005
   Gomaa W. H., 2013, INT J COMPUTER APPL, V68, P13, DOI 10.5120/11638-7118
   Gunther F, 2015, BEHAV RES METHODS, V47, P930, DOI 10.3758/s13428-014-0529-0
   Guo XL, 2013, ANN TOURISM RES, V41, P20, DOI 10.1016/j.annals.2012.11.009
   Guo XL, 2012, TOURISM ECON, V18, P1361, DOI 10.5367/te.2012.0179
   Hartigan J. A., 1979, Applied Statistics, V28, P100, DOI 10.2307/2346830
   Holman D, 2018, HEALTH-LONDON, V22, P389, DOI 10.1177/1363459317695630
   Hota PK, 2020, J BUS ETHICS, V166, P89, DOI 10.1007/s10551-019-04129-4
   Hou JH, 2018, SCIENTOMETRICS, V115, P869, DOI 10.1007/s11192-018-2695-9
   Hsiao T. M., 2017, P ASS INFORM SCI TEC, V54, P170, DOI DOI 10.1002/PRA2.2017
   Huang GQ, 2010, SERV IND J, V30, P1593, DOI 10.1080/02642060903580631
   Hussein AS, 2016, PROCEEDINGS OF THE 2016 SAI COMPUTING CONFERENCE (SAI), P269, DOI 10.1109/SAI.2016.7555994
   Ishchenko I., 2019, OPEN SEMANTIC TECHNO, P151
   Jena SK, 2017, TOURISM ECON, V23, P1235, DOI 10.1177/1354816616674611
   Jeong YK, 2014, J INFORMETR, V8, P197, DOI 10.1016/j.joi.2013.12.001
   Ji Xiaonan, 2015, AMIA Annu Symp Proc, V2015, P1927
   Juttner U, 2011, SUPPLY CHAIN MANAG, V16, P246, DOI 10.1108/13598541111139062
   Kaufman LR, 1990, FINDING GROUPS DATA, P725
   Kawamura T, 2018, SCIENTOMETRICS, V116, P941, DOI 10.1007/s11192-018-2783-x
   Keating B, 2009, INT J TOUR RES, V11, P403, DOI 10.1002/jtr.706
   Ketchen DJ, 2007, J OPER MANAG, V25, P573, DOI 10.1016/j.jom.2006.05.010
   Kiaei H, 2015, J PETROL SCI ENG, V135, P152, DOI 10.1016/j.petrol.2015.08.019
   Kim HJ, 2016, J INFORMETR, V10, P954, DOI 10.1016/j.joi.2016.07.007
   Klemm M., 2001, International Journal of Tourism Research, V3, P367, DOI 10.1002/jtr.351
   Knoth Petr, 2017, CEUR WORKSHOP P, V1888, P14
   Kodinariya T.M., 2013, INT J ADV RES COMPUT, V1, P90
   Koopman R, 2017, SCIENTOMETRICS, V111, P1157, DOI 10.1007/s11192-017-2305-2
   Lambert DM, 2000, IND MARKET MANAG, V29, P65, DOI 10.1016/S0019-8501(99)00113-3
   Landauer TK, 1998, DISCOURSE PROCESS, V25, P259, DOI 10.1080/01638539809545028
   Larkey L. S., 1999, Digital 99 Libraries. Fourth ACM Conference on Digital Libraries, P179
   Law R, 2014, INT J CONTEMP HOSP M, V26, P727, DOI 10.1108/IJCHM-08-2013-0367
   Lee HK, 2015, TOURISM MANAGE, V46, P148, DOI 10.1016/j.tourman.2014.06.014
   Lee M. D., 2005, P ANN M COGN SCI COU, V27
   Leydesdorff L, 1998, SCIENTOMETRICS, V43, P5, DOI 10.1007/BF02458391
   Leydesdorff L, 2006, J AM SOC INF SCI TEC, V57, P1616, DOI 10.1002/asi.20335
   Ling LY, 2011, J CHINA TOUR RES, V7, P20, DOI 10.1080/19388160.2011.551044
   Liu DR, 2011, J AM SOC INF SCI TEC, V62, P246, DOI 10.1002/asi.21459
   Liu M., 2017, ARXIV171111508
   Liu SB, 2012, SCIENTOMETRICS, V91, P495, DOI 10.1007/s11192-011-0575-7
   Maletic JI, 2000, PROC INT C TOOLS ART, P46, DOI 10.1109/TAI.2000.889845
   MARSHAKOVA IV, 1973, NAUCH-TEKHN INFORM 2, P3
   Mayan S.N.A., 2017, GLOB J SOC SCI STUD, V3, P1
   Medina-Munoz D, 2000, ANN TOURISM RES, V27, P737, DOI 10.1016/S0160-7383(99)00104-8
   Meyer D., 2007, Current Issues in Tourism, V10, P558, DOI 10.2167/cit313.0
   Mongeon P, 2016, SCIENTOMETRICS, V106, P213, DOI 10.1007/s11192-015-1765-5
   Mora L, 2019, TECHNOL FORECAST SOC, V142, P56, DOI 10.1016/j.techfore.2018.07.019
   Nerur SP, 2008, STRATEG MANAGE J, V29, P319, DOI 10.1002/smj.659
   Ng CK, 2018, ENTERP INF SYST-UK, V12, P820, DOI 10.1080/17517575.2018.1464666
   Niraula Nobal, 2013, Statistical Language and Speech Processing. First International Conference, SLSP 2013. Proceedings: LNCS 7978, P188, DOI 10.1007/978-3-642-39593-2_17
   Ozcinar H, 2015, TEACH TEACH EDUC, V47, P42, DOI 10.1016/j.tate.2014.12.006
   Pakhira MK, 2004, PATTERN RECOGN, V37, P487, DOI 10.1016/j.patcog.2003.06.005
   Peng DX, 2012, J OPER MANAG, V30, P467, DOI 10.1016/j.jom.2012.06.002
   Pillay M, 2013, APPL GEOGR, V36, P49, DOI 10.1016/j.apgeog.2012.06.005
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Ramaprasad A., 2014, P 2 INT C BIG DAT AN
   Rauchfleisch A, 2018, JCOM-J SCI COMMUN, V17, DOI 10.22323/2.17030207
   Rendon E., 2011, INT J COMP COMMUN, V5, P27
   Rodriguez-Prieto O, 2019, SCIENTOMETRICS, V120, P105, DOI 10.1007/s11192-019-03125-9
   Schwartz K, 2008, J SUSTAIN TOUR, V16, P298, DOI 10.1080/09669580802154108
   Selivanov D., 2016, TEXT2VEC
   Seuring S, 2008, J CLEAN PROD, V16, P1699, DOI 10.1016/j.jclepro.2008.04.020
   Shiau WL, 2017, INT J INFORM MANAGE, V37, P390, DOI 10.1016/j.ijinfomgt.2017.04.007
   Shiau WL, 2016, ENTERP INF SYST-UK, V10, P815, DOI 10.1080/17517575.2015.1019570
   Sigala M, 2008, J CLEAN PROD, V16, P1589, DOI 10.1016/j.jclepro.2008.04.021
   Sims R, 2009, J SUSTAIN TOUR, V17, P321, DOI 10.1080/09669580802359293
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Small H, 2011, PRO INT CONF SCI INF, P783
   SMITH SLJ, 1994, ANN TOURISM RES, V21, P582, DOI 10.1016/0160-7383(94)90121-X
   Song HY, 2009, TOURISM ECON, V15, P813, DOI 10.5367/000000009789955099
   Szpilko D, 2017, PROCEDIA ENGINEER, V182, P687, DOI 10.1016/j.proeng.2017.03.180
   Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
   Telfer D. J., 2000, Tourism Geographies, V2, P421, DOI 10.1080/146166800750035521
   Telfer DJ, 1996, ANN TOURISM RES, V23, P635, DOI 10.1016/0160-7383(95)00087-9
   Tepelus CM, 2005, J CLEAN PROD, V13, P99, DOI 10.1016/j.jclepro.2003.12.018
   Theuvsen L, 2004, ANN TOURISM RES, V31, P475, DOI 10.1016/j.annals.2003.12.007
   Torres R, 2003, ANN TOURISM RES, V30, P546, DOI 10.1016/S0160-7383(02)00103-2
   Trujillo CM, 2018, SCI ADV, V4, DOI 10.1126/sciadv.1701130
   Tso A., 2005, International Journal of Hospitality Management, V24, P301, DOI 10.1016/j.ijhm.2004.09.002
   Vachon S, 2008, INT J PROD ECON, V111, P299, DOI 10.1016/j.ijpe.2006.11.030
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   VANRAAN AFJ, 1990, NATURE, V347, P626, DOI 10.1038/347626a0
   Wang SH, 2017, SCIENTOMETRICS, V111, P1017, DOI 10.1007/s11192-017-2298-x
   WARD JH, 1963, J AM STAT ASSOC, V58, P236, DOI 10.2307/2282967
   Wickelmaier F, 2003, SOUND QUALITY RES UN, V46, P1
   Wie BW, 2005, TOURISM MANAGE, V26, P203, DOI 10.1016/j.tourman.2003.09.020
   Wieland A, 2013, INT J PHYS DISTR LOG, V43, P300, DOI 10.1108/IJPDLM-08-2012-0243
   Yaghtin M, 2019, SCIENTOMETRICS, V119, P987, DOI 10.1007/s11192-019-03058-3
   Yang S, 2009, J TRAVEL RES, V47, P425, DOI 10.1177/0047287508326533
   Yilmaz Y., 2006, International Journal of Contemporary Hospitality Management, V18, P341, DOI 10.1108/09596110610665348
   Yilmaz Y, 2006, INT J PRODUCT PERFOR, V55, P371, DOI 10.1108/17410400610671417
   Yong-Hak J., 2013, WEB SCI
   Zacharia, 2001, J BUSINESS LOGISTICS, V22, P1, DOI [10.1002/j.2158-1592.2001.tb00001.x, DOI 10.1002/J.2158-1592.2001.TB00001.X]
   Zambelli A. E., 2016, F1000RESEARCH, V5
   Zhang X, 2009, TOURISM MANAGE, V30, P345, DOI 10.1016/j.tourman.2008.12.010
   Zhang Y, 2009, TOURISM MANAGE, V30, P278, DOI 10.1016/j.tourman.2008.07.004
NR 134
TC 7
Z9 7
U1 4
U2 36
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD OCT
PY 2020
VL 125
IS 1
BP 233
EP 269
DI 10.1007/s11192-020-03608-0
EA JUL 2020
PG 37
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA NL7AC
UT WOS:000550610700005
DA 2022-08-23
ER

PT S
AU Reid, E
   Chen, HC
AF Reid, E
   Chen, HC
BE Kantor, P
   Muresan, G
   Roberts, F
   Zeng, DD
   Wang, FY
   Chen, H
   Merkle, RC
TI Mapping the contemporary terrorism research domain: Researchers,
   publications, and institutions analysis
SO INTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS
SE Lecture Notes in Computer Science
LA English
DT Article; Proceedings Paper
CT IEEE International Conference on Intelligence and Security Informatics
   (ISI 2005)
CY MAY 19-20, 2005
CL Atlanta, GA
SP IEEE, Rutgers State Univ, Univ Arizona, Georgia Inst Technol, Natl Sci Fdn, Intelligence Technol Innovat Ctr, US Dept Homeland Secur
ID AUTHOR COCITATION ANALYSIS; EVOLUTION; COUNTRY; SCIENCE
AB The ability to map the contemporary terrorism research domain involves mining, analyzing, charting, and visualizing a research area according to experts, institutions, topics, publications, and social networks. As the increasing flood of new, diverse, and disorganized digital terrorism studies continues, the application of domain visualization techniques are increasingly critical for understanding the growth of scientific research, tracking the dynamics of the field, discovering potential new areas of research, and creating a big picture of the field's intellectual structure as well as challenges. In this paper, we present an overview of contemporary terrorism research by applying domain visualization techniques to the literature and author citation data from the years 1965 to 2003. The data were gathered from ten databases such as the ISI Web of Science then analyzed using an integrated knowledge mapping framework that includes selected techniques such as self-organizing map (SOM), content map analysis, and co-citation analysis. The analysis revealed (1) 42 key terrorism researchers and their institutional affiliations; (2) their influential publications; (3) a shift from focusing on terrorism as a low-intensity conflict to an emphasis on it as a strategic threat to world powers with increased focus on Osama Bin Laden; and (4) clusters of terrorism researchers who work in similar research areas as identified by co-citation and block-modeling maps.
C1 Univ Arizona, Dept Management Infomat Syst, Tucson, AZ 85721 USA.
RP Reid, E (通讯作者)，Univ Arizona, Dept Management Infomat Syst, Tucson, AZ 85721 USA.
EM ednareid@eller.arizona.edu; hchen@eller.arizona.edu
RI White, Howard D/A-7034-2009
CR Andrews JE, 2003, J MED LIBR ASSOC, V91, P47
   Bergen P. L., 2001, HOLY WAR INC INSIDE
   BORGMAN CL, 2002, ANN REV INFORMATION
   BOYACK KW, 2003, C NAT AC SCI NAOS NA
   Chen CM, 2001, COMPUTER, V34, P65, DOI 10.1109/2.910895
   Chen H., 2004, IEEE COMPUTER SOC, P50
   CHUA C, 2002, J ASSOC INF SYST, V2, P146
   Crenshaw M, 2000, POLIT PSYCHOL, V21, P405, DOI 10.1111/0162-895X.00195
   CRONIN B, 2002, SPAC SPAT TECHN WORK
   EGGERS S, IN PRESS MED INFORMA
   FERLIGOJ A, 1996, J COMPUTING INFORMAT, V4, P63
   GARFIELD E, 2002, ASIST 2002
   Garfield E., 1992, SCI PUBL POLICY, V19, P321, DOI DOI 10.1093/SPP/19.5.321
   Gordon A, 2004, STUD CONFL TERROR, V27, P79, DOI 10.1080/10576100490275076
   Gordon A, 1999, TERROR POLIT VIOLENC, V11, P141, DOI 10.1080/09546559908427510
   Huang Z, 2004, J NANOPART RES, V6, P325, DOI 10.1007/s11051-004-4117-6
   Huang Z, 2003, J NANOPART RES, V5, P333, DOI 10.1023/A:1025556800994
   HUGHES G, 2003, ANAL THIS AGE
   *ISI, 2003, INC CIT THRESH
   *ISI, 2003, DOES ISI ID HIGHL CI
   KENNEDY LW, 2003, DEV FDN POLICY RELEV
   LIN X, 2001, AUTHORLINK INSTANT A
   MCCAIN KW, 1990, J AM SOC INFORMATION, V41
   *PUBL INF RES INC, 2004, NAM
   REID E, 2004, 2 S INT SEC INF ISI
   Reid EOF, 1997, INFORM PROCESS MANAG, V33, P91, DOI 10.1016/S0306-4573(96)00052-0
   REID EOF, 2004, P 36 INT C SYST SCI
   REID EOF, 1983, ANAL TERRORISM LIT B
   SCHMID A, 1988, POLITICAL TOERRORISM
   SHIFFRIN RM, 2004, C NAT AC SCI HELD MA
   White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-W
   WHITE HD, 2001, JOINT INT C ASS COMP
   WILKINSON P, 2003, WEST GEN M PREP PEAC
NR 33
TC 8
Z9 8
U1 0
U2 11
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 3-540-25999-6
J9 LECT NOTES COMPUT SC
PY 2005
VL 3495
BP 322
EP 339
PG 18
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Interdisciplinary Applications; Computer
   Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BCN19
UT WOS:000230114100027
DA 2022-08-23
ER

PT J
AU Cheng, MY
   Tsai, HC
   Liu, CL
AF Cheng, Min-Yuan
   Tsai, Hsing-Chih
   Liu, Chih-Lung
TI Artificial intelligence approaches to achieve strategic control over
   project cash flows
SO AUTOMATION IN CONSTRUCTION
LA English
DT Article
DE Construction industry; Project success; Cash flows; Artificial
   intelligence; K-means clustering; Genetic algorithm; Fuzzy logic; Neural
   network
ID NEURAL INFERENCE SYSTEM; MANAGEMENT; NETWORKS; MODELS
AB The ability over the course of a construction project to make reliable predictions regarding cash flows enhances project cost management. This paper uses artificial intelligence (AI) approaches to predict cash flow trends for Such projects in order to develop appropriate strategies that apply factors such as float, process execution time, construction rate and resource demand to project cash flow control. AI approaches involved in this paper include K-means clustering, genetic algorithm (GA), fuzzy logic (FL), and neural network (NN). K-means clustering is employed to categorize similar projects. while the other approaches are used to develop the Evolutionary Fuzzy Neural Inference Model (EFNIM), a knowledge learning model. FL and NN are employed in the EFNIM to develop a neural-fuzzy model that can deal with uncertainties and knowledge mapping. CA is used to optimize the membership functions of FL and NN parameters globally. The major target of this AI learning is to address sequential cash flow trends. This trained result is furthermore applied to a strategic project cash flow control. This cash flow control affects project performance within the banana envelope of the S-curve for project management. Crown Copyright (c) 2008 Published by Elsevier B.V. All rights reserved.
C1 [Cheng, Min-Yuan; Tsai, Hsing-Chih; Liu, Chih-Lung] Natl Taiwan Univ Sci & Technol, Dept Construct Engn, Taipei 106, Taiwan.
RP Tsai, HC (通讯作者)，Natl Taiwan Univ Sci & Technol, Dept Construct Engn, 43 Sec 4,Keelung Rd, Taipei 106, Taiwan.
EM tsaihsingchih@gmail.com
RI Cheng, Min-Yuan/G-3510-2013
CR ALMOND B, 1979, ENG PROCESS ECON, V4, P455, DOI 10.1016/0377-841X(79)90003-2
   Barraza GA, 2000, J CONSTR ENG M ASCE, V126, P142, DOI 10.1061/(ASCE)0733-9364(2000)126:2(142)
   Blyth K, 2006, ENG CONSTR ARCHIT MA, V13, P82, DOI 10.1108/09699980610646511
   Boussabaine A. H., 1998, CONSTRUCTION MANAGEM, V16, P471, DOI DOI 10.1080/014461998372240
   BOUSSABAINE AH, 1999, ENG CONSTRUCTION ARC, V6, P213
   Chen HL, 2005, J CONSTR ENG M, V131, P669, DOI 10.1061/(ASCE)0733-9364(2005)131:6(669)
   Cheng MY, 2008, J COMPUT CIVIL ENG, V22, P272, DOI 10.1061/(ASCE)0887-3801(2008)22:4(272)
   Cheng MY, 2003, J CONSTR ENG M, V129, P461, DOI 10.1061/(ASCE)0733-9364(2003)129:4(461)
   Chua DKH, 1997, EXPERT SYST APPL, V13, P317, DOI 10.1016/S0957-4174(97)00046-8
   Cioffi D. F., 2005, International Journal of Project Management, V23, P215, DOI 10.1016/j.ijproman.2004.08.001
   Gen M., 1997, GENETIC ALGORITHMS E
   Gormley FM, 2007, EUR J OPER RES, V182, P923, DOI 10.1016/j.ejor.2006.07.041
   Gorzalczany MB, 2000, PROCEEDINGS OF IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY 2000, VOLS 1 AND 2, P503, DOI 10.1109/ICIT.2000.854204
   Haykin S, 2010, NEURAL NETWORKS COMP, P1
   Hegazy T, 2003, J CONSTR ENG M ASCE, V129, P396, DOI 10.1061/(ASCE)0733-9364(2003)129:4(396)
   Holland J. H., 1975, ADAPTATION NEURAL AR
   Jagielska I, 1999, NEUROCOMPUTING, V24, P37, DOI 10.1016/S0925-2312(98)00090-3
   Jarrah R, 2007, J CONSTR ENG M, V133, P235, DOI 10.1061/(ASCE)0733-9364(2007)133:3(235)
   Kaka A. P., 1991, CONSTRUCTION MANAGEM, V9, P291, DOI DOI 10.1080/01446199100000023
   Kenley R., 1989, CONSTR MANAGE EC, V7, P3, DOI DOI 10.1080/01446198900000002
   Khosrowshahi F, 2007, COMPUT-AIDED CIV INF, V22, P527, DOI 10.1111/j.1467-8667.2007.00508.x
   KLIR GJ, 1995, IEEE INT C, V1, P25
   LIATSIS P, 1995, P IEEE INT S IND EL, V2, P792
   LOKMIC L, 2000, P IEEE INNS ENNS INT, V6, P343
   LOWE JG, 1993, J APPL EXPERT SYSTEM, V1, P134
   MacQueen J., 1967, P 5 BERK S MATH STAT, P281
   Maier HR, 2000, J COMPUT CIVIL ENG, V14, P183, DOI 10.1061/(ASCE)0887-3801(2000)14:3(183)
   Maulik U, 2000, PATTERN RECOGN, V33, P1455, DOI 10.1016/S0031-3203(99)00137-5
   Michalewicz, 1996, GENETIC ALGORITHMS D
   Navon R, 1996, J CONSTR ENG M ASCE, V122, P22, DOI 10.1061/(ASCE)0733-9364(1996)122:1(22)
   Park HK, 2005, J MANAGE ENG, V21, P164, DOI 10.1061/(ASCE)0742-597X(2005)21:4(164)
   RUSSELL JS, 1991, J PERFORM CONSTR FAC, V5, P163, DOI DOI 10.1061/(ASCE)0887-3828(1991)5:3(163)
   Shou YY, 2006, PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, P176
   Singer M, 1992, Med Anthropol, V14, P1
   STALLWORTHY EA, 1979, ENG PROCESS ECON, V4, P29, DOI 10.1016/0377-841X(79)90013-5
   SUHANIC G, 1986, 30 ANN M AM ASS COST
   Tou J. T., 1974, PATTERN RECOGNITION
   Touran A, 2004, J CONSTR ENG M ASCE, V130, P719, DOI 10.1061/(ASCE)0733-9364(2004)130:5(719)
   ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X
NR 39
TC 33
Z9 35
U1 2
U2 47
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0926-5805
EI 1872-7891
J9 AUTOMAT CONSTR
JI Autom. Constr.
PD JUL
PY 2009
VL 18
IS 4
BP 386
EP 393
DI 10.1016/j.autcon.2008.10.005
PG 8
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA 444NH
UT WOS:000265987200002
DA 2022-08-23
ER

PT J
AU Li, J
   Goerlandt, F
   Li, KW
AF Li, Jie
   Goerlandt, Floris
   Li, Kai Way
TI Slip and Fall Incidents at Work: A Visual Analytics Analysis of the
   Research Domain
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE slip and fall; bibliometric analysis; knowledge mapping; citation
   network; VOSviewer; HistCite
ID FLOOR SLIPPERINESS; SAFETY SCIENCE; PREVENT FALLS; OLDER-ADULTS;
   RISK-FACTORS; FRICTION; RESISTANCE; ACCIDENTS; INJURIES; TRIPS
AB Slip and fall incidents at work remain an important class of injury and fatality causing mechanisms. An extensive body of safety research has accumulated on this topic. This article presents an analysis of this research domain. Two bibliometric visualization tools are applied: VOSviewer and HistCite. Samples of 618 slip and fall related articles are obtained from the Web of Science database. Networks of institutions, authors, terms, and chronological citation relationships are established. Collaboration and research activities of the slip and fall research community show that most contributors are from the United States, with the (now closed) Liberty Mutual Research Institute for Safety the most influential research organization. The results of a term clustering analysis show that the slip and fall research can be grouped into three sub-domains: epidemiology, gait/biomechanics, and tribology. Of these, early research focused mainly on tribology, whereas research on gait/biomechanics and epidemiological studies are relatively more recent. Psychological aspects of slip and fall incident occurrence represent a relatively under-investigated research topic, in which future contributions may provide new insights and safety improvements. Better linking of this research domain with other principles and methods in safety science, such as safety management and resilience, may also present valuable future development paths.
C1 [Li, Jie] Shanghai Maritime Univ, Sch Ocean Sci & Engn, Dept Safety Sci & Engn, Pudong New Area, Shanghai 201306, Peoples R China.
   [Li, Jie] Beijing Inst Technol, State Key Lab Explos Sci & Technol, Beijing 100081, Peoples R China.
   [Goerlandt, Floris] Dalhousie Univ, Dept Ind Engn, Halifax, NS B3H 4R2, Canada.
   [Li, Kai Way] Chung Hua Univ, Dept Ind Management, Hsinchu 30012, Taiwan.
RP Li, KW (通讯作者)，Chung Hua Univ, Dept Ind Management, Hsinchu 30012, Taiwan.
EM lijie_jerry@126.com; floris.goerlandt@dal.ca; kai@chu.edu.tw
RI Li, Jie/I-1983-2014
OI Li, Jie/0000-0002-4096-2795; Goerlandt, Floris/0000-0002-9772-419X; Li,
   Kai Way/0000-0003-0138-183X
FU National Natural Science Foundation of China [51874042, 51904185];
   Natural Sciences and Engineering Research Council of Canada (NSERC)
FX This study was supported by the National Natural Science Foundation of
   China (No. 51874042 and No. 51904185). The contributions by the second
   author are supported by the Natural Sciences and Engineering Research
   Council of Canada (NSERC), through the Discovery Grants Program. The
   authors wish to thank the three anonymous reviewers for their insightful
   and constructive comments, which have helped improve an earlier version
   of this article.
CR Amin MT, 2019, PROCESS SAF ENVIRON, V126, P366, DOI 10.1016/j.psep.2019.04.015
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], 2009, GEPHI OPEN SOURCE SO
   Aparicio-Martinez P, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16204024
   BEAVER DD, 1979, SCIENTOMETRICS, V1, P133, DOI 10.1007/BF02016966
   Bentley TA, 1998, ERGONOMICS, V41, P1859, DOI 10.1080/001401398186027
   Brady RA, 2000, J BIOMECH, V33, P803, DOI 10.1016/S0021-9290(00)00037-3
   Briganti M, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16030320
   CARLSOO S, 1962, ERGONOMICS, V5, P271, DOI 10.1080/00140136208930585
   Cham R, 2002, GAIT POSTURE, V15, P159, DOI 10.1016/S0966-6362(01)00150-3
   Cham R, 2002, SAFETY SCI, V40, P559, DOI 10.1016/S0925-7535(01)00059-5
   Cham R, 2001, J BIOMECH, V34, P1439, DOI 10.1016/S0021-9290(01)00116-6
   Chang WR, 2004, APPL ERGON, V35, P401, DOI 10.1016/j.apergo.2004.01.006
   Chang WR, 2001, ERGONOMICS, V44, P1217, DOI 10.1080/00140130110085574
   Chang WR, 2001, ERGONOMICS, V44, P1233, DOI 10.1080/00140130110085583
   Chang WR, 2002, SAFETY SCI, V40, P593, DOI 10.1016/S0925-7535(01)00061-3
   COLE JR, 1972, SCIENCE, V178, P368, DOI 10.1126/science.178.4059.368
   Courtney TK, 2001, ERGONOMICS, V44, P1118, DOI 10.1080/00140130110085538
   Dahl E, 2010, INT MARIT HEALTH, V61, P1
   Edwards N, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16091598
   Garfield E, 2002, P ASIST ANNU, V39, P14, DOI 10.1002/meet.1450390102
   Garfield E., 2006, INFORM WISSENSCHAFT, V57, P391
   Grant MJ, 2009, HEALTH INFO LIBR J, V26, P91, DOI 10.1111/j.1471-1842.2009.00848.x
   GRONQVIST R, 1995, ERGONOMICS, V38, P224, DOI 10.1080/00140139508925100
   GRONQVIST R, 1989, ERGONOMICS, V32, P979, DOI 10.1080/00140138908966859
   Gronqvist R, 2001, ERGONOMICS, V44, P1167, DOI 10.1080/00140130110085556
   Hanson JP, 1999, ERGONOMICS, V42, P1619, DOI 10.1080/001401399184712
   Hartung B, 2017, GERIATR NURS, V38, P412, DOI 10.1016/j.gerinurse.2017.02.002
   Haslam RA, 1999, SAFETY SCI, V32, P33, DOI 10.1016/S0925-7535(99)00009-0
   Jensen OC, 2005, AM J IND MED, V47, P161, DOI 10.1002/ajim.20119
   Jin RY, 2019, SAFETY SCI, V113, P285, DOI 10.1016/j.ssci.2018.12.006
   Kemmlert K, 2001, APPL ERGON, V32, P149, DOI 10.1016/S0003-6870(00)00051-X
   KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103
   Kim SC, 2016, INT J ENV RES PUB HE, V13, DOI 10.3390/ijerph13111063
   Kiyoshi-Teo H, 2019, GERIATR NURS, V40, P290, DOI 10.1016/j.gerinurse.2018.11.005
   Kong PW, 2013, SAFETY SCI, V60, P203, DOI 10.1016/j.ssci.2013.07.016
   Larsson A, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16142451
   LEAMON TB, 1995, ERGONOMICS, V38, P487, DOI 10.1080/00140139508925120
   Leclerc BS, 2010, REV EPIDEMIOL SANTE, V58, P3, DOI 10.1016/j.respe.2009.10.008
   Leclercq S, 1999, SAFETY SCI, V31, P95, DOI 10.1016/S0925-7535(98)00064-2
   Lee S, 2017, INT J ENV RES PUB HE, V14, DOI 10.3390/ijerph14020163
   Li J, 2017, J LOSS PREVENT PROC, V49, P103, DOI 10.1016/j.jlp.2016.06.003
   Li J, 2015, SAFETY SCI, V74, P70, DOI 10.1016/j.ssci.2014.12.003
   Li KW, 2004, SAFETY SCI, V42, P547, DOI 10.1016/j.ssci.2003.08.006
   Lockhart TE, 2003, ERGONOMICS, V46, P1136, DOI 10.1080/0014013031000139491
   MANNING DP, 1988, J OCCUP ACCID, V10, P121, DOI 10.1016/0376-6349(88)90026-0
   Marigold DS, 2002, J NEUROPHYSIOL, V88, P339, DOI 10.1152/jn.00691.2001
   Masud T, 2001, AGE AGEING, V30, P3, DOI 10.1093/ageing/30.suppl_4.3
   Merigo JM, 2019, SAFETY SCI, V115, P66, DOI 10.1016/j.ssci.2019.01.029
   Moon Y, 2017, ARCH PHYS MED REHAB, V98, P783, DOI 10.1016/j.apmr.2016.08.460
   Myung R, 1993, INT J IND ERGONOM, V11, P313, DOI DOI 10.1016/0169-8141(93)90081-N
   Nasarwanji MF, 2019, SAFETY SCI, V120, P625, DOI 10.1016/j.ssci.2019.08.007
   Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.066133
   Perkins P. J., 1978, ASTM STP, P71, DOI DOI 10.1520/STP28738S
   PERKINS PJ, 1983, ERGONOMICS, V26, P73, DOI 10.1080/00140138308963314
   PRICE DJD, 1976, J AM SOC INFORM SCI, V27, P292, DOI 10.1002/asi.4630270505
   PRITCHARD A, 1969, J DOC, V25, P348
   REDFERN MS, 1994, ERGONOMICS, V37, P511, DOI 10.1080/00140139408963667
   Redfern MS, 2001, ERGONOMICS, V44, P1138, DOI 10.1080/00140130110085547
   Saadat S, 2016, INJURY, V47, P1404, DOI 10.1016/j.injury.2016.05.001
   Scott KA, 2018, AM J IND MED, V61, P111, DOI 10.1002/ajim.22796
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   STRANDBERG L, 1981, J OCCUP ACCID, V3, P153, DOI 10.1016/0376-6349(81)90009-2
   STRANDBERG L, 1985, ERGONOMICS, V28, P131, DOI 10.1080/00140138508963123
   STRANDBERG L, 1983, ERGONOMICS, V26, P11, DOI 10.1080/00140138308963309
   SWENSEN EE, 1992, HUM FACTORS, V34, P67, DOI 10.1177/001872089203400108
   TISSERAND M, 1985, ERGONOMICS, V28, P1027, DOI 10.1080/00140138508963225
   Valero P, 1998, BEHAV INFORM TECHNOL, V17, P3, DOI 10.1080/014492998119625
   van Eck NJ, 2010, SCIENTOMETRICS, V82, P581, DOI 10.1007/s11192-010-0173-0
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   van Nunen K, 2018, SAFETY SCI, V108, P248, DOI 10.1016/j.ssci.2017.08.011
   Wilkinson A, 2018, INT J NURS STUD, V82, P13, DOI 10.1016/j.ijnurstu.2018.03.002
   Yang YF, 2019, SAFETY SCI, V120, P14, DOI 10.1016/j.ssci.2019.06.022
NR 76
TC 12
Z9 12
U1 5
U2 29
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD DEC 2
PY 2019
VL 16
IS 24
AR 4972
DI 10.3390/ijerph16244972
PG 18
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA KC6VK
UT WOS:000507312700093
PM 31817818
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Gan, MX
   Kwon, OC
AF Gan, Mingxin
   Kwon, O-Chol
TI A knowledge-enhanced contextual bandit approach for personalized
   recommendation in dynamic domains
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Recommender system; Knowledge graph; Contextual multi-armed bandit;
   Dynamic domain
ID DIVERSITY; ACCURACY; SYSTEMS
AB Recently, contextual multiarmed bandits (CMAB)-based recommendation has shown promise for appli-cations in dynamic domains such as news or short video recommendation, where items are changing over time. There are two key challenges in constructing a CMAB-based system. First, a user's historical rating data are usually sparse, which restricts the precise representation of dynamic contextual information. Second, it is difficult to design a personalized recommendation policy because of the high diversity of users' selection behaviors over time. Therefore, existing CMAB-based recommendation methods mainly focus on the improvement of recommendation accuracy. This leads to difficulty in capturing the dynamic personalized preferences of users. To overcome these limitations, we design a novel knowledge-enhanced contextual-bandit approach that combines the dynamic recommendation strategy of the bandit algorithm and the knowledge representation of the knowledge graph. We argue that a user's selection history for items reflects his/her favorite attributes, which will be repeated in the future. Accordingly we propose the knowledge-enhanced CMAB model, which leverages the contextual information of both users and items obtained by knowledge graph-based embedding. We validate the performance of the proposed model using two public datasets. Experimental results show that our method outperforms state-of-the-art methods in terms of both recommendation accuracy and diversity. (C) 2022 Elsevier B.V. All rights reserved.
C1 [Gan, Mingxin; Kwon, O-Chol] Univ Sci & Technol Beijing, Sch Econ & Management, Dept Management Sci & Engn, Beijing 100083, Peoples R China.
   [Kwon, O-Chol] Kim Il Sung Univ, Dept Informat Sci, Pyongyang, North Korea.
RP Gan, MX (通讯作者)，Univ Sci & Technol Beijing, Sch Econ & Management, Dept Management Sci & Engn, Beijing 100083, Peoples R China.
EM ganmx@ustb.edu.cn
FU National Natural Science Foundation of China [71871019, 71471016]
FX This study is supported by the National Natural Science Foundation of
   China (71871019, 71471016) .
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
   Agrawal S., 2013, INT C MACH LEARN, P127, DOI DOI 10.5555/3042817.3043073
   Auer P, 2003, SIAM J COMPUT, V32, P48, DOI 10.1137/s0097539701398375
   Auer P, 2002, MACH LEARN, V47, P235, DOI 10.1023/A:1013689704352
   Bobadilla J, 2012, INT J INTELL SYST, V27, P939, DOI 10.1002/int.21556
   Bordes A., 2013, P 26 INT C NEUR INF
   Chapelle O., 2011, ADV NEURAL INFORM PR, P2249, DOI DOI 10.5555/2986459.2986710
   Chen MH, 2015, ADV ENG INFORM, V29, P830, DOI 10.1016/j.aei.2015.04.005
   Chen R, 2018, IEEE ACCESS, V6, P64301, DOI 10.1109/ACCESS.2018.2877208
   Diao QM, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P193, DOI 10.1145/2623330.2623758
   Ebrahimi F, 2016, IRAN CONF ELECTR ENG, P218, DOI 10.1109/IranianCEE.2016.7585521
   Gan MX, 2013, DECIS SUPPORT SYST, V55, P811, DOI 10.1016/j.dss.2013.03.006
   Goldberg K, 2001, INFORM RETRIEVAL, V4, P133, DOI 10.1023/A:1011419012209
   Gong SJ, 2009, PROCEEDINGS OF THE 2009 PACIFIC-ASIA CONFERENCE ON CIRCUITS, COMMUNICATIONS AND SYSTEM, P690, DOI 10.1109/PACCS.2009.66
   Goyal N., 2012, P 25 ANN C LEARN THE
   Guo Qingyu, 2022, IEEE Transactions on Knowledge and Data Engineering, V34, P3549, DOI 10.1109/TKDE.2020.3028705
   Gutowski N, 2021, INFORM SCIENCES, V546, P378, DOI 10.1016/j.ins.2020.08.106
   Gutowski N, 2019, SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING, P1647, DOI 10.1145/3297280.3297440
   Hamilton WL, 2017, ADV NEUR IN, V30
   Hopfgartner F., 2014, INT C CROSS LANG EV, V213, P250
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Huang LW, 2021, KNOWL-BASED SYST, V213, DOI 10.1016/j.knosys.2020.106706
   Javari A, 2015, KNOWL INF SYST, V44, P609, DOI 10.1007/s10115-014-0779-2
   Jeong B, 2010, INFORM SCIENCES, V180, P602, DOI 10.1016/j.ins.2009.10.016
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Jin YR, 2020, IEEE ACCESS, V8, P55032, DOI 10.1109/ACCESS.2020.2982037
   Kipf T., 2016, 5 INT C LEARN REPR, P1
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Langford J., 2008, ADV NEURAL INFORM PR, P817
   Lee D, 2019, INFORM SYST RES, V30, P239, DOI 10.1287/isre.2018.0800
   Li L., 2010, P 19 INT C WORLD WID, P661, DOI [DOI 10.1145/1772690.1772758, 10.1145/1772690.1772758]
   Li S, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P539, DOI 10.1145/2911451.2911548
   Liebman E, 2019, MIS QUART, V43, P765, DOI 10.25300/MISQ/2019/14750
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lin Y, 2021, KNOWL-BASED SYST, V233, DOI 10.1016/j.knosys.2021.107484
   Liu F, 2020, KNOWL-BASED SYST, V205, DOI 10.1016/j.knosys.2020.106170
   Liu X, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P887, DOI 10.1145/2766462.2767775
   Margaris D, 2019, PROCEEDINGS OF THE 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2019), P1160, DOI 10.1145/3341161.3345620
   Marlin B, 2004, ADV NEUR IN, V16, P627
   Mudambi SM, 2010, MIS QUART, V34, P185
   Nachev A, 2005, ICAI '05: PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, P846
   Qiu JZ, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2110, DOI 10.1145/3219819.3220077
   Qu YR, 2019, 1ST INTERNATIONAL WORKSHOP ON DEEP LEARNING PRACTICE FOR HIGH-DIMENSIONAL SPARSE DATA WITH KDD (DLP-KDD 2019), DOI 10.1145/3326937.3341257
   Reafee W, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0154848
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Schedl M, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P947, DOI 10.1145/2766462.2767763
   Song KH, 2021, KNOWL-BASED SYST, V221, DOI 10.1016/j.knosys.2021.106835
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Tang XL, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1891, DOI 10.1145/3292500.3330705
   Turcotte J, 2015, J COMPUT-MEDIAT COMM, V20, P520, DOI 10.1111/jcc4.12127
   Wang FR, 2020, CONCURR COMP-PRACT E, V32, DOI 10.1002/cpe.5703
   Wang H, 2018, 2018 IEEE 3RD INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA), P78, DOI 10.1109/ICCCBDA.2018.8386490
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu LB, 2019, ACM T INFORM SYST, V37, DOI 10.1145/3298988
   Wu S, 2016, IEEE T KNOWL DATA EN, V28, P2000, DOI 10.1109/TKDE.2016.2562621
   Yang B., 2015, P INT C LEARN REPR
   Ye YT, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P679, DOI 10.1145/3357384.3358014
   Yu K, 2004, IEEE T KNOWL DATA EN, V16, P56, DOI 10.1109/TKDE.2004.1264822
   Yu X., 2013, IJCAI HINA, V27
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhang JD, 2017, IEEE T KNOWL DATA EN, V29, P798, DOI 10.1109/TKDE.2016.2641439
   Zhao H, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P635, DOI 10.1145/3097983.3098063
   Zhao Q, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P320, DOI 10.1145/3240323.3240367
   Zhou T, 2010, P NATL ACAD SCI USA, V107, P4511, DOI 10.1073/pnas.1000488107
   Zhou X, 2015, J COMPUT SYST SCI, V81, P717, DOI 10.1016/j.jcss.2014.11.016
NR 73
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD SEP 5
PY 2022
VL 251
AR 109158
DI 10.1016/j.knosys.2022.109158
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3C0FK
UT WOS:000828308100016
DA 2022-08-23
ER

PT J
AU Chen, JY
   Hu, P
   Jimenez-Ruiz, E
   Holter, OM
   Antonyrajah, D
   Horrocks, I
AF Chen, Jiaoyan
   Hu, Pan
   Jimenez-Ruiz, Ernesto
   Holter, Ole Magnus
   Antonyrajah, Denvar
   Horrocks, Ian
TI OWL2Vec*: embedding of OWL ontologies
SO MACHINE LEARNING
LA English
DT Article
DE Ontology; Ontology embedding; Word embedding; Web ontology language;
   OWL2Vec*; Ontology completion
AB Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attention has been paid to developing robust methods for embedding OWL (Web Ontology Language) ontologies, which contain richer semantic information than plain knowledge graphs, and have been widely adopted in domains such as bioinformatics. In this paper, we propose a random walk and word embedding based ontology embedding method named OWL2Vec*, which encodes the semantics of an OWL ontology by taking into account its graph structure, lexical information and logical constructors. Our empirical evaluation with three real world datasets suggests that OWL2Vec* benefits from these three different aspects of an ontology in class membership prediction and class subsumption prediction tasks. Furthermore, OWL2Vec* often significantly outperforms the state-of-the-art methods in our experiments.
C1 [Chen, Jiaoyan; Hu, Pan; Horrocks, Ian] Univ Oxford, Dept Comp Sci, Oxford, England.
   [Hu, Pan] City Univ London, London, England.
   [Jimenez-Ruiz, Ernesto; Holter, Ole Magnus] Univ Oslo, Dept Informat, Oslo, Norway.
   [Antonyrajah, Denvar] Samsung Res, Staines, England.
RP Chen, JY (通讯作者)，Univ Oxford, Dept Comp Sci, Oxford, England.
EM jiaoyan.chen@cs.ox.ac.uk
RI Jimenez-Ruiz, Ernesto/L-6460-2014
OI Jimenez-Ruiz, Ernesto/0000-0002-9083-4599
FU SIRIUS Centre for Scalable Data Access (Research Council of Norway)
   [237889]; Samsung Research UK; Siemens AG; EPSRC project AnaLOG
   [EP/P025943/1]; EPSRC project OASIS [EP/S032347/1]; EPSRC project UK
   FIRES [EP/S019111/1]; EPSRC project AIDA project (Alan Turing Institute)
FX This work was supported by the SIRIUS Centre for Scalable Data Access
   (Research Council of Norway, project 237889), Samsung Research UK,
   Siemens AG, and the EPSRC projects AnaLOG (EP/P025943/1), OASIS
   (EP/S032347/1), UK FIRES (EP/S019111/1) and the AIDA project (Alan
   Turing Institute).
CR Alshargi F., 2018, METRICS EVALUATING Q
   [Anonymous], 2011, J MACH LEARN RES
   Arora Sanjeev, 2019, ICLR
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bechhofer S., 2004, W3C RECOMMENDATION, V10
   Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen H, 2020, KR
   CHEN J, 2019, INT SEMANTIC WEB C, P110
   CHEN J, 2020, P WEB C, V2020, P1537
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dooley DM, 2018, NPJ SCI FOOD, V2, DOI 10.1038/s41538-018-0032-6
   Dragoni M, 2018, LECT NOTES COMPUT SC, V11137, P53, DOI 10.1007/978-3-030-00668-6_4
   *G, 2008, NUCLEIC ACIDS RES, V36, pD440
   Garg D., 2019, ADV NEURAL INFORM PR, P5595
   Glimm B, 2014, J AUTOM REASONING, V53, P245, DOI 10.1007/s10817-014-9305-1
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Guan NN, 2019, KNOWL-BASED SYST, V164, P38, DOI 10.1016/j.knosys.2018.10.008
   Hao B., 2020, P 28 INT C COMPUTATI, P657
   Hao J., 2019, KDD
   Hoehndorf F. Z, 2018, BIOINFORMATICS, V35
   Hoehndorf M, 2020, BRIEFINGS BIOINFORMA
   Hoehndorf R., 2019, IJCAI
   Horrocks I., 2020, ICBO 2020 INT FOOD O
   Horrocks I., 2017, INTRO DESCRIPTION LO, DOI [10.1017/9781139025355.008, DOI 10.1017/9781139025355.008]
   Horrocks I, 2008, COMMUN ACM, V51, P58, DOI 10.1145/1409360.1409377
   Jimenez-Ruiz E, 2021, ESWC WORKSH DEEPONTO
   Jimenez-Ruiz O.M, 2019, INT SEM WEB C POST D
   Lee J, 2020, EUR SEM WEB C ESWC
   Leskovec J, 2014, MINING OF MASSIVE DATASETS, 2ND EDITION, P1
   Li C, 2020, BENCHMARKING STUDY E
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Miaschi A, 2020, 5TH WORKSHOP ON REPRESENTATION LEARNING FOR NLP (REPL4NLP-2020), P110
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Moon C, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2215, DOI 10.1145/3132847.3133095
   MYKLEBUST E, 2019, ISWC, P490
   Paulheim H, 2015, LECT NOTES COMPUT SC, V9366, P180, DOI 10.1007/978-3-319-25007-6_11
   Peters M., 2018, IMPROVING LANGUAGE U
   Polleres S, 2020, ARXIV PREPRINT ARXIV
   Ristoski P, 2019, SEMANT WEB, V10, P721, DOI 10.3233/SW-180317
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9981, P498, DOI 10.1007/978-3-319-46523-4_30
   Rossi A., 2020, ARXIV PREPRINT ARXIV
   Shervashidze N, 2011, J MACH LEARN RES, V12, P2539
   Smaili FZ, 2018, BIOINFORMATICS, V34, P52, DOI 10.1093/bioinformatics/bty259
   Soylu A, 2018, SEMANT WEB, V9, P627, DOI 10.3233/SW-180293
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Wang CJ, 2019, LECT NOTES COMPUT SC, V11778, P664, DOI 10.1007/978-3-030-30793-6_38
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang XL, 2018, PROC CVPR IEEE, P6857, DOI 10.1109/CVPR.2018.00717
   Yanardag P, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1365, DOI 10.1145/2783258.2783417
   Yang B., 2014, ARXIV PREPRINT ARXIV
NR 53
TC 3
Z9 3
U1 2
U2 5
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0885-6125
EI 1573-0565
J9 MACH LEARN
JI Mach. Learn.
PD JUL
PY 2021
VL 110
IS 7
BP 1813
EP 1845
DI 10.1007/s10994-021-05997-6
EA JUN 2021
PG 33
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TC9WO
UT WOS:000662170800001
OA Green Submitted, hybrid, Green Accepted
DA 2022-08-23
ER

PT J
AU Chen, Y
   Wang, YP
   Zhou, D
AF Chen, Yuan
   Wang, Yupeng
   Zhou, Dian
TI Knowledge Map of Urban Morphology and Thermal Comfort: A Bibliometric
   Analysis Based on CiteSpace
SO BUILDINGS
LA English
DT Review
DE urban morphology; thermal comfort; CiteSpace; bibliometric analysis
ID HEAT-ISLAND MITIGATION; CLIMATE-CHANGE; GREEN INFRASTRUCTURE; BUILDING
   HEIGHT; AIR-QUALITY; BLOCK FORM; ENVI-MET; IMPACT; DESIGN; HOT
AB In the context of global climate change, urban morphology is closely related to thermal comfort and contributes to sustainable urban development. Academics started to pay attention to related topics and carried out many studies during the last decades. This paper aims to summarize the research achievements and the development track for future studies. The Web of Science database and CiteSpace were used in this paper to conduct a bibliometric analysis of 556 studies in related fields from 1993 to 2020. Using a three-level co-occurrence analysis of 446 keywords, 1187 cocited literature clusters, and 15 landmark studies, the research topics and mainstream research frameworks were identified. The results show that with the increasing participation of disciplines such as computer science, ecology, and chemistry, the purpose of future research will shift to a focus on anthropogenic heat emissions, energy consumption, air pollution, and other aspects, and new research tools will be needed. In addition to building-scale and block-scale morphology, urban-scale morphology and green infrastructure will become the focus in the future. This study provides a systematic review of research about urban morphology and thermal comfort, which can inspire other researchers and policy makers.</p>
C1 [Chen, Yuan; Wang, Yupeng; Zhou, Dian] Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xian 710049, Peoples R China.
RP Wang, YP (通讯作者)，Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xian 710049, Peoples R China.
EM y.chen@stu.xjtu.edu.cn; wang-yupeng@xjtu.edu.cn; dian-z@mail.xjtu.edu.cn
FU National Natural Science Foundation of China [52078416]
FX FundingThis research was funded by the National Natural Science
   Foundation of China, grant number 52078416.
CR Akbari H, 2016, J CIV ENG MANAG, V22, P1, DOI 10.3846/13923730.2015.1111934
   Ali-Toudert F, 2007, SOL ENERGY, V81, P742, DOI 10.1016/j.solener.2006.10.007
   AlKhaled S, 2020, URBAN CLIM, V31, DOI 10.1016/j.uclim.2019.100570
   Allegrini J, 2017, URBAN CLIM, V21, P278, DOI 10.1016/j.uclim.2017.07.005
   American Society of Heating Refrigerating and Air-Conditioning Engineers (ASHRAE), 2004, ASHRAE STANDARD 55
   Baruti MM, 2020, SUSTAIN CITIES SOC, V62, DOI 10.1016/j.scs.2020.102380
   Baruti MM, 2019, INT J BIOMETEOROL, V63, P1449, DOI 10.1007/s00484-019-01757-3
   Cai Z, 2018, SUSTAIN CITIES SOC, V39, P487, DOI 10.1016/j.scs.2018.02.033
   Charalampopoulos I, 2013, URBAN ECOSYST, V16, P217, DOI 10.1007/s11252-012-0252-5
   Chatzidimitriou A, 2017, SUSTAIN CITIES SOC, V33, P85, DOI 10.1016/j.scs.2017.05.019
   Chen C, 2016, CITESPACE PRACTICAL
   Chen CM, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0223994
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen YP, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12072974
   Coutts AM, 2016, REMOTE SENS ENVIRON, V186, P637, DOI 10.1016/j.rse.2016.09.007
   DAES, 2019, WORLD URB PROSP 2018
   de Abreu-Harbicha LV, 2015, LANDSCAPE URBAN PLAN, V138, P99, DOI 10.1016/j.landurbplan.2015.02.008
   de Freitas CR, 2017, INT J BIOMETEOROL, V61, P487, DOI 10.1007/s00484-016-1228-6
   de Munck CS, 2013, GEOSCI MODEL DEV, V6, P1941, DOI 10.5194/gmd-6-1941-2013
   Emmanuel R, 2007, CLIM RES, V34, P241, DOI 10.3354/cr00694
   Endler C, 2010, INT J BIOMETEOROL, V54, P45, DOI 10.1007/s00484-009-0251-2
   Fahmy M, 2009, BUILD ENVIRON, V44, P1907, DOI 10.1016/j.buildenv.2009.01.010
   Fernandes ME, 2020, URBE-CURITIBA, V12, DOI 10.1590/2175-3369.012.e20190247
   Gago EJ, 2013, RENEW SUST ENERG REV, V25, P749, DOI 10.1016/j.rser.2013.05.057
   Galal OM, 2020, SUSTAIN CITIES SOC, V54, DOI 10.1016/j.scs.2019.102011
   Guo LL, 2018, NEW ARCH, V5, P28, DOI 10.14621/tna.20180104
   Hajat S, 2010, J EPIDEMIOL COMMUN H, V64, P753, DOI 10.1136/jech.2009.087999
   Hami A, 2019, SUSTAIN CITIES SOC, V49, DOI 10.1016/j.scs.2019.101634
   Hong B, 2017, SUSTAIN CITIES SOC, V34, P358, DOI 10.1016/j.scs.2017.07.005
   Huang KT, 2017, ENERG BUILDINGS, V154, P448, DOI 10.1016/j.enbuild.2017.08.054
   Huang X, 2019, ISPRS J PHOTOGRAMM, V152, P119, DOI 10.1016/j.isprsjprs.2019.04.010
   Jamei E, 2020, RENEW SUST ENERG REV, V134, DOI 10.1016/j.rser.2020.110362
   Jamei E, 2017, SOL ENERGY, V144, P681, DOI 10.1016/j.solener.2017.01.023
   Jamei E, 2016, RENEW SUST ENERG REV, V54, P1002, DOI 10.1016/j.rser.2015.10.104
   Javanroodi K, 2018, APPL ENERG, V231, P714, DOI 10.1016/j.apenergy.2018.09.116
   Jiang YF, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9122224
   Johansson E, 2006, INT J BIOMETEOROL, V51, P119, DOI 10.1007/s00484-006-0047-6
   Johansson E, 2006, BUILD ENVIRON, V41, P1326, DOI 10.1016/j.buildenv.2005.05.022
   Kantzioura A, 2015, SUSTAIN CITIES SOC, V19, P331, DOI 10.1016/j.scs.2015.03.006
   Ketterer C, 2014, LANDSCAPE URBAN PLAN, V122, P78, DOI 10.1016/j.landurbplan.2013.11.003
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   Kotharkar R, 2019, GEOGR PANNONICA, V23, P318, DOI 10.5937/gp23-24251
   Kruger EL, 2011, BUILD ENVIRON, V46, P621, DOI 10.1016/j.buildenv.2010.09.006
   Kwok YT, 2019, INT J CLIMATOL, V39, P5292, DOI 10.1002/joc.6140
   Lai DY, 2019, SCI TOTAL ENVIRON, V661, P337, DOI 10.1016/j.scitotenv.2019.01.062
   Lee H, 2016, LANDSCAPE URBAN PLAN, V148, P37, DOI 10.1016/j.landurbplan.2015.12.004
   Letzel MO, 2012, METEOROL Z, V21, P575, DOI 10.1127/0941-2948/2012/0356
   Leung K.S., 2010, URBAN GEOMETRY INDOO, P63
   Li GG, 2020, BUILD ENVIRON, V169, DOI 10.1016/j.buildenv.2019.106587
   Li GD, 2018, SUSTAIN CITIES SOC, V38, P736, DOI 10.1016/j.scs.2018.01.033
   Manoli G, 2019, NATURE, V573, P55, DOI 10.1038/s41586-019-1512-9
   Mavrogianni A, 2012, BUILD ENVIRON, V55, P117, DOI 10.1016/j.buildenv.2011.12.003
   Mendes A, 2013, J TOXICOL ENV HEAL A, V76, P333, DOI 10.1080/15287394.2013.757213
   Meng LC, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12062384
   Middel A, 2014, LANDSCAPE URBAN PLAN, V122, P16, DOI 10.1016/j.landurbplan.2013.11.004
   Morakinyo TE, 2020, SCI TOTAL ENVIRON, V719, DOI 10.1016/j.scitotenv.2020.137461
   Morakinyo TE, 2016, BUILD ENVIRON, V103, P262, DOI 10.1016/j.buildenv.2016.04.025
   Morini E, 2018, SOL ENERGY, V162, P14, DOI 10.1016/j.solener.2017.12.064
   Mostofa T, 2020, INT TRANS J ENG MANA, V11, DOI 10.14456/ITJEMAST.2020.27
   Natanian J, 2020, ENERG BUILDINGS, V224, DOI 10.1016/j.enbuild.2020.110283
   Natanian J, 2020, SUSTAIN CITIES SOC, V56, DOI 10.1016/j.scs.2020.102094
   Ng E, 2012, BUILD ENVIRON, V47, P256, DOI 10.1016/j.buildenv.2011.07.014
   Norton BA, 2015, LANDSCAPE URBAN PLAN, V134, P127, DOI 10.1016/j.landurbplan.2014.10.018
   OKE TR, 1982, Q J ROY METEOR SOC, V108, P1, DOI 10.1002/qj.49710845502
   Oliveira S, 2011, BUILD ENVIRON, V46, P2186, DOI 10.1016/j.buildenv.2011.04.034
   Peng SS, 2012, ENVIRON SCI TECHNOL, V46, P696, DOI 10.1021/es2030438
   Perini K, 2017, ENERG BUILDINGS, V152, P373, DOI 10.1016/j.enbuild.2017.07.061
   Perini K, 2014, URBAN FOR URBAN GREE, V13, P495, DOI 10.1016/j.ufug.2014.03.003
   Pigliautile I, 2020, BUILD ENVIRON, V171, DOI 10.1016/j.buildenv.2019.106641
   Quan JL, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16122140
   Algeciras JAR, 2016, BUILD ENVIRON, V101, P85, DOI 10.1016/j.buildenv.2016.02.026
   Ronchi S, 2020, SUSTAIN CITIES SOC, V63, DOI 10.1016/j.scs.2020.102459
   Rupp RF, 2015, ENERG BUILDINGS, V105, P178, DOI 10.1016/j.enbuild.2015.07.047
   Salata F, 2016, SUSTAIN CITIES SOC, V26, P318, DOI 10.1016/j.scs.2016.07.005
   Sanaieian H, 2014, RENEW SUST ENERG REV, V38, P551, DOI 10.1016/j.rser.2014.06.007
   Santamouris M, 2014, SOL ENERGY, V103, P682, DOI 10.1016/j.solener.2012.07.003
   Shafaghat A, 2016, ENVIRON CLIM TECHNOL, V17, P61, DOI 10.1515/rtuect-2016-0006
   Shafaghat A, 2016, SUSTAIN CITIES SOC, V21, P35, DOI 10.1016/j.scs.2015.11.001
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Soon WWH, 2018, EARTH-SCI REV, V185, P80, DOI 10.1016/j.earscirev.2018.05.013
   Srivanit M, 2020, J BUILD ENG, V30, DOI 10.1016/j.jobe.2020.101262
   Stone B, 2010, ENVIRON HEALTH PERSP, V118, P1425, DOI 10.1289/ehp.0901879
   Taleb D, 2013, RENEW ENERG, V50, P747, DOI 10.1016/j.renene.2012.07.030
   Taleghani M, 2015, BUILD ENVIRON, V83, P65, DOI 10.1016/j.buildenv.2014.03.014
   Tan Z, 2016, ENERG BUILDINGS, V114, P265, DOI 10.1016/j.enbuild.2015.06.031
   Thom JK, 2016, URBAN FOR URBAN GREE, V20, P233, DOI 10.1016/j.ufug.2016.08.016
   Toparlar Y, 2017, RENEW SUST ENERG REV, V80, P1613, DOI 10.1016/j.rser.2017.05.248
   van Hove LWA, 2015, BUILD ENVIRON, V83, P91, DOI 10.1016/j.buildenv.2014.08.029
   Vandentorren S, 2006, EUR J PUBLIC HEALTH, V16, P583, DOI 10.1093/eurpub/ckl063
   Wang YF, 2014, BUILD ENVIRON, V77, P88, DOI 10.1016/j.buildenv.2014.03.021
   Wang YP, 2016, SUSTAIN CITIES SOC, V27, P122, DOI 10.1016/j.scs.2016.04.013
   Wang YP, 2016, ENERG BUILDINGS, V114, P2, DOI 10.1016/j.enbuild.2015.06.046
   Wong MMF, 2019, URBAN CLIM, V28, DOI 10.1016/j.uclim.2019.100460
   Wu F, 2018, J CLEAN PROD, V204, P344, DOI 10.1016/j.jclepro.2018.09.067
   Wu WJ, 2019, TRANSPORT RES D-TR E, V67, P553, DOI 10.1016/j.trd.2018.12.023
   Xie XX, 2020, RENEW ENERG, V150, P943, DOI 10.1016/j.renene.2019.11.148
   Xu XD, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11133683
   Xu Y, 2017, LANDSCAPE URBAN PLAN, V167, P212, DOI 10.1016/j.landurbplan.2017.06.018
   Xue J, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12198083
   Yang XS, 2016, BUILDINGS, V6, DOI 10.3390/buildings6010002
   Yang ZW, 2020, BUILD ENVIRON, V167, DOI 10.1016/j.buildenv.2019.106477
   Yao XB, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12208687
   Younsi SA, 2018, ADV SCI TECHNOL INN, P75, DOI 10.1007/978-3-319-70548-4_27
   Yu K, 2019, REMOTE SENS-BASEL, V11, DOI 10.3390/rs11050497
   Zaki SA, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12072962
   Zeng YY, 2019, SCI TOTAL ENVIRON, V663, P329, DOI 10.1016/j.scitotenv.2019.01.262
   Zhang L, 2018, BUILD ENVIRON, V130, P27, DOI 10.1016/j.buildenv.2017.12.014
   Zhao D, 2020, FORESTS, V11, DOI 10.3390/f11080825
   Zhu Z, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12051876
   Zuo J, 2015, J CLEAN PROD, V92, P1, DOI 10.1016/j.jclepro.2014.12.078
NR 111
TC 1
Z9 1
U1 52
U2 75
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-5309
J9 BUILDINGS-BASEL
JI BUILDINGS-BASEL
PD OCT
PY 2021
VL 11
IS 10
AR 427
DI 10.3390/buildings11100427
PG 23
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Construction & Building Technology; Engineering
GA WV2XY
UT WOS:000717102400001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhou, Q
   Kong, HB
   He, BM
   Zhou, SY
AF Zhou, Qin
   Kong, Hai-Bo
   He, Bao-Mei
   Zhou, Su-Ya
TI Bibliometric Analysis of Bronchopulmonary Dysplasia in Extremely
   Premature Infants in the Web of Science Database Using CiteSpace
   Software
SO FRONTIERS IN PEDIATRICS
LA English
DT Review
DE bronchopulmonary dysplasia; extremely pre-mature infants; bibliometrics;
   CiteSpace; Web of Science
ID CHRONIC LUNG-DISEASE; PULMONARY-HYPERTENSION; CAFFEINE THERAPY; GLOBAL
   RESEARCH; KNOWLEDGE; TRENDS; APNEA
AB Objectives: To review the literature related to bronchopulmonary dysplasia in extremely pre-mature infants, summarize research direction, and report trends. Methods: CiteSpace is a Java application which supports visual exploration with knowledge discovery in bibliographic databases. Relevant articles from 2008 to 2020 were retrieved from the Web of Science Core Collection database, and we extracted the following data: title, abstract, year, keywords, author, organization, journal and cited literature. We downloaded the data into CiteSpace (version 5.7.R3) to summarize countries, institutions, journals, and authors. We visualized the data with a knowledge map, collaborative network analysis, cluster analysis, and burst keyword analysis. Results: We identified 610 articles on bronchopulmonary dysplasia in extremely pre-mature infants. The United States had the most articles on this topic (302 articles), followed by Canada (49 articles) and Germany (44 articles). The top three institutions, high-yield journals, and authors were all from the United States. The most common keywords were neurodevelopmental disorders, active perinatal care, mechanical ventilation, inflammation, pulmonary hypertension, low-dose hydrocortisone, development, and patent ductus arteriosus. Conclusions: This study illustrates the trends and frontiers in the study of bronchopulmonary dysplasia in extremely pre-mature infants. The current research direction is to identify the risk factors in developing bronchopulmonary dysplasia in extremely pre-mature infants.
C1 [Zhou, Qin; Kong, Hai-Bo; He, Bao-Mei] Hangzhou Med Coll, Peoples Hosp, Zhejiang Prov Peoples Hosp, Dept Pediat, Hangzhou, Peoples R China.
   [Zhou, Su-Ya] Hangzhou Childrens Hosp, Dept Neonatol, Hangzhou, Peoples R China.
RP Zhou, SY (通讯作者)，Hangzhou Childrens Hosp, Dept Neonatol, Hangzhou, Peoples R China.
EM suya.zhou@163.com
OI zhou, suya/0000-0003-1046-352X
CR Ahn SY, 2021, STEM CELL TRANSL MED, V10, P1129, DOI 10.1002/sctm.20-0330
   Amaro CM, 2018, J PEDIATR-US, V196, P52, DOI 10.1016/j.jpeds.2018.01.010
   Arjaans S, 2018, PAEDIATR PERINAT EP, V32, P258, DOI 10.1111/ppe.12444
   Chen Chaomei, 2005, AMIA Annu Symp Proc, P121
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Clyman RI, 2020, AM J PERINAT, V37, P216, DOI 10.1055/s-0039-1697672
   Clyman RI, 2019, J PEDIATR-US, V205, P41, DOI 10.1016/j.jpeds.2018.09.012
   Doyle LW, 2017, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD001145.pub4
   Doyle LW, 2006, PEDIATRICS, V117, P75, DOI 10.1542/peds.2004-2843
   Dufault B, 2011, AM J EPIDEMIOL, V174, P1101, DOI 10.1093/aje/kwr241
   EL-Khuffash A, 2015, J PEDIATR-US, V167, P1354, DOI 10.1016/j.jpeds.2015.09.028
   Gao Y, 2019, INT IMMUNOPHARMACOL, V72, P374, DOI 10.1016/j.intimp.2019.03.045
   Jefferies AL, 2012, PAED CHILD HEALT-CAN, V17, P573, DOI 10.1093/pch/17.10.573
   Krishnan U, 2017, J PEDIATR-US, V188, P24, DOI 10.1016/j.jpeds.2017.05.029
   Liang CC, 2018, SAGE OPEN MED, V6, DOI 10.1177/2050312118800199
   Lodha A, 2015, JAMA PEDIATR, V169, P33, DOI 10.1001/jamapediatrics.2014.2223
   Mourani PM, 2015, CLIN PERINATOL, V42, P839, DOI 10.1016/j.clp.2015.08.010
   Mund M, 2014, INT J ENV RES PUB HE, V11, P5792, DOI 10.3390/ijerph110605792
   Namba F, 2019, PEDIATR INT, V61, P945, DOI 10.1111/ped.14001
   Powell SB, 2019, J PEDIATR-US, V210, P209, DOI 10.1016/j.jpeds.2019.02.029
   Schmidt B, 2006, NEW ENGL J MED, V354, P2112, DOI 10.1056/NEJMoa054065
   Schmidt B, 2007, NEW ENGL J MED, V357, P1893, DOI 10.1056/NEJMoa073679
   Smith VC, 2004, J PEDIATR-US, V144, P799, DOI 10.1016/j.jpeds.2004.03.026
   Stoll BJ, 2015, JAMA-J AM MED ASSOC, V314, P1039, DOI 10.1001/jama.2015.10244
   Willis KA, 2019, CONGENIT HEART DIS, V14, P27, DOI 10.1111/chd.12691
   Yao LM, 2020, CHEMOSPHERE, V245, DOI 10.1016/j.chemosphere.2019.125627
   Zheng MS, 2017, ENVIRON SCI POLLUT R, V24, P20409, DOI 10.1007/s11356-017-9711-0
NR 27
TC 1
Z9 1
U1 50
U2 97
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-2360
J9 FRONT PEDIATR
JI Front. Pediatr.
PD AUG 20
PY 2021
VL 9
DI 10.3389/fped.2021.705033
PG 9
WC Pediatrics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pediatrics
GA UR2QA
UT WOS:000696597400001
PM 34490163
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Sun, ST
   Dustdar, S
   Ranjan, R
   Morgan, G
   Dong, YS
   Wang, LZ
AF Sun, Shengtao
   Dustdar, Schahram
   Ranjan, Rajiv
   Morgan, Graham
   Dong, Yusen
   Wang, Lizhe
TI Remote Sensing Image Interpretation With Semantic Graph-Based Methods: A
   Survey
SO IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE
   SENSING
LA English
DT Article
DE Semantics; Remote sensing; Ontologies; Big Data; Data models; Earth;
   Data mining; Remote sensing (RS) image interpretation; remotes sensing
   big data; RS geological interpretation; semantic graph-based method;
   semantic knowledge graph
ID SENSED BIG DATA; DATA FUSION; RETRIEVAL; CLASSIFICATION; CHALLENGES;
   EVOLUTION; FRAMEWORK; MODEL
AB With the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, RS image interpretation has drawn remarkable attention and achieved progress. However, the semantic relationship and domain knowledge hidden in massive RS images have not been fully exploited. To the best of our knowledge, a comprehensive review of recent achievements regarding semantic graph-based methods for comprehension and interpretation of RS images is still lacking. Specifically, this article discusses the main challenges of RS image interpretation and presents a systematic survey of typical semantic graph-based methodologies for RS knowledge representation and understanding, including the Ontology Model, Geo-Information Tupu, and Semantic Knowledge Graph. Furthermore, we categorize and summarize how the existing technologies address different challenges in RS image interpretation based on semantic graph-based methods, which indicates that the semantic information about potential relationships and prior knowledge of variant RS targets are central to the solution. In addition, a case study of RS geological interpretation based on the semantic knowledge graph is demonstrated to show the promising capability of intelligent RS image interpretation. Finally, the future directions are discussed for further research.
C1 [Sun, Shengtao] Yanshan Univ, Dept Comp Sci & Engn, Qinhuangdao 066004, Hebei, Peoples R China.
   [Dustdar, Schahram] TU Wien, Comp Sci, A-1040 Vienna, Austria.
   [Ranjan, Rajiv; Morgan, Graham] Newcastle Univ, Sch Comp Sci, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England.
   [Dong, Yusen] China Univ Geosci, Fac Earth Sci, Wuhan 430079, Peoples R China.
   [Wang, Lizhe] China Univ Geosci, Sch Comp Sci, Wuhan 430079, Peoples R China.
RP Wang, LZ (通讯作者)，China Univ Geosci, Sch Comp Sci, Wuhan 430079, Peoples R China.
EM xysst@ysu.edu.cn; dustdar@dsg.tuwien.ac.at; raj.ranjan@ncl.ac.uk;
   graham.morgan@ncl.ac.uk; ysdong@cug.edu.cn; lizhe.wang@gmail.com
RI Dustdar, Schahram/G-9877-2015; Wang, Lizhe/L-7453-2014
OI Dustdar, Schahram/0000-0001-6872-8821; Dong, Yusen/0000-0002-6424-0623;
   Wang, Lizhe/0000-0003-2766-0845
FU National Natural Science Foundation of China [U21A2013, 41925007];
   Strategic Priority Research Program of the Chinese Academy of Sciences
   [XDA19090128]; Natural Science Foundation of Hebei Province
   [D2022203002]; Central Guidance on Local Science and Technology
   Development Fund of Hebei Province [202260201013242]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant U21A2013 and Grant 41925007 and in part
   by the Strategic Priority Research Program of the Chinese Academy of
   Sciences under Grant XDA19090128, in part by the Natural Science
   Foundation of Hebei Province under Grant D2022203002, and in part by the
   Central Guidance on Local Science and Technology Development Fund of
   Hebei Province under Grant 202260201013242. (Corresponding author: Lizhe
   Wang.)
CR Amiri K, 2018, CAN J REMOTE SENS, V44, P551, DOI 10.1080/07038992.2019.1569507
   Asadzadeh S, 2016, INT J APPL EARTH OBS, V47, P69, DOI 10.1016/j.jag.2015.12.004
   Bai YQ, 2017, IEEE GEOSC REM SEN M, V5, P72, DOI 10.1109/MGRS.2016.2635018
   Balti H., 2019, PROC INT C BIG DATA, P119
   Bharambe U, 2016, INT GEOSCI REMOTE SE, P7601, DOI 10.1109/IGARSS.2016.7730982
   Bishop C, 2018, INT J APPL EARTH OBS, V64, P267, DOI 10.1016/j.jag.2017.08.005
   Boualleg Y, 2019, IEEE GEOSCI REMOTE S, V16, P1944, DOI 10.1109/LGRS.2019.2911855
   Chen Jun, 2019, Geomatics and Information Science of Wuhan University, V44, P38, DOI 10.13203/j.whugis20180441
   Chen Zhu, 2018, Yingyong Shengtai Xuebao, V29, P4135, DOI 10.13287/j.1001-9332.201812.010
   Cheng G, 2020, IEEE J-STARS, V13, P3735, DOI 10.1109/JSTARS.2020.3005403
   Cheng G, 2018, IEEE T GEOSCI REMOTE, V56, P2811, DOI 10.1109/TGRS.2017.2783902
   Cheng YY, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12060972
   Chi MM, 2016, P IEEE, V104, P2207, DOI 10.1109/JPROC.2016.2598228
   Farah M, 2016, 2016 2ND INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES FOR SIGNAL AND IMAGE PROCESSING (ATSIP), P606, DOI 10.1109/ATSIP.2016.7523153
   Ghamisi P, 2019, IEEE GEOSC REM SEN M, V7, P6, DOI 10.1109/MGRS.2018.2890023
   Ghamisi P, 2017, IEEE GEOSC REM SEN M, V5, P8, DOI 10.1109/MGRS.2016.2616418
   Ghazouani F, 2019, IEEE T GEOSCI REMOTE, V57, P8775, DOI 10.1109/TGRS.2019.2922908
   Grainger T, 2016, PROCEEDINGS OF 3RD IEEE/ACM INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS, (DSAA 2016), P420, DOI 10.1109/DSAA.2016.51
   Gu Haiyan, 2018, Geomatics and Information Science of Wuhan University, V43, P31, DOI 10.13203/j.whugis20150468
   Guo HD, 2017, INT J DIGIT EARTH, V10, P1, DOI 10.1080/17538947.2016.1264490
   Gupta N, 2020, FRONTIER APPL NATURE, P302
   Hecker C, 2019, IEEE GEOSC REM SEN M, V7, P51, DOI 10.1109/MGRS.2019.2899193
   Hoffman R.R., 2018, REMOTE SENSING COGNI, P1
   [黄茂军 Huang Maojun], 2004, [地理与地理信息科学, Geography and geo-information science], V20, P1
   Huang XH, 2018, IEEE 20TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS / IEEE 16TH INTERNATIONAL CONFERENCE ON SMART CITY / IEEE 4TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS), P83, DOI 10.1109/HPCC/SmartCity/DSS.2018.00043
   Jiang B., 2017, INT C SPAC INF NETW, P329
   Kalinicheva E, 2020, IEEE J-STARS, V13, P1450, DOI 10.1109/JSTARS.2020.2982631
   Kateryna T., 2018, ACM T INFORM SYST, V37, P1
   Kim M, 2018, IEEE J-STARS, V11, P4604, DOI 10.1109/JSTARS.2018.2880783
   Kostovska Ana, 2020, Discovery Science. 23rd International Conference, DS 2020. Proceedings. Lecture Notes in Artificial Intelligence. Subseries of Lecture Notes in Computer Science (LNAI 12323), P140, DOI 10.1007/978-3-030-61527-7_10
   Kurte KR, 2017, IEEE J-STARS, V10, P29, DOI 10.1109/JSTARS.2016.2547992
   [李国庆 Li Guoqing], 2017, [计算机研究与发展, Journal of Computer Research and Development], V54, P267
   Li JY, 2019, NATL SCI REV, V6, P1082, DOI 10.1093/nsr/nwz058
   Li Lin, 2018, Geomatics and Information Science of Wuhan University, V43, P2243, DOI 10.13203/j.whugis20180232
   Li L, 2017, COMPUT GEOSCI-UK, V99, P1, DOI 10.1016/j.cageo.2016.10.006
   Li QP, 2018, IEEE T GEOSCI REMOTE, V56, P7147, DOI 10.1109/TGRS.2018.2848901
   Li WJ, 2019, EARTH SCI INFORM, V12, P599, DOI 10.1007/s12145-019-00402-2
   Li YS, 2021, INFORM FUSION, V67, P94, DOI 10.1016/j.inffus.2020.10.008
   [刘畅 Liu Chang], 2019, [小型微型计算机系统, Journal of Chinese Computer Systems], V40, P2644
   Liu J, 2019, IEEE J-STARS, V12, P2821, DOI 10.1109/JSTARS.2019.2920077
   Liu P, 2018, REMOTE SENS-BASEL, V10, DOI 10.3390/rs10050711
   Long Y, 2021, IEEE J-STARS, V14, P4205, DOI 10.1109/JSTARS.2021.3070368
   [骆剑承 Luo Jiancheng], 2016, [地球信息科学学报, Journal of Geo-Information Science], V18, P578
   Ma Y, 2015, FUTURE GENER COMP SY, V51, P47, DOI 10.1016/j.future.2014.10.029
   Mai GC, 2020, T GIS, V24, P623, DOI 10.1111/tgis.12629
   Marino K, 2017, PROC CVPR IEEE, P20, DOI 10.1109/CVPR.2017.10
   Martinez-Romo J, 2016, J ASSOC INF SCI TECH, V67, P71, DOI 10.1002/asi.23365
   Matevosyan H, 2017, IEEE GEOSC REM SEN M, V5, P19, DOI 10.1109/MGRS.2017.2720263
   Moalla M, 2020, IEEE T GEOSCI REMOTE, V58, P7022, DOI 10.1109/TGRS.2020.2978763
   Nieland S., 2017, THESIS TECHNISCHE U
   Nikoohemat S., 2018, REMOTE SENS-BASEL, V10, P1
   Nizzoli L, 2020, DECIS SUPPORT SYST, V136, DOI 10.1016/j.dss.2020.113346
   Ouyang S, 2021, REMOTE SENS-BASEL, V13, DOI 10.3390/rs13010119
   Perkins D. Nikolaus, 2016, U.S. Patent, Patent No. [9727976B1, 9727976]
   Potnis AV, 2021, ISPRS INT J GEO-INF, V10, DOI 10.3390/ijgi10010032
   Qu B, 2016, INT CONF COMP INFO, P124
   Rotich G, 2018, IEEE APP IMG PAT
   Shenoi S, 2015, INT J CRIT INFR PROT, V8, P1, DOI [10.1016/j.ijcip.2014.12.003, 10.1080/17538947.2014.927538]
   Sudmanns M, 2020, INT J DIGIT EARTH, V13, P832, DOI 10.1080/17538947.2019.1585976
   Sun J, 2019, IEEE T GEOSCI REMOTE, V57, P4294, DOI 10.1109/TGRS.2018.2890513
   Sun ST, 2017, FUTURE GENER COMP SY, V76, P499, DOI 10.1016/j.future.2016.10.018
   [唐常春 Tang Changchun], 2020, [地理研究, Geographical Research], V39, P2626
   Tang GA, 2015, J GEOGR SCI, V25, P1452, DOI 10.1007/s11442-015-1245-0
   Thomas C., 2018, ONTOLOGY INFORM SCI
   Uma V, 2018, INT CONF ADV COMPU, P47
   van Luijt B, 2020, IEEE SOFTWARE, V37, P89, DOI 10.1109/MS.2019.2957526
   [王家耀 Wang Jiayao], 2017, [测绘学报, Acta Geodetica et Cartographica Sinica], V46, P1226
   Wang LZ, 2020, WIRES DATA MIN KNOWL, V10, DOI 10.1002/widm.1371
   Wang LZ, 2018, FUTURE GENER COMP SY, V78, P353, DOI 10.1016/j.future.2016.06.009
   Wang S, 2019, ISPRS INT J GEO-INF, V8, DOI 10.3390/ijgi8040184
   Wang X, 2021, IEEE T GEOSCI REMOTE, V59, P7918, DOI 10.1109/TGRS.2020.3044655
   [王志华 Wang Zhihua], 2021, [地球信息科学学报, Journal of Geo-Information Science], V23, P16
   White AR, 2019, INT J APPL EARTH OBS, V74, P37, DOI 10.1016/j.jag.2018.08.026
   Wu QY, 2020, INT J SOFTW ENG KNOW, V30, P765, DOI 10.1142/S0218194020400069
   Wu YZ, 2017, IEEE INT SYMP PARAL, P715, DOI 10.1109/ISPA/IUCC.2017.00111
   Xie R., 2017, RADIO ENG, V47, P1
   Xu C, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12040607
   Yan B., 2019, THESIS U CALIFORNIA
   [杨存建 Yang Cunjian], 2020, [地球信息科学学报, Journal of Geo-Information Science], V22, P697
   Yang MY, 2017, ISPRS J PHOTOGRAMM, V131, P15, DOI 10.1016/j.isprsjprs.2017.07.010
   Yao XW, 2016, IEEE T GEOSCI REMOTE, V54, P3660, DOI 10.1109/TGRS.2016.2523563
   Yokoya N, 2017, IEEE GEOSC REM SEN M, V5, P29, DOI 10.1109/MGRS.2016.2637824
   Zangeneh P., 2020, ADV ENG INFORMAT, V46, P1
   Zhang B, 2019, P IEEE, V107, P2294, DOI 10.1109/JPROC.2019.2948454
   Zhang HP, 2018, ISPRS INT J GEO-INF, V7, DOI 10.3390/ijgi7080328
   [张洪岩 Zhang Hongyan], 2020, [地球信息科学学报, Journal of Geo-Information Science], V22, P653
   [张焜 Zhang Kun], 2016, [遥感信息, Remote Sensing Information], V31, P115
   Zhang YY, 2021, INTEGR MED RES, V10, DOI 10.1016/j.imr.2021.100726
   Zhong YF, 2017, REMOTE SENS-BASEL, V9, DOI 10.3390/rs9101030
   [周成虎 Zhou Chenghu], 2020, [地理学报, Acta Geographica Sinica], V75, P2593
   [朱建章 Zhu Jianzhang], 2016, [中国图象图形学报, Journal of Image and Graphics], V21, P1425
   Zhu PP, 2020, IEEE T GEOSCI REMOTE, V58, P4047, DOI 10.1109/TGRS.2019.2960466
   Zhu QQ, 2017, IEEE T GEOSCI REMOTE, V55, P5525, DOI 10.1109/TGRS.2017.2709802
   Zhunchen Luo, 2019, Knowledge Science, Engineering and Management. 12th International Conference, KSEM 2019. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 11776), P88, DOI 10.1007/978-3-030-29563-9_9
NR 94
TC 0
Z9 0
U1 3
U2 3
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1939-1404
EI 2151-1535
J9 IEEE J-STARS
JI IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens.
PY 2022
VL 15
BP 4544
EP 4558
DI 10.1109/JSTARS.2022.3176612
PG 15
WC Engineering, Electrical & Electronic; Geography, Physical; Remote
   Sensing; Imaging Science & Photographic Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Physical Geography; Remote Sensing; Imaging Science &
   Photographic Technology
GA 2E9HO
UT WOS:000812531800003
OA gold
DA 2022-08-23
ER

PT J
AU Song, W
   Guo, JJ
   Fu, RJ
   Liu, T
   Liu, LZ
AF Song, Wei
   Guo, Jingjin
   Fu, Ruiji
   Liu, Ting
   Liu, Lizhen
TI A Knowledge Graph Embedding Approach for Metaphor Processing
SO IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
LA English
DT Article
DE Metaphor processing; Knowledge graph embedding; Metaphor generation;
   Metaphor interpretation; Metaphor detection
AB Metaphor is a figure of speech that describes one thing (a target) by mentioning another thing (a source) in a way that is not literally true. Metaphor understanding is an interesting but challenging problem in natural language processing. This paper presents a novel method for metaphor processing based on knowledge graph (KG) embedding. Conceptually, we abstract the structure of a metaphor as an attribute-dependent relation between the target and the source. Each specific metaphor can be represented as a metaphor triple (target, attribute, source). Therefore, we can model metaphor triples just like modeling fact triples in a KG and exploit KG embedding techniques to learn better representations of concepts, attributes and concept relations. In this way, metaphor interpretation and generation could be seen as KG completion, while metaphor detection could be viewed as a representation learning enhanced concept pair classification problem. Technically, we build a Chinese metaphor KG in the form of metaphor triples based on simile recognition, and also extract concept-attribute collocations to help describe concepts and measure concept relations. We extend the translation-based and the rotation-basedKGembedding models to jointly optimizemetaphor KG embedding and concept-attribute collocation embedding.
   Experimental results demonstrate the effectiveness of our method. Simile recognition is feasible for building the metaphor triple resource. The proposed models improve the performance on metaphor interpretation and generation, and the learned representations also benefit nominal metaphor detection compared with strong baselines.
C1 [Song, Wei; Guo, Jingjin; Liu, Lizhen] Capital Normal Univ, Coll Informat Engn, Beijing 100048, Peoples R China.
   [Song, Wei; Guo, Jingjin; Liu, Lizhen] Capital Normal Univ, Acad Multidisciplinary Studies, Beijing 100048, Peoples R China.
   [Fu, Ruiji] iFLYTEK Res & iFLYTEK AI Res Hebei, State Key Lab Cognit Intelligence, Langfang 230088, Peoples R China.
   [Liu, Ting] Harbin Inst Technol, Res Ctr Social Comp & Informat Retrieval, Harbin 150001, Peoples R China.
RP Liu, LZ (通讯作者)，Capital Normal Univ, Coll Informat Engn, Beijing 100048, Peoples R China.; Liu, LZ (通讯作者)，Capital Normal Univ, Acad Multidisciplinary Studies, Beijing 100048, Peoples R China.
EM wsong@cnu.edu.cn; jjguo@cnu.edu.cn; rjfu@iflytek.com;
   tliu@ir.hit.edu.cn; Iizliu_cnu@163.com
FU Beijing Natural Science Foundation [4 192 017]; National Natural Science
   Foundation of China [61 876 113, 61 876 112]; Project of High-level
   Teachers in Beijing Municipal Universities in the Period of 13th
   Five-year Plan [CITTCD20170322]; Capital Building for Sci-Tech
   InnovationFundamental Scientific Research Funds
FX This work was supported in part by the Beijing Natural Science
   Foundation under Grant 4 192 017, in part by the National Natural
   Science Foundation of China under Grants 61 876 113 and 61 876 112, and
   in part by the Project of High-level Teachers in Beijing Municipal
   Universities in the Period of 13th Five-year Plan (CIT&TCD20170322) and
   Capital Building for Sci-Tech InnovationFundamental Scientific Research
   Funds.
CR Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cila N, 2014, INT J DES, V8, P15
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dinh E.-L. Do, 2016, P 4 WORKSH MET NLP, P28, DOI [DOI 10.18653/V1/W16-1104, 10.18653/v1/W16-1104]
   Fass D., 1991, Computational Linguistics, V17, P49
   Gao G., 2018, P 2018 C EMP METH NA, P607
   Gedigian M., 2006, P 3 WORKSH SCAL NAT, P41, DOI DOI 10.3115/1621459.1621467
   Gentner D, 2001, ANALOGICAL MIND, P199
   Han X., 2018, P C EMP METH NAT LAN, P139
   Jang Hyeju, 2015, P 16 ANN M SPEC INT, P384
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kingma D. P., 2017, ARXIV
   Lafferty J. D., 2001, P INT C MACH LEARN, P282, DOI DOI 10.1038/NPROT.2006.61
   Lakoff G., 1980, METAPHORS WE LIVE
   Lakoff J. E., 1991, CSRP0105 U CAL BERK
   [李斌 LI Bin], 2008, [中文信息学报, Journal of Chinese Information Processing], V22, P27
   Li H., 2013, T ASSOC COMPUT LING, V1, P379
   Li J., 2018, P 2018 C N AM CHAPTE, V2, P175, DOI 10.18653/v1/N18-2028
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu L, 2018, COMPLEXITY, DOI 10.1155/2018/7531547
   Mao R, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1222
   Martin J. H., 1994, Computational Intelligence, V10, P134, DOI 10.1111/j.1467-8640.1994.tb00161.x
   Martin James H., 1990, COMPUTATIONAL MODEL
   Mei J., 1984, SYNONYMY THESAURUS C, V1983
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Mohler Michael, 2013, P 1 WORKSH MET NLP, P27
   Nakagawa M., 2006, P 28 ANN M COGN SCI, P937
   Neuman Y, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0062343
   Niculae V., 2013, P JOINT S SEM PROC T, P110
   NICULAE V, 2013, P 51 ANN M ASS COMP, P00089
   Niculae Vlad, 2014, P 2014 C EMP METH NA, P2008
   ORTONY A, 1979, PSYCHOL REV, V86, P161, DOI 10.1037/0033-295X.86.3.161
   Qadir Ashequl, 2016, P 2016 C N AM CHAPT, P1223, DOI DOI 10.18653/V1/N16-1146
   Rei M., 2017, ARXIV170900575
   Rzepka R., 2013, P 2 INT WORKSH COMP, P937
   Schuster M, 1997, IEEE T SIGNAL PROCES, V45, P2673, DOI 10.1109/78.650093
   Shutova E., 2010, P NAACL 2010 LOS ANG, P1029
   Shutova E., 2016, P 2016 C N AM CHAPT, P160
   Shutova E., 2017, P EACL VAL, P523
   Shutova Ekaterina, 2013, HLT NAACL, P978
   Shutova Ekaterina, 2010, P 23 INT C COMP LING, P1002
   Steen GJ, 2010, CONV EVI LANG COMMUN, V14, P1
   Su C, 2017, NEUROCOMPUTING, V219, P300, DOI 10.1016/j.neucom.2016.09.030
   Sun Z., 2019, P 7 INT C LEARN REPR
   Tsvetkov Y, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P248
   Turney P. D., 2011, P C EMP METH NAT LAN, P680
   Tyler LK, 2000, BRAIN LANG, V75, P195, DOI 10.1006/brln.2000.2353
   Veale T., 2007, AAAI, P1471
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao P., 2016, P 7 INT C COMP CREAT, P230
   Yarowsky David, 1993, P WORKSH HUM LANG TE, P266, DOI DOI 10.3115/1075671.1075731
   Zeiler M.D., 2012, ARXIV PREPRINT ARXIV
   Zeng JL, 2020, AAAI CONF ARTIF INTE, V34, P9515
NR 55
TC 1
Z9 1
U1 6
U2 19
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2329-9290
EI 2329-9304
J9 IEEE-ACM T AUDIO SPE
JI IEEE-ACM Trans. Audio Speech Lang.
PY 2021
VL 29
BP 406
EP 420
DI 10.1109/TASLP.2020.3040507
PG 15
WC Acoustics; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Acoustics; Engineering
GA PK8QW
UT WOS:000602703300006
DA 2022-08-23
ER

PT J
AU Zeb, A
   Ul Haq, A
   Zhang, DF
   Chen, JD
   Gong, ZG
AF Zeb, Adnan
   Ul Haq, Anwar
   Zhang, Defu
   Chen, Junde
   Gong, Zhiguo
TI KGEL: A novel end-to-end embedding learning framework for knowledge
   graph completion
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge graph; Link prediction; Weighted graph convolutional network;
   Tensor train decomposition; Tensor factorization
AB Knowledge graphs (KGs) have recently become increasingly popular due to the broad range of essential applications in various downstream tasks including intelligent search, personalized recommendations, intelligent financial data analytics, etc. During an automated construction of a KG, the knowledge facts from multiple knowledge sources are automatically extracted in the form of triples, and these observed triples are used to derive new unobserved triples for KG completion (also known as link prediction). State-of-the-art link prediction methods are known to be primarily KG embedding models, among which tensor factorization models have recently drawn much attention due to their scalability and expressive feature embeddings, and hence, perform well for link prediction. However, these embedding models consider each KG triple individually and fail to capture the useful information present in the neighborhood of a node. To this end, we propose a novel end-to-end KG embedding learning framework that consists of an encoder of a dual weighted graph convolutional network, and a decoder of a novel fully expressive tensor factorization model. The proposed encoder extends weighted graph convolutional network to generate two rich and high quality embedding vectors for each node by aggregating information from the neighboring nodes. The proposed decoder has a flexible and powerful tensor representation form of the Tensor Train decomposition that takes benefit of the two representations of each node in its embedding space to accurately model the KG triples. We also derive a bound on the size of the embeddings for full expressivity and show that our proposed tensor factorization model is fully expressive. Additionally, we show the relationship of our tensor factorization model to previous tensor factorization models. The experimental results show the effectiveness of the proposed framework that consistently marks performance gains over several previous models on recent standard link prediction datasets.
C1 [Zeb, Adnan; Ul Haq, Anwar; Zhang, Defu; Chen, Junde] Xiamen Univ, Sch Informat, Xiamen 361005, Fujian, Peoples R China.
   [Gong, Zhiguo] Univ Macau, State Key Lab Internet Things Smart City, Macau, Peoples R China.
   [Gong, Zhiguo] Univ Macau, Dept Comp & Informat Sci, Macau, Peoples R China.
RP Zhang, DF (通讯作者)，Xiamen Univ, Sch Informat, Xiamen 361005, Fujian, Peoples R China.
EM adnanzeb@stu.xmu.edu.cn; anwar@uom.edu.pk; dfzhang@xmu.edu.cn;
   chen2wo@126.com; fstzgg@um.edu.mo
FU National Natural Science Foundation of China [61672439]
FX This work is partially supported by the National Natural Science
   Foundation of China (Grant no. 61672439).
CR Alobaidi M, 2018, COMPUT METH PROG BIO, V165, P117, DOI 10.1016/j.cmpb.2018.08.010
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Cai L, 2018, NAACL, P1470
   Chang S., 2018, SCALING KNOWLEDGE AC
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Cheng PY, 2018, ICCAD-IEEE ACM INT, DOI 10.1145/3240765.3243487
   Das R., 2018, ICLR
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Edgar M., 2019, BIG DAT INN GATH WEB
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Hogan A., 2020, ARXIV PREPRINT ARXIV
   Ioffe S., 2015, PR MACH LEARN RES, V37, P448
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kertkeidkachorn N, 2018, IEICE T INF SYST, VE101D, P90, DOI 10.1587/transinf.2017SWP0006
   Kingma D. P., 2017, ARXIV
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Lin X.V., 2018, ARXIV180810568
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Liu S., 2019, 191104129 ARXIV
   Malik KM, 2020, EXPERT SYST APPL, V145, DOI 10.1016/j.eswa.2019.113120
   Martinez-Rodriguez JL, 2018, EXPERT SYST APPL, V113, P339, DOI 10.1016/j.eswa.2018.07.017
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nguyen D.Q., 2019, P NAACL HLT 2019, P2180, DOI DOI 10.18653/V1/N19-1226
   Nguyen D. Q., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Oseledets IV, 2011, SIAM J SCI COMPUT, V33, P2295, DOI 10.1137/090752286
   Park N, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P596, DOI 10.1145/3292500.3330855
   Pittman R. J., 2017, EBAY BLOG
   Sabour S, 2017, ADV NEURAL INFORM PR, P3856
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Shang C, 2019, AAAI CONF ARTIF INTE, P3060
   Shen Y., 2018, NEURIPS, V31, P6786
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   Sun Z., 2018, P ICLR
   Sun Z., 2019, ARXIV191103903
   Szegedy C, 2016, PROC CVPR IEEE, P2818, DOI 10.1109/CVPR.2016.308
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wilkinson J.H., 1965, ALGEBRAIC EIGENVALUE
   Xinyi Z, 2018, PROC INT C LEARN REP
   Yang B., 2015, P INT C LEARN REPR I
   Yang F, 2017, ADV NEUR IN, V30
   Ying R, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P974, DOI 10.1145/3219819.3219890
   Zhang S., 2019, COMPUT SOC NETW, V6, P11, DOI [10.1186/s40649-019-0069-y, DOI 10.1186/S40649-019-0069-Y]
   Zhao XJ, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER), P56, DOI 10.1109/SANER.2017.7884609
NR 52
TC 5
Z9 5
U1 10
U2 46
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD APR 1
PY 2021
VL 167
AR 114164
DI 10.1016/j.eswa.2020.114164
EA FEB 2021
PG 10
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA QP0XN
UT WOS:000623561000003
DA 2022-08-23
ER

PT J
AU Wang, Q
   Hao, YS
   Chen, F
AF Wang, Qi
   Hao, Yongsheng
   Chen, Feng
TI Deepening the IDA* algorithm for knowledge graph reasoning through
   neural network architecture
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph; IDA* algorithm; Markov decision process; Reinforcement
   learning; Neural network
AB Inferring missing links in Knowledge Graphs (KGs) is a key evaluation task for KG reasoning, which aims to find relations for a given entity pair. Existing research often employs the IDA* (Iterative Deepening A*) algorithm for the path discovery task owing to its efficiency and accuracy. However, it relies on heuristics to set cost functions and is also difficult to utilize useful context information in the search process. In this paper, we propose the Deep-IDA* framework which applies neural networks and reinforcement learning (RL) to empower the IDA* algorithm to tackle the path discovery problem in KG reasoning. We model KG reasoning as a Markov Decision Process (MDP) and divide our Deep-IDA* framework and the resulting path into two parts: path-finding and path-reasoning. For path-finding, we propose a policy network to model the cost from the source to a candidate location. In this process, we employ the GCN (Graph Convolutional Network) to embed the observable sub-track, then employ the LSTM (Long Short-Term Memory) to record the historical trajectory, and introduce the attention to utilize the context information, and finally form policy. For path-reasoning with the searched candidate paths passed from the former process, we employ a value network to estimate the cost from the candidate to the destination entity, using the GNN (Graph Neural Networks) to learn a message-passing algorithm that solves the path inference problem, and using the GRU (Gated Recurrent Unit) to update the historical information. Finally, the actor-learner algorithm is utilized to minimize the sum of the losses of the two parts. Experiment results on three datasets demonstrate the effectiveness and efficiency of our framework. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Wang, Qi] Fudan Univ, Shanghai, Peoples R China.
   [Hao, Yongsheng] Nanjing Univ Informat Sci & Technol, Network Ctr, Nanjing, Peoples R China.
   [Chen, Feng] Chongqing Univ Posts & Telecommun, Sch Software Engn, Chongqing, Peoples R China.
RP Wang, Q (通讯作者)，Fudan Univ, Shanghai, Peoples R China.; Wang, Q (通讯作者)，220 Handan Rd, Shanghai, Peoples R China.
EM qiwang1101@163.com; yongshenghao@yahoo.com; ifengcn@foxmail.com
RI Wang, Qi/CAH-9366-2022
OI Wang, Qi/0000-0003-3249-8459
CR [Anonymous], 2018, ADV NEURAL INFORM PR
   Bordes A., 2013, ADV NEURAL INFORM PR
   Chen Wenhu, 2018, ARXIV PREPRINT ARXIV
   Cho K., 2014, ARXIV14061078, P1724
   Das Rajarshi, 2016, ARXIV PREPRINT ARXIV
   Das Rajarshi, 2017, ARXIV PREPRINT ARXIV
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dunning I, 2018, ARXIV PREPRINT ARXIV
   Dzeroski S, 2001, MACH LEARN, V43, P7, DOI 10.1023/A:1007694015589
   Glorot X., 2010, P 13 INT C ART INT S, P249
   HART PE, 1968, IEEE T SYST SCI CYB, VSSC4, P100, DOI 10.1109/TSSC.1968.300136
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   kovic Velic, 2017, ARXIV PREPRINT ARXIV
   Kudlur M., 2015, ARXIV PREPRINT ARXIV
   Lan Z, 2019, INT C LEARN REPR
   Lao N., 2011, P C EMP METH NAT LAN
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Li Y., 2015, ARXIV PREPRINT ARXIV
   Lillicrap T. P., 2015, CORR
   Lin Y, 29 AAAI C ART INT
   Mnih V, 2015, NATURE, V518, P529, DOI 10.1038/nature14236
   Neelakantan A., 2015, 2015 AAAI SPRING S S
   Nickel Maximilian, 2011, ICML, V11
   Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
   Seo M., 2016, ARXIV PREPRINT ARXIV
   Silver D, 2017, NATURE, V550, P354, DOI 10.1038/nature24270
   Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
   Toutanova Kristina., 2015, P 2015 C EMPIRICAL M
   Wang Jingyuan., 2019, P 25 ACM SIGKDD INT
   Wang Q, 2020, NEUROCOMPUTING
   Wang Q, 2020, KNOWL-BASED SYST, V209, DOI 10.1016/j.knosys.2020.106421
   Wang Q, 2020, KNOWL-BASED SYST, V197, DOI 10.1016/j.knosys.2020.105910
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang W.Y., 2017, ARXIV PREPRINT ARXIV
   Wang Z., 2014, 28 AAAI C ART INT
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Wu ZH, 2021, IEEE T NEUR NET LEAR, V32, P4, DOI 10.1109/TNNLS.2020.2978386
   Yang B., 2014, ABS14126575 CORR
   Yang B., 2014, ARXIV PREPRINT ARXIV
   Yang F, 2017, ADV NEUR IN, V30
NR 41
TC 3
Z9 3
U1 3
U2 19
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD MAR 14
PY 2021
VL 429
BP 101
EP 109
DI 10.1016/j.neucom.2020.12.040
EA JAN 2021
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QD2QC
UT WOS:000615368600010
DA 2022-08-23
ER

PT J
AU Bucher, B
   Tiainen, E
   von Brasch, TE
   Janssen, P
   Kotzinos, D
   Ceh, M
   Rijsdijk, M
   Folmer, E
   Van Damme, MD
   Zhral, M
AF Bucher, Benedicte
   Tiainen, Esa
   von Brasch, Thomas Ellett
   Janssen, Paul
   Kotzinos, Dimitris
   Ceh, Marjan
   Rijsdijk, Martijn
   Folmer, Erwin
   Van Damme, Marie-Dominique
   Zhral, Mehdi
TI Conciliating Perspectives from Mapping Agencies and Web of Data on
   Successful European SDIs: Toward a European Geographic Knowledge Graph
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE SDI; open data; metadata; ontologies; linked data; provenance; mediation
ID SEMANTIC WEB
AB Spatial Data Infrastructures (SDIs) are a key asset for Europe. This paper concentrates on unsolved issues in SDIs in Europe related to the management of semantic heterogeneities. It studies contributions and competences from two communities in this field: cartographers, authoritative data providers, and geographic information scientists on the one hand, and computer scientists working on the Web of Data on the other. During several workshops organized by the EuroSDR and Eurogeographics organizations, the authors analyzed their complementarity and discovered reasons for the difficult collaboration between these communities. They have different and sometimes conflicting perspectives on what successful SDIs should look like, as well as on priorities. We developed a proposal to integrate both perspectives, which is centered on the elaboration of an open European Geographical Knowledge Graph. Its structure reuses results from the literature on geographical information ontologies. It is associated with a multifaceted roadmap addressing interrelated aspects of SDIs.
C1 [Bucher, Benedicte; Van Damme, Marie-Dominique; Zhral, Mehdi] Univ Gustave Eiffel, LASTIG, IGN, ENSG, F-94160 St Mande, France.
   [Tiainen, Esa] Finnish Geospatial Res Inst FGI, Natl Land Survey Finland, Opastinsilta 12 C, Helsinki 00520, Finland.
   [von Brasch, Thomas Ellett] Kartverket, Kartverksveien 21, N-3511 Honefoss, Norway.
   [Janssen, Paul] Geonovum, Barchman Wuytierslaan 10, NL-3818 LH Amersfoort, Netherlands.
   [Kotzinos, Dimitris] Univ Cergy Pontoise, Paris Seine Univ, ETIS Lab, CNRS,ENSEA,UMR 8051, 2 Av A Chauvin, F-95000 Pontoise, France.
   [Ceh, Marjan] Univ Ljubljana, Fac Civil & Geodet Engn, Jamova Cesta 2, Ljubljana 1000, Slovenia.
   [Rijsdijk, Martijn; Folmer, Erwin] Kadaster, Hofstr 110, NL-7311 KZ Apeldoorn, Netherlands.
RP Bucher, B (通讯作者)，Univ Gustave Eiffel, LASTIG, IGN, ENSG, F-94160 St Mande, France.
EM benedicte.bucher@ign.fr; esa.tiainen@nls.fi;
   thomas.ellett@kartverket.no; p.janssen@geonovum.nl;
   Dimitrios.Kotzinos@u-cergy.fr; Marjan.Ceh@fgg.uni-lj.si;
   Martijn.Rijsdijk@kadaster.nl; erwin.folmer@kadaster.nl;
   marie-dominique.van-damme@ign.fr; mehdi.zrhal@ign.fr
RI Čeh, Marjan/ABD-1992-2021
OI Čeh, Marjan/0000-0001-5768-4712; Folmer, Erwin/0000-0002-7845-1763;
   Kotzinos, Dimitris/0000-0002-3678-4092; Zrhal, Mehdi/0000-0002-7583-411X
FU EuroSDR; European Union [690462]; ERA4CS, an ERA-NET
FX This work has partly been funded by EuroSDR and also partly by ERA4CS,
   an ERA-NET initiated by JPI Climate with co-funding from the European
   Union (Grant no 690462) for the URCLIM project.
CR [Anonymous], 2001, SCI AM
   [Anonymous], LINKED DATA
   [Anonymous], 2015, BUILDING EUROPEAN SP
   Archer P., STUDY BUSINESS MODEL
   Atzemoglou A., 2016, B GEOL SOC GREECE, V50, P1683
   Atzemoglou A., 2014, P 10 INT HYDR C THES
   Ballatore A, 2015, ISPRS INT J GEO-INF, V4, P471, DOI 10.3390/ijgi4020471
   Bereta K., REPRESENTATION QUERY
   Bucher B., 2018, P EUROSDR EUR WORKSH
   Bucher B., 2018, PROJECT REPORT
   Bucher B., 2018, P REP EUR EUROSDR WO
   Ceh M., 2006, PROGR SPATIAL DATA H, P417
   Crompvoets J, 2004, INT J GEOGR INF SCI, V18, P665, DOI 10.1080/13658810410001702030
   Debruyne C, 2017, LECT NOTES COMPUT SC, V10588, P66, DOI 10.1007/978-3-319-68204-4_6
   Ehrlinger L., 2016, JOINT P POSTERS DEMO, V1695, P13
   Fonseca F., 2006, SPAT COGNIT COMPUT, V6, P309, DOI DOI 10.1207/S15427633SCC0604_2
   Frank AU, 2001, INT J GEOGR INF SCI, V15, P667, DOI 10.1080/13658810110061144
   Goodwin J., 2008, T GIS, V12, P19, DOI [10.1111/j.1467-9671.2008.01133.x, DOI 10.1111/J.1467-9671.2008.01133.X]
   Hamdi F., 2015, ARXIV150304864
   Heath T., 2011, SYNTHESIS LECT SEMAN, DOI [DOI 10.2200/S00334ED1V01Y201102WBE001, 10.2200/s00334ed1v01y201102wbe001]
   Keet C.M., 2013, ENCY SYSTEMBIOL
   Kobilarov G, 2009, LECT NOTES COMPUT SC, V5554, P723, DOI 10.1007/978-3-642-02121-3_53
   Kritikos K, 2015, LECT NOTES COMPUT SC, V9070, P59, DOI 10.1007/978-3-662-46703-9_3
   Kuhn W, 2003, INT J GEOGR INF SCI, V17, P405, DOI 10.1080/1365881031000114116
   Kuhn W., 2003, IMPLEMENTING SEMANTI
   Masser I, 1999, INT J GEOGR INF SCI, V13, P67, DOI 10.1080/136588199241463
   Masson V.W., 2019, URBAN CLIM, P31
   Noy N, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1365, DOI 10.1145/3308558.3313685
   Osterwalder A., 2010, BUSINESS MODEL GENER, P26
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Probst F., 2004, P 7 AGILE C GEOGR IN
   Ronzhin S., NEXT GENERATION SPAT
   Ronzhin S., 2018, OPEN DATA EXPOSED, P173, DOI DOI 10.1007/978-94-6265-261-3_9
   Ronzhin S, 2019, INFORMATION, V10, DOI 10.3390/info10100310
   Scharffe F., 2012, P 26 AAAI C ART INT
   Semic Interoperability Community, LINK DAT SHOWC LDS P
   Shapes Constraint Language (SHACL), 2017, HOLG KNUBL DIM KONT
   Smole D, 2011, INT J GEOGR INF SCI, V25, P1809, DOI 10.1080/13658816.2011.556640
   Tambassi T, 2017, PHILOS GEOONTOLOGIES
   Taylor J., 2019, QUEUE, V17, P48, DOI 10.1145/3329781.3332266
   Tiainen E., FINNISH INITIATIVE F
   UN-GGIM: EuropeWork Group on Data Integration, 2019, REP TERR DIM SDG IND
   van den Brink L, 2014, INT J SPAT DATA INFR, V9, P59, DOI 10.2902/1725-0463.2014.09.art3
   White House The Office of Management and Budget, 2002, A16 WHIT HOUS
   Wilcke X., 2017, DATA SCI, V1, P39, DOI [10.3233/ds-170007, DOI 10.3233/DS-170007, 10.3233/DS-170007]
NR 45
TC 5
Z9 5
U1 1
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2220-9964
J9 ISPRS INT GEO-INF
JI ISPRS Int. Geo-Inf.
PD FEB
PY 2020
VL 9
IS 2
AR 62
DI 10.3390/ijgi9020062
PG 23
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Physical Geography; Remote Sensing
GA KY3CX
UT WOS:000522449700002
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Pang, YH
   Liu, J
   Liu, LZ
   Yu, ZT
   Zhang, K
AF Pang, Yihe
   Liu, Jie
   Liu, Lizhen
   Yu, Zhengtao
   Zhang, Kai
TI A Deep Neural Network Model for Joint Entity and Relation Extraction
SO IEEE ACCESS
LA English
DT Article
DE Automatic knowledge graph construction; entity and relation extraction;
   deep neural networks; natural language processing; pointer networks;
   relational triplet extraction; sequence-to-sequence learning
AB Joint extraction of entities and their relations from the text is an essential issue in automatic knowledge graph construction, which is also known as the joint extraction of relational triplets. The relational triplets in sentence are complicated, multiple and different relational triplets may have overlaps, which is commonly seen in reality. However, multiple pairs of triplets cannot be efficiently extracted in most of the previous works. To mitigate this problem, we propose a deep neural network model based on the sequenceto-sequence learning, namely, the hybrid dual pointer networks (HDP), which extracts multiple pairs of triplets from the given sentence by generating the hybrid dual pointer sequence. In experiments, we tested our model using the New York Times (NYT) public dataset. The experimental results demonstrated that our model outperformed the state-of-the-art work, and achieved a 17.1% improvement on the F1 values.
C1 [Pang, Yihe; Liu, Jie; Liu, Lizhen] Capital Normal Univ, Dept Informat Engn, Beijing 100048, Peoples R China.
   [Liu, Jie; Zhang, Kai] Capital Normal Univ, Res Ctr Language Intelligence, Beijing 100048, Peoples R China.
   [Liu, Jie; Zhang, Kai] Beijing Adv Innovat Ctr Imaging Theory & Technol, Beijing 100048, Peoples R China.
   [Yu, Zhengtao] Kunming Univ Sci & Technol, Dept Informat Engn & Automat, Kunming 650504, Yunnan, Peoples R China.
RP Liu, J (通讯作者)，Capital Normal Univ, Dept Informat Engn, Beijing 100048, Peoples R China.; Liu, J (通讯作者)，Capital Normal Univ, Res Ctr Language Intelligence, Beijing 100048, Peoples R China.; Liu, J (通讯作者)，Beijing Adv Innovat Ctr Imaging Theory & Technol, Beijing 100048, Peoples R China.
EM liujie@cnu.edu.cn
FU Key Special Projects of National Key Research and Development Program of
   China [2018YFC0830100]; National Natural Science Foundation of China
   [61672361]; Beijing Municipal Education Commission through the Beijing
   Natural Fund Joint Funding Project [KZ201910028039]
FX This work was supported in part by the Key Special Projects of National
   Key Research and Development Program of China under Grant
   2018YFC0830100, in part by the National Natural Science Foundation of
   China under Grant 61672361, and in part by the Beijing Municipal
   Education Commission through the Beijing Natural Fund Joint Funding
   Project under Grant KZ201910028039.
CR Adel H., 2017, P 2017 C EMP METH NA, P1723, DOI [10.18653/v1/D17-1181, DOI 10.18653/V1/D17-1181]
   [Anonymous], 2018, P 2018 C EMP METH NA
   Gong YX, 2016, PROCEEDINGS OF THE THIRD INTERNATIONAL SYMPOSIUM - MANAGEMENT, INNOVATION & DEVELOPMENT, BKS ONE & TWO, P101
   Gormley MR, 2015, P 2015 C EMP METH NA, P1774, DOI DOI 10.18653/V1/D15-1205
   Gulcehre C, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P140, DOI 10.18653/v1/p16-1014
   Gupta P., 2016, COLING, P2537
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Katiyar A, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P917, DOI 10.18653/v1/P17-1085
   Kingma D. P., 2015, P INT C LEARN REPR I
   Li Q, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P402, DOI 10.3115/v1/p14-1038
   Miwa M, 2014, P 2014 C EMP METH NA, P1858, DOI DOI 10.3115/V1/D14-1200
   Miwa M, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1105
   Ratinov L., 1955, P 13 C COMP NAT LANG, P147
   RIEDEL S, 2010, P 2010 EUR C MACH, V6323, P148
   See A, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1073, DOI 10.18653/v1/P17-1099
   Tan Z, 2019, AAAI CONF ARTIF INTE, P7080
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Vinyals Oriol, 2015, ADV NEURAL INFORM PR, P2674
   Wang LL, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1298
   Wang SL, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4461
   Zeng XR, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P506
   Zheng SC, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1227, DOI 10.18653/v1/P17-1113
   Zheng SC, 2017, NEUROCOMPUTING, V257, P59, DOI 10.1016/j.neucom.2016.12.075
NR 24
TC 3
Z9 5
U1 3
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 179143
EP 179150
DI 10.1109/ACCESS.2019.2949086
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA KF8JT
UT WOS:000509483800143
OA gold
DA 2022-08-23
ER

PT J
AU Turki, H
   Taieb, MAH
   Shafee, T
   Lubiana, T
   Jemielniak, D
   Ben Aouicha, M
   Gayo, JEL
   Youngstrom, EA
   Banat, M
   Das, D
   Mietchen, D
AF Turki, Houcemeddine
   Taieb, Mohamed Ali Hadj
   Shafee, Thomas
   Lubiana, Tiago
   Jemielniak, Dariusz
   Ben Aouicha, Mohamed
   Gayo, Jose Emilio Labra
   Youngstrom, Eric A.
   Banat, Mus'ab
   Das, Diptanshu
   Mietchen, Daniel
CA WikiProject COVID-19
TI Representing COVID-19 information in collaborative knowledge graphs: The
   case of Wikidata
SO SEMANTIC WEB
LA English
DT Article
DE Public health surveillance; Wikidata; knowledge graph; COVID-19; SPARQL;
   community curation; FAIR data; linked open data
ID LINKED DATA; EVOLUTION; IMPACT
AB Information related to the COVID-19 pandemic ranges from biological to bibliographic, from geographical to genetic and beyond. The structure of the raw data is highly complex, so converting it to meaningful insight requires data curation, integration, extraction and visualization, the global crowdsourcing of which provides both additional challenges and opportunities. Wikidata is an interdisciplinary, multilingual, open collaborative knowledge base of more than 90 million entities connected by well over a billion relationships. It acts as a web-scale platform for broader computer-supported cooperative work and linked open data, since it can be written to and queried in multiple ways in near real time by specialists, automated tools and the public. The main query language, SPARQL, is a semantic language used to retrieve and process information from databases saved in Resource Description Framework (RDF) format.
   Here, we introduce four aspects of Wikidata that enable it to serve as a knowledge base for general information on the COVID-19 pandemic: its flexible data model, its multilingual features, its alignment to multiple external databases, and its multidisciplinary organization. The rich knowledge graph created for COVID-19 in Wikidata can be visualized, explored, and analyzed for purposes like decision support as well as educational and scholarly research.
C1 [Turki, Houcemeddine] Univ Sfax, Fac Med Sfax, Sfax, Tunisia.
   [Turki, Houcemeddine; Taieb, Mohamed Ali Hadj; Ben Aouicha, Mohamed] Univ Sfax, Fac Sci Sfax, Data Engn & Semant Res Unit, Sfax, Tunisia.
   [Shafee, Thomas] La Trobe Univ, Melbourne, Vic, Australia.
   [Lubiana, Tiago] Univ Sao Paulo, Computat Syst Biol Lab, Sao Paulo, SP, Brazil.
   [Jemielniak, Dariusz] Kozminski Univ, Dept Management Networked & Digital Soc, Warsaw, Poland.
   [Gayo, Jose Emilio Labra] Univ Oviedo, Web Semant Oviedo WESO Res Grp, Oviedo, Spain.
   [Youngstrom, Eric A.] Univ N Carolina, Dept Psychol & Neurosci, CB 3270,Davie Hall, Chapel Hill, NC 27599 USA.
   [Banat, Mus'ab] Hashemite Univ, Fac Med, Zarqa, Jordan.
   [Das, Diptanshu] Inst Child Hlth ICH, Kolkata, India.
   [Das, Diptanshu] Med Superspecialty Hosp, Kolkata, India.
   [Mietchen, Daniel] Univ Virginia, Sch Data Sci, Charlottesville, VA 22904 USA.
   [Mietchen, Daniel] Fraunhofer Inst Biomed Engn, Biomed Data & Bioeth, Wurzburg, Germany.
RP Mietchen, D (通讯作者)，Univ Virginia, Sch Data Sci, Charlottesville, VA 22904 USA.; Mietchen, D (通讯作者)，Fraunhofer Inst Biomed Engn, Biomed Data & Bioeth, Wurzburg, Germany.
EM daniel.mietchen@ibmt.fraunhofer.de
RI Jemielniak, Dariusz/M-9949-2019; Turki, Houcemeddine/J-8929-2013;
   Youngstrom, Eric A./ABE-3258-2020; Labra-Gayo, Jose Emilio/L-9181-2014;
   Mietchen, Daniel/A-7748-2009
OI Jemielniak, Dariusz/0000-0002-3745-7931; Turki,
   Houcemeddine/0000-0003-3492-2014; Youngstrom, Eric
   A./0000-0003-2251-6860; Labra-Gayo, Jose Emilio/0000-0001-8907-5348; Ben
   Aouicha, Mohamed/0000-0002-2277-5814; Das,
   Diptanshu/0000-0002-7221-5022; Shafee, Thomas/0000-0002-2298-7593;
   Lubiana Alves, Tiago/0000-0003-2473-2313; Mietchen,
   Daniel/0000-0001-9488-1870; Hadj Taieb, Mohamed Ali/0000-0002-2786-8913
FU Ministry of Higher Education and Scientific Research in Tunisia (MoHESR)
   [PRFCOV19-D1-P1]; Spanish Ministry of Economy and Competitiveness
   [TIN2017-88877-R]; Alfred P. Sloan Foundation [G-2019-11458]; Polish
   National Science Center [2019/35/B/HS6/01056]
FX The work done by Houcemeddine Turki, Mohamed Ali Hadj Taieb and Mohamed
   Ben Aouicha was supported by the Ministry of Higher Education and
   Scientific Research in Tunisia (MoHESR) in the framework of Federated
   Research Project PRFCOV19-D1-P1. The work done by Jose Emilio Labra Gayo
   was partially funded by the Spanish Ministry of Economy and
   Competitiveness (Society challenges: TIN2017-88877-R). The work done by
   Daniel Mietchen was supported in part by the Alfred P. Sloan Foundation
   under grant number G-2019-11458. The work done by Dariusz Jemielniak was
   funded by Polish National Science Center grant no 2019/35/B/HS6/01056.
   We thank the Wikidata community, Egon Willighagen (Maastricht
   University, Netherlands), Toby Hudson (University of Sydney, Australia),
   Adam Shorland (Wikimedia Deutschland, Germany), and Mahir Morshed
   (University of Illinois at Urbana-Champaign, United States of America)
   for useful comments and discussions about the topic of this research
   paper.
CR Abian D, 2018, LECT NOTES COMPUT SC, V10546, P142, DOI 10.1007/978-3-319-74497-1_14
   Addshore D., 2020, **DATA OBJECT**, DOI [10.5281/ zenodo.3977414, DOI 10.5281/ZENODO.3977414]
   Allison-Cassin S., 2018, CODE4LIB J, V40
   Alocci D, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0144578
   Angles R., 2019, P 13 A MEND INT WORK
   Aturban M, 2018, ACM-IEEE J CONF DIG, P321, DOI 10.1145/3197026.3203880
   Balaraman V, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1787, DOI 10.1145/3184558.3191641
   Bellomarini L., 2020, ARXIV PREPRINT ARXIV
   Benjakob O., 2021, METARESEARCH CITATIO, DOI [10.1101/2021.03.01.433379, DOI 10.1101/2021.03.01.433379]
   Boccone A., 2019, BIBLIOTHECAE, V8, P227, DOI [10.6092/issn.2283-9364/9503, DOI 10.6092/ISSN.2283-9364/9503]
   Burgstaller-Muehlbacher S, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw015
   Chaari L, 2020, EPMA J, V11, P133, DOI 10.1007/s13167-020-00207-0
   Chrzanowski J, 2021, J MED INTERNET RES, V23, DOI 10.2196/26331
   Ciechanowski L, 2020, J BUS RES, V117, P322, DOI 10.1016/j.jbusres.2020.06.012
   Cohen J., 1988, STAT POWER ANAL BEHA
   Dadzie AS, 2011, SEMANT WEB, V2, P89, DOI 10.3233/SW-2011-0037
   Darari F, 2021, JURNAL ILMU KOMPUTER, V14, P39, DOI [10.21609/jiki.v14i1. 941, DOI 10.21609/JIKI.V14I1.941]
   Darari F, 2016, LECT NOTES COMPUT SC, V9671, P170, DOI 10.1007/978-3-319-38791-8_10
   De Giacomo G., 2018, COMPREHENSIVE GUIDE, P187, DOI 10.1007/ 978- 3- 319- 61893- 7_ 11
   De Melo G., 2010, PRINCIPLES CONSTRUCT, P149
   Debattista J, 2018, SEMANT WEB, V9, P859, DOI 10.3233/SW-180306
   Desvars-Larrive A, 2020, SCI DATA, V7, DOI 10.1038/s41597-020-00609-9
   Dimitrov D., 2020, ARXIV PREPRINT ARXIV
   Domingo-Fernandez D., 2020, COVID 19 KNOWLEDGE G, DOI [10.1101/2020.04.14.040667, DOI 10.1101/2020.04.14.040667]
   Dubey S, 2020, DIABETES METAB SYND, V14, P779, DOI 10.1016/j.dsx.2020.05.035
   Eberhand D.M., 2020, ETHNOLOGUE LANGUAGES
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Ekins Sean, 2016, F1000Res, V5, P150, DOI 10.12688/f1000research.8013.1
   Erxleben F, 2014, LECT NOTES COMPUT SC, V8796, P50, DOI 10.1007/978-3-319-11964-9_4
   Farber M, 2018, SEMANT WEB, V9
   Farda-Sarbas M, 2019, PROCEEDINGS OF THE 15TH INTERNATIONAL SYMPOSIUM ON OPEN COLLABORATION (OPENSYM), DOI 10.1145/3306446.3340833
   Finn RD, 2012, NUCLEIC ACIDS RES, V40, pD9, DOI 10.1093/nar/gkr1195
   Fontelo P, 2007, STUD HEALTH TECHNOL, V129, P817
   Freitas F., 2009, RECIIS, V3, P7, DOI [10.3395/reciis.v3i1.239-n, DOI 10.3395/RECIIS.V3I1.239-N]
   Guidotti E., 2020, J OPEN SOURCE SOFTWA, V5, P2376, DOI [10.21105/joss.02376, DOI 10.21105/JOSS.02376]
   Hagedorn G, 2011, ZOOKEYS, P127, DOI 10.3897/zookeys.150.2189
   He YQ, 2020, SCI DATA, V7, DOI 10.1038/s41597-020-0523-6
   Heilman JM, 2015, J MED INTERNET RES, V17, DOI 10.2196/jmir.4069
   Henriksson A, 2013, P 2013 WORKSH BIOM N, P36
   Hernandez D., 2015, P 11 INT WORKSH SCAL, P32
   Hitzler P, 2013, SEMANT WEB, V4, P233, DOI 10.3233/SW-130117
   Hu XJ, 2011, J INFORMETR, V5, P27, DOI 10.1016/j.joi.2010.07.004
   Huss JW, 2008, PLOS BIOL, V6, P1398, DOI 10.1371/journal.pbio.0060175
   Iglesias E., 2020, ARXIV200807176
   Ismayilov A, 2018, SEMANT WEB, V9, P493, DOI 10.3233/SW-170277
   Jaradeh MY, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P243, DOI 10.1145/3360901.3364435
   Jemielniak D., 2014, COMMON KNOWLEDGE ETH
   Jemielniak D, 2019, J MED INTERNET RES, V21, DOI 10.2196/11429
   Jette N, 2010, MED CARE, V48, P1105, DOI 10.1097/MLR.0b013e3181ef9d3e
   Juul S, 2020, SYST REV-LONDON, V9, DOI 10.1186/s13643-020-01371-0
   Kaffee LA, 2018, PROCEEDINGS OF THE 14TH INTERNATIONAL SYMPOSIUM ON OPEN COLLABORATION (OPENSYM'18), DOI 10.1145/3233391.3233965
   Kaffee Lucie-Aimee, 2017, P 13 INT S OP COLL, V14, P1, DOI [10.1145/3125433.3125465, DOI 10.1145/3125433.3125465]
   Kagan D, 2020, GIGASCIENCE, V9, DOI 10.1093/gigascience/giaa085
   Keegan B.C., 2020, ARXIV PREPRINT ARXIV
   Keegan B, 2013, AM BEHAV SCI, V57, P595, DOI 10.1177/0002764212469367
   Keegan BC, 2015, PROCEEDINGS OF THE 2015 ACM INTERNATIONAL CONFERENCE ON COMPUTER-SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING (CSCW'15), P533, DOI 10.1145/2675133.2675238
   Kirk A., 2016, DATA VISUALISATION H
   Klein M., 2013, CODE4LIB J, P22
   Konieczny P, 2010, J INF TECHNOL POLITI, V7, P263, DOI 10.1080/19331681.2010.489408
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Liu Fang, 2006, AMIA Annu Symp Proc, P1012
   Liu Y., 2020, ONTOLOGICAL BIOINFOR, DOI [10.20944/preprints202003.0413.v1, DOI 10.20944/PREPRINTS202003.0413.V1]
   Lu Wang L., 2020, ARXIV200410706
   Lublin DM, 2000, TRANSFUSION, V40, P1285, DOI 10.1046/j.1537-2995.2000.40111285.x
   Majumder MS, 2020, LANCET GLOB HEALTH, V8, pE627, DOI 10.1016/S2214-109X(20)30113-3
   Malyshev S, 2018, LECT NOTES COMPUT SC, V11137, P376, DOI 10.1007/978-3-030-00668-6_23
   Manske M., 2019, WELLCOME OPEN RES, V4, P114, DOI [10.12688/ wellcomeopenres.15355.2, DOI 10.12688/WELLCOMEOPENRES.15355.2]
   Mietchen D., 2015, RES IDEAS OUTCOMES, V1, pe7573, DOI [10.3897/rio.1. e7573, DOI 10.3897/RIO.1.E7573, 10.3897/rio.1.e7573, DOI 10.3897/RI0.1.E7573]
   Mietchen D., 2020, WORKSH OP CIT OP SCH, DOI [10.5281/zenodo. 4019954, DOI 10.5281/ZENODO.4019954]
   Mortensen JM, 2015, J AM MED INFORM ASSN, V22, P640, DOI 10.1136/amiajnl-2014-002901
   Motta E., 2014, P INT C CONS LINK DA, V1264, P73, DOI DOI 10.5555/2877789.2877796
   Nielsen FA, 2017, IN SY AP IN WE HC, V10577, P237, DOI 10.1007/978-3-319-70407-4_36
   Nunn J., 2019, STANDARDISED DATA IN, DOI [10.31219/osf.io/5q47h, DOI 10.31219/OSF.IO/5Q47H]
   Ordun C., 2020, ARXIV200503082
   Ostaszewski M, 2020, SCI DATA, V7, DOI 10.1038/s41597-020-0477-8
   Page R., 2016, RIO, V2, P8767, DOI [10.3897/rio.2.e8767, DOI 10.3897/RIO.2.E8767, DOI 10.3897/RI0.2.E8767]
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Penev L., 2017, RIO, V3, DOI [10.3897/rio.3.e12431, DOI 10.3897/RIO.3.E12431]
   Prud'hommeaux E, 2014, P 10 INT C SEM SYST, P32, DOI DOI 10.1145/2660517.2660523
   Putman TE, 2017, DATABASE-OXFORD, DOI 10.1093/database/bax025
   Queralt-Rosinach N, 2020, DATABASE-OXFORD, DOI 10.1093/database/baaa015
   Rahimi A., 2020, P CAN SOC MECH ENG I
   RDA COVID-19 Working Group, 2020, REC GUID DAT SHAR RE, DOI [10.15497/ rda00052, DOI 10.15497/RDA00052]
   Rico M, 2018, 33RD ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, P323, DOI 10.1145/3167132.3167164
   Ringler D, 2017, LECT NOTES ARTIF INT, V10505, P366, DOI 10.1007/978-3-319-67190-1_33
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9982, P186, DOI 10.1007/978-3-319-46547-0_20
   Rodgers RPC, 2004, STUD HEALTH TECHNOL, V107, P425
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Samuel J., 2018, COMPANION WEB C 2018, P23
   Schriml LM, 2020, SCI DATA, V7, DOI 10.1038/s41597-020-0524-5
   Sebei H, 2018, SOC NETW ANAL MIN, V8, DOI 10.1007/s13278-018-0507-0
   Shafee T, 2017, J EPIDEMIOL COMMUN H, V71, P1122, DOI 10.1136/jech-2016-208601
   Tanon TP, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P1419, DOI 10.1145/2872427.2874809
   Taraborelli D., 2017, WIKICITE 2017 REPORT, DOI [10.6084/m9. figshare.5648233, DOI 10.6084/M9.FIGSHARE.5648233]
   Terryn AR, 2020, LANG RESOUR EVAL, V54, P385, DOI 10.1007/s10579-019-09453-9
   Turki H., 2020, **DATA OBJECT**, DOI [10.5281/zenodo.4022591, DOI 10.5281/ZENODO.4022591]
   Turki H., 2020, ZENODO, DOI [10.5281/zenodo.4008358, DOI 10.5281/ZENODO.4008358]
   Turki H, 2019, J BIOMED INFORM, V99, DOI 10.1016/j.jbi.2019.103292
   Turki H, 2018, J BIOMED INFORM, V83, P217, DOI 10.1016/j.jbi.2018.05.011
   United Nations Development Programme, 2020, HUM DEV REP 2020 NEX, P343
   Vincent N, 2018, PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), DOI 10.1145/3173574.3174140
   Vossen P, 2016, KNOWL-BASED SYST, V110, P60, DOI 10.1016/j.knosys.2016.07.013
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Waagmeester A., 2019, BIODIVERSITY INFORM, V3, DOI [10.3897/biss.3.35206, DOI 10.3897/BISS.3.35206]
   Waagmeester A, 2021, BMC BIOL, V19, DOI 10.1186/s12915-020-00940-y
   Waagmeester A, 2020, ELIFE, V9, DOI 10.7554/eLife.52614
   Wyatt L., 2020, WIKICITE ANN REPORT, DOI [10.5281/zenodo.3869809, DOI 10.5281/ZENODO.3869809]
   Xu B, 2020, LANCET INFECT DIS, V20, P534, DOI 10.1016/S1473-3099(20)30119-5
   Zaveri A, 2016, SEMANT WEB, V7, P63, DOI 10.3233/SW-150175
   Zhang YJ, 2018, J BIOMED INFORM, V81, P83, DOI 10.1016/j.jbi.2018.03.011
   Zhang ZQ, 2020, J INFECTION, V81, P170, DOI 10.1016/j.jinf.2020.03.018
NR 111
TC 1
Z9 1
U1 8
U2 8
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2022
VL 13
IS 2
BP 233
EP 264
DI 10.3233/SW-210444
PG 32
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA YV6TV
UT WOS:000752861100006
OA Green Submitted, Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Adams, B
   Janowicz, K
AF Adams, Benjamin
   Janowicz, Krzysztof
TI Thematic signatures for cleansing and enriching place-related linked
   data
SO INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
LA English
DT Article
DE geographic knowledge discovery; ontology; semantics
ID KNOWLEDGE; DBPEDIA
AB There has been significant progress transforming semi-structured data about places into knowledge graphs that can be used in a wide variety of geographic information systems such as digital gazetteers or geographic information retrieval systems. For instance, in addition to information about events, actors, and objects, DBpedia contains data about hundreds of thousands of places from Wikipedia and publishes it as Linked Data. Repositories that store data about places are among the most interlinked hubs on the Linked Data cloud. However, most content about places resides in unstructured natural language text, and therefore it is not captured in these knowledge graphs. Instead, place representations are limited to facts such as their population counts, geographic locations, and relations to other entities, for example, headquarters of companies or historical figures. In this paper, we present a novel method to enrich the information stored about places in knowledge graphs using thematic signatures that are derived from unstructured text through the process of topic modeling. As proof of concept, we demonstrate that this enables the automatic categorization of articles into place types defined in the DBpedia ontology (e.g., mountain) and also provides a mechanism to infer relationships between place types that are not captured in existing ontologies. This method can also be used to uncover miscategorized places, which is a common problem arising from the automatic lifting of unstructured and semi-structured data.
C1 [Adams, Benjamin] Univ Auckland, Dept Comp Sci, Ctr eRes, Auckland 1, New Zealand.
   [Janowicz, Krzysztof] Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA.
RP Adams, B (通讯作者)，Univ Auckland, Dept Comp Sci, Ctr eRes, Auckland 1, New Zealand.
EM b.adams@auckland.ac.nz
OI Adams, Benjamin/0000-0002-1657-9809
CR [Anonymous], 2013, CROWDSOURCING GEOGRA, DOI DOI 10.1007/978-94-007-4587-2_12
   Berkes F., 2003, NAVIGATING SOCIO ECO
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Blei DM, 2014, ANNU REV STAT APPL, V1, P203, DOI 10.1146/annurev-statistics-022513-115657
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bowman A.W., 1997, APPL SMOOTHING TECHN, V18
   Cresswell T., 2004, PLACE SHORT INTRO
   Entrikin N., 1991, BETWEENNESS PLACE GE
   Gahegan M, 2014, LECT NOTES COMPUT SC, V8728, P142, DOI 10.1007/978-3-319-11593-1_10
   Graham M, 2014, ANN ASSOC AM GEOGR, V104, P746, DOI 10.1080/00045608.2014.910087
   Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101
   Hastie Trevor, 2009, ELEMENTS STAT LEARNI
   Hinton GE, 2006, SCIENCE, V313, P504, DOI 10.1126/science.1127647
   Janowicz K, 2012, T GIS, V16, P351, DOI 10.1111/j.1467-9671.2012.01342.x
   Jones CB, 2008, INT J GEOGR INF SCI, V22, P1045, DOI 10.1080/13658810701850547
   Kruskal J. B, 1978, MULTIDIMENSIONAL SCA, V11
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Massey Doreen B, 2005, FOR SPACE
   Medelyan O, 2009, INT J HUM-COMPUT ST, V67, P716, DOI 10.1016/j.ijhcs.2009.05.004
   Montello DR, 2003, SPAT COGN COMPUT, V3, P185, DOI DOI 10.1080/13875868.2003.9683761
   ROSCH E, 1975, COGNITIVE PSYCHOL, V7, P573, DOI 10.1016/0010-0285(75)90024-9
   Rubner Y, 2000, INT J COMPUT VISION, V40, P99, DOI 10.1023/A:1026543900054
   Steyvers Mark, 2007, HDB LATENT SEMANTIC, V427, P424, DOI DOI 10.1109/MSP.2010.938079
   Teh YW, 2006, J AM STAT ASSOC, V101, P1566, DOI 10.1198/016214506000000302
   Vasardani Maria, 2013, Spatial Information Theory. 11th International Conference, COSIT 2013. Proceedings: LNCS 8116, P299, DOI 10.1007/978-3-319-01790-7_17
   WARD JH, 1963, J AM STAT ASSOC, V58, P236, DOI 10.2307/2282967
   Winter S, 2009, SPAT COGN COMPUT, V9, P171, DOI 10.1080/13875860903144675
NR 27
TC 21
Z9 22
U1 0
U2 28
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1365-8816
EI 1362-3087
J9 INT J GEOGR INF SCI
JI Int. J. Geogr. Inf. Sci.
PD APR 3
PY 2015
VL 29
IS 4
BP 556
EP 579
DI 10.1080/13658816.2014.989855
PG 24
WC Computer Science, Information Systems; Geography; Geography, Physical;
   Information Science & Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Geography; Physical Geography; Information Science &
   Library Science
GA CJ0TF
UT WOS:000355190200003
DA 2022-08-23
ER

PT J
AU Alkhamees, MA
   Alnuem, MA
   Al-Saleem, SM
   Al-Ssulami, AM
AF Alkhamees, Majed A.
   Alnuem, Mohammed A.
   Al-Saleem, Saleh M.
   Al-Ssulami, Abdulrakeeb M.
TI A semantic metric for concepts similarity in knowledge graphs
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article; Early Access
DE Concept similarity; semantic metrics; semantic similarity; structural
   knowledge
ID FRAMEWORK; ENSEMBLE; MODEL
AB Semantic similarity between concepts concerns expressing the degree of similarity in meaning between two concepts in a computational model. This problem has recently attracted considerable attention from researchers in attempting to automate the understanding of word meanings to expedite the classification of users' opinions and attitudes embedded in text. In this article, a semantic similarity metric is presented. The proposed metric, namely, weighted information-content (wic), exploits the information content of the least common subsumer of two compared concepts and the depth information in knowledge graphs such as DBPedia and YAGO. The two similarity components were combined using calibrated cooperative contributions from both similarity components. A statistical test using the Spearman correlations on well-known human judgement word-similarity data sets showed that the wic metric produced more highly correlated similarities compared with state-of-the-art metrics. In addition, a real-world aspect category classification was evaluated, which exhibited further increased accuracy and recall.
C1 [Alkhamees, Majed A.; Alnuem, Mohammed A.; Al-Saleem, Saleh M.] King Saud Univ, Dept Informat Syst, POB 51178, Riyadh 11543, Saudi Arabia.
   [Al-Ssulami, Abdulrakeeb M.] Taiz Univ, Dept Comp Sci, Taizi, Yemen.
RP Alkhamees, MA (通讯作者)，King Saud Univ, Dept Informat Syst, POB 51178, Riyadh 11543, Saudi Arabia.
EM majedkh@gmail.com
OI AlKhamees, Majed A./0000-0002-1904-9491
FU Deanship of Scientific Research (DSR), King Saud University, Riyadh,
   Saudi Arabia; initiative of DSR Graduate Students Research Support (GSR)
FX The authors would like to thank the Deanship of Scientific Research
   (DSR), King Saud University, Riyadh, Saudi Arabia, for supporting this
   research through the initiative of DSR Graduate Students Research
   Support (GSR).
CR Agirre E, P HUM LANG TECHN 200, P19
   Araque O, 2019, KNOWL-BASED SYST, V165, P346, DOI 10.1016/j.knosys.2018.12.005
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Finkelstein L., P 10 INT C WORLD WID, P406
   Hardalov M, 2016, LECT NOTES ARTIF INT, V9883, P172, DOI 10.1007/978-3-319-44748-3_17
   Hill F, 2015, COMPUT LINGUIST, V41, P665, DOI 10.1162/COLI_a_00237
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Jiang JJ, P 10 RES COMP LING I, P19
   Keramatfar A, 2021, J INF SCI, DOI 10.1177/0165551521990617
   Kulmanov M, 2020, BRIEF BIOINFORM, V2020
   Leacock C, 1998, WORDNET, V49, P283
   Lee JW, 2013, J INF SCI, V39, P608, DOI 10.1177/0165551513480100
   Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005
   Lin D., 1998, Machine Learning. Proceedings of the Fifteenth International Conference (ICML'98), P296
   Lin P, 2019, ACM J DATA INF QUAL, V11, DOI 10.1145/3286488
   Madani Y, 2020, NEURAL COMPUT APPL, V32, P8655, DOI 10.1007/s00521-019-04357-9
   Mihalcea R, P 21 NAT C ART INT A, P775
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936
   Onan A., 28 SIGN PROC COMM AP, P1
   Onan A, 2021, IEEE ACCESS, V9, P7701, DOI 10.1109/ACCESS.2021.3049734
   Onan A, 2019, IEEE ACCESS, V7, P145614, DOI 10.1109/ACCESS.2019.2945911
   Onan A, 2021, CONCURR COMP-PRACT E, V33, DOI 10.1002/cpe.5909
   Onan A, 2021, COMPUT APPL ENG EDUC, V29, P675, DOI 10.1002/cae.22252
   Onan A, 2021, COMPUT APPL ENG EDUC, V29, P572, DOI 10.1002/cae.22253
   Onan A, 2020, COMPUT APPL ENG EDUC, V28, P117, DOI 10.1002/cae.22179
   Onan A, 2018, J INF SCI, V44, P28, DOI 10.1177/0165551516677911
   Onan A, 2017, INFORM PROCESS MANAG, V53, P814, DOI 10.1016/j.ipm.2017.02.008
   Onan A, 2016, EXPERT SYST APPL, V57, P232, DOI 10.1016/j.eswa.2016.03.045
   Pontiki M, P 9 INT WORKSH SEM E, P486
   Pontiki M, P 10 INT WORKSH SEM, P19
   RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528
   Resnik P, 1995, INT JOINT CONF ARTIF, P448
   RUBENSTEIN H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657
   Sharma K, 2019, ACM T INTEL SYST TEC, V10, DOI 10.1145/3305260
   Shekarpour S, 2015, J WEB SEMANT, V30, P39, DOI 10.1016/j.websem.2014.06.002
   Shin S, 2015, J INF SCI, V41, P178, DOI 10.1177/0165551514560669
   Sousa RT, 2020, BMC BIOINFORMATICS, V21, DOI 10.1186/s12859-019-3296-1
   STEIGER JH, 1980, PSYCHOL BULL, V87, P245, DOI 10.1037/0033-2909.87.2.245
   Wasti SH, 2020, CONCURR COMP-PRACT E, V2020
   Wieting J., P 57 ANN M ASS COMP, P4344
   WU ZB, 1994, 32ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P133
   Zhang XC, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2019.03.004
   Zhu GG, 2018, EXPERT SYST APPL, V101, P8, DOI 10.1016/j.eswa.2018.02.011
   Zhu GG, 2017, KNOWL-BASED SYST, V130, P30, DOI 10.1016/j.knosys.2017.05.021
   Zhu GG, 2017, IEEE T KNOWL DATA EN, V29, P72, DOI 10.1109/TKDE.2016.2610428
NR 47
TC 0
Z9 0
U1 6
U2 17
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
AR 01655515211020580
DI 10.1177/01655515211020580
EA JUN 2021
PG 14
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA SN7GE
UT WOS:000658454900001
DA 2022-08-23
ER

PT J
AU Luo, WH
   Cai, FT
   Wu, CN
   Meng, XK
AF Luo, Wenhui
   Cai, Fengtian
   Wu, Chuna
   Meng, Xingkai
TI Bayesian Network-Based Knowledge Graph Inference for Highway
   Transportation Safety Risks
SO ADVANCES IN CIVIL ENGINEERING
LA English
DT Article
AB Accurate inference of knowledge about highway transportation safety risks forms a crucial aspect of building a knowledge graph. Based on the data related to highway transportation accidents, this study has developed a Bayesian network model. The initial identification of the network nodes is through expert scoring. The network structure is then constructed by utilizing the prior expert knowledge and K2 greedy search algorithm. Later, the network parameters are trained via the expectation-maximization (EM) algorithm. Finally, knowledge about highway transportation safety risks is inferred using the junction tree algorithm. A comparison is made between the trained conditional and actual probabilities during the network parameter training to verify the validity of the proposed model that accords with expert experience, thereby proving the model validity. Further, its main "causal chain" is inferred to be an improper emergency response-human failure-accident occurrence, where the probability of driver failure is 82%, and the probability of accident occurrence is 68% by taking "a certain road traffic accident" as an example. There is consistency between the inference results and the actual accident sequence that suggests the effectiveness of the proposed knowledge inference method.
C1 [Luo, Wenhui; Cai, Fengtian; Wu, Chuna; Meng, Xingkai] Minist Transport, Res Inst Highway, Beijing 100088, Peoples R China.
RP Luo, WH (通讯作者)，Minist Transport, Res Inst Highway, Beijing 100088, Peoples R China.
EM 815345591@163.com; ft.cai@rioh.cn; cn.wu@rioh.cn; xk.meng@rion.cn
FU Special Fund of Science and Technology Innovation of Research Institute
   of Highway Ministry of Transport [2019-C507]; Special Fund Project of
   Basic Research Expenses of Central Public Welfare Research Institutes
   [2020-9066]
FX This work was supported by the Special Fund 2019-C507 of Science and
   Technology Innovation of Research Institute of Highway Ministry of
   Transport, and the Special Fund Project of Basic Research Expenses of
   Central Public Welfare Research Institutes 2020-9066.
CR Annervaz K.M., 2018, LEARNING DATASETS KN
   Chen Yu, 2016, Computer Engineering and Applications, V52, P104, DOI 10.3778/j.issn.1002-8331.1512-0029
   COOPER GF, 1992, MACH LEARN, V9, P309, DOI 10.1007/BF00994110
   Godin F., 2019, USING TERNARY REWARD
   Jiang JC, 2017, KNOWL-BASED SYST, V138, P58, DOI 10.1016/j.knosys.2017.09.030
   LAURITZEN SL, 1995, COMPUT STAT DATA AN, V19, P191, DOI 10.1016/0167-9473(93)E0056-A
   Liu CC, 2021, J MATER CIVIL ENG, V33, DOI 10.1061/(ASCE)MT.1943-5533.0003611
   Liu CC, 2020, J MATER CIVIL ENG, V32, DOI 10.1061/(ASCE)MT.1943-5533.0003317
   [刘峤 Liu Qiao], 2017, [计算机学报, Chinese Journal of Computers], V40, P1275
   Liu YB, 2017, EXPERT SYST APPL, V83, P114, DOI 10.1016/j.eswa.2017.04.036
   Lu QW, 2020, J HYDROL, V589, DOI 10.1016/j.jhydrol.2020.125353
   Rajabi MM, 2016, J HYDROL, V536, P255, DOI 10.1016/j.jhydrol.2016.02.029
   Ribeiro B, 2019, NEUROCOMPUTING, V326, P123, DOI 10.1016/j.neucom.2017.01.119
   SeyedHassani A, 2019, J PARALLEL DISTR COM, V125, P72, DOI 10.1016/j.jpdc.2018.11.003
   Xia Deng-you, 2015, Journal of Northeastern University. Natural Science, V36, P897, DOI 10.3969/j.issn.1005-3026.2015.06.030
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Yang Li-gong, 2013, Transactions of Beijing Institute of Technology, V33, P600
   Yin D.L., 2017, J WUT INFORM MANAGEM, V39, P514
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
NR 19
TC 4
Z9 4
U1 17
U2 35
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-8086
EI 1687-8094
J9 ADV CIV ENG
JI Adv. Civ. Eng.
PD MAR 5
PY 2021
VL 2021
AR 6624579
DI 10.1155/2021/6624579
PG 11
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA QX5EA
UT WOS:000629368200001
OA gold
DA 2022-08-23
ER

PT J
AU Bone, J
   Ferreira, JC
   Ribeiro, R
   Cadete, G
AF Bone, Joao
   Ferreira, Joao C.
   Ribeiro, Ricardo
   Cadete, Goncalo
TI DisBot: A Portuguese Disaster Support Dynamic Knowledge Chatbot
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE disaster management; community resilience; situational awareness;
   natural language processing; chatbots; knowledge graphs
ID DESIGN SCIENCE; SYSTEMS
AB This paper presents DisBot, the first Portuguese speaking chatbot that uses social media retrieved knowledge to support citizens and first-responders in disaster scenarios, in order to improve community resilience and decision-making. It was developed and tested using Design Science Research Methodology (DSRM), being progressively matured with field specialists through several design and development iterations. DisBot uses a state-of-the-art Dual Intent Entity Transformer (DIET) architecture to classify user intents, and makes use of several dialogue policies for managing user conversations, as well as storing relevant information to be used in further dialogue turns. To generate responses, it uses real-world safety knowledge, and infers a dynamic knowledge graph that is dynamically updated in real-time by a disaster-related knowledge extraction tool, presented in previous works. Through its development iterations, DisBot has been validated by field specialists, who have considered it to be a valuable asset in disaster management.
C1 [Bone, Joao; Ferreira, Joao C.; Ribeiro, Ricardo] Inst Univ Lisboa ISCTE IUL, P-1649026 Lisbon, Portugal.
   [Bone, Joao; Ferreira, Joao C.; Cadete, Goncalo] INOV INESC Inovacao Inst Novas Tecnol, P-1000029 Lisbon, Portugal.
   [Ferreira, Joao C.] Inst Univ Lisboa ISCTE IUL, ISTAR, P-1649026 Lisbon, Portugal.
   [Ribeiro, Ricardo] INESC ID Lisboa, P-1000029 Lisbon, Portugal.
   [Cadete, Goncalo] Univ Lisbon, Inst Super Tecn, P-1049001 Lisbon, Portugal.
RP Bone, J (通讯作者)，Inst Univ Lisboa ISCTE IUL, P-1649026 Lisbon, Portugal.; Bone, J (通讯作者)，INOV INESC Inovacao Inst Novas Tecnol, P-1000029 Lisbon, Portugal.
EM jmbbe@iscte-iul.pt; jcafa@iscte-iul.pt; ricardo.ribeiro@iscte-iul.pt;
   goncalo.cadete@tecnico.ulisboa.pt
RI Ferreira, João Carlos Amaro/B-5351-2009; Cadete, Goncalo/K-5868-2017
OI Ferreira, João Carlos Amaro/0000-0002-6662-0806; Cadete,
   Goncalo/0000-0001-6049-5306; Ribeiro, Ricardo/0000-0002-2058-693X; Bone,
   Joao/0000-0002-3359-9780
FU Portuguese National funds through FITEC - Programa Interface;
   Infrastress Project-The Infrastress project H2020; FCT, Fundacao para a
   Ciencia e a Tecnologia [UIDB/50021/2020]
FX This work has been partially supported by Portuguese National funds
   through FITEC - Programa Interface, with reference CIT "INOV - INESC
   Inovacao - Financiamento Base", and by the Infrastress Project-The
   Infrastress project is an H2020 financed project, aiming to address
   cyber-physical security of Sensitive Industrial Plants and Sites (SIPS),
   improve resilience and protection capabilities of SIPS exposed to large
   scale, combined, cyber-physical threats and hazards, and guarantee
   continuity of operations, while mitigating effects in the infrastructure
   itself, the environment, and citizens in vicinity, at a reasonable cost.
   This work was also supported by national funds through FCT, Fundacao
   para a Ciencia e a Tecnologia, under project UIDB/50021/2020.
CR Baby CJ, 2017, 2017 INNOVATIONS IN POWER AND ADVANCED COMPUTING TECHNOLOGIES (I-PACT)
   Bajic I. V., 2017, P IEEE INT S CIRC SY, P1
   Barrett M. P, 2018, FRAMEWORK IMPROVING
   Belfin RV, 2019, INT CONF ADVAN COMPU, P717, DOI 10.1109/ICACCS.2019.8728499
   Bernstein P., 1996, GODS REMARKABLE STOR
   Bone J, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10176083
   Bunk T., 2004, ARXIV200409936
   Cadete G, 2018, SAFETY AND RELIABILITY - SAFE SOCIETIES IN A CHANGING WORLD, P1459
   Chan HY, 2019, INT J DISAST RISK RE, V41, DOI 10.1016/j.ijdrr.2019.101313
   D'silva GM, 2017, 2017 INTERNATIONAL CONFERENCE ON I-SMAC (IOT IN SOCIAL, MOBILE, ANALYTICS AND CLOUD) (I-SMAC), P658, DOI 10.1109/I-SMAC.2017.8058261
   Dale R, 2016, NAT LANG ENG, V22, P811, DOI 10.1017/S1351324916000243
   Dias M, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10072303
   Farahani RZ, 2011, ELSEV INSIGHT, P1
   Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625
   Huang CY, 2018, IN C IND ENG ENG MAN, P1791, DOI 10.1109/IEEM.2018.8607399
   International Organization for Standardization, 2003, INF TECHN PROC ASS 2
   ISACA, 2014, IMPLEMENTING NIST CY
   Jurafsky D., 2018, SPEECH LANGUAGE PROC
   Kadariya D, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP 2019), P138, DOI [10.1109/SMARTCOMP.2019.00043, 10.1109/smartcomp.2019.00043]
   Kshetri N, 2020, IT PROF, V22, P63, DOI 10.1109/MITP.2019.2951851
   Lafferty J., 2001, P 18 INT C MACH LEAR
   Maniou TA, 2020, FUTURE INTERNET, V12, DOI 10.3390/fi12070109
   Markus ML, 2002, MIS QUART, V26, P179
   Peffers K, 2007, J MANAGE INFORM SYST, V24, P45, DOI 10.2753/MIS0742-1222240302
   Prat N., 2014, P PAC AS C INF SYST
   Ranoliya BR, 2017, 2017 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), P1525, DOI 10.1109/ICACCI.2017.8126057
   Sermet Y, 2018, ENVIRON MODELL SOFTW, V108, P51, DOI 10.1016/j.envsoft.2018.06.003
   Shum HY, 2018, FRONT INFORM TECH EL, V19, P10, DOI 10.1631/FITEE.1700826
   Tsai MH, 2019, WATER-SUI, V11, DOI 10.3390/w11020234
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Vlasov V., 2019, ARXIV191000486
   Weizenbaum J., 1983, Communications of the ACM, V26, P23, DOI 10.1145/357980.357991
   Wu L, 2018, AAAI CONF ARTIF INTE, P5569
NR 33
TC 4
Z9 4
U1 6
U2 15
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD DEC
PY 2020
VL 10
IS 24
AR 9082
DI 10.3390/app10249082
PG 20
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA PL1UI
UT WOS:000602915300001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Chen, F
   Huang, YF
AF Chen, Fang
   Huang, Yongfeng
TI Knowledge-enhanced neural networks for sentiment analysis of Chinese
   reviews
SO NEUROCOMPUTING
LA English
DT Review
DE Sentiment analysis; Neural networks; Knowledge graph
AB Sentiment analysis aims to extract structured opinions from unstructured reviews and determine their sentiment polarities. However, existing sentiment analysis systems fail to identify aspect-opinion pairs and perform poorly on small training corpora. To address these issues, we propose a novel framework to model aspect-opinion pair identification and aspect-level sentiment classification as a joint text classification task. Moreover, we incorporate external knowledge into neural networks to compensate for the lack of training data. In our approach, context features extracted from review sentences and external knowledge retrieved from a sentiment knowledge graph are used to identify aspect-opinion pairs and determine their sentiment polarities. In this way, our model is able to provide more detailed sentiment analysis results and achieve better performance with limited training corpora. We evaluate our approach using a Chinese car review dataset. Experimental results show that the knowledge-enhanced neural networks consistently outperform the conventional models. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Chen, Fang; Huang, Yongfeng] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China.
RP Chen, F (通讯作者)，Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China.
EM f-chen17@mails.tsinghua.edu.cn; yfhuang@mail.tsinghua.edu.cn
OI Huang, Yongfeng/0000-0003-3825-2230
FU Ministry of Science and Technology of China [2018YFC1604002]; National
   Natural Science Foundation of China [U1536201, U1705261]
FX We are especially grateful to Zhigang Yuan and Sixing Wu for their
   fruitful comments and corrections. This work is supported by the
   Ministry of Science and Technology of China (Grant no. 2018YFC1604002)
   and the National Natural Science Foundation of China (Grant nos.
   U1536201 and U1705261).
CR Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
   Cambria E, 2018, AAAI CONF ARTIF INTE, P1795
   Cambria E, 2016, IEEE INTELL SYST, V31, P102, DOI 10.1109/MIS.2016.31
   Chen P, 2017, P 2017 C EMP METH NA, P452, DOI DOI 10.18653/V1/D17-1047
   Kingma D. P., 2014, P 3 INT C LEARN REPR, P1, DOI DOI 10.1038/S41598-021-83424-5
   Kiritchenko S., 2014, P 8 INT WORKSH SEM E, V2014, P437, DOI DOI 10.3115/V1/S14-2076
   Lin YK, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2124
   Liu Bing, 2012, SYNTHSIS LECT HUM LA, V5.1, P1, DOI DOI 10.2200/S00416ED1V01Y201204HLT016
   Liu LZ, 2013, J COMPUT, V8, P878, DOI 10.4304/jcp.8.4.878-883
   Liu P, P 2015 C EMP METH NA, P1433
   Ma DH, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4068
   Ma YK, 2018, AAAI CONF ARTIF INTE, P5876
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Sapienza A, 2017, INT CONF DAT MIN WOR, P902, DOI 10.1109/ICDMW.2017.124
   Tang D., 2015, ARXIV151201100
   Tang HJ, 2016, PROCEEDINGS OF 2016 5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT), P214, DOI 10.1109/ICCSNT.2016.8070151
   Tay Y, 2018, P 2018 C EMP METH NA, P3443
   Teng Z., 2016, P 2016 C EMP METH NA, P1629
   Wagner A, 2014, J APPL GEOD, V8, P223, DOI 10.1515/jag-2014-0014
   Wang W., 2017, ARXIV170201776
   Wang WY, 2017, AAAI CONF ARTIF INTE, P3316
   Wang Y, 2016, PROC C EMPIRICAL MET, P606, DOI [10.18653/v1/D16-1058, DOI 10.18653/V1/D16-1058]
   Yang M, 2019, NEURAL NETWORKS, V117, P240, DOI 10.1016/j.neunet.2019.05.021
   Yang M, 2018, NEUROCOMPUTING, V307, P91, DOI 10.1016/j.neucom.2018.04.042
   Zeng D., 2014, P COLING, P2335
   Zeng D., 2015, P C EMP METH NAT LAN, P1753, DOI DOI 10.18653/V1/D15-1203
NR 26
TC 22
Z9 23
U1 19
U2 79
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD NOV 27
PY 2019
VL 368
BP 51
EP 58
DI 10.1016/j.neucom.2019.08.054
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JD3VN
UT WOS:000489905200005
DA 2022-08-23
ER

PT J
AU Li, SY
   Wang, H
   Pan, R
   Mao, MZ
AF Li, Shuangyin
   Wang, Heng
   Pan, Rong
   Mao, Mingzhi
TI MemoryPath: A deep reinforcement learning framework for incorporating
   memory component into knowledge graph reasoning
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph reasoning; Memory component; Deep reinforcement
   learning; Link prediction; Attentional path; Force forward
AB Knowledge Graph (KG) is identified as a major area in artificial intelligence, which is used for many real -world applications. The task of knowledge graph reasoning has been widely used and proven to be effective, which aims to find these reasonable paths for various relations to solve the issue of incompleteness in KGs. However, many previous works on KG reasoning, such as path-based or reinforcement learning-based methods, are too reliant on the pre-training, where the paths from the head entity and the target entity must be given to pre-train the model, which would easily lead the model to overfit on the given paths seen in the pre-training. To address this issue, we propose a novel reasoning model named MemoryPath with a deep reinforcement learning framework, which incorporates Long Short Term Memory (LSTM) and graph attention mechanism to form the memory component. The well-designed memory component can get rid of the pre-training so that the model doesn't depend on the given target entity for training. A tailored mechanism of reinforcement learning is presented in this proposed deep reinforcement framework to optimize the training procedure, where two metrics, Mean Selection Rate (MSR) and Mean Alternative Rate (MAR), are defined to quantitatively measure the complexities of the query relations. Meanwhile, three different training mechanisms, Action Dropout, Reward Shaping and Force Forward, are proposed to optimize the training process of the proposed MemoryPath. The proposed MemoryPath is validated on two datasets from FB15K-237 and NELL-995 on different tasks including fact prediction, link prediction and success rate in finding paths. The experimental results demonstrate that the tailored mechanism of reinforcement learning make the MemoryPath achieves state-of-the-art performance comparing with the other models. Also, the qualitative analysis indicates that the MemoryPath can store the learning process and automatically find the promising paths for a reasoning task during the training, and shows the effectiveness of the memory component. (c) 2020 Elsevier B.V. All rights reserved.
C1 [Li, Shuangyin] South China Normal Univ, Sch Comp Sci, Guangzhou, Guangdong, Peoples R China.
   [Wang, Heng] Tencent, Guangzhou, Guangdong, Peoples R China.
   [Pan, Rong; Mao, Mingzhi] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China.
RP Li, SY (通讯作者)，South China Normal Univ, Sch Comp Sci, Guangzhou, Guangdong, Peoples R China.; Pan, R (通讯作者)，Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China.
EM shuangyinli@scnu.edu.cn; jimmyhwang@tencent.com; panr@sysu.edu.cn;
   mcsmmz@mail.sysu.edu.cn
FU National Natural Science Foundation of China [62006083]; Basic and
   Applied Basic Research Fund of Guangdong Province [2019B1515120085]
FX This work was supported by National Natural Science Foundation of China
   (No. 62006083), and Basic and Applied Basic Research Fund of Guangdong
   Province (No. 2019B1515120085). Shuangyin Li and Rong Pan are the
   corresponding authors.
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2014, ARXIV14063676
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Chen W., 2018, P 2018 C N AM CHAPT, V1, P1823, DOI [10.18653/v1/N18-1165, DOI 10.18653/V1/N18-1165]
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Das R., 2018, ICLR
   Das Rajarshi, 2019, ICLR
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dong L, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P260
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Dubey M, 2018, LECT NOTES COMPUT SC, V11136, P108, DOI 10.1007/978-3-030-00671-6_7
   Guu K., 2015, ARXIV150601094
   Hildebrandt M., 2020, ARXIV200100461
   Jagvaral B, 2020, EXPERT SYST APPL, V142, DOI 10.1016/j.eswa.2019.112960
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kingma D. P., 2014, P 3 INT C LEARN REPR, P1, DOI DOI 10.1038/S41598-021-83424-5
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Li Y, 2018, IEEE 2018 INTERNATIONAL CONGRESS ON CYBERMATICS / 2018 IEEE CONFERENCES ON INTERNET OF THINGS, GREEN COMPUTING AND COMMUNICATIONS, CYBER, PHYSICAL AND SOCIAL COMPUTING, SMART DATA, BLOCKCHAIN, COMPUTER AND INFORMATION TECHNOLOGY, P1821, DOI 10.1109/Cybermatics_2018.2018.00303
   Lin X.V., 2018, C EMPIRICAL METHODS, P3243
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu ZH, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P2395
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Neelakantan A, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P156
   Ng AY, 1999, MACHINE LEARNING, PROCEEDINGS, P278
   Shen Y., 2018, NEURIPS, V31, P6786
   Shi C., 2019, AAAI
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Toutanova K., 2015, P 2015 C EMP METH NA, P1499, DOI DOI 10.18653/V1/D15-1174
   Toutanova K, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1434
   Velickovic P., 2018, STAT-US, V1050, P4
   Wang H., 2019, P EMNLP, P2623, DOI [10.18653/v1/D19-1264, DOI 10.18653/V1/D19-1264]
   Wang Q, 2020, NEUROCOMPUTING
   Wang WY, 2016, AAMAS'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS, P1327
   Wang ZK, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI), P211, DOI 10.1109/ISI.2018.8587330
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Yang B., 2019, ARXIV190209091
   Yang F., 2017, ADV NEURAL INFORM PR, P2319
NR 39
TC 4
Z9 4
U1 5
U2 54
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JAN 2
PY 2021
VL 419
BP 273
EP 286
DI 10.1016/j.neucom.2020.08.032
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OS4XR
UT WOS:000590168700005
DA 2022-08-23
ER

PT J
AU Dogan, T
   Atas, H
   Joshi, V
   Atakan, A
   Rifaioglu, AS
   Nalbat, E
   Nightingale, A
   Saidi, R
   Volynkin, V
   Zellner, H
   Cetin-Atalay, R
   Martin, M
   Atalay, V
AF Dogan, Tunca
   Atas, Heval
   Joshi, Vishal
   Atakan, Ahmet
   Rifaioglu, Ahmet Sureyya
   Nalbat, Esra
   Nightingale, Andrew
   Saidi, Rabie
   Volynkin, Vladimir
   Zellner, Hermann
   Cetin-Atalay, Rengul
   Martin, Maria
   Atalay, Volkan
TI CROssBAR: comprehensive resource of biomedical relations with knowledge
   graph representations
SO NUCLEIC ACIDS RESEARCH
LA English
DT Article
ID PRIMARY OSTEOSARCOMA; CANCER; NETWORKS; ONTOLOGY; BREAST
AB Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produced using different technologies and scattered across individual computational resources without any explicit connections to each other, which hinders extensive and integrative multi-omics-based analysis. We aimed to address this issue by developing a new data integration/representation methodology and its application by constructing a biological data resource. CROssBAR is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database. CROssBAR is enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules. These complex sets of entities and relationships are displayed to users via easy-tointerpret, interactive knowledge graphs within an open-access service. CROssBAR knowledge graphs incorporate relevant genes-proteins, molecular interactions, pathways, phenotypes, diseases, as well as known/predicted drugs and bioactive compounds, and they are constructed on-the-fly based on simple non-programmatic user queries. These intensely processed heterogeneous networks are expected to aid systems-level research, especially to infer biological mechanisms in relation to genes, proteins, their ligands, and diseases.
C1 [Dogan, Tunca] Hacettepe Univ, Dept Comp Engn, TR-06800 Ankara, Turkey.
   [Dogan, Tunca] Hacettepe Univ, Inst Informat, TR-06800 Ankara, Turkey.
   [Dogan, Tunca; Atas, Heval; Nalbat, Esra; Cetin-Atalay, Rengul] METU, Canc Syst Biol Lab, Grad Sch Informat, TR-06800 Ankara, Turkey.
   [Dogan, Tunca; Joshi, Vishal; Nightingale, Andrew; Saidi, Rabie; Volynkin, Vladimir; Zellner, Hermann; Martin, Maria] European Bioinformat Inst EMBL EBI, European Mol Biol Lab, Hinxton CB10 1SD, Cambs, England.
   [Atakan, Ahmet; Rifaioglu, Ahmet Sureyya; Atalay, Volkan] METU, Dept Comp Engn, TR-06800 Ankara, Turkey.
   [Atakan, Ahmet] EBYU, Dept Comp Engn, TR-24002 Erzincan, Turkey.
   [Rifaioglu, Ahmet Sureyya] Iskenderun Tech Univ, Dept Comp Engn, TR-31200 Antakya, Turkey.
   [Cetin-Atalay, Rengul] Univ Chicago, Sect Pulm & Crit Care Med, Chicago, IL 60637 USA.
RP Dogan, T (通讯作者)，Hacettepe Univ, Dept Comp Engn, TR-06800 Ankara, Turkey.; Dogan, T (通讯作者)，Hacettepe Univ, Inst Informat, TR-06800 Ankara, Turkey.; Dogan, T (通讯作者)，METU, Canc Syst Biol Lab, Grad Sch Informat, TR-06800 Ankara, Turkey.; Dogan, T (通讯作者)，European Bioinformat Inst EMBL EBI, European Mol Biol Lab, Hinxton CB10 1SD, Cambs, England.
EM tuncadogan@gmail.com
RI Dogan, Tunca/B-5274-2017; Atalay, Volkan/M-2256-2016
OI Dogan, Tunca/0000-0002-1298-9763; Martin,
   Maria-Jesus/0000-0001-5454-2815; Atas Guvenilir,
   Heval/0000-0003-3180-7010; Nalbat, Esra/0000-0002-4051-9793; Atalay,
   Volkan/0000-0001-7850-0601
FU Newton/Katip Celebi Institutional Links program by TUBITAK, Turkey
   [116E930]; British Council, UK [116E930]; TUBITAK-ARDEB 1001 program
   [118Z774]; European Molecular Biology Laboratory
FX Newton/Katip Celebi Institutional Links program by TUBITAK, Turkey and
   British Council, UK [116E930, project acronym: CROssBAR]; TUBITAK-ARDEB
   1001 program [118Z774]; European Molecular Biology Laboratory core
   funds. Funding for open access charge: Newton/Katip Celebi Institutional
   Links program by TUBITAK, Turkey and British Council, UK [116E930,
   CROssBAR]; TUBITAK-ARDEB 1001 program [118Z774]; European Molecular
   Biology Laboratory core funds.
CR Bahrami A, 2007, ARCH PATHOL LAB MED, V131, P792
   Cong Q, 2018, IEEE INT C BIOINFORM, P1628, DOI 10.1109/BIBM.2018.8621568
   Costa RLB, 2018, BREAST CANCER RES TR, V169, P397, DOI 10.1007/s10549-018-4697-y
   Cote R, 2010, NUCLEIC ACIDS RES, V38, pW155, DOI 10.1093/nar/gkq331
   Cui CC, 2017, ACTA PHARM SIN B, V7, P3, DOI 10.1016/j.apsb.2016.11.001
   Donmez A, 2020, BIOINFORMATICS, V36, P4227, DOI 10.1093/bioinformatics/btaa496
   Ernst P, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0549-5
   Fabregat A, 2018, NUCLEIC ACIDS RES, V46, pD649, DOI [10.1093/nar/gkx1132, 10.1093/nar/gkz1031]
   Fadare O, 2007, INT J GYNECOL PATHOL, V26, P21, DOI 10.1097/01.pgp.0000225840.36750.a2
   Franz M, 2018, NUCLEIC ACIDS RES, V46, pW60, DOI 10.1093/nar/gky311
   Gordon DE, 2020, NATURE, V583, P459, DOI 10.1038/s41586-020-2286-9
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Hsieh YH, 2019, CANCER MED-US, V8, P4484, DOI 10.1002/cam4.2329
   Huang C, 2019, CANCERS, V11, DOI 10.3390/cancers11121869
   Kanehisa M, 2014, NUCLEIC ACIDS RES, V42, pD199, DOI 10.1093/nar/gkt1076
   King MC, 2003, SCIENCE, V302, P643, DOI 10.1126/science.1088759
   Kutmon M, 2016, NUCLEIC ACIDS RES, V44, pD488, DOI 10.1093/nar/gkv1024
   Lheureux S, 2019, LANCET, V393, P1240, DOI 10.1016/S0140-6736(18)32552-2
   Li X, 2015, ONCOGENESIS, V4, DOI 10.1038/oncsis.2015.1
   Liekens AML, 2011, GENOME BIOL, V12, DOI 10.1186/gb-2011-12-6-r57
   Livingston KM, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0559-3
   Messina A, 2018, BMC SYST BIOL, V12, DOI 10.1186/s12918-018-0616-4
   Messina A, 2018, ADV INTELL SYST, V611, P299, DOI 10.1007/978-3-319-61566-0_28
   Nicholson D.N., 2020, EXPANDING DATABASE D, DOI [10.1101/730085, DOI 10.1101/730085]
   Pareja-Tobes P., 2015, BIO4J HIGH PERFORMAN
   Richardson P, 2020, LANCET, V395, pE30, DOI 10.1016/S0140-6736(20)30304-4
   Rifaioglu AS, 2021, BIOINFORMATICS, V37, P693, DOI 10.1093/bioinformatics/btaa858
   Rifaioglu AS, 2020, CHEM SCI, V11, P2531, DOI 10.1039/c9sc03414e
   Rifaioglu AS, 2019, BRIEF BIOINFORM, V20, P1878, DOI 10.1093/bib/bby061
   Rivals I, 2007, BIOINFORMATICS, V23, P401, DOI 10.1093/bioinformatics/btl633
   Shannon P, 2003, GENOME RES, V13, P2498, DOI 10.1101/gr.1239303
   Stebbing J, 2020, LANCET INFECT DIS, V20, P400, DOI 10.1016/S1473-3099(20)30132-8
   Szklarczyk D, 2019, NUCLEIC ACIDS RES, V47, pD607, DOI 10.1093/nar/gky1131
   Szklarczyk D, 2016, NUCLEIC ACIDS RES, V44, pD380, DOI 10.1093/nar/gkv1277
   Turki H, 2019, J BIOMED INFORM, V99, DOI 10.1016/j.jbi.2019.103292
   Waagmeester A, 2020, ELIFE, V9, DOI 10.7554/eLife.52614
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Whetzel PL, 2011, NUCLEIC ACIDS RES, V39, pW541, DOI 10.1093/nar/gkr469
   Whibley C, 2009, NAT REV CANCER, V9, P95, DOI 10.1038/nrc2584
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Wu F, 2020, NATURE, V579, P265, DOI 10.1038/s41586-020-2008-3
   Wu J, 2020, WORLD J GASTROENTERO, V26, P2286, DOI 10.3748/wjg.v26.i19.2286
   Xia Y, 2019, FRONT PHARMACOL, V10, DOI 10.3389/fphar.2019.01029
   Xie R, 2017, CANCER RES, V77, P6499, DOI 10.1158/0008-5472.CAN-17-0360
   Yeh CT, 2012, AM J RESP CRIT CARE, V186, P1180, DOI 10.1164/rccm.201207-1180OC
   Yuan JB, 2020, KNOWL INF SYST, V62, P317, DOI 10.1007/s10115-019-01351-4
   Zhang CT, 2020, J MED VIROL, V92, P1441, DOI 10.1002/jmv.25788
NR 47
TC 4
Z9 4
U1 13
U2 24
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0305-1048
EI 1362-4962
J9 NUCLEIC ACIDS RES
JI Nucleic Acids Res.
PD SEP 20
PY 2021
VL 49
IS 16
AR e96
DI 10.1093/nar/gkab543
EA JUN 2021
PG 24
WC Biochemistry & Molecular Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology
GA UY6XS
UT WOS:000701664900006
PM 34181736
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Yan, ZH
   Peng, R
   Wang, YQ
   Li, WD
AF Yan, Zhihuan
   Peng, Rong
   Wang, Yaqian
   Li, Weidong
TI CTEA: Context and Topic Enhanced Entity Alignment for knowledge graphs
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph embedding; Entity alignment; Entity contexts; Topic
   model
AB We study the problem of finding entities referring to the same real world object in multilingual knowledge graphs(KGs), i.e., entity alignment for multilingual KGs. Recently, embedding-based entity alignment methods get extended attention in this area. Most of them firstly embed the entities in low dimensional vectors space via relation structure of entities, and then align entities via these learned embeddings combined with some entity similarity function. Even achieved promising performances, these methods are defective in utilizing entity contexts and entity topic information. In this paper, we propose a novel entity alignment framework CTEA (Context and Topic Enhanced Entity Alignment), which integrates entity context information and entity topic information to help alignment. This framework learns entity topic distributions from their attributes with a specially designed topic model BTM4EA, and the learned entity topic distributions are used to filter some weakly correlated entities for each entity to be aligned. Meanwhile, we embed KGs to low dimensional vectors space via translation-based KG embedding model and mine context information from these vectors with an attention attached Convolutional Neural Network(CNN). The entity embeddings, entity contexts and entity topics are combined to get the final alignment results. Extended experiments reveal that our method achieves promising performances in most cases. (C) 2020 Published by Elsevier B.V.
C1 [Yan, Zhihuan; Peng, Rong; Wang, Yaqian; Li, Weidong] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
RP Peng, R (通讯作者)，Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
EM zhihuanyan@whu.edu.cn; rongpeng@whu.edu.cn; yaqian@whu.edu.cn;
   weidonghappy@whu.edu.cn
FU National Key Research and Development Plan of China [2017YFB0503702,
   2016YFB0501801]; National Natural Science Foundation of China [61170026]
FX This work is supported by the National Key Research and Development Plan
   of China under Grant Nos. 2017YFB0503702, 2016YFB0501801, and National
   Natural Science Foundation of China under Grant No. 61170026.
CR Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cao YX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1452
   Chen MH, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3998
   Chen XM, 2017, CHIN AUTOM CONGR, P2115, DOI 10.1109/CAC.2017.8243121
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Duchi J, 2011, J MACH LEARN RES, V12, P2121
   Hao YC, 2016, COMM COM INF SC, V650, P3, DOI 10.1007/978-981-10-3168-7_1
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kipf Thomas N., 2017, INT C LEARN REPR ICL
   Li C., 2019, EMNLP IJCNLP, P2723, DOI 10.18653/v1/D19-1274
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Navigli R, 2012, ARTIF INTELL, V193, P217, DOI 10.1016/j.artint.2012.07.001
   Nguyen D. Q., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Qian W, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4286
   Qu M., ABS190703179 CORR
   Rebele T, 2016, LECT NOTES COMPUT SC, V9982, P177, DOI 10.1007/978-3-319-46547-0_19
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Trisedya BD, 2019, AAAI CONF ARTIF INTE, P297
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Xu K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3156
   Yan ZH, 2019, IEEE IJCNN
   Yang B., 2015, ICLR
   Yang Hsiu-Wei, 2019, P 2019 C EMP METH NA, P4430
   Ye R, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4135
   Yu WR, 2013, IEEE DATA MINING, P1271, DOI 10.1109/ICDM.2013.32
   Zeng W., ABS191208404 CORR
   Zhang QH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5429
   Zhu H, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4258
NR 36
TC 8
Z9 9
U1 9
U2 27
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD OCT 14
PY 2020
VL 410
BP 419
EP 431
DI 10.1016/j.neucom.2020.06.054
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OD4CB
UT WOS:000579799300037
DA 2022-08-23
ER

PT J
AU Oh, B
   Seo, S
   Hwang, J
   Lee, D
   Lee, KH
AF Oh, Byungkook
   Seo, Seungmin
   Hwang, Jimin
   Lee, Dongho
   Lee, Kyong-Ho
TI Open-world knowledge graph completion for unseen entities and relations
   via attentive feature aggregation
SO INFORMATION SCIENCES
LA English
DT Article
DE Knowledge graph embedding; Link prediction; Graph-based learning; Graph
   convolutional networks; Deep learning; Attention model
AB Most of knowledge graph completion (KGC) models are designed for static KGs where entity and relation sets are fixed. These approaches are inherently transductive because they simply predict the plausibility of facts whose entities and relations had to previously appear in the training phase. However, since entities and relations are constantly added, removed, or changed over time, KGC models should be able to generalize to out-of-KG entities and relations in evolving KGs, which are more suited to real-world scenarios. Moreover, incorporating global graph-structured information into KGC models is another challenging issue. To overcome these issues, this paper proposes a novel Inductive KG Embedding (IKGE) model for open-world KGC, which accommodates out-of-KG entities and relations. Unlike training individual unique embeddings, the proposed model fundamentally learns an embedding generator function to elaborately generate fact embeddings in an inductive manner. Specifically, the feature information of each fact is extracted as a vector from its entity-related and relation-related side information via an attention mechanism. Then, to score a given fact, our neighborhood feature aggregator hierarchically accumulates the feature information of multi-hop neighbors. Experimental results show that IKGE outperforms existing approaches in both transductive and inductive setups by successfully aggregating neighborhood features.(c) 2021 Elsevier Inc. All rights reserved.
C1 [Oh, Byungkook; Seo, Seungmin; Hwang, Jimin; Lee, Dongho; Lee, Kyong-Ho] Yonsei Univ, Dept Comp Sci, Seoul, South Korea.
RP Lee, KH (通讯作者)，Yonsei Univ, Dept Comp Sci, Seoul, South Korea.
EM smseo@icl.yonsei.ac.kr; jmhwang@icl.yonsei.ac.kr;
   dhlee@icl.yonsei.ac.kr; khlee89@yonsei.ac.kr
FU National Research Foundation of Korea (NRF) - Korea government (MSIP)
   [NRF-2019R1A2B5B01070555]; Ministry of Science, ICT & Future Planning
FX This research was supported by the National Research Foundation of Korea
   (NRF) grant funded by the Korea government (MSIP; Ministry of Science,
   ICT & Future Planning) (No. NRF-2019R1A2B5B01070555) .
CR Amador-Dominguez E, 2021, INFORM SCIENCES, V564, P85, DOI 10.1016/j.ins.2021.02.018
   [Anonymous], ARXIV170905584
   Bahdanau D., 2014, ARXIV
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bruna J., 2014, 2 INT C LEARNING REP
   Carlson Andrew, 2010, P 24 C ART INT AAAI
   Cheng J, 2016, ARXIV160106733, P551, DOI [10.18653/v1/d16-1053, DOI 10.18653/V1/D16-1053]
   dos Santos C., ARXIV160203609
   Douglas B.L., ARXIV11015211
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Guo XB, 2020, INFORM SCIENCES, V540, P263, DOI 10.1016/j.ins.2020.06.041
   Hamilton Will, 2017, NEURIPS
   Hayashi T., J AMBIENT INTELL HUM
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Kim Y., 2014, PROC C EMPIRICAL MET, P1746, DOI [10.3115/v1/d14-1181, 10.3115/v1/D14-1181]
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lin Z, 2017, 5 INT C LEARN REPR I
   Lin ZY, 2021, INFORM SCIENCES, V580, P1, DOI 10.1016/j.ins.2021.08.018
   Luong MT, 2015, EFFECTIVE APPROACHES
   Mazumder S., ARXIV180206024
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Neelakantan A, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P156
   Nguyen D.Q., ARXIV170308098
   Nguyen D.Q., 2016, P 20 SIGNLL C COMP N, P40, DOI DOI 10.18653/V1/K16-1005
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   NIEPERT M, 2016, ICML, V48
   Oh B, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P257, DOI 10.1145/3269206.3271769
   Perozzi Bryan, 2014, P 20 ACM SIGKDD INT
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Shah H, 2019, AAAI CONF ARTIF INTE, P3044
   Shanavas N, 2020, INFORM SCIENCES, V525, P172, DOI 10.1016/j.ins.2020.03.006
   Shi BX, 2018, AAAI CONF ARTIF INTE, P1957
   Shin S, 2020, INFORM SCIENCES, V523, P234, DOI 10.1016/j.ins.2020.02.065
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sutskever I., 2014, ADV NEURAL INF PROCE
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   TROUILLON T, 2016, ICML, V48
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Velickovi P., 2017, ARXIV171010903
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang DS, 2021, INFORM SCIENCES, V579, P454, DOI 10.1016/j.ins.2021.08.015
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   [Ямалетдинова Мунира Фадитовна Yamaletdinova Munira F.], 2020, [Пищевая промышленность, Food Industry, Pishchevaya promyshlennost'], P23, DOI 10.24411/0235-2486-2020-10092
   Yang B., 2015, P INT C LEARN REPR I
   Yin W., 2016, T ASSOC COMPUT LING, V4, P259, DOI [DOI 10.1162/TACL_A_00097, 10.1162/tacl_a_00097, DOI 10.1162/TACL_A_00244]
   Zhang RC, 2020, INFORM SCIENCES, V521, P144, DOI 10.1016/j.ins.2020.02.016
NR 50
TC 1
Z9 1
U1 4
U2 4
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD MAR
PY 2022
VL 586
BP 468
EP 484
DI 10.1016/j.ins.2021.11.085
PG 17
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZS1MZ
UT WOS:000768237300002
DA 2022-08-23
ER

PT J
AU Lan, LTH
   Tuan, TM
   Ngan, TT
   Son, LH
   Giang, NL
   Ngoc, VTN
   Hai, PV
AF Hong Lan, Luong Thi
   Tuan, Tran Manh
   Ngan, Tran Thi
   Son, Le Hoang
   Giang, Nguyen Long
   Nhu Ngoc, Vo Truong
   Hai, Pham Van
TI A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and
   Extensions in Decision Making
SO IEEE ACCESS
LA English
DT Article
DE Fuzzy logic; Fuzzy sets; Testing; Electronic mail; Decision making;
   Inference mechanisms; Computational modeling; Complex fuzzy set; complex
   fuzzy inference system; fuzzy knowledge graph; fast inference search
   algorithm; Sugeno complex fuzzy inference systems; Tsukamoto complex
   fuzzy inference systems; complex fuzzy measures; complex fuzzy integrals
ID ANFIS; PERFORMANCE; SETS
AB Context and Background:Complex fuzzy theory has a strong practical implication in many real-world applications. Complex Fuzzy Inference System (CFIS) is a powerful technique to overcome the challenges of uncertain, periodic data. However, a question is raised for CFIS: How can we deduce and predict the result in case there is little knowledge about data information and rule base? This is significance because many real applications do not have enough knowledge of rule base for inference so that the performance of systems may be low. Thus, it is necessary to have an approximate reasoning method to represent and derive final results. Motivation: Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been proposed with a specific inference mechanism according to the Mamdani type. A new improvement so-called the Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R) has been designed to utilize granular computing with complex similarity measures to reduce the rule base so as to gain better performance in decision-making problems. However in M-CFIS-R, testing data are checked by matching with each rule in the rule base, which leads to a high cost of computational time. Besides, if the testing data contain records that are not inferred by the rule base, the output cannot be generated. This happens in real commerce systems in which the rule base is small at the time of creation and needs to feed with new rules. Methodology: In order to handle those issues, this article first time proposes the Fuzzy Knowledge Graph to represent the rule base in terms of linguistic labels and their relationships according to the rule set. An adjacent matrix of Fuzzy Knowledge Graph is generated for inference. When a record in the Testing dataset is given, it would be fuzzified and labelled. Each component in the record is checked with the Fuzzy Knowledge Graph by the inference mechanism in approximate reasoning called Fast Inference Search Algorithm. Then, we derive the label of the new record by the Max-Min operator. Besides, we also propose four extensions of Mamdani Complex Fuzzy Inference System Rule Reduction including Sugeno Complex Fuzzy Inference Systems, Tsukamoto Complex Fuzzy Inference Systems, Complex Fuzzy Measures and Complex Fuzzy Integrals in M-CFIS-R. Results: The experiments on the UCI Machine Learning datasets show that the proposed method classifies samples as correctly as M-CFIS-R with very lower run time (6.45 times on average). The experiments are performed through many tests via 2 main scenarios. Conclusion: The proposed system has good performance in reducing the computational time of inference with acceptable accuracy. It has ability to work with systems having limited knowledge and rule base.
C1 [Hong Lan, Luong Thi] Vietnam Acad Sci & Technol, Grad Univ Sci & Technol, Hanoi 100000, Vietnam.
   [Hong Lan, Luong Thi; Tuan, Tran Manh; Ngan, Tran Thi] Thuyloi Univ, Fac Comp Sci & Engn, Hanoi 100000, Vietnam.
   [Giang, Nguyen Long] Vietnam Acad Sci & Technol, Inst Informat Technol, Hanoi 100000, Vietnam.
   [Son, Le Hoang] Vietnam Natl Univ, VNU Informat Technol Inst, Hanoi 100000, Vietnam.
   [Son, Le Hoang] Vietnam Natl Univ, VNU Univ Sci, Hanoi 100000, Vietnam.
   [Son, Le Hoang] Sejong Univ, Coll Elect & Informat Engn, Seoul 05006, South Korea.
   [Nhu Ngoc, Vo Truong] Hanoi Med Univ, Sch Odontostomatol, Hanoi 100000, Vietnam.
   [Hai, Pham Van] Hanoi Univ Sci & Technol, Sch Informat Commun Technol, Hanoi 100000, Vietnam.
RP Hai, PV (通讯作者)，Hanoi Univ Sci & Technol, Sch Informat Commun Technol, Hanoi 100000, Vietnam.
EM haipv@soict.hust.edu.vn
RI Giang, Nguyen/AAV-3016-2021; Pham, Hai V./Q-8380-2019; Van Pham,
   Hai/AAA-5116-2022; Tran, Tuan/AAV-4913-2021
OI Pham, Hai V./0000-0001-8325-1662; Tran, Tuan/0000-0002-1117-7253; Lan,
   Luong Thi Hong/0000-0002-4083-2253; Hoang Son, Le/0000-0001-6356-0046;
   Giang, Nguyen Long/0000-0001-6184-1469
FU Vietnam National Foundation for Science and Technology Development
   (NAFOSTED) [102.05-2019.316]
FX This research was funded by Vietnam National Foundation for Science and
   Technology Development (NAFOSTED) under grant number 102.05-2019.316.
CR Adoko AC, 2019, GEOTECH GEOL ENG, V37, P1533, DOI 10.1007/s10706-018-0706-5
   Al-Qudah Y, 2019, INT J MATH COMPUT SC, V14, P149
   Alcantud JCR, 2018, INFORM FUSION, V41, P48, DOI 10.1016/j.inffus.2017.08.005
   [Anonymous], 2019, NEURAL COMPUT APPL, DOI DOI 10.1016/J.VEHCOM.2018.11.002
   [Anonymous], **DATA OBJECT**
   [Anonymous], **DATA OBJECT**
   [Anonymous], **DATA OBJECT**
   Azad A, 2019, J HYDROL, V571, P214, DOI 10.1016/j.jhydrol.2019.01.062
   Bakhshipour A, 2020, J FOOD MEAS CHARACT, V14, P1402, DOI 10.1007/s11694-020-00390-8
   Bas E, 2021, GRANULAR COMPUT, V6, P619, DOI 10.1007/s41066-020-00220-8
   Chao L, 2019, APPL SOFT COMPUT, V78, P150, DOI 10.1016/j.asoc.2019.02.032
   Chen IY, 2020, PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, P19
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Chen ZF, 2011, IEEE T FUZZY SYST, V19, P305, DOI 10.1109/TFUZZ.2010.2096469
   Egrioglu E, 2019, ENG APPL ARTIF INTEL, V82, P175, DOI 10.1016/j.engappai.2019.03.024
   Faizi S., 2019, THESIS
   Fan JP, 2019, IEEE ACCESS, V7, P105546, DOI 10.1109/ACCESS.2019.2932267
   Garg H, 2020, ARAB J SCI ENG, V45, P2017, DOI 10.1007/s13369-019-03925-4
   Garg H, 2019, ARAB J SCI ENG, V44, P2679, DOI 10.1007/s13369-018-3413-x
   Hajek P, 2014, 2014 11TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), P232, DOI 10.1109/FSKD.2014.6980838
   He LL, 2019, IEEE ACCESS, V7, P101231, DOI 10.1109/ACCESS.2019.2931361
   Heim P, 2009, LECT NOTES COMPUT SC, V5887, P182, DOI 10.1007/978-3-642-10543-2_21
   Hajek J, 2016, MARK MANAG INNOV, P206
   Hernandez-Aguila A, 2016, IEEE INT FUZZY SYST, P1294, DOI 10.1109/FUZZ-IEEE.2016.7737838
   JANG JSR, 1993, IEEE T SYST MAN CYB, V23, P665, DOI 10.1109/21.256541
   Kapadia B., 2020, STUD INDIAN PLACE NA, V40, P104
   Karaboga D, 2019, ARTIF INTELL REV, V52, P2263, DOI 10.1007/s10462-017-9610-2
   Klement EP, 2010, IEEE T FUZZY SYST, V18, P178, DOI 10.1109/TFUZZ.2009.2039367
   Son LH, 2017, FUZZY OPTIM DECIS MA, V16, P359, DOI 10.1007/s10700-016-9249-5
   Son LH, 2017, APPL INTELL, V46, P652, DOI 10.1007/s10489-016-0856-1
   Son LH, 2017, APPL INTELL, V46, P1, DOI 10.1007/s10489-016-0811-1
   Son LH, 2016, APPL SOFT COMPUT, V46, P284, DOI 10.1016/j.asoc.2016.05.009
   Li X, 2020, HEALTH INF SCI SYST, V8, DOI 10.1007/s13755-020-0102-4
   Liu H, 2020, J INTELL FUZZY SYST, V38, P495, DOI 10.3233/JIFS-179424
   Liu Y, 2019, NEUROCOMPUTING, V365, P94, DOI 10.1016/j.neucom.2019.07.042
   Long JW, 2020, APPL SOFT COMPUT, V91, DOI 10.1016/j.asoc.2020.106205
   Man JY, 2007, NAFIPS 2007 - 2007 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY, P415, DOI 10.1109/NAFIPS.2007.383875
   Mapari B. G., 2016, IOSR J MATH, V12, P148
   Ngan T. T., 2020, FRONTIERS INTELLIGEN, P11
   Paik B, 2020, SOFT COMPUT, V24, P5217, DOI 10.1007/s00500-019-04273-z
   Pancho DP, 2013, IEEE T FUZZY SYST, V21, P1133, DOI 10.1109/TFUZZ.2013.2245130
   Petrova E, 2019, ARCHIT ENG DES MANAG, V15, P334, DOI 10.1080/17452007.2018.1530092
   Thong PH, 2016, ENG APPL ARTIF INTEL, V56, P121, DOI 10.1016/j.engappai.2016.08.009
   Thong PH, 2016, KNOWL-BASED SYST, V109, P48, DOI 10.1016/j.knosys.2016.06.023
   Viet PV, 2015, 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), P192, DOI 10.1109/KSE.2015.33
   Pourjavad E, 2018, INT J FUZZY SYST, V20, P901, DOI 10.1007/s40815-017-0378-y
   Priyadarshi Neeraj, 2020, Advances in Computational Intelligence. Proceedings of Second International Conference on Computational Intelligence 2018. Advances in Intelligent Systems and Computing (988), P3, DOI 10.1007/978-981-13-8222-2_1
   Qiu J, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10062166
   Ramot D, 2002, IEEE T FUZZY SYST, V10, P171, DOI 10.1109/91.995119
   Razavi R, 2019, ENG APPL COMP FLUID, V13, P560, DOI 10.1080/19942060.2019.1620130
   Ren Lijuan, 2019, 2019 IEEE 3rd International Conference on Circuits, Systems and Devices (ICCSD), P163, DOI 10.1109/ICCSD.2019.8842938
   Sagir A., 2017, Pertanika J. Sci. Technol., V25, P15
   Sagir AM, 2017, PERTANIKA J SCI TECH, V25, P43
   Selvachandran G, 2021, IEEE T FUZZY SYST, V29, P716, DOI 10.1109/TFUZZ.2019.2961350
   Semero YK, 2020, IET GENER TRANSM DIS, V14, P470, DOI 10.1049/iet-gtd.2019.0869
   Shao LX, 2017, 2017 IEEE/ACIS 15TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING RESEARCH, MANAGEMENT AND APPLICATIONS (SERA), P339, DOI 10.1109/SERA.2017.7965749
   Shi LX, 2017, BIOMED RES INT, V2017, DOI 10.1155/2017/2858423
   Sihag Parveen, 2019, ISH Journal of Hydraulic Engineering, V25, P132, DOI 10.1080/09715010.2017.1381861
   Son LH, 2015, EXPERT SYST APPL, V42, P51, DOI 10.1016/j.eswa.2014.07.026
   Sooraj TR, 2017, ADV INTELL SYST, V517, P315, DOI 10.1007/978-981-10-3174-8_28
   Sousa RT, 2020, BMC BIOINFORMATICS, V21, DOI 10.1186/s12859-019-3296-1
   Subbulakshmi S., 2019, MALAYA J MATEMATIK, V1, P535
   Tairidis G. K., 2020, NATURAL RISK MANAGEM, P169
   Tao XH, 2020, WORLD WIDE WEB, V23, P2341, DOI 10.1007/s11280-020-00810-1
   Tiwari Laxmikant, 2020, Computer Vision and Machine Intelligence in Medical Image Analysis. International Symposium, ISCMM 2019. Advances in Intelligent Systems and Computing (992), P33, DOI 10.1007/978-981-13-8798-2_4
   Ngan TT, 2018, ROM J INF SCI TECH, V21, P344
   Tu C, 2018, UNCERTAIN OPER RES, P241, DOI 10.1007/978-981-10-7817-0_22
   Tuan TM, 2020, MATHEMATICS-BASEL, V8, DOI 10.3390/math8050707
   Ullah K, 2020, COMPLEX INTELL SYST, V6, P15, DOI 10.1007/s40747-019-0103-6
   Verborgh R, 2016, J WEB SEMANT, V37-38, P184, DOI 10.1016/j.websem.2016.03.003
   Wang HZ, 2019, 2019 34TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING WORKSHOPS (ASEW 2019), P134, DOI 10.1109/ASEW.2019.00043
   Wang ZH, 2014, METHOD ENZYMOL, V548, P1, DOI 10.1016/B978-0-12-397918-6.00001-X
   Wu MQ, 2020, IEEE ACCESS, V8, P10208, DOI 10.1109/ACCESS.2019.2963260
   Xu SY, 2019, IEEE ACCESS, V7, P6510, DOI 10.1109/ACCESS.2018.2890022
   Yazdanbakhsh O, 2019, INT J APPROX REASON, V105, P417, DOI 10.1016/j.ijar.2018.10.018
   Yoo S, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112965
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
   ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X
NR 78
TC 9
Z9 9
U1 5
U2 16
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 164899
EP 164921
DI 10.1109/ACCESS.2020.3021097
PG 23
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NT5KK
UT WOS:000572979500001
OA gold
DA 2022-08-23
ER

PT J
AU Gong, PZ
   Liu, J
   Yang, YH
   He, HH
AF Gong, Peizhu
   Liu, Jin
   Yang, Yihe
   He, Huihua
TI Towards Knowledge Enhanced Language Model for Machine Reading
   Comprehension
SO IEEE ACCESS
LA English
DT Article
DE Task analysis; Semantics; Knowledge engineering; Knowledge based
   systems; Bit error rate; Encoding; Syntactics; Machine reading
   comprehension; knowledge graph embedding; BERT; capsule network
AB Machine reading comprehension is a crucial and challenging task in natural language processing (NLP). Recently, knowledge graph (KG) embedding has gained massive attention as it can effectively provide side information for downstream tasks. However, most previous knowledge-based models do not take into account the structural characteristics of the triples in KGs, and only convert them into vector representations for direct accumulation, leading to deficiencies in knowledge extraction and knowledge fusion. In order to alleviate this problem, we propose a novel deep model KCF-NET, which incorporates knowledge graph representations with context as the basis for predicting answers by leveraging capsule network to encode the intrinsic spatial relationship in triples of KG. In KCF-NET, we fine-tune BERT, a highly performance contextual language representation model, to capture complex linguistic phenomena. Besides, a novel fusion structure based on multi-head attention mechanism is designed to balance the weight of knowledge and context. To evaluate the knowledge expression and reading comprehension ability of our model, we conducted extensive experiments on multiple public datasets such as WN11, FB13, SemEval-2010 Task 8 and SQuAD. Experimental results show that KCF-NET achieves state-of-the-art results in both link prediction and MRC tasks with negligible parameter increase compared to BERT-Base, and gets competitive results in triple classification task with significantly reduced model size.
C1 [Gong, Peizhu; Liu, Jin; Yang, Yihe] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China.
   [He, Huihua] Shanghai Normal Univ, Coll Educ, Shanghai 200234, Peoples R China.
RP He, HH (通讯作者)，Shanghai Normal Univ, Coll Educ, Shanghai 200234, Peoples R China.
EM hehuihua@shnu.edu.cn
OI Gong, Peizhu/0000-0002-9556-089X; Liu, Jin/0000-0001-7249-698X
FU National Natural Science Foundation of China [61872231, 61701297]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 61872231 and Grant 61701297.
CR An Bo, 2018, NAACL, V1, P745
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chang SW, 2020, IEEE ACCESS, V8, P79876, DOI 10.1109/ACCESS.2020.2990700
   Dhingra B, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1832, DOI 10.18653/v1/P17-1168
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   Hermann K. M., 2015, 29 ANN C NEUR INF PR
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hu MH, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4099
   Ji G., 2016, P 13 AAAI C ART INT, P1
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Lai G., 2017, P 2017 C EMP METH NA, P785, DOI DOI 10.18653/V1/D17-1082
   LIN Y, 2015, P 29 AAAI C ART INT, P1
   Liu J, 2019, J AMB INTEL HUM COMP, DOI [10.1007/s12652-019-01344-9, DOI 10.1007/S12652-019-01344-9, 10.1007/ S12652-019-01344-9]
   Liu J, 2018, SOFT COMPUT, V22, P6705, DOI 10.1007/s00500-018-3181-2
   Liu J, 2018, SOFT COMPUT, V22, P4295, DOI 10.1007/s00500-017-2852-8
   Liu SS, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9183698
   Liu WJ, 2020, AAAI CONF ARTIF INTE, V34, P2901
   Long Teng, 2017, P 2017 C EMP METH NA, P825
   Mihaylov T, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P821
   Miller A., 2016, EMNLP 2016
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nguyen D. Q., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Pan B., 2018, ADV NEURAL INFORM PR, P6092
   Rajpurkar P., 2016, P C EMP METH NAT LAN
   Sahu SK, 2018, UKSIM INT CONF COMP, P1, DOI 10.1109/UKSim.2018.00012
   Seo Minjoon, 2016, ARXIV161101603
   Soares LB, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P2895
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Toutanova K, 2018, BERT PRETRAINING DEE, DOI DOI 10.18653/V1/N19-1423
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Wang C., 2018, P 57 ANN M ASS COMP, P2263
   Wang C., 2016, ARXIV160807905
   Wang C, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P2263
   Wang LL, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1298
   Wang W, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1705
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J. E., 2015, 29 ANN C NEUR INF PR
   Wu SC, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P2361, DOI 10.1145/3357384.3358119
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xie R., 2016, 2016 IEEE ENERGY CON, P1, DOI [10.1109/ECCE.2016.7854840, DOI 10.1109/ECCE.2016.7854840]
   Yang A, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P2346
   Yang BS, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1436, DOI 10.18653/v1/P17-1132
   Yao L., 2019, CORR
   Yu A W, 2018, P 6 INT C LEARNING R
   Zhang C, 2018, INTERSPEECH, P1773, DOI 10.21437/Interspeech.2018-2158
   Zhang CX, 2019, ASIAPAC SIGN INFO PR, P165, DOI 10.1109/APSIPAASC47483.2019.9023283
   Zhang Z, 2018, EMNLP, P3198, DOI DOI 10.18653/V1/D18-1358
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
NR 49
TC 1
Z9 1
U1 2
U2 13
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 224837
EP 224851
DI 10.1109/ACCESS.2020.3044308
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA PM3SD
UT WOS:000603722500001
OA gold
DA 2022-08-23
ER

PT J
AU Song, MN
   Zhao, W
   HaiHong, E
AF Song, Meina
   Zhao, Wen
   HaiHong, E.
TI KGAnet: a knowledge graph attention network for enhancing natural
   language inference
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Natural language processing; Natural language inference; External
   knowledge
AB Natural language inference (NLI) is the basic task of many applications such as question answering and paraphrase recognition. Existing methods have solved the key issue of how the NLI model can benefit from external knowledge. Inspired by this, we attempt to further explore the following two problems: (1) how to make better use of external knowledge when the total amount of such knowledge is constant and (2) how to bring external knowledge to the NLI model more conveniently in the application scenario. In this paper, we propose a novel joint training framework that consists of a modified graph attention network, called the knowledge graph attention network, and an NLI model. We demonstrate that the proposed method outperforms the existing method which introduces external knowledge, and we improve the performance of multiple NLI models without additional external knowledge.
C1 [Song, Meina; Zhao, Wen; HaiHong, E.] Beijing Univ Posts & Telecommun, Beijing, Peoples R China.
RP HaiHong, E (通讯作者)，Beijing Univ Posts & Telecommun, Beijing, Peoples R China.
EM luzhizhaowen@bupt.edu.cn; ehaihong@bupt.edu.cn
FU National Natural Science Foundation of China [61902034]; Engineering
   Research Center of Information Networks, Ministry of Education
FX This work is supported by the National Natural Science Foundation of
   China (No. 61902034) and Engineering Research Center of Information
   Networks, Ministry of Education.
CR Annervaz K. M., 2018, PROC C N AM CHAPTER, P313
   [Anonymous], 2017, ARXIV170507664
   Atwood James, 2016, ADV NEURAL INFORM PR, P1993, DOI DOI 10.5555/3157096.3157320
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bowman S. R., 2015, P 2015 C EMP METH NA
   Bromley J., 1993, International Journal of Pattern Recognition and Artificial Intelligence, V7, P669, DOI 10.1142/S0218001493000339
   Bruna J., 2013, P 2 INT C LEARN REPR
   Chen Q, 2017, P 56 ANN M ASS COMP, V1, P2406
   Chen Q., 2016, ABS160906038 CORR
   Cho S., 2017, ARXIV171202047
   Defferrard M, 2016, ARXIV160609375
   Duvenaud David K, 2015, ADV NEURAL INFORM PR, P2224, DOI DOI 10.1021/ACS.JCIM.5B00572
   Guan J, 2019, AAAI CONF ARTIF INTE, P6473
   Hamilton W. L., 2017, ARXIV170602216
   Henaff M., 2015, ABS150605163 CORR
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Kim S.-K., 2018, ABS180511360 CORR
   Kingma D. P., 2017, ARXIV
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Lecun Y., 1995, HDB BRAIN THEORY NEU
   Lin BY, 2019, ABS190902151 CORR
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mou L., 2015, ARXIV151208422
   Nie Y., 2017, P 2 WORKSH EV VECT S P 2 WORKSH EV VECT S
   Niepert M, 2016, PR MACH LEARN RES, V48
   Parikh A., 2016, P 2016 C EMP METH NA, P2249, DOI DOI 10.18653/V1/D16-1244
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Sabour S., 2017, ADV NEURAL INFORM PR, P3856
   Sen P, 2008, AI MAG, V29, P93, DOI 10.1609/aimag.v29i3.2157
   Shen T., 2018, ARXIV180110296
   Shen T., 2017, ARXIV170904696
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Talman A, 2018, ARXIV180808762
   Tan CQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4411
   Urtasun R., 2015, ARXIV151106361
   Velickovic P., 2018, P INT C LEARN REPR A
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wang XY, 2019, AAAI CONF ARTIF INTE, P7208
   Wang Z, 2017, ARXIV170203814
   Williams A., 2018, P C N AM CHAPT ASS C, P1112, DOI [10.18653/v1/N18-1101, DOI 10.18653/V1/N18-1101]
   Yoon Deunsol, 2018, ARXIV180807383
   Zhang J., 2017, ARXIV170904348
   Zhong GQ, 2018, INT C PATT RECOG, P1462, DOI 10.1109/ICPR.2018.8545107
NR 43
TC 5
Z9 5
U1 3
U2 11
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0941-0643
EI 1433-3058
J9 NEURAL COMPUT APPL
JI Neural Comput. Appl.
PD SEP
PY 2020
VL 32
IS 18
SI SI
BP 14963
EP 14973
DI 10.1007/s00521-020-04851-5
EA JUN 2020
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NX4AB
UT WOS:000543610000001
OA hybrid
DA 2022-08-23
ER

PT J
AU Chen, JG
   Su, YY
   Si, HY
   Chen, JD
AF Chen, Jianguo
   Su, Yangyue
   Si, Hongyun
   Chen, Jindao
TI Managerial Areas of Construction and Demolition Waste: A Scientometric
   Review
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Review
DE construction and demolition waste; CDW; management; scientometric
   analysis; review
ID LIFE-CYCLE ASSESSMENT; INFORMATION MODELING BIM; COMPARATIVE
   ENVIRONMENTAL EVALUATION; BUILDING REFURBISHMENT; ECONOMIC-FEASIBILITY;
   REDUCTION MANAGEMENT; AGGREGATE PRODUCTION; MAPPING SOFTWARE; CARBON
   EMISSION; ASSESSMENT LCA
AB In past decades, the massive generation of construction and demolition waste (CDW) was increasingly threatening the public environment and humanity health worldwide. A large amount of research has been devoted to the CDW from difference perspectives. However, few scholars have attempted to summarize and review the extant studies, especially in the managerial areas of CDW (MA-CDW). This paper fills this gap via a systematic and quantitative review in the CDW management field. Employing the scientometric analysis method, a total of 261 articles published from 2006 to 2018 were collected to construct the knowledge map and comprehensive framework for MA-CDW. Results show that the overall evolutionary trend of MA-CDW was from basic management concepts to internal and external challenges analysis, to organizational strategy and innovative management practices. The major MA-CDW knowledge domains were identified and summarized into four pillars, namely: (1) factor and challenge; (2) composition and quantification; (3) assessment and comparison; and (4) technology and method. Based on the trend, knowledge gaps and future research directions were found out and discussed. This study contributes to the existing MA-CDW knowledge by presenting a comprehensive knowledge framework. Furthermore, these findings can provide the researchers and practitioners with an in-depth understanding for the sustainable governance of CDW.
C1 [Chen, Jianguo; Su, Yangyue; Si, Hongyun; Chen, Jindao] Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China.
RP Su, YY; Si, HY (通讯作者)，Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China.
EM jgchen@tongji.edu.cn; 1710260@tongji.edu.cn; sihongyun@tongji.edu.cn;
   chenjindao@tongji.edu.cn
RI Chen, Jindao/L-8522-2019; Si, Hongyun/AAY-2908-2021
OI Chen, Jindao/0000-0002-6294-829X; si, hong yun/0000-0003-2367-4756
CR Ajayi SO, 2018, WASTE MANAGE, V75, P60, DOI 10.1016/j.wasman.2018.01.025
   Ajayi SO, 2017, WASTE MANAGE, V59, P330, DOI 10.1016/j.wasman.2016.10.040
   Ajayi SO, 2015, RESOUR CONSERV RECY, V102, P101, DOI 10.1016/j.resconrec.2015.06.001
   Akinade OO, 2018, J CLEAN PROD, V180, P375, DOI 10.1016/j.jclepro.2018.01.022
   Akinade OO, 2015, RESOUR CONSERV RECY, V105, P167, DOI 10.1016/j.resconrec.2015.10.018
   Al-Sari MI, 2012, WASTE MANAGE RES, V30, P122, DOI 10.1177/0734242X11423066
   Ansell C, 2008, J PUBL ADM RES THEOR, V18, P543, DOI 10.1093/jopart/mum032
   Arm M, 2017, WASTE BIOMASS VALORI, V8, P1491, DOI 10.1007/s12649-016-9661-7
   Bakshan A, 2017, RESOUR CONSERV RECY, V117, P274, DOI 10.1016/j.resconrec.2016.10.006
   Baldwin A, 2009, RENEW ENERG, V34, P2067, DOI 10.1016/j.renene.2009.02.008
   Banias G, 2011, WASTE MANAGE, V31, P2497, DOI 10.1016/j.wasman.2011.07.018
   Begum RA, 2007, RESOUR CONSERV RECY, V51, P190, DOI 10.1016/j.resconrec.2006.09.004
   Begum RA, 2006, RESOUR CONSERV RECY, V48, P86, DOI 10.1016/j.resconrec.2006.01.004
   Begum RA, 2009, RESOUR CONSERV RECY, V53, P321, DOI 10.1016/j.resconrec.2009.01.005
   Blengini GA, 2010, J CLEAN PROD, V18, P1021, DOI 10.1016/j.jclepro.2010.01.027
   Chen C, 2016, CITESPACE PRACTICAL
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen JD, 2017, J CLEAN PROD, V168, P645, DOI 10.1016/j.jclepro.2017.09.072
   Chen X, 2017, J CLEAN PROD, V141, P799, DOI 10.1016/j.jclepro.2016.09.164
   Chileshe N, 2016, INT J OPER PROD MAN, V36, P332, DOI 10.1108/IJOPM-01-2014-0024
   Cobo MJ, 2011, J AM SOC INF SCI TEC, V62, P1382, DOI 10.1002/asi.21525
   Coelho A, 2013, J CLEAN PROD, V39, P338, DOI 10.1016/j.jclepro.2012.08.024
   da Rocha CG, 2009, RESOUR CONSERV RECY, V54, P104, DOI 10.1016/j.resconrec.2009.07.004
   Department for Environment Food and Rural Affairs, 2007, WAST STRAT ENGL
   Di Maria A, 2018, WASTE MANAGE, V75, P3, DOI 10.1016/j.wasman.2018.01.028
   Ding ZK, 2018, J CLEAN PROD, V176, P676, DOI 10.1016/j.jclepro.2017.12.101
   Ding ZK, 2016, WASTE MANAGE, V51, P130, DOI 10.1016/j.wasman.2016.03.001
   Diyamandoglu V, 2015, RESOUR CONSERV RECY, V100, P21, DOI 10.1016/j.resconrec.2015.04.006
   Duran X, 2006, RESOUR CONSERV RECY, V46, P302, DOI 10.1016/j.resconrec.2005.08.003
   European Commission, 2008, 200898EC EUR COMM
   Faleschini F, 2016, WASTE MANAGE, V49, P146, DOI 10.1016/j.wasman.2016.01.013
   Fu PH, 2017, MATH PROBL ENG, V2017, DOI 10.1155/2017/9081628
   Galvez-Martos JL, 2018, RESOUR CONSERV RECY, V136, P166, DOI 10.1016/j.resconrec.2018.04.016
   Ganbat T, 2018, ADV CIV ENG, V2018, DOI 10.1155/2018/8351679
   Ghanbari M, 2017, ADV MATER SCI ENG, V2017, DOI 10.1155/2017/7458285
   Ghose A, 2017, J CLEAN PROD, V165, P119, DOI 10.1016/j.jclepro.2017.07.099
   Penteado CSG, 2016, WASTE MANAGE RES, V34, P1026, DOI 10.1177/0734242X16657605
   He QH, 2017, INT J PROJ MANAG, V35, P670, DOI 10.1016/j.ijproman.2016.08.001
   Hiete M, 2011, BUILD RES INF, V39, P333, DOI 10.1080/09613218.2011.576849
   Hoglmeier K, 2013, RESOUR CONSERV RECY, V78, P81, DOI 10.1016/j.resconrec.2013.07.004
   Hong Kong Government, 2006, ENV PROT DEP ENV REP
   Hossain MU, 2018, CONSTR BUILD MATER, V173, P474, DOI 10.1016/j.conbuildmat.2018.04.066
   Hossain MU, 2018, J CLEAN PROD, V177, P387, DOI 10.1016/j.jclepro.2017.12.233
   Hossain MU, 2017, WASTE MANAGE, V69, P325, DOI 10.1016/j.wasman.2017.07.043
   Hossain MU, 2016, RESOUR CONSERV RECY, V109, P67, DOI 10.1016/j.resconrec.2016.02.009
   Hou JH, 2018, SCIENTOMETRICS, V115, P869, DOI 10.1007/s11192-018-2695-9
   Huang BJ, 2018, RESOUR CONSERV RECY, V129, P36, DOI 10.1016/j.resconrec.2017.09.029
   Jaillon L, 2014, AUTOMAT CONSTR, V39, P195, DOI 10.1016/j.autcon.2013.09.006
   Jimenez-Rivero A, 2017, J CLEAN PROD, V167, P1335, DOI 10.1016/j.jclepro.2017.05.068
   Jimenez-Rivero A, 2017, RESOUR CONSERV RECY, V116, P116, DOI 10.1016/j.resconrec.2016.09.014
   Kartam N, 2004, WASTE MANAGE, V24, P1049, DOI 10.1016/j.wasman.2004.06.003
   Kim YC, 2017, WASTE MANAGE RES, V35, P1285, DOI 10.1177/0734242X17736381
   Kleemann F, 2017, RESOUR CONSERV RECY, V123, P37, DOI 10.1016/j.resconrec.2016.06.010
   Kofoworola OF, 2009, WASTE MANAGE, V29, P731, DOI 10.1016/j.wasman.2008.07.004
   Kourmpanis B, 2008, WASTE MANAGE RES, V26, P573, DOI 10.1177/0734242X08091554
   Kucukvar M, 2014, WASTE MANAGE RES, V32, P500, DOI 10.1177/0734242X14536457
   Li H, 2005, AUTOMAT CONSTR, V14, P323, DOI 10.1016/j.autcon.2004.08.007
   Li JR, 2018, J CLEAN PROD, V172, P1399, DOI 10.1016/j.jclepro.2017.10.138
   Li JR, 2015, RESOUR CONSERV RECY, V105, P29, DOI 10.1016/j.resconrec.2015.10.009
   Li X, 2017, AUTOMAT CONSTR, V84, P195, DOI 10.1016/j.autcon.2017.09.011
   Li YS, 2013, AUTOMAT CONSTR, V35, P142, DOI 10.1016/j.autcon.2013.05.002
   Li YK, 2018, INT J PROJ MANAG, V36, P342, DOI 10.1016/j.ijproman.2017.05.008
   Li ZD, 2014, RESOUR CONSERV RECY, V91, P27, DOI 10.1016/j.resconrec.2014.07.013
   Liu Z, 2015, AUTOMAT CONSTR, V59, P1, DOI 10.1016/j.autcon.2015.07.020
   Llatas C, 2011, WASTE MANAGE, V31, P1261, DOI 10.1016/j.wasman.2011.01.023
   Lu WS, 2016, J CLEAN PROD, V112, P521, DOI 10.1016/j.jclepro.2015.06.106
   Lu WS, 2015, RESOUR CONSERV RECY, V105, P49, DOI 10.1016/j.resconrec.2015.10.013
   Lu WS, 2013, RENEW SUST ENERG REV, V23, P214, DOI 10.1016/j.rser.2013.03.007
   Lu WS, 2011, WASTE MANAGE, V31, P1252, DOI 10.1016/j.wasman.2011.01.018
   Lu WS, 2011, WASTE MANAGE, V31, P680, DOI 10.1016/j.wasman.2010.12.004
   Lu WS, 2010, RESOUR CONSERV RECY, V55, P201, DOI 10.1016/j.resconrec.2010.09.010
   Mahpour A, 2018, RESOUR CONSERV RECY, V134, P216, DOI 10.1016/j.resconrec.2018.01.026
   Mastrucci A, 2017, RESOUR CONSERV RECY, V123, P54, DOI 10.1016/j.resconrec.2016.07.003
   Menegaki M, 2018, CURR OPIN GREEN SUST, V13, P8, DOI 10.1016/j.cogsc.2018.02.010
   Ng S., 2018, CONSTRUCTION DEMOLIT
   Nunes KRA, 2007, WASTE MANAGE, V27, P1531, DOI 10.1016/j.wasman.2006.09.007
   Ocholla DN, 2010, J INFORMETR, V4, P492, DOI 10.1016/j.joi.2010.05.001
   Ortiz O, 2010, WASTE MANAGE, V30, P646, DOI 10.1016/j.wasman.2009.11.013
   Osmani M, 2008, WASTE MANAGE, V28, P1147, DOI 10.1016/j.wasman.2007.05.011
   Pan XL, 2018, J INFORMETR, V12, P481, DOI 10.1016/j.joi.2018.03.005
   Pollack J, 2015, INT J PROJ MANAG, V33, P236, DOI 10.1016/j.ijproman.2014.04.011
   Poon CS, 2013, J CONSTR ENG M, V139, P466, DOI 10.1061/(ASCE)CO.1943-7862.0000631
   Pouris A, 2011, SCIENTOMETRICS, V86, P541, DOI 10.1007/s11192-010-0277-6
   Rosado LP, 2017, J CLEAN PROD, V151, P634, DOI 10.1016/j.jclepro.2017.03.068
   Seror N, 2018, WASTE MANAGE, V75, P22, DOI 10.1016/j.wasman.2018.01.027
   Smol M, 2015, J CLEAN PROD, V95, P45, DOI 10.1016/j.jclepro.2015.02.051
   Soheili F, 2017, ASLIB J INFORM MANAG, V69, P215, DOI 10.1108/AJIM-01-2017-0027
   Solis-Guzman J, 2009, WASTE MANAGE, V29, P2542, DOI 10.1016/j.wasman.2009.05.009
   SOLISGUZMAN J, 2018, SUSTAINABILITY-BASEL, V10, DOI DOI 10.3390/su10051359
   Song JB, 2016, SCIENTOMETRICS, V107, P1111, DOI 10.1007/s11192-016-1918-1
   Srour IM, 2013, WASTE MANAGE RES, V31, P1170, DOI 10.1177/0734242X13479430
   Tam VWY, 2014, WASTE MANAGE RES, V32, P441, DOI 10.1177/0734242X14527636
   Tam VWY, 2010, WASTE MANAGE, V30, P291, DOI 10.1016/j.wasman.2009.09.044
   Tam VWY, 2009, J CLEAN PROD, V17, P688, DOI 10.1016/j.jclepro.2008.11.015
   TAM VWY, 2016, SUSTAINABILITY-BASEL, V8, DOI DOI 10.3390/SU8020190
   Tukker A, 2000, ENVIRON IMPACT ASSES, V20, P435, DOI 10.1016/S0195-9255(99)00045-1
   Udawatta N, 2015, RESOUR CONSERV RECY, V101, P73, DOI 10.1016/j.resconrec.2015.05.003
   Vieira DR, 2016, CONSTR BUILD MATER, V124, P656, DOI 10.1016/j.conbuildmat.2016.07.125
   Vilches A, 2017, ENERG BUILDINGS, V135, P286, DOI 10.1016/j.enbuild.2016.11.042
   Saez PV, 2013, RESOUR CONSERV RECY, V75, P52, DOI 10.1016/j.resconrec.2013.03.009
   Vitale P, 2017, WASTE MANAGE, V60, P311, DOI 10.1016/j.wasman.2016.10.002
   Wang JY, 2018, J CLEAN PROD, V172, P3154, DOI 10.1016/j.jclepro.2017.11.087
   Wang JY, 2015, J CLEAN PROD, V92, P237, DOI 10.1016/j.jclepro.2014.12.076
   Wang JY, 2010, RESOUR CONSERV RECY, V54, P931, DOI 10.1016/j.resconrec.2010.01.012
   Won J, 2017, AUTOMAT CONSTR, V79, P3, DOI 10.1016/j.autcon.2017.02.002
   Wu HY, 2017, PROCEEDINGS OF THE 20TH INTERNATIONAL SYMPOSIUM ON ADVANCEMENT OF CONSTRUCTION MANAGEMENT AND REAL ESTATE, P1157, DOI 10.1007/978-981-10-0855-9_102
   Wu HY, 2016, J CLEAN PROD, V112, P494, DOI 10.1016/j.jclepro.2015.08.096
   Wu HY, 2015, J MATER CYCLES WASTE, V17, P637, DOI 10.1007/s10163-015-0404-9
   Wu ZZ, 2017, WASTE MANAGE, V60, P290, DOI 10.1016/j.wasman.2016.09.001
   Wu ZZ, 2014, WASTE MANAGE, V34, P1683, DOI 10.1016/j.wasman.2014.05.010
   Xu JP, 2012, INT J CIV ENG, V10, P1
   Xue XL, 2018, NAT HAZARDS, V90, P477, DOI 10.1007/s11069-017-3040-y
   Yalcinkaya M, 2015, AUTOMAT CONSTR, V59, P68, DOI 10.1016/j.autcon.2015.07.012
   Yang H, 2017, WASTE MANAGE, V63, P393, DOI 10.1016/j.wasman.2017.01.026
   Yeheyis M, 2013, CLEAN TECHNOL ENVIR, V15, P81, DOI 10.1007/s10098-012-0481-6
   Yu X, 2015, CHIN J POPUL RESOUR, V13, P320, DOI 10.1080/10042857.2015.1056286
   Yuan HP, 2017, J CLEAN PROD, V157, P84, DOI 10.1016/j.jclepro.2017.04.137
   Yuan HP, 2013, J CLEAN PROD, V39, P1, DOI 10.1016/j.jclepro.2012.08.016
   Yuan HP, 2011, WASTE MANAGE, V31, P670, DOI 10.1016/j.wasman.2010.10.030
   Zhang LZ, 2015, INT J COMPUT INT SYS, V8, P128, DOI 10.1080/18756891.2014.963988
   Zhao W, 2011, RESOUR CONSERV RECY, V55, P933, DOI 10.1016/j.resconrec.2011.04.011
   Zhao W, 2010, RESOUR CONSERV RECY, V54, P377, DOI 10.1016/j.resconrec.2009.09.003
NR 122
TC 34
Z9 35
U1 13
U2 62
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD NOV
PY 2018
VL 15
IS 11
AR 2350
DI 10.3390/ijerph15112350
PG 20
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA HC2OB
UT WOS:000451640500024
PM 30356018
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Guo, P
   Li, Q
   Guo, HD
   Li, HM
   Yang, LB
AF Guo, Ping
   Li, Qin
   Guo, Haidong
   Li, Huimin
   Yang, Lingbo
TI A Bibliometric and Visual Analysis of Global Urban Resilience Research
   in 2011-2020: Development and Hotspots
SO SUSTAINABILITY
LA English
DT Article
DE urban resilience; bibliometric analysis; knowledge map; research trend
ID ECOSYSTEM SERVICES; CLIMATE-CHANGE; ENVIRONMENTAL JUSTICE; GREEN
   INFRASTRUCTURE; SUSTAINABILITY; MANAGEMENT; FRAMEWORK; GARDENS; CITIES;
   TRANSFORMATION
AB Urban resilience (UR), which promotes the implementation of resilient cities, has received widespread attention. The purpose of this study is to visualize the knowledge background, research status, and knowledge structure of relevant literatures by using a Citespace based scientometrics survey. The results show that UR is an increasingly popular topic, with 2629 articles published during the study period. (1) The most prolific publications and journals involved in the flourishment of UR research were identified by co-citation. The United States was the most productive contributor, with numerous publications and active institutions. Journal of Cleaner Production, Sustainability, International Journal of Disaster Risk Reduction were the three most cited journals. (2) Co-occurrence analysis was employed to determine the highly productive keywords, and subject categories in the UR domain, including "environmental science & ecology", "environmental sciences, "science & technology", "environmental studies", "green & sustainable science & technology", and "water resources". (3) The diversity of highly cited authors in different countries and regions confirmed the evolution of UR studies. (4) Furthermore, the classification of UR knowledge was performed in the form of clusters and knowledge structure to achieve ten distinct sub-domains (e.g., Urban floods and stormwater management, Urban ecosystem services, Urban landscapes, and Trauma). This study provides an overview of UR research and research topics so that future researchers can identify their research topics and partners.
C1 [Guo, Ping; Li, Huimin] Xian Univ Architecture & Technol, Sch Civil Engn, Xian 710055, Peoples R China.
   [Li, Qin] Beijing Univ Civil Engn & Architecture, Sch Architecture & Urban Planning, Beijing 100044, Peoples R China.
   [Guo, Haidong] Lanzhou Jiaotong Univ, Sch Civil Engn, Lanzhou 730070, Peoples R China.
   [Yang, Lingbo] Shaanxi Iron & Steel Grp Co Ltd, Xian 710018, Peoples R China.
RP Li, Q (通讯作者)，Beijing Univ Civil Engn & Architecture, Sch Architecture & Urban Planning, Beijing 100044, Peoples R China.; Guo, HD (通讯作者)，Lanzhou Jiaotong Univ, Sch Civil Engn, Lanzhou 730070, Peoples R China.
EM guoping@xauat.edu.cn; liqin@bucea.edu.cn; ghd_lzjt@outlook.com;
   li_huimin2005@126.com; sgjtylb@163.com
OI Guo, Ping/0000-0002-0293-2501
FU 13th Five-Year Plan for Education and Science of Beijing [CDDB19167];
   China Construction Education Association Project [2019061]; Special fund
   support for basic scientific research business expenses of municipal
   Universities of Beijing University of Civil Engineering and Architecture
   [x20055]
FX This research was funded by 2019 annual project of the 13th Five-Year
   Plan for Education and Science of Beijing (CDDB19167); China
   Construction Education Association Project (2019061); and Special fund
   support for basic scientific research business expenses of municipal
   Universities of Beijing University of Civil Engineering and
   Architecture(x20055).
CR Ahern J, 2014, LANDSCAPE URBAN PLAN, V125, P254, DOI 10.1016/j.landurbplan.2014.01.020
   Ahern J, 2013, LANDSCAPE ECOL, V28, P1203, DOI 10.1007/s10980-012-9799-z
   Ahern J, 2011, LANDSCAPE URBAN PLAN, V100, P341, DOI 10.1016/j.landurbplan.2011.02.021
   Andersson E, 2014, AMBIO, V43, P445, DOI 10.1007/s13280-014-0506-y
   [Anonymous], 2015, AUST J EMERG MANAG, V30, P9
   [Anonymous], 2016, URBAN RESILIENCE EME
   Barthel S, 2013, ECOL ECON, V86, P224, DOI 10.1016/j.ecolecon.2012.06.018
   Barthel S, 2010, GLOBAL ENVIRON CHANG, V20, P255, DOI 10.1016/j.gloenvcha.2010.01.001
   Barthelemy M, 2011, PHYS REP, V499, P1, DOI 10.1016/j.physrep.2010.11.002
   Bendt P, 2013, LANDSCAPE URBAN PLAN, V109, P18, DOI 10.1016/j.landurbplan.2012.10.003
   Broto VC, 2013, GLOBAL ENVIRON CHANG, V23, P92, DOI 10.1016/j.gloenvcha.2012.07.005
   Butler D, 2014, PROCEDIA ENGINEER, V89, P347, DOI 10.1016/j.proeng.2014.11.198
   Calkin DE, 2014, P NATL ACAD SCI USA, V111, P746, DOI 10.1073/pnas.1315088111
   Cao WJ, 2020, PSYCHIAT RES, V287, DOI 10.1016/j.psychres.2020.112934
   Casal-Campos A, 2018, ENVIRON SCI TECHNOL, V52, P9008, DOI 10.1021/acs.est.8b01193
   Casal-Campos A, 2015, ENVIRON SCI TECHNOL, V49, P8307, DOI 10.1021/es506144f
   Chandler D., 2016, ROUTLEDGE HDB INT RE
   Chelleri L, 2015, ENVIRON URBAN, V27, P181, DOI 10.1177/0956247814550780
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chu ZF, 2022, INT J ROCK MECH MIN, V149, DOI 10.1016/j.ijrmms.2021.104994
   Coaffee J, 2018, J CONTING CRISIS MAN, V26, P403, DOI 10.1111/1468-5973.12233
   Colding J, 2013, GLOBAL ENVIRON CHANG, V23, P1039, DOI 10.1016/j.gloenvcha.2013.05.006
   Colding J, 2013, ECOL ECON, V86, P156, DOI 10.1016/j.ecolecon.2012.10.016
   Davoudi S, 2012, PLAN THEORY PRACT, V13, P299, DOI 10.1080/14649357.2012.677124
   Dumitru A, 2020, ENVIRON SCI POLICY, V112, P107, DOI 10.1016/j.envsci.2020.05.024
   Edenhofer O, 2014, NAT CLIM CHANGE, V4, P663, DOI 10.1038/nclimate2327
   Elmqvist T., 2014, SOLUTIONS, V5, P26, DOI DOI 10.1007/S11069-014-1116-5
   Elmqvist T, 2019, NAT SUSTAIN, V2, P267, DOI 10.1038/s41893-019-0250-1
   Ernstson H, 2013, LANDSCAPE URBAN PLAN, V109, P7, DOI 10.1016/j.landurbplan.2012.10.005
   Evans DH, 2013, P NATL ACAD SCI USA, V110, P12595, DOI 10.1073/pnas.1306539110
   Feng ML, 2022, SCI TOTAL ENVIRON, V805, DOI 10.1016/j.scitotenv.2021.150248
   Fletcher TD, 2015, URBAN WATER J, V12, P525, DOI 10.1080/1573062X.2014.916314
   Folke C, 2010, ECOL SOC, V15, DOI 10.5751/es-03610-150420
   Frantzeskaki N, 2018, SUSTAIN SCI, V13, P1045, DOI 10.1007/s11625-018-0562-5
   Garschagen M, 2013, NAT HAZARDS, V67, P25, DOI 10.1007/s11069-011-9753-4
   Glaeser EL, 2013, J ECON SOCIOL, V14, P75, DOI 10.17323/1726-3247-2013-4-75-94
   Godschalk DR, 2003, NAT HAZARDS REV, V4, P136, DOI 10.1061/(ASCE)1527-6988(2003)4:3(136)
   Ribeiro PJG, 2019, SUSTAIN CITIES SOC, V50, DOI 10.1016/j.scs.2019.101625
   Gomez-Baggethun E, 2013, ECOL ECON, V86, P235, DOI 10.1016/j.ecolecon.2012.08.019
   Gotham KF, 2011, ECOL SOC, V16, DOI 10.5751/ES-04292-160312
   Gunawardena KR, 2017, SCI TOTAL ENVIRON, V584, P1040, DOI 10.1016/j.scitotenv.2017.01.158
   Habitat III, NEW URB AG
   Hale JD, 2012, P I CIVIL ENG-ENG SU, V165, P59, DOI 10.1680/ensu.2012.165.1.59
   Hammond MJ, 2015, URBAN WATER J, V12, P14, DOI 10.1080/1573062X.2013.857421
   He BJ, 2021, SUSTAIN CITIES SOC, V75, DOI 10.1016/j.scs.2021.103361
   Hosseini S, 2016, RELIAB ENG SYST SAFE, V145, P47, DOI 10.1016/j.ress.2015.08.006
   House-Peters LA, 2011, WATER RESOUR RES, V47, DOI 10.1029/2010WR009624
   Hunt A, 2011, CLIMATIC CHANGE, V104, P13, DOI 10.1007/s10584-010-9975-6
   Jabareen Y, 2013, CITIES, V31, P220, DOI 10.1016/j.cities.2012.05.004
   Jiang YF, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9122224
   Johannessen A, 2017, ECOL SOC, V22, DOI 10.5751/ES-08870-220101
   Kabisch N, 2016, ECOL SOC, V21, DOI 10.5751/ES-08373-210239
   Kapucu N, 2021, URBAN GOVERNANCE, DOI [10.1016/j.ugj.2021.09.001, DOI 10.1016/J.UGJ.2021.09.001]
   Katz JS, 1997, RES POLICY, V26, P1, DOI 10.1016/S0048-7333(96)00917-1
   Kong LQ, 2020, J CLEAN PROD, V273, DOI 10.1016/j.jclepro.2020.123142
   Lam NSN, 2014, J COASTAL RES, P473, DOI 10.2112/SI70-080.1
   da Silva LBL, 2020, INT J DISAST RISK RE, V50, DOI 10.1016/j.ijdrr.2020.101865
   Leichenko R, 2011, CURR OPIN ENV SUST, V3, P164, DOI 10.1016/j.cosust.2010.12.014
   Li J., 2016, CITESPACE SCI TEXT M
   Li T, 2020, SCI TOTAL ENVIRON, V723, DOI 10.1016/j.scitotenv.2020.138113
   Li X, 2017, AUTOMAT CONSTR, V84, P195, DOI 10.1016/j.autcon.2017.09.011
   Liao KH, 2012, ECOL SOC, V17, DOI 10.5751/ES-05231-170448
   Lin BB, 2015, BASIC APPL ECOL, V16, P189, DOI 10.1016/j.baae.2015.01.005
   Lovell ST, 2013, LANDSCAPE ECOL, V28, P1447, DOI 10.1007/s10980-013-9912-y
   Manuel-Navarrete D, 2015, GLOBAL ENVIRON CHANG, V35, P558, DOI 10.1016/j.gloenvcha.2015.08.012
   Marcus L, 2014, ECOL SOC, V19, DOI 10.5751/ES-06939-190455
   Marvuglia A, 2020, RENEW SUST ENERG REV, V124, DOI 10.1016/j.rser.2020.109788
   Masnavi MR, 2019, INT J ENVIRON SCI TE, V16, P567, DOI 10.1007/s13762-018-1860-2
   McPhearson T, 2016, NATURE, V538, P165, DOI 10.1038/538165a
   McPhearson T, 2016, BIOSCIENCE, V66, P198, DOI 10.1093/biosci/biw002
   McPhearson T, 2015, ECOSYST SERV, V12, P152, DOI 10.1016/j.ecoser.2014.07.012
   Meerow S, 2017, LANDSCAPE URBAN PLAN, V159, P62, DOI 10.1016/j.landurbplan.2016.10.005
   Meerow S, 2016, LANDSCAPE URBAN PLAN, V147, P38, DOI 10.1016/j.landurbplan.2015.11.011
   Meng CH, 2020, J CLEAN PROD, V277, DOI 10.1016/j.jclepro.2020.122802
   Mugume SN, 2015, WATER RES, V81, P15, DOI 10.1016/j.watres.2015.05.030
   Ouyang M, 2012, STRUCT SAF, V36-37, P23, DOI 10.1016/j.strusafe.2011.12.004
   Pelling M, 2011, ECOL SOC, V16
   Peponi A, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17072463
   Ruan JE, 2021, INT J DISAST RISK RE, V66, DOI 10.1016/j.ijdrr.2021.102578
   Sandifer PA, 2015, ECOSYST SERV, V12, P1, DOI 10.1016/j.ecoser.2014.12.007
   Schoennagel T, 2017, P NATL ACAD SCI USA, V114, P4582, DOI 10.1073/pnas.1617464114
   Shao Y., 2015, URBAN PLAN INT, V30, P48
   Sharifi A, 2021, SUSTAIN CITIES SOC, V74, DOI 10.1016/j.scs.2021.103190
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Stanton-Geddes Z., 2013, BUILDING URBAN RESIL
   Tress B, 2005, LANDSCAPE URBAN PLAN, V70, P177, DOI 10.1016/j.landurbplan.2003.10.013
   Ward S, 2016, WATER-SUI, V8, DOI 10.3390/w8110526
   Wilby RL, 2012, PROG PHYS GEOG, V36, P348, DOI 10.1177/0309133312438908
   Wolch JR, 2014, LANDSCAPE URBAN PLAN, V125, P234, DOI 10.1016/j.landurbplan.2014.01.017
   Wu F, 2018, J CLEAN PROD, V204, P344, DOI 10.1016/j.jclepro.2018.09.067
   Wu K, 2022, J ENG MECH, V148, DOI 10.1061/(ASCE)EM.1943-7889.0002052
   Wu K, 2021, INT J APPL MECH, V13, DOI 10.1142/S1758825121500940
   Zhang XL, 2018, CITIES, V72, P141, DOI 10.1016/j.cities.2017.08.009
   Zhao DX, 2021, COMPUT ELECTRON AGR, V182, DOI 10.1016/j.compag.2021.105990
   Zhao DX, 2018, SOIL SCI SOC AM J, V82, P1347, DOI 10.2136/sssaj2018.03.0100
   Zhao L, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11236648
NR 97
TC 3
Z9 3
U1 33
U2 33
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JAN
PY 2022
VL 14
IS 1
AR 229
DI 10.3390/su14010229
PG 23
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA YV2ZQ
UT WOS:000752601000001
OA gold
DA 2022-08-23
ER

PT J
AU Mat, SRT
   Ab Razak, MF
   Kahar, MNM
   Arif, JM
   Mohamad, S
   Firdaus, A
AF Mat, Sharfah Ratibah Tuan
   Ab Razak, Mohd Faizal
   Kahar, Mohd Nizam Mohmad
   Arif, Juliza Mohamad
   Mohamad, Salwana
   Firdaus, Ahmad
TI Towards a systematic description of the field using bibliometric
   analysis: malware evolution
SO SCIENTOMETRICS
LA English
DT Article
DE Bibliometric; Android malware; Web of science; Intrusion detection
   system
ID INTRUSION DETECTION SYSTEM; MOBILE PHISHING ATTACKS; ANDROID MALWARE;
   SECURITY; RISK; CLASSIFICATION; BEHAVIORS; SIGNATURE; NETWORKS; DEFENSE
AB Malware is a blanket term for Trojan, viruses, spyware, worms, and other files that are purposely created to harm computers, mobile devices, or computer networks. Malware commonly steals, encrypts, damages, and causes a mess in these devices. The growth of malware attacks has a consequence on the growth and attractiveness of mobile features in mobile devices. Most malware research aims to probe the different methods of preventing, analysing, and detecting malware attacks. This paper aims to demonstrate an exhaustive knowledge map of the Android malware by collecting a ten (10) year dataset from the Web of Science database. A bibliometric analysis was employed for analysing articles published between 2010 and 2019. Using the keyword "malware", 5622 articles were retrieved. After scrutinising with the keywords of "Android malware", 1278 articles were then collected. This study provides an overview of the articles, productivity, research area, the Web of Science categories, authors, high-cited articles, institutions, and impact journals examining malware. Research activities are continued by placing terms in the classification of malware detection systems that outline important areas in malware research. From the analysis, it can be concluded that the highest number of publications focusing on malware studies came from the continent of Asia. Additionally, this study discusses the challenges of malware studies in the recent research studies as well as the future direction.
C1 [Mat, Sharfah Ratibah Tuan; Ab Razak, Mohd Faizal; Kahar, Mohd Nizam Mohmad; Arif, Juliza Mohamad; Mohamad, Salwana; Firdaus, Ahmad] Univ Malaysia Pahang, Fac Comp, Kuantan 26300, Pahang, Malaysia.
RP Ab Razak, MF (通讯作者)，Univ Malaysia Pahang, Fac Comp, Kuantan 26300, Pahang, Malaysia.
EM sharfah0206@gmail.com; faizalrazak@ump.edu.my; mnizam@ump.edu.my;
   juliza.m.arif@gmail.com; salwanamohamad@ump.edu.my; firdausza@ump.edu.my
RI Razak, Mohd Faizal Ab/O-2027-2018
OI Ab Razak, Mohd Faizal/0000-0002-7388-4340
FU Ministry of Higher Education FRGS [RACER/1/2019/ICT03/UMP//2
   (RDU192613), UIC190807]; University Malaysia Pahang
FX The work is funded by the Ministry of Higher Education FRGS under
   Project ID: RACER/1/2019/ICT03/UMP//2 (RDU192613) and UIC190807. The
   authors thank anonymous reviewers for their constructive comments, and
   University Malaysia Pahang for the support.
CR Aafer Y, 2013, L N INST COMP SCI SO, V127, P86
   Ab Razak MF, 2019, MEASUREMENT, V133, P396, DOI 10.1016/j.measurement.2018.10.034
   Ab Razak MF, 2016, J NETW COMPUT APPL, V75, P58, DOI 10.1016/j.jnca.2016.08.022
   Adebayo OS, 2019, SECUR COMMUN NETW, V2019, DOI 10.1155/2019/2850932
   Ahmad P, 2020, DENT TRAUMATOL, V36, P89, DOI 10.1111/edt.12534
   Akour, 2017, 2016 2 INT C OP SOUR, DOI 10.1109/OSSCOM.2016.7863750
   Alali M, 2018, COMPUT SECUR, V74, P323, DOI 10.1016/j.cose.2017.09.011
   Ali W, 2019, INT J COMPUT SCI NET, V19, P15
   Almin SB, 2015, PROCEDIA COMPUT SCI, V45, P407, DOI 10.1016/j.procs.2015.03.170
   Amin M, 2020, FUTURE GENER COMP SY, V102, P112, DOI 10.1016/j.future.2019.07.070
   An N, 2017, PROCEEDINGS OF THE 2017 12TH INTERNATIONAL CONFERENCE ON MALICIOUS AND UNWANTED SOFTWARE (MALWARE), P47, DOI 10.1109/MALWARE.2017.8323956
   [Anonymous], 2018, APP STORE CELEBRATES
   [Anonymous], 2018, SECURITY INTELLIGENT, DOI 10.1007/978-3-319-76451-1
   Anuar, 2019, RISE BLOCKCHAIN BIBL, DOI 10.1007/s11192-019-03170-4
   Anuar, 2013, FRONT INFORM TECH EL, DOI 10.1631/fitee.1601491
   Arabo A, 2013, I C CONTR SYS COMP S, P526, DOI 10.1109/CSCS.2013.27
   Arzt S, 2014, ACM SIGPLAN NOTICES, V49, P259, DOI [10.1145/2666356.2594299, 10.1145/2594291.2594299]
   Aung Z, 2013, INT J SCI TECHNOL RE, V2, P228
   Baker HK, 2020, REV FINANC ECON, V38, P3, DOI 10.1002/rfe.1095
   Basu K, 2020, IEEE T INF FOREN SEC, V15, P512, DOI 10.1109/TIFS.2019.2924549
   Belaoued M, 2019, INT J DISTRIB SENS N, V15, DOI 10.1177/1550147719889907
   BESHARATI E, 2018, J AMB INTEL HUM COMP
   Blanco-Mesa F, 2017, J INTELL FUZZY SYST, V32, P2033, DOI 10.3233/JIFS-161640
   Bonilla CA, 2015, SCIENTOMETRICS, V105, P1239, DOI 10.1007/s11192-015-1747-7
   Chebyshev V., 2019, IT THREAT EVOLUTION, pSTATISTICS
   Chen DH, 2019, J HEALTHC ENG, V2019, DOI 10.1155/2019/1649363
   Chen HT, 2017, COMPUT HUM BEHAV, V75, P958, DOI 10.1016/j.chb.2017.06.011
   Computer Hope, 2019, COMP VS SMARTPH
   Counterpoint, 2019, GLOB SMARTPH MARK SH
   Cyber Security in Parallel and Distributed Computing, 2019, CYBER SECURITY PARAL, DOI 10.1002/9781119488330
   Daimi, 2017, COMPUTER NETWORK SEC, P1, DOI [10.1007/978-3-319-58424-9, DOI 10.1007/978-3-319-58424-9]
   DAVI L, 2011, PRIVILEGE ESCALATION
   De Lorenzo A, 2020, J INF SECUR APPL, V50, DOI 10.1016/j.jisa.2019.102423
   Deshpande P, 2018, INT J SYST ASSUR ENG, V9, P567, DOI 10.1007/s13198-014-0277-7
   Dobran B, 2019, 27 TERRIFYING RANSOM
   Dockrill, 2018, SCIENCEALERT
   Ellegaard O, 2015, SCIENTOMETRICS, V105, P1809, DOI 10.1007/s11192-015-1645-z
   Enago Academy, 2018, CHIN OV US HIGH NUMB
   Faruki P, 2015, IEEE COMMUN SURV TUT, V17, P998, DOI 10.1109/COMST.2014.2386139
   Feizollah A, 2017, COMPUT SECUR, V65, P121, DOI 10.1016/j.cose.2016.11.007
   Feizollah A, 2015, DIGIT INVEST, V13, P22, DOI 10.1016/j.diin.2015.02.001
   Feng Y, 2014, 22ND ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (FSE 2014), P576, DOI 10.1145/2635868.2635869
   Galetsi P, 2020, HEALTH INFO LIBR J, V37, P5, DOI 10.1111/hir.12286
   Garg S, 2020, FUTURE GENER COMP SY, V104, P105, DOI 10.1016/j.future.2019.09.038
   Gautam P, 2020, INT J MATH ENG MANAG, V5, P1156, DOI 10.33889/IJMEMS.2020.5.6.088
   Goel D, 2018, COMPUT SECUR, V73, P519, DOI 10.1016/j.cose.2017.12.006
   Gorla A, 2014, 36TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2014), P1025, DOI 10.1145/2568225.2568276
   Guanghui, 2020, SPRINGER NATURE THUY, DOI 10.1007/978-3-030-15235-2
   Haider W, 2016, FUTURE INTERNET, V8, DOI 10.3390/fi8030029
   Ham HS, 2014, J APPL MATH, DOI 10.1155/2014/594501
   Hu XL, 2018, EURASIP J WIREL COMM, DOI 10.1186/s13638-018-1158-6
   Huda S, 2018, FUTURE GENER COMP SY, V83, P193, DOI 10.1016/j.future.2017.12.037
   International Center, 2019, RES CHIN
   Iwami S, 2020, SCIENTOMETRICS, V122, P3, DOI 10.1007/s11192-019-03284-9
   Johnson, 2016, IOT DEVICES BEING IN
   Jose S, 2018, J PHYS CONF SER, V1000, DOI 10.1088/1742-6596/1000/1/012049
   Kabakus AT, 2015, DIGIT INVEST, V13, P1, DOI 10.1016/j.diin.2015.01.001
   Kim T, 2019, IEEE T INF FOREN SEC, V14, P773, DOI 10.1109/TIFS.2018.2866319
   Koucham O, 2015, 2015 SAI INTELLIGENT SYSTEMS CONFERENCE (INTELLISYS), P1010, DOI 10.1109/IntelliSys.2015.7361267
   Kuntz M., CHALLENGES STRATEGIE, V17, P68
   Lanet J., 2018, INFORM TECHNOLOGY, DOI 10.1007/978-3-030-12942-2
   Li J, 2018, IEEE T IND INFORM, V14, P3216, DOI 10.1109/TII.2017.2789219
   Liang JW, 2019, APPL SOFT COMPUT, V75, P712, DOI 10.1016/j.asoc.2018.12.001
   Library, 2019, MEAS RES IMP
   Library U, 2020, RES METH GUID
   Ljubas, 2020, IT SPECIALISTS WARN
   Lookout, 2019, WHAT IS MOB THREAT
   Lopes J, 2019, 2019 7TH INTERNATIONAL SYMPOSIUM ON DIGITAL FORENSICS AND SECURITY (ISDFS)
   Lu CC, 2019, FRONT PUBLIC HEALTH, V7, DOI 10.3389/fpubh.2019.00384
   Luo JL, 2020, J CLEAN PROD, V273, DOI 10.1016/j.jclepro.2020.122945
   Macmanus, 2012, FUTURE CONNECTED CAR
   Magdum, 2015, TECHNIQUES, V14, P6170
   Martin I, 2019, FUTURE GENER COMP SY, V97, P295, DOI 10.1016/j.future.2019.03.006
   Mas'Ud, 2014, ICISA 2014 2014 5 IN, DOI 10.1109/ICISA.2014.6847364
   Mas'ud MZ., 2014, RES J INFORM TECHNOL, V6, P325, DOI [10.3923/rjit.2014.325.341, DOI 10.3923/RJIT.2014.325.341]
   Mcafee, 2019, RANS ATT GREW 118 NE
   McAfee, 2019, MOB MALW CONT INCR C
   Merigo JM, 2017, AUST ACCOUNT REV, V27, P71, DOI 10.1111/auar.12109
   Merigo JM, 2015, APPL SOFT COMPUT, V27, P420, DOI 10.1016/j.asoc.2014.10.035
   Mobliciti, 2020, CURR TRENDS ANDR MOB
   Moon D, 2016, J SUPERCOMPUT, V72, P2520, DOI 10.1007/s11227-015-1506-9
   Moran, 2020, ANDROID MALWARE TAKE
   Narudin FA, 2016, SOFT COMPUT, V20, P343, DOI 10.1007/s00500-014-1511-6
   Niazi RA, 2019, IEEE ACCESS, V7, P88656, DOI 10.1109/ACCESS.2019.2924968
   Or-Meir O, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3329786
   Ospina-Mateus H, 2019, SCIENTOMETRICS, V121, P793, DOI 10.1007/s11192-019-03234-5
   Palmer, 2019, MOBILE MALWARE ATTAC
   Parati, 2015, INT J ENG COMPUTER S, V4, P12146
   Park M, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19092148
   Peiravian N, 2013, PROC INT C TOOLS ART, P300, DOI 10.1109/ICTAI.2013.53
   Prashar A, 2020, J CLEAN PROD, V245, DOI 10.1016/j.jclepro.2019.118665
   Qamar A, 2019, FUTURE GENER COMP SY, V97, P887, DOI 10.1016/j.future.2019.03.007
   Raparelli E, 2019, INT J REMOTE SENS, V40, P9070, DOI 10.1080/01431161.2019.1569793
   Rastogi V, 2014, IEEE T INF FOREN SEC, V9, P99, DOI 10.1109/TIFS.2013.2290431
   Rehman ZU, 2018, COMPUT ELECTR ENG, V69, P828, DOI 10.1016/j.compeleceng.2017.11.028
   Reuters T., 2008, THOMSON REUTERS, DOI 10.1097/NCN.0b013e31819ec9ac
   Sanders, 2019, 15 CRAZY MALWARE VIR
   Sanz B, 2013, ADV INTELL SYST, V189, P289
   Seals T., 2020, THREAT POST
   Security, 2020, COVID 19 PANDEMIC SP
   Seo SH, 2014, J NETW COMPUT APPL, V38, P43, DOI 10.1016/j.jnca.2013.05.008
   Shabtai A, 2014, COMPUT SECUR, V43, P1, DOI 10.1016/j.cose.2014.02.009
   Shabtai A, 2012, J INTELL INF SYST, V38, P161, DOI 10.1007/s10844-010-0148-x
   Shabtai A, 2010, J SYST SOFTWARE, V83, P1524, DOI 10.1016/j.jss.2010.03.046
   Shankar DS, 2020, INTERDISCIP NEUROSUR, V19, DOI 10.1016/j.inat.2019.100615
   Sharma Kavita, 2019, International Journal of E-Services and Mobile Applications, V11, P1, DOI 10.4018/IJESMA.2019040101
   Sharma K., 2018, NEXT GENERATION NETW, V638, P555, DOI [10.1007/978-981-10-6005-2_56, DOI 10.1007/978-981-10-6005-2_56]
   Sharma K, 2018, COMPUT ELECTR ENG, V71, P416, DOI 10.1016/j.compeleceng.2018.08.003
   Sharma K, 2016, PROCEDIA COMPUT SCI, V78, P19, DOI 10.1016/j.procs.2016.02.005
   Sheen S, 2015, NEUROCOMPUTING, V151, P905, DOI 10.1016/j.neucom.2014.10.004
   Shrivastava G, 2022, ENERGY SYST, V13, P567, DOI 10.1007/s12667-019-00359-7
   Shrivastava G, 2019, MULTIMED TOOLS APPL, V78, P35713, DOI 10.1007/s11042-019-07899-1
   Shrivastava G, 2021, EXPERT SYST, V38, DOI 10.1111/exsy.12468
   Shrivastava G, 2017, SCALABLE COMPUT-PRAC, V18, P243, DOI 10.12694/scpe.v18i3.1304
   Shukla N, 2020, COMPUT METH PROG BIO, V183, DOI 10.1016/j.cmpb.2019.105075
   Singhal S., 2019, RECENT FINDINGS INTE, P229, DOI [10.1007/978-981-10-8639-7, DOI 10.1007/978-981-10-8639-7]
   Spring, 2019, THREAT POST
   Srikanth K., 2017, INT J ENG TECHNOLOGY, V7, P297, DOI [10.14419/ijet.v7i1.1.9706, DOI 10.14419/IJET.V7I1.1.9706]
   Statista, 2019, NUMB SMARTPH US WORL
   Suarez-Tangil G, 2018, IET INFORM SECUR, V12, P404, DOI 10.1049/iet-ifs.2017.0460
   Suarez-Tangil G, 2014, EXPERT SYST APPL, V41, P1104, DOI 10.1016/j.eswa.2013.07.106
   Subba B, 2017, TENCON IEEE REGION, P2006, DOI 10.1109/TENCON.2017.8228190
   Taheri R, 2020, FUTURE GENER COMP SY, V105, P230, DOI 10.1016/j.future.2019.11.034
   Tahir M, 2019, INT J ADV COMPUT SC, V10, P305
   TALAL M, 2019, TELECOMMUNICATION SY
   Tam K, 2017, ACM COMPUT SURV, V49, DOI 10.1145/3017427
   Thompson N, 2017, COMPUT SECUR, V70, P376, DOI 10.1016/j.cose.2017.07.003
   Venkatraman S, 2019, J INF SECUR APPL, V47, P377, DOI 10.1016/j.jisa.2019.06.006
   Verkijika SF, 2019, COMPUT HUM BEHAV, V101, P286, DOI 10.1016/j.chb.2019.07.034
   Wang W, 2014, IEEE T INF FOREN SEC, V9, P1869, DOI 10.1109/TIFS.2014.2353996
   WANG X, 2018, IEEE T MOBILE COMPUT
   Wazid M, 2019, IEEE CONSUM ELECTR M, V8, P56, DOI 10.1109/MCE.2018.2881291
   Webofknowledge, 2018, WEB SCI CORE COLLECT
   Wei FG, 2014, CCS'14: PROCEEDINGS OF THE 21ST ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P1329, DOI 10.1145/2660267.2660357
   Whitwam, 2020, ANDROID ANTIVIRUS AP
   Wu DJ, 2012, ASIA JT CONF INF SEC, P62, DOI 10.1109/AsiaJCIS.2012.18
   Wu F, 2019, INT WIREL COMMUN, P61
   Yang AM, 2019, IEEE ACCESS, V7, P106043, DOI 10.1109/ACCESS.2019.2929919
   Yang C, 2014, LECT NOTES COMPUT SC, V8712, P163, DOI 10.1007/978-3-319-11203-9_10
   Yao RQ, 2020, INT J BIOL SCI, V16, P27, DOI 10.7150/ijbs.37496
   Ye N, 2020, J CLEAN PROD, V272, DOI 10.1016/j.jclepro.2020.122679
   Yerima SY, 2015, IET INFORM SECUR, V9, P313, DOI 10.1049/iet-ifs.2014.0099
   Yerima SY, 2014, IET INFORM SECUR, V8, P25, DOI 10.1049/iet-ifs.2013.0095
   Yerima SY, 2013, INT CON ADV INFO NET, P121, DOI 10.1109/AINA.2013.88
   Yu DJ, 2016, J INTELL FUZZY SYST, V30, P2727, DOI 10.3233/IFS-152026
   Yu L, 2013, INT CONF INSTR MEAS, P1338, DOI 10.1109/IMCCC.2013.297
   Yuan ZL, 2016, TSINGHUA SCI TECHNOL, V21, P114
   Yuan ZL, 2014, SIGCOMM'14: PROCEEDINGS OF THE 2014 ACM CONFERENCE ON SPECIAL INTEREST GROUP ON DATA COMMUNICATION, P371, DOI [10.1145/2619239.2631434, 10.1145/2740070.2631434]
   ZHANG M, CCS 14 P 21 ACM C CO, P1105
   Zhang Y, 2020, J HAZARD MATER, V400, DOI 10.1016/j.jhazmat.2020.123290
   Zhang Y, 2020, CATENA, V194, DOI 10.1016/j.catena.2020.104798
   Zheng M, 2013, IEEE INT CONF TRUST, P163, DOI 10.1109/TrustCom.2013.25
   Zhou YJ, 2012, P IEEE S SECUR PRIV, P95, DOI 10.1109/SP.2012.16
   Zhu HJ, 2018, NEUROCOMPUTING, V272, P638, DOI 10.1016/j.neucom.2017.07.030
   2011, ENG SOLUTIONS SUSTAI, V23, P1
NR 155
TC 7
Z9 7
U1 7
U2 22
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAR
PY 2021
VL 126
IS 3
BP 2013
EP 2055
DI 10.1007/s11192-020-03834-6
EA FEB 2021
PG 43
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA RC7ES
UT WOS:000616460200001
PM 33583978
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Li, J
   Jovanovic, A
   Klimek, P
   Guo, XH
AF Li, Jie
   Jovanovic, Aleksandar
   Klimek, Peter
   Guo, Xiaohong
TI Bibliometric analysis of fracking scientific literature
SO SCIENTOMETRICS
LA English
DT Article
DE Fracking; Hydraulic fracturing; Shale gas; Bibliometric; Knowledge
   mapping
ID NATURAL-GAS; METHANE
AB This study uses bibliometric methods to analyze the scientific literature of fracking. Web of Science database, including the Science Citation Index, Sciences Citation Index and Conference Proceedings Citation Index-Science were used to collect the data. The analysis done in the paper looks at the annual distribution of publications, countries, institutes, authors, journals and categories. Furthermore, key topics and highly-cited papers were analyzed. The results show that fracking as a new research term appears in the Web of Science records from 1953 and its presence in the Web of Science has been growing ever since, becoming a hot topic recently. The countries with most of the contributions have been USA, China and Canada, whereas the Russian Academy of Sciences, University of Oklahoma and Tohoku University were the three institutions with most publications in fracking research. The publications have been concentrated in several journals, led by the Journal of Petroleum Technology, HeN"mND1/2oe XoD center dot ND(1)cmD(2)o and International Journal of Rock Mechanics and Mining Sciences, and categorized mainly in geosciences multidisciplinary, Engineering Petroleum and Energy Fuels. The study has identified that terms of fracking can be divided into three main clusters, related to "drilling methods", "exploitation/extraction process" and the "geoscience aspects". The highly cited papers in the period 1953-2013 were collected and analyzed, in order to show the papers with highest impact in fracking area.
C1 [Li, Jie; Jovanovic, Aleksandar; Guo, Xiaohong] Capital Univ Econ & Business, Sch Safety & Environm Engn, Beijing, Peoples R China.
   [Li, Jie; Jovanovic, Aleksandar] Steinbeis Adv Risk Technol, Stuttgart, Germany.
   [Jovanovic, Aleksandar; Klimek, Peter] European Virtual Inst Integrated Risk Management, Stuttgart, Germany.
   [Jovanovic, Aleksandar] Univ Stuttgart, ZIRIUS Stuttgart Res Ctr Interdisciplinary Risk &, D-70174 Stuttgart, Germany.
   [Klimek, Peter] Med Univ Vienna, Sect Sci Complex Syst, A-1090 Vienna, Austria.
RP Jovanovic, A (通讯作者)，Capital Univ Econ & Business, Sch Safety & Environm Engn, Beijing, Peoples R China.
EM jovanovic@risk-technologies.com
RI Li, Jie/I-1983-2014; Klimek, Peter/AAC-2723-2019
OI Li, Jie/0000-0002-4096-2795; Klimek, Peter/0000-0003-1187-6713;
   Jovanovic, Aleksandar/0000-0002-2624-4586
CR Davies R, 2013, MAR PETROL GEOL, V45, P171, DOI 10.1016/j.marpetgeo.2013.03.016
   de Rijke K, 2013, ANTHROPOL TODAY, V29, P13, DOI 10.1111/1467-8322.12017
   GARFIELD E, 1970, NATURE, V227, P669, DOI 10.1038/227669a0
   Garfield E, 2009, J INFORMETR, V3, P173, DOI 10.1016/j.joi.2009.03.009
   Ho Y. S., 2012, SCIENTOMETRICS, V96, P1
   Howarth RW, 2011, CLIMATIC CHANGE, V106, P679, DOI 10.1007/s10584-011-0061-5
   IRGC, 2013, RISK GOV GUID UNC GA
   Jovanovic A., 2012, THINK PIEC IRGC WORK
   Jovanovic AS, 2013, J RISK RES, V16, P275, DOI 10.1080/13669877.2012.729516
   Jovanovic AS, 2013, J RISK RES, V16, P271, DOI 10.1080/13669877.2012.743162
   Kerr RA, 2010, SCIENCE, V328, P1624, DOI 10.1126/science.328.5986.1624
   Osborn SG, 2011, P NATL ACAD SCI USA, V108, P8172, DOI 10.1073/pnas.1100682108
   Ren JL, 2013, WATER RESOUR MANAG, V27, P4679, DOI 10.1007/s11269-013-0435-8
   Rivard C, 2014, INT J COAL GEOL, V126, P64, DOI 10.1016/j.coal.2013.12.004
   US-Energy-Information-Administration, 2013, ANN EN OUTL 2013 PRO
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
NR 16
TC 15
Z9 17
U1 4
U2 115
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2015
VL 105
IS 2
BP 1273
EP 1284
DI 10.1007/s11192-015-1739-7
PG 12
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA CU1ED
UT WOS:000363261600036
DA 2022-08-23
ER

PT J
AU Wang, YZ
   Wang, HZ
   He, JW
   Lu, WB
   Gao, SL
AF Wang, Yuzhuo
   Wang, Hongzhi
   He, Junwei
   Lu, Wenbo
   Gao, Shuolin
TI TAGAT: Type-Aware Graph Attention neTworks for reasoning over knowledge
   graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge reasoning; Knowledge graph embedding; Type-aware; Graph
   attention network; Hierarchical attention mechanism
AB With the rapid development of knowledge graphs (KGs), various AI-related applications have been affected positively. However, even though some KGs are relatively large, they still suffer from incompleteness. This has mighty promoted the development of reasoning over KGs to complement them. However, most existing reasoning methods only focus on semantics in the KG, ignoring potential or valuable information hidden in it, the most typical ones are neighborhood information and type information. This limits not only the reasoning performance but also the interpretability of the embedding space. To this end, we propose a Type-Aware Graph Attention neTworks (TAGAT) for the reasoning task over KGs. Except for combining type-related information during the embedding process, TAGAT further adopts a hierarchical attention mechanism to realize the perception of type and neighborhood information meticulously. For each entity, different attention levels respectively focus on considering the contribution of different relations, different types under each relation and different entity of each type under each relation. Moreover, the embedding space of TAGAT is constrained by the type information and naturally has better type-related interpretability, which greatly complements the defects of existing KGC models. Extensive experiments validate the high reasoning performance and the relatively ideal interpretability of our model. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Wang, Yuzhuo; Wang, Hongzhi; He, Junwei; Lu, Wenbo; Gao, Shuolin] Harbin Inst Technol, Harbin, Peoples R China.
   [Wang, Hongzhi] Peng Cheng Lab, Shenzhen, Peoples R China.
RP Wang, HZ (通讯作者)，Harbin Inst Technol, Harbin, Peoples R China.
EM wangyuzh@hit.edu.cn; wangzh@hit.edu.cn; 1180500313@stu.hit.edu.cn;
   luwenbo@hit.edu.cn; 19S103244@stu.hit.edu.cn
OI Wang, Hongzhi/0000-0002-7521-2871
CR Agirre E, 2014, COMPUT LINGUIST, V40, P57, DOI 10.1162/COLI_a_00164
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Dasgupta SS, 2018, P 2018 C EMPIRICAL M, P2001
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dong L, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P260
   Faloutsos M, 1999, COMP COMM R, V29, P251
   Hao J., 2021, ABS210308115 CORR
   Hao JH, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1709, DOI 10.1145/3292500.3330838
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Kingma D. P., 2017, ARXIV
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   Krompass D, 2014, 2014 INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA), P18, DOI 10.1109/DSAA.2014.7058046
   Li J, 2019, COMM COM INF SC, V1134, P50, DOI 10.1007/978-981-15-1956-7_5
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Ma SH, 2017, LECT NOTES ARTIF INT, V10534, P717, DOI 10.1007/978-3-319-71249-9_43
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Newman MEJ, 2006, P NATL ACAD SCI USA, V103, P8577, DOI 10.1073/pnas.0601602103
   Nguyen D. Q., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Niu G., 2020, P C EMP METH NAT LAN, P1172
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun Z., 2019, 7 INT C LEARN REPR
   Tissot H., 2018, P 10 INT JOINT C KNO, V1, P70, DOI [10.5220/0006923700700079, DOI 10.5220/0006923700700079]
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vaswani A, 2017, ADV NEURAL INF PROCE, P5998
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xiong C, 2015, P 2015 INT C THEOR I, P111, DOI DOI 10.1145/2808194.2809446
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Yang B., 2015, P INT C LEARN REPR I
   Zhang RC, 2018, AAAI CONF ARTIF INTE, P2046
   Zhang Z, 2020, AAAI CONF ARTIF INTE, V34, P9612
   Zhe Jin, 2019, Knowledge Science, Engineering and Management. 12th International Conference, KSEM 2019. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 11775), P537, DOI 10.1007/978-3-030-29551-6_48
NR 40
TC 0
Z9 0
U1 4
U2 13
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD DEC 5
PY 2021
VL 233
AR 107500
DI 10.1016/j.knosys.2021.107500
EA OCT 2021
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WK7QW
UT WOS:000709919000006
DA 2022-08-23
ER

PT J
AU Yan, SR
   Lin, KJ
   Zheng, XL
   Wang, HS
AF Yan, Surong
   Lin, Kwei-Jay
   Zheng, Xiaolin
   Wang, Haosen
TI LkeRec: Toward Lightweight End-to-End Joint Representation Learning for
   Building Accurate and Effective Recommendation
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE End-to-end joint learning; knowledge graph embedding; self-attention;
   scalability; recommendation
AB Explicit and implicit knowledge about users and items have been used to describe complex and heterogeneous side information for recommender systems (RSs). Many existing methods use knowledge graph embedding (KGE) to learn the representation of a user-item knowledge graph (KG) in low-dimensional space. In this article, we propose a lightweight end-to-end joint learning framework for fusing the tasks of KGE and RSs at the model level. Our method proposes a lightweight KG embedding method by using bidirectional bijection relation-type modeling to enable scalability for large graphs while using self-adaptive negative sampling to optimize negative sample generating. Our method further generates the integrated views for users and items based on relation-types to explicitly model users' preferences and items' features, respectively. Finally, we add virtual "recommendation" relations between the integrated views of users and items to model the preferences of users on items, seamlessly integrating RS with user-item KG over a unified graph. Experimental results on multiple datasets and benchmarks show that our method can achieve a better accuracy of recommendation compared with existing state-of-the-art methods. Complexity and runtime analysis suggests that our method can gain a lower time and space complexity than most of existing methods and improve scalability.
C1 [Yan, Surong; Wang, Haosen] Zhejiang Univ Finance & Econ, Sch Informat Management & Artificial Intelligence, Hangzhou 310018, Peoples R China.
   [Lin, Kwei-Jay] Univ Calif Irvine, Dept Elect Engn & Comp Sci, Irvine, CA 92697 USA.
   [Zheng, Xiaolin] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
RP Yan, SR (通讯作者)，Zhejiang Univ Finance & Econ, Sch Informat Management & Artificial Intelligence, Hangzhou 310018, Peoples R China.
EM surong.y@gmail.com; klin@uci.edu; xlzheng@cs.zju.edu.cn;
   haosenwang97@163.com
FU Natural Science Foundation of China [61972337, 61502414]; Ministry of
   Science and Technology of Taiwan [MOST 108-2633-E-002-001]; National
   Taiwan University [NTU108L104039]
FX This work is supported in part by the Natural Science Foundation of
   China (Grant Nos.61972337, and 61502414), the Ministry of Science and
   Technology of Taiwan (MOST 108-2633-E-002-001), and National Taiwan
   University (NTU108L104039).
CR Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Bordes Antoine, 2011, AAAI
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Cai L, 2018, NAACL, P1470
   Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Cen YK, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1358, DOI 10.1145/3292500.3330964
   Chang SY, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P119, DOI 10.1145/2783258.2783296
   Chen M., 2016, P IJCAI, P1511
   Chen XC, 2020, ACM T INFORM SYST, V38, DOI 10.1145/3402521
   Das R, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P132
   Dong YX, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P135, DOI 10.1145/3097983.3098036
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   Feng J., 2016, COLING, P641
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Hamilton WL, 2017, ADV NEUR IN, V30
   He RN, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P161, DOI 10.1145/3109859.3109882
   He XN, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P639, DOI 10.1145/3397271.3401063
   He XN, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P355, DOI 10.1145/3077136.3080777
   Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Jin BW, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P659, DOI 10.1145/3397271.3401072
   Li XP, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P305, DOI 10.1145/3097983.3098077
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu Fan, 2020, IEEE T KNOWL DATA EN, V2020
   Liu ZM, 2017, AAAI CONF ARTIF INTE, P154
   Long MS, 2017, ADV NEUR IN, V30
   Lu YC, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P4, DOI 10.1145/3240323.3240365
   Luo C, 2014, IEEE DATA MINING, P917, DOI 10.1109/ICDM.2014.64
   Luo Y., 2015, P 2015 C EMP METH NA, P1656, DOI DOI 10.18653/V1/D15-1191
   McAuley J., 2013, P 7 ACM C REC SYST, P165, DOI DOI 10.1145/2507157.2507163
   McAuley J, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P43, DOI 10.1145/2766462.2767755
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Shi C., 2015, P 24 ACM INT C INF K, P453
   Shi C, 2019, IEEE T KNOWL DATA EN, V31, P357, DOI 10.1109/TKDE.2018.2833443
   Sun Y, 2013, SIGKDD EXPLOR NEWSL, V14, P20, DOI DOI 10.1145/2481244.2481248
   Sun Z., 2019, 7 INT C LEARN REPR
   Trouillon T, 2016, PR MACH LEARN RES, V48
   van der Merwe R, 2019, ARCH REC, V40, P239, DOI 10.1080/23257962.2017.1388224
   Velivckovic Petar, 2017, P 6 INT C LEARN REPR
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2000, DOI 10.1145/3308558.3313411
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang X, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P99, DOI 10.1145/3366423.3380098
   Wang X, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P165, DOI 10.1145/3331184.3331267
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Yang B., 2014, ABS14126575 CORR
   Ying R, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P974, DOI 10.1145/3219819.3219890
   Yu X, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P283, DOI 10.1145/2556195.2556259
   Yu Xiao, 2013, INT JOINT C ART INT, V27
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhang RC, 2020, ACM T INFORM SYST, V38, DOI 10.1145/3406116
   Zhang YF, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1449, DOI 10.1145/3132847.3132892
   Zhao H, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P635, DOI 10.1145/3097983.3098063
   Zheng J., 2017, INT J DATA SCI ANALY, V3, P35
NR 63
TC 0
Z9 0
U1 2
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1046-8188
EI 1558-2868
J9 ACM T INFORM SYST
JI ACM Trans. Inf. Syst.
PD JUL
PY 2022
VL 40
IS 3
AR 54
DI 10.1145/3486673
PG 28
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0E1NE
UT WOS:000776450500013
DA 2022-08-23
ER

PT J
AU Zhang, X
   Wang, X
   Yun, W
   Gao, C
   Han, MT
   Liu, H
AF Zhang, Xuan
   Wang, Xu
   Yun, Wei
   Gao, Chen
   Han, Mengting
   Liu, Hui
TI Pattern-based software process modeling for dependability
SO JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS
LA English
DT Article
DE aspect-oriented modeling; dependability; knowledge graph; pattern;
   software process modeling
ID CYBER-PHYSICAL SYSTEMS; REQUIREMENTS; UML4SPM; AUTOMATION; EXECUTION;
   FRAMEWORK; WEB
AB Traditional process modeling focuses on modeling activities for functional requirements. For dependability requirements, a knowledge-based aspect-oriented software process modeling approach is proposed. First, we extend the pattern and context to the knowledge graph triplet structure to describe dependability-oriented knowledge patterns. By applying the patterns, dependability requirements can be organized into dependability-related activities that are integrated into the software process. Then, aspect-oriented modeling patterns based on Petri nets are introduced to support the integration of these dependability-related activities and model dependability-oriented software processes. Finally, the modeling performance and the subjective usability of the patterns are evaluated by 110 students with different degrees. The results indicate that these two indexes are on the positive track. Hence, the patterns may be the backbone of dependability-oriented software process modeling.
C1 [Zhang, Xuan; Yun, Wei; Gao, Chen; Han, Mengting; Liu, Hui] Yunnan Univ, Sch Software, Kunming 650091, Yunnan, Peoples R China.
   [Zhang, Xuan] Key Lab Software Engn Yunnan, Kunming, Yunnan, Peoples R China.
   [Wang, Xu] Yunnan Univ, Sch Econ, Kunming, Yunnan, Peoples R China.
RP Zhang, X (通讯作者)，Yunnan Univ, Sch Software, Kunming 650091, Yunnan, Peoples R China.
EM zhxuan@ynu.edu.cn
FU Data Driven Software Engineering Innovative Research Team Funding of
   Yunnan Province [2017HC012]; Science Foundation of Key Laboratory of
   Software Engineering of Yunnan Province [2015SE202]; Natural Science
   Foundation of Yunnan Province [2016FB106]; National Social Science
   Foundation of China [18BJL104]; National Natural Science Foundation of
   China [61262025, 61502413, 61862063]
FX Data Driven Software Engineering Innovative Research Team Funding of
   Yunnan Province, Grant/Award Number: 2017HC012; Science Foundation of
   Key Laboratory of Software Engineering of Yunnan Province, Grant/Award
   Number: 2015SE202; Natural Science Foundation of Yunnan Province,
   Grant/Award Number: 2016FB106; National Social Science Foundation of
   China, Grant/Award Number: 18BJL104; National Natural Science Foundation
   of China, Grant/Award Numbers: 61262025, 61502413, 61862063
CR Akkaya I, 2016, P IEEE, V104, P997, DOI 10.1109/JPROC.2015.2512265
   Al Balushi TH, 2013, EXPERT SYST, V30, P129, DOI 10.1111/j.1468-0394.2012.00625.x
   Albert B., 2013, MEASURING USER EXPER
   Garcia-Garcia JA, 2018, J SOFTW-EVOL PROC, V30, DOI 10.1002/smr.1982
   Alexander C., 1979, TIMELESS WAY BUILDIN
   Anderson R., 2008, SECURITY ENG
   [Anonymous], 2015, 60261 IEC
   ARC Advisory Group, 2018, AUT SOFTW EXP DISCR
   Asghari SA, 2014, IEEE T IND INFORM, V10, P481, DOI 10.1109/TII.2013.2248373
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Avizienis A, 2004, IEEE T DEPEND SECURE, V1, P11, DOI 10.1109/TDSC.2004.2
   BASILI V, 1994, GOAL QUESTION METRIC
   Bendraou R, 2005, LECT NOTES COMPUT SC, V3713, P17
   Bendraou R, 2007, EUROMICRO CONF PROC, P314
   Bendraou R, 2006, IEEE INT ENTERP DIST, P297
   Bendraou R, 2012, J SOFTW-EVOL PROC, V24, P765, DOI 10.1002/smr.494
   Bendraou R, 2009, LECT NOTES COMPUT SC, V5543, P148, DOI 10.1007/978-3-642-01680-6_15
   Bhatti ZE, 2017, IEEE T IND INFORM, V13, P17, DOI 10.1109/TII.2016.2610185
   Binotto A, P 13 IFAC S INF CONT, P1423
   Briand LC, 2004, IEEE T SOFTWARE ENG, V30, P770, DOI 10.1109/TSE.2004.79
   Daramola O., 2012, 2012 IEEE Second International Workshop on Requirements Patterns (RePa 2012), P54, DOI 10.1109/RePa.2012.6359973
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   de Freitas EP, 2007, LECT NOTES COMPUT SC, V4765, P55
   DEWI, 2019, EU PROJ DEP EMB WIR
   Elahi G, 2009, LECT NOTES COMPUTER, V5829
   Ericson A. Clifton, 2005, HAZARD ANAL TECHNIQU
   Franch X., 2013, MANAGING REQUIREMENT
   Frey G, 2000, IEEE SYS MAN CYBERN, P2431, DOI 10.1109/ICSMC.2000.884356
   Gandhi RA, 2011, ACM T SOFTW ENG METH, V20, DOI 10.1145/2000799.2000802
   Garcia-Borgonon L, 2014, INFORM SOFTWARE TECH, V56, P103, DOI 10.1016/j.infsof.2013.10.001
   Givehchi O, 2017, IEEE T IND INFORM, V13, P3370, DOI 10.1109/TII.2017.2740434
   Hanisch H.-M., 2009, IFAC P VOLUMES, V42, P211
   Hastbacka D, 2013, MULTIMED TOOLS APPL, V65, P47, DOI 10.1007/s11042-012-1134-9
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Howard M, 2006, SECURE DEV LIFE CYCL
   IEC 61508, 2010, 61508 IEC, V2nd ed.
   Jackson D, 2009, COMMUN ACM, V52, P78, DOI 10.1145/1498765.1498787
   Jamro M, 2015, IEEE T IND INFORM, V11, P1119, DOI 10.1109/TII.2015.2469257
   Jin Z., 2018, ENV MODELING BASED R
   Johnson TL, 2007, CONTROL ENG PRACT, V15, P1403, DOI 10.1016/j.conengprac.2006.07.005
   Kim J, 2010, IEEE T IND INFORM, V6, P568, DOI 10.1109/TII.2010.2060206
   Lopez C, 2008, LECT NOTES COMPUTER, V5333
   Lyu M.R., 1996, HDB SOFTWARE RELIABI, V222
   Lastra JLM, 2006, IEEE T IND INFORM, V2, P1, DOI 10.1109/TII.2005.862144
   Maxim B. R, 2014, SOFTWARE ENG PRACTIT
   Musa D, 1999, SOFTWARE RELIABILITY
   Niemela E, 2008, ENASE 2008: PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING, P169
   Nysetvold A. G., 2006, ADV TOPICS DATABASE, P79, DOI [10.4018/978-1-59140-935-9.ch005, DOI 10.4018/978-1-59140-935-9.CH005]
   Oppenheim A.N., 1992, QUESTIONNAIRE DESIGN
   OWASP (the open web application security project), 2009, SOFTW ASS MAT MOD GU
   Palomares C, 2017, EMPIR SOFTW ENG, V22, pCP3, DOI 10.1007/s10664-016-9485-x
   Potter Bruce, 2009, Network Security, V2009, P15, DOI 10.1016/S1353-4858(09)70008-X
   Provenzano L, 2017, ASIA PAC SOFWR ENG, P713, DOI 10.1109/APSEC.2017.91
   Roque AD, 2019, J CONTROL AUTOM ELEC, V30, P700, DOI 10.1007/s40313-019-00483-w
   SAE, 2016, 12016 SAE
   Saeki M, 2013, INT J SOFTW ENG KNOW, V23, P695, DOI 10.1142/S0218194013500174
   Salini P., 2012, INT J COMPUTER APPL, V49, P21, DOI DOI 10.5120/7671-0953
   Schutz D, 2013, IEEE T IND INFORM, V9, P2397, DOI 10.1109/TII.2012.2229285
   Souag A, 2013, 2013 IEEE 37TH ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE WORKSHOPS (COMPSACW), P101, DOI 10.1109/COMPSACW.2013.124
   Steger M, 2018, IEEE T IND INFORM, V14, P2181, DOI 10.1109/TII.2017.2776250
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Supakkul S, 2010, Proceedings of the 2010 IEEE 18th International Conference on Requirements Engineering (RE2010), P179, DOI 10.1109/RE.2010.31
   United Stated Department of Defense, 2000, MILSTD882D
   Vyatkin V, 2013, IEEE T IND INFORM, V9, P1234, DOI 10.1109/TII.2013.2258165
   Wang DD, 2011, INT SYMP EMP SOFTWAR, P434, DOI 10.1109/ESEM.2011.62
   Wehrmeister MA, 2008, ISORC 2008: 11TH IEEE SYMPOSIUM ON OBJECT/COMPONENT/SERVICE-ORIENTED REAL-TIME DISTRIBUTED COMPUTING - PROCEEDINGS, P234, DOI 10.1109/ISORC.2008.67
   Wehrmeister MA, 2013, IEEE T IND INFORM, V9, P2373, DOI 10.1109/TII.2013.2240308
   Yan JH, 2018, FRONT COMPUT SCI-CHI, V12, P55, DOI 10.1007/s11704-016-5228-9
   Yousfi A, 2018, IEEE T IND INFORM, V14, P3358, DOI 10.1109/TII.2017.2777847
   Zhang X, 2018, FRONT ENV SCI ENG, V12, P1, DOI 10.1007/s11783-018-1059-2
   Zhou JL, 2015, INT REQUIR ENG CONF, P247, DOI 10.1109/RE.2015.7320431
NR 72
TC 1
Z9 1
U1 3
U2 8
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2047-7473
EI 2047-7481
J9 J SOFTW-EVOL PROC
JI J. Softw.-Evol. Proc.
PD SEP
PY 2020
VL 32
IS 9
AR e2262
DI 10.1002/smr.2262
EA MAR 2020
PG 21
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NO3YE
UT WOS:000521621000001
DA 2022-08-23
ER

PT J
AU Assi, A
   Mcheick, H
   Dhifli, W
AF Assi, Ali
   Mcheick, Hamid
   Dhifli, Wajdi
TI Data linking over RDF knowledge graphs: A survey
SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
LA English
DT Article
DE data linking; instance matching; knowledge graph; record linkage;
   semantic web; web of data
ID ENTITY RESOLUTION; RECORD LINKAGE; DISCOVERY; ALGORITHM; ALIGNMENT
AB Instance matching (IM) is the process of matching instances across Knowledge Bases (KBs) that refer to the same real-world object (eg, the same person in two different KBs). Several approaches in the literature were developed to perform this process using different algorithmic techniques and search strategies. In this article, we aim to provide the rationale for IM and to survey the existing algorithms for performing this task. We begin by identifying the importance of such a process and define it formally. We also provide a new classification of these approaches depending on the "source of evidence," which can be considered as the context information integrated explicitly or implicitly in the IM process. We survey and discuss the state-of-the-art IM methods regarding the context information. We, furthermore, describe and compare different state-of-the-art IM approaches in relation to several criteria. Such a comprehensive comparative study constitutes an asset and a guide for future research in IM.
C1 [Assi, Ali] Univ Quebec Montreal, Montreal, PQ, Canada.
   [Mcheick, Hamid] Univ Quebec Chicoutimi, Chicoutimi, PQ, Canada.
   [Dhifli, Wajdi] Univ Lille, CHU Lille, ULR 2694 Metr Evaluat Technol Sante & Prat Med, F-59000 Lille, France.
RP Dhifli, W (通讯作者)，Univ Lille, CHU Lille, ULR 2694 Metr Evaluat Technol Sante & Prat Med, F-59000 Lille, France.
EM wajdi.dhifli@univ-lille.fr
RI ; Mcheick, Hamid/A-6250-2018
OI Assi, Ali Ch./0000-0003-3709-1519; Mcheick, Hamid/0000-0002-2589-8609;
   Dhifli, Wajdi/0000-0002-6580-8778
CR Abiteboul S., 1995, FDN DATABASES LOGICA
   Achichi Manel, 2016, Ingenierie des systemes d'information, V21, P11, DOI 10.3166/ISI.21.5-6.11-29
   Achichi M, 2019, J WEB SEMANT, V55, P108, DOI 10.1016/j.websem.2018.12.003
   Achichi M, 2016, LECT NOTES COMPUT SC, V10024, P3, DOI 10.1007/978-3-319-49004-5_1
   Aidan H, 2010, P WORKSH NEW FORMS R
   Al-Bakri M, 2015, AAAI CONF ARTIF INTE, P9
   Al-Bakri M, 2016, FRONT ARTIF INTEL AP, V285, P698, DOI 10.3233/978-1-61499-672-9-698
   [Anonymous], [No title captured]
   Araujo S, 2015, IEEE T KNOWL DATA EN, V27, P1397, DOI 10.1109/TKDE.2014.2365779
   Assi A, 2019, LECT NOTES ARTIF INT, V11606, P422, DOI [10.1007/978-3-030-22999-3_37, 10.1016/j.knosys.2019.104925]
   Assi A, 2019, IEEE INT CONF BIG DA, P1028, DOI 10.1109/BigData47090.2019.9006348
   Atencia M, 2012, P INT C KNOWL ENG KN, P144
   Atencia M, 2014, FRONT ARTIF INTEL AP, V263, P15, DOI 10.3233/978-1-61499-419-0-15
   Aydar M, 2015, P 10 INT WORKSH ONT, P224
   Baxter R., 2003, P ACM SIGKDD, V3, P25, DOI DOI 10.1007/978-3-319-11257-2
   Berti-Equille L, 2018, PROC VLDB ENDOW, V11, P880, DOI 10.14778/3204028.3204032
   Bhattacharya I., 2007, ACM T KNOWL DISCOV D, V1, P1, DOI [10.1145/1217299.1217304, DOI 10.1145/1217299.1217304]
   Brizan D.G., 2006, COMMUNICATIONS IIMA, V6, P41
   Chen MH, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1511
   Chen X., 2018, OPEN J BIG DATA, V4, P30
   Christen P., 2007, QUALITY MEASURES DAT, P127, DOI DOI 10.1007/978-3-540-44918-8_6
   Christen P, 2012, IEEE T KNOWL DATA EN, V24, P1537, DOI 10.1109/TKDE.2011.127
   Ciosici MR, 2019, ARXIV190400929
   Cruz IF, 2009, PROC VLDB ENDOW, V2, P1586, DOI 10.14778/1687553.1687598
   De Melo, 2013, P 27 AAAI C ART INT, V27
   De Vries T, 2011, ACM T KNOWL DISCOV D, V5, DOI 10.1145/1921632.1921635
   Dean J, 2004, USENIX ASSOCIATION PROCEEDINGS OF THE SIXTH SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION (OSDE '04), P137
   Dey A. K., 2000, THESIS
   Ding L, 2010, IEEE ENG MED BIO, P5145, DOI 10.1109/IEMBS.2010.5626178
   Doran P., 2007, P 16 ACM C INF KNOWL, P61, DOI DOI 10.1145/1321440.1321451
   Dorneles CF, 2011, KNOWL INF SYST, V27, P1, DOI 10.1007/s10115-010-0285-0
   Efthymiou V., 2019, ADV DATABASE TECHNOL, P373
   Elfeky MG, 2002, PROC INT CONF DATA, P17, DOI 10.1109/ICDE.2002.994694
   Elmagarmid AK, 2007, IEEE T KNOWL DATA EN, V19, P1, DOI 10.1109/TKDE.2007.250581
   Euzenat J, 2013, ONTOLOGY MATCHING, V18
   Farah H., 2017, P KNOWL CAPT C K CAP, P7, DOI [10.1145/3148011., DOI 10.1145/3148011]
   Faria D, 2013, LECT NOTES COMPUT SC, V8185, P527, DOI 10.1007/978-3-642-41030-7_38
   Ferrara A., 2008, P 3 INT C ONT MATCH, P37
   Ferraram A., 2013, SEMANTIC WEB ONTOLOG, P169
   Gal A, 2014, PROC VLDB ENDOW, V7, P1711, DOI 10.14778/2733004.2733068
   GALE D, 1962, AM MATH MON, V69, P9, DOI 10.2307/2312726
   Gao NN, 2018, PATTERN RECOGN, V75, P214, DOI 10.1016/j.patcog.2017.05.011
   Getoor L, 2012, PROC VLDB ENDOW, V5, P2018, DOI 10.14778/2367502.2367564
   Ghemmaz W, 2016, PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 2 (WEBIST), P274, DOI 10.5220/0005908102740279
   Ghemmaz W, 2015, 2015 5TH INTERNATIONAL CONFERENCE ON INFORMATION & COMMUNICATION TECHNOLOGY AND ACCESSIBILITY (ICTA)
   Glimm B, 2012, P LINK DAT WEB LDOW
   Golub GH., 2012, MATRIX COMPUTATIONS
   Guan SP, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON BIG KNOWLEDGE (IEEE ICBK 2017), P33, DOI 10.1109/ICBK.2017.15
   Gueret Christophe, 2012, The Semantic Web: Research and Applications. Proceedings 9th Extended Semantic Web Conference (ESWC 2012), P87, DOI 10.1007/978-3-642-30284-8_13
   Halpin H., 2011, P 2011 INT C DISC ME, P25
   Hao YC, 2016, COMM COM INF SC, V650, P3, DOI 10.1007/978-981-10-3168-7_1
   Hassan MM, 2015, P LINK DAT WEB LDOW
   Hassanzadeh O., 2009, PROC VLDB ENDOW, V2, P1282, DOI DOI 10.14778/1687627.1687771
   Haveliwala T.H., 2002, P 11 INT C WORLD WID, P517, DOI DOI 10.1145/511446.511513
   Hess RF, 2010, J VISION, V10, DOI 10.1167/10.13.22
   Hitzler P, 2012, OWL 2 WEB ONTOLOGY L, VSecond
   Hitzler P, 2017, OWL 2 WEB ONTOLOGY L
   Hu  W., 2011, P 20 INT C WORLD WID, P87
   Huhtala Y, 1999, COMPUT J, V42, P100, DOI 10.1093/comjnl/42.2.100
   Isele, 2011, P 14 INT WORKSH WEB
   Isele R, 2013, THESIS
   Isele R, 2012, PROC VLDB ENDOW, V5, P1638, DOI 10.14778/2350229.2350276
   Isele R, 2013, J WEB SEMANT, V23, P2, DOI 10.1016/j.websem.2013.06.001
   Jentzsch A, 2015, P 6 INT WORKSH CONS, V1426
   Jimenez-Ruiz E., 2011, LNCS, P273, DOI DOI 10.1007/978-3-642-25073-6_18
   Joachims T., 1999, SVM SUPPORT VECTOR M, V19, P4
   Karpathy Andrej, 2014, ADV NEURAL INFORM PR, P1889
   Kejriwal M, 2015, J WEB SEMANT, V35, P102, DOI 10.1016/j.websem.2015.07.002
   Khiat A., P 12 INT WORKSH ONT, P135
   Kitchenham B., 2004, PROCEDURES PERFORMIN, V33, P1
   Klyne G., 2004, RESOURCE DESCRIPTION
   Kohler H, 2015, PROC VLDB ENDOW, V8, P1118
   Kopcke H, 2010, DATA KNOWL ENG, V69, P197, DOI 10.1016/j.datak.2009.10.003
   Kolb L., 2012, DATENBANK SPEKTRUM, V13, P23
   KOZA JR, 1994, STAT COMPUT, V4, P87
   Lesnikova T, 2015, P 2015 ACM S DOC ENG, P39, DOI [10.1145/2682571.2797089, DOI 10.1145/2682571.2797089]
   Lesnikova T, 2014, P 3 ESWC WORKSH KNOW
   Li C, 2015, AAAI CONF ARTIF INTE, P4178
   Li Ding TF, 2010, P 2 WEB SCI C RAL NC
   Li JZ, 2013, KNOWL-BASED SYST, V50, P112, DOI 10.1016/j.knosys.2013.06.004
   Lyu XF, 2017, 2017 IEEE 5TH WORKSHOP ON WIDE BANDGAP POWER DEVICES AND APPLICATIONS (WIPDA), P158, DOI 10.1109/WiPDA.2017.8170540
   Melnik S, 2002, PROC INT CONF DATA, P117, DOI 10.1109/ICDE.2002.994702
   Meusel R, 2014, LECT NOTES COMPUT SC, V8796, P277, DOI 10.1007/978-3-319-11964-9_18
   Michelson M, 2006, AAAI, V21, P440
   Mikolov T., 2013, PROBLEM SOLVING ALGO, P3111
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Moher D, 2009, ANN INTERN MED, V151, P264, DOI 10.7326/0003-4819-151-4-200908180-00135
   MUNKRES J, 1957, J SOC IND APPL MATH, V5, P32, DOI 10.1137/0105003
   Navigli R, 2012, ARTIF INTELL, V193, P217, DOI 10.1016/j.artint.2012.07.001
   Nentwig M, 2017, SEMANT WEB, V8, P419, DOI 10.3233/SW-150210
   Ngomo Axel-Cyrille Ngonga, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P378, DOI 10.1007/978-3-642-35176-1_24
   Ngomo A.-C.N., 2011, P 6 INT WORKSH ONT M, P25
   Ngomo A.C.N., 2011, P IJCAI, P2312, DOI [DOI 10.5591/978-1-57735-516-8/IJCAI11-385, DOI 10.5555/2283696.2283783]
   Ngomo A.-C.N., 2012, P EXT SEM WEB C ESWC, P149, DOI DOI 10.1007/978-3-642-30284-8_17
   Ngomo ACN, 2014, LECT NOTES COMPUT SC, V8796, P17, DOI 10.1007/978-3-319-11964-9_2
   Nguyen K, 2012, JOINT INT SEM TECHN, P113
   Nguyen K, 2015, P INT SEM WEB C
   Nguyen K, 2012, P 7 INT C ONTOLOGY M, V946, P1
   Nguyen K, 2016, IEICE T INF SYST, VE99D, P1521, DOI 10.1587/transinf.2015EDP7392
   Nikolov A, 2010, LINK DAT WEB LDOW 20, V628
   Nikolov A, 2010, P LINK DAT WEB LDOW, V628
   Page L., 1999, PAGERANK CITATION RA
   Pan SJ, 2010, IEEE T KNOWL DATA EN, V22, P1345, DOI 10.1109/TKDE.2009.191
   Papadakis G., 2010, P 12 INT C INF INT W, P453
   Papadakis G, 2016, PROC VLDB ENDOW, V9, P684
   Papadakis G, 2015, PROC VLDB ENDOW, V9, P312
   Papadakis G, 2014, PROC VLDB ENDOW, V7, P1929, DOI 10.14778/2733085.2733098
   Papaleo L, 2014, LECT NOTES ARTIF INT, V8876, P373, DOI 10.1007/978-3-319-13704-9_29
   Pernelle N, 2013, P 2 INT WORKSH OP DA, P6
   Pernelle N, 2013, J WEB SEMANT, V23, P16, DOI 10.1016/j.websem.2013.07.001
   Pershina M, 2015, PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA, P1585, DOI 10.1109/BigData.2015.7363924
   Raad J, 2018, LECT NOTES COMPUT SC, V11136, P391, DOI 10.1007/978-3-030-00671-6_23
   Rowe M., 2009, P LINK DAT WEB LDOW
   sais F, 2007, THESIS, P11
   Sais F., 2010, P 5 INT WORKSH ONT M, P172
   Sais F, 2009, LECT NOTES COMPUT SC, V5480, P66, DOI 10.1007/978-3-642-00685-2_3
   Sandler, 2008, P LINK DAT WEB LDOW, P1
   Scharffe F., 2009, P IJCAI 2009 WORKSH
   Scharffe F, 2011, ABS11074502 CORR
   Schmachtenberg M, 2014, LECT NOTES COMPUT SC, V8796, P245, DOI 10.1007/978-3-319-11964-9_16
   Shao C, 2016, J COMPUT SCI TECH-CH, V31, P185, DOI 10.1007/s11390-016-1620-z
   Shu Rong, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P460, DOI 10.1007/978-3-642-35176-1_29
   Siangliulue P, 2015, PROCEEDINGS OF THE 2015 ACM INTERNATIONAL CONFERENCE ON COMPUTER-SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING (CSCW'15), P937, DOI 10.1145/2675133.2675239
   Simonini G, 2019, IEEE T KNOWL DATA EN, V31, P1208, DOI 10.1109/TKDE.2018.2852763
   Sismanis Y., 2006, VLDB, P691
   Sleeman J, 2010, P AAAI SPRING S LINK
   Song DZ, 2013, ACM J DATA INF QUAL, V4, DOI 10.1145/2435221.2435223
   Song DZ, 2011, LECT NOTES COMPUT SC, V7031, P649, DOI 10.1007/978-3-642-25073-6_41
   Soru T., 2014, P 10 INT C SEM SYST, P41, DOI DOI 10.1145/2660517.2660532
   Soru T, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1025, DOI 10.1145/2736277.2741642
   Spohr D, 2011, LECT NOTES COMPUT SC, V7031, P665, DOI 10.1007/978-3-642-25073-6_42
   Suchanek FM, 2011, PROC VLDB ENDOW, V5, P157, DOI 10.14778/2078331.2078332
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Symeonidou D, 2017, LECT NOTES COMPUT SC, V10587, P661, DOI 10.1007/978-3-319-68288-4_39
   Symeonidou D, 2014, LECT NOTES COMPUT SC, V8796, P33, DOI 10.1007/978-3-319-11964-9_3
   Taheri A., 2012, P JOINT INT SEM TECH, P129
   Tang Y, 2018, P 13 INT WORKSH ONT, P179
   Vieille L., 1986, P EXP DAT C
   Volz J, 2009, LECT NOTES COMPUT SC, V5823, P650, DOI 10.1007/978-3-642-04930-9_41
   Wartena C, 2018, 27 INT C COMP LING N, P2610
   Whang SE, 2013, IEEE T KNOWL DATA EN, V25, P1111, DOI 10.1109/TKDE.2012.43
   Wolger S., 2011, SURVEY DATA INTERLIN
   Wolger S, 2011, INTERLINKING D UNPUB
   Xiao C, 2011, ACM T DATABASE SYST, V36, DOI 10.1145/2000824.2000825
   Yamamoto Y, 2011, DATABASE, V2011, DOI DOI 10.1093/DATABASE/BAR013
   Zhu H, 2017, IEEE IND ELEC, P6425, DOI 10.1109/IECON.2017.8217119
NR 146
TC 3
Z9 3
U1 0
U2 10
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-0626
EI 1532-0634
J9 CONCURR COMP-PRACT E
JI Concurr. Comput.-Pract. Exp.
PD OCT 10
PY 2020
VL 32
IS 19
AR e5746
DI 10.1002/cpe.5746
EA APR 2020
PG 40
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NP5YU
UT WOS:000528843000001
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Ranganathan, V
   Barbosa, D
AF Ranganathan, Varun
   Barbosa, Denilson
TI HOPLoP: multi-hop link prediction over knowledge graph embeddings
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Link Prediction; Knowledge Graph Embeddings; Multi-hop reasoning
AB Large-scale Knowledge Graphs (KGs) support applications such as Web search and personal assistants and provide training data for numerous Natural Language Processing tasks. Nevertheless, building KGs with high accuracy and domain coverage remains difficult, and neither manual nor automatic efforts are up to par. Link Prediction (LP) is one of many tasks aimed at addressing this problem. Its goal is to find missing links between entities in the KG based on structural by exploiting regularities in the graph structure. Recent years have seen two approaches emerge: using KG embeddings, and modelling complex relations by exploiting correlations between individual links and longer paths connecting the same pair of entities. For the latter, state-of-the-art methods traverse the KG itself and are hampered both by incompleteness and skewed degree distributions found in most KGs, resulting in some entities being overly represented in the training set leading to poor generalization. We present HOPLoP: an efficient and effective multi-hop LP meta method that performs the equivalent to path traversals on the KG embedding space instead of the KG itself, marrying both ideas. We show how to train and tune our method with different underlying KG embeddings, and report on experiments on many benchmarks, showing both that HOPLoP improves each LP method on its own and that it consistently outperforms the previous state-of-the-art by a good margin. Finally, we describe a way to interpret paths generated by HOPLoP when used with TransE.
C1 [Ranganathan, Varun; Barbosa, Denilson] Univ Alberta, Dept Comp Sci, Edmonton, AB, Canada.
RP Barbosa, D (通讯作者)，Univ Alberta, Dept Comp Sci, Edmonton, AB, Canada.
EM vrangana@ualberta.ca; denilson@ualberta.ca
OI Ranganathan, Varun/0000-0001-6865-3217
FU Natural Science Research Council of Canada; Alberta Innovates Graduate
   Student Scholarship
FX This work was funded in part by grants from the Natural Science Research
   Council of Canada and an Alberta Innovates Graduate Student Scholarship.
CR Abadi M, 2016, ACM SIGPLAN NOTICES, V51, P1, DOI [10.1145/3022670.2976746, 10.1145/2951913.2976746]
   Adnan K, 2019, INT J ENG BUS MANAG, V11, DOI 10.1177/1847979019890771
   Aggarwal N., 2017, C INF KNOWL MAN, V17
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Bianchi F, 2020, STUD SEMANTIC WEB, V47, P49, DOI 10.3233/SSW200011
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Chami I., 2020, P 58 ANN M ASS COMP, P6901, DOI [10.18653/v1/2020.acl-main.617, DOI 10.18653/V1/2020.ACL-MAIN.617]
   Chen W., 2018, P 2018 C N AM CHAPT, V1, P1823, DOI [10.18653/v1/N18-1165, DOI 10.18653/V1/N18-1165]
   Chung J., 2014, 28 C NEARAL INFORM P, P1, DOI DOI 10.1109/IJCNN.2015.7280624
   CsuYji BC., 2001, APPROXIMATION ARTIFI, V24, P7
   Das R., 2018, ICLR
   Das R, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P132
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ding BY, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P110
   Gal Y., 2016, UNCERTAINTY DEEP LEA
   Gardner M., 2014, P 2014 C EMP METH NA, P397, DOI [10.3115/v1/D14-1044, DOI 10.3115/V1/D14-1044]
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Guu K., 2015, ARXIV150601094
   Haeb-Umbach R, 1994, IEEE T SPEECH AUDI P, V2, P353, DOI 10.1109/89.279287
   Halpern, 2017, REASONING UNCERTAINT
   Hamilton WL, 2018, ADV NEUR IN, V31
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hosmer DW, 2013, WILEY SER PROBAB ST, P89
   Ji H., 2011, P 49 ANN M ASS COMP, V1, P1148
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Jordan M. I., 1997, ADV PSYCHOL, V121, P471, DOI [DOI 10.1016/S0166-4115(97)80111-2, 10.1016/s0166-4115(97)80111-2]
   Kadlec R., 2017, P 2 WORKSH REPR LEAR, P69, DOI [10.18653/v1/W17-2609, DOI 10.18653/V1/W17-2609]
   King DB, 2015, ACS SYM SER, V1214, P1
   Kingma D. P., 2017, ARXIV
   Kolda TG, 2009, SIAM REV, V51, P455, DOI 10.1137/07070111X
   Koller D, 2009, PROBABILISTIC GRAPHI
   LACROIX T, 2018, P MACHINE LEARNING R, V80, P2863
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Lao N, 2010, MACH LEARN, V81, P53, DOI 10.1007/s10994-010-5205-8
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Lin X.V., 2018, ARXIV180810568
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Mahdisoltani F., 2015, CIDR 2015
   Meilicke C, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3137
   Mesquita F., 2019, P 2019 C EMP METH NA, P749
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Moschitti A., 2017, QUESTION ANSWERING K, P181
   Nayyeri M., 2019, ARXIV190807141
   Neelakantan A, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P156
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Pascanu R., 2013, INT C MACH LEARN, P1310
   Pinter Y., 2018, P 2018 C EMP METH NA, P1741, DOI [10.18653/v1/D18-1201, DOI 10.18653/V1/D18-1201]
   Raghavan S, 2003, PROC INT CONF DATA, P405, DOI 10.1109/ICDE.2003.1260809
   Ranganathan V, 2020, COMM COM INF SC, V1167, P483, DOI 10.1007/978-3-030-43823-4_39
   Ren H., 2020, INT C LEARN REPR
   REN H, 2020, NEURAL INFORM PROCES
   Robinson J.A., 2001, HDB AUTOMATED REASON
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Ruder S., 2016, ARXIV PREPRINT ARXIV
   RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Shen Y., 2018, NEURIPS, V31, P6786
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun Z., 2019, 7 INT C LEARN REPR
   Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
   Toutanova K, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1434
   Trouillon T, 2016, PR MACH LEARN RES, V48
   TUCKER LR, 1966, PSYCHOMETRIKA, V31, P279, DOI 10.1007/BF02289464
   Vashishth S., 2020, INT C LEARN REPR
   Vrandei D., 2012, P 21 INT C WORLD WID, P1063, DOI DOI 10.1145/2187980.2188242
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Yang B., 2015, 3 INT C LEARN REPR
NR 73
TC 0
Z9 0
U1 3
U2 9
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
PD MAR
PY 2022
VL 25
IS 2
SI SI
BP 1037
EP 1065
DI 10.1007/s11280-021-00972-6
EA DEC 2021
PG 29
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZK2AM
UT WOS:000729211000004
DA 2022-08-23
ER

PT J
AU Geng, YX
   Chen, JY
   Ye, ZQ
   Yuan, ZG
   Zhang, W
   Chen, HJ
AF Geng, Yuxia
   Chen, Jiaoyan
   Ye, Zhiquan
   Yuan, Zonggang
   Zhang, Wei
   Chen, Huajun
TI Explainable zero-shot learning via attentive graph convolutional network
   and knowledge graphs
SO SEMANTIC WEB
LA English
DT Article
DE Zero-shot learning; knowledge graph; explainable AI; knowledge-based
   learning; graph convolutional network
AB Zero-shot learning (ZSL) which aims to deal with new classes that have never appeared in the training data (i.e., unseen classes) has attracted massive research interests recently. Transferring of deep features learned from training classes (i.e., seen classes) are often used, but most current methods are black-box models without any explanations, especially textual explanations that are more acceptable to not only machine learning specialists but also common people without artificial intelligence expertise. In this paper, we focus on explainable ZSL, and present a knowledge graph (KG) based framework that can explain the transferability of features in ZSL in a human understandable manner. The framework has two modules: an attentive ZSL learner and an explanation generator. The former utilizes an Attentive Graph Convolutional Network (AGCN) to match class knowledge from WordNet with deep features learned from CNNs (i.e., encode inter-class relationship to predict classifiers), in which the features of unseen classes are transferred from seen classes to predict the samples of unseen classes, with impressive (important) seen classes detected, while the latter generates human understandable explanations for the transferability of features with class knowledge that are enriched by external KGs, including a domain-specific Attribute Graph and DBpedia. We evaluate our method on two benchmarks of animal recognition. Augmented by class knowledge from KGs, our framework generates promising explanations for the transferability of features, and at the same time improves the recognition accuracy.
C1 [Geng, Yuxia; Ye, Zhiquan; Chen, Huajun] Zhejiang Univ, Coll Comp Sci & Technol, 38 Zheda Rd, Hangzhou, Peoples R China.
   [Chen, Jiaoyan] Univ Oxford, Dept Comp Sci, 15 Parks Rd, Oxford OX1 3QD, England.
   [Yuan, Zonggang] Huawei Technol Co Ltd, NAIE Prod Dept, 101 Software Ave, Nanjing, Peoples R China.
   [Zhang, Wei] Alibaba Grp, Bussiness Platform Dept, 969 Wenyi West Rd, Hangzhou, Peoples R China.
   [Chen, Huajun] AZFT Joint Lab, Knowledge Engine Grp, 1818-2 Wenyi West Rd, Hangzhou, Peoples R China.
RP Chen, HJ (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technol, 38 Zheda Rd, Hangzhou, Peoples R China.; Chen, HJ (通讯作者)，AZFT Joint Lab, Knowledge Engine Grp, 1818-2 Wenyi West Rd, Hangzhou, Peoples R China.
EM gengyx@zju.edu.cn; jiaoyan.chen@cs.ox.ac.uk; yezq@zju.edu.cn;
   yuanzonggang@huawei.com; lantu.zw@alibaba-inc.com; huajunsir@zju.edu.cn
FU national key research program [2018YFB1402800, NSFC91846204/U19B2027];
   Alibaba-ZJU Frontier Technology Research Center; AIDA project (Alan
   Turing Institute); Samsung Research UK; Siemens AG; EPSRC project AnaLOG
   [EP/P025943/1]; EPSRC project OASIS [EP/S032347/1]; EPSRC project UK
   FIRES [EP/S019111/1]; SIRIUS Centre for Scalable Data Access (Research
   Council of Norway)
FX This work is funded by national key research program 2018YFB1402800,
   NSFC91846204/U19B2027, and supported by Alibaba-ZJU Frontier Technology
   Research Center. Jiaoyan Chen's contribution is supported by the AIDA
   project (Alan Turing Institute), the SIRIUS Centre for Scalable Data
   Access (Research Council of Norway), Samsung Research UK, Siemens AG,
   and the EPSRC projects AnaLOG (EP/P025943/1), OASIS (EP/S032347/1) and
   UK FIRES (EP/S019111/1).
CR Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170035.170072
   Agrawal R., 1994, P 20 INT C VER LARG, P487
   Akata Z, 2013, PROC CVPR IEEE, P819, DOI 10.1109/CVPR.2013.111
   Biran O., 2017, ICJAI 17 WORKSH EXPL, P8
   Biran O, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1461
   Caruana R, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1721, DOI 10.1145/2783258.2788613
   Changpinyo S, 2016, PROC CVPR IEEE, P5327, DOI 10.1109/CVPR.2016.575
   Chen H, 2019, HUMAN CENTRIC TRANSF
   Chen JY, 2020, KR2020: PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P850
   Chen JY, 2018, SIXTEENTH INTERNATIONAL CONFERENCE ON PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P349
   Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
   Farhadi A, 2009, PROC CVPR IEEE, P1778, DOI 10.1109/CVPRW.2009.5206772
   Frome Andrea, 2013, ADV NEURAL INFORM PR, P2121
   Geng Y., 2020, GENERATIVE ADVERSARI
   Guidotti R, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3236009
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Johnson M., 2017, T ASSOC COMPUT LING, V5, P339, DOI DOI 10.1162/TACL_A_00065
   Kampffmeyer M, 2019, PROC CVPR IEEE, P11479, DOI 10.1109/CVPR.2019.01175
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Kodirov E, 2017, PROC CVPR IEEE, P4447, DOI 10.1109/CVPR.2017.473
   Kulesza T., 2015, P ACM INT C INT US I, V15, P126, DOI [10.1145/2678025.2701399, DOI 10.1145/2678025.2701399]
   Lampert CH, 2014, IEEE T PATTERN ANAL, V36, P453, DOI 10.1109/TPAMI.2013.140
   Lampert CH, 2009, PROC CVPR IEEE, P951, DOI 10.1109/CVPRW.2009.5206594
   Landecker W, 2013, 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING (CIDM), P32, DOI 10.1109/CIDM.2013.6597214
   Larochelle Hugo, 2008, AAAI, P646
   Lecue F, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1779
   Lee CW, 2018, PROC CVPR IEEE, P1576, DOI 10.1109/CVPR.2018.00170
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Levy O, 2017, P 21 C COMP NAT LANG, P333, DOI [10.18653/v1/K17-1034, DOI 10.18653/V1/K17-1034]
   Li Qing, 2018, PROC C EMPIRICAL MET, P1338
   Lipton ZC, 2018, COMMUN ACM, V61, P36, DOI 10.1145/3233231
   Liu TL, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2365
   Logeswaran L, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3449
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   Mihalcea R., 2004, P 2004 C EMP METH NA, P404, DOI DOI 10.3115/1219044.1219064
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Miller G., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Morgado P, 2017, PROC CVPR IEEE, P2037, DOI 10.1109/CVPR.2017.220
   Nadeau D, 2007, LINGUIST INVESTIG, V30, P3
   Ngomo A.C.N., 2011, P IJCAI, P2312, DOI [DOI 10.5591/978-1-57735-516-8/IJCAI11-385, DOI 10.5555/2283696.2283783]
   Norouzi M., 2014, ICLR
   Palatucci M., 2009, P ADV NEUR INF PROC, P1410, DOI DOI 10.1007/116694874
   Pan SJ, 2010, IEEE T KNOWL DATA EN, V22, P1345, DOI 10.1109/TKDE.2009.191
   Paulheim H, 2018, CEUR WORKSHOP PROC
   Pennington J., 2014, EMNLP, P1532
   Pershina M, 2015, PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA, P1585, DOI 10.1109/BigData.2015.7363924
   Qin PD, 2020, AAAI CONF ARTIF INTE, V34, P8673
   Qin Y., 2017, P 26 INT JOINT C ART, P2627, DOI [10.24963/ijcai.2017/366, DOI 10.24963/IJCAI.2017/366]
   Ratcliffe D, 2014, LECT NOTES ARTIF INT, V8876, P429, DOI 10.1007/978-3-319-13704-9_33
   Ribeiro MT, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1135, DOI 10.1145/2939672.2939778
   Rijhwani S, 2019, AAAI CONF ARTIF INTE, P6924
   Rudin C, 2013, J MACH LEARN RES, V14, P3441
   Russakovsky O, 2015, INT J COMPUT VISION, V115, P211, DOI 10.1007/s11263-015-0816-y
   Selvaraju RR, 2018, LECT NOTES COMPUT SC, V11217, P540, DOI 10.1007/978-3-030-01261-8_32
   Srivastava M., 2017, 171205972 CORR
   Tiddi Ilaria, 2014, The Semantic Web: Trends and Challenges. 11th International Conference (ESWC 2014). Proceedings: LNCS 8465, P333, DOI 10.1007/978-3-319-07443-6_23
   Velickovic P., 2018, P INT C LEARN REPR A
   Wang XL, 2018, PROC CVPR IEEE, P6857, DOI 10.1109/CVPR.2018.00717
   Weiss Karl, 2016, Journal of Big Data, V3, DOI 10.1186/s40537-016-0043-6
   Weld DS, 2019, COMMUN ACM, V62, P70, DOI 10.1145/3282486
   Welinder P., 2010, CNSTR2010001 CAL I T
   Wu Yuting, 2019, EMNLP IJCNLP, P240
   Xian YQ, 2019, IEEE T PATTERN ANAL, V41, P2251, DOI 10.1109/TPAMI.2018.2857768
   Xian YQ, 2016, PROC CVPR IEEE, P69, DOI 10.1109/CVPR.2016.15
   Yang Z, 2016, P 2016 C N AM CHAPTE, P1480
   Ye ZQ, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3014
   Yosinski J, 2014, ADV NEUR IN, V27
   Zhang J., 2019, P NAACL HLT, P1031
   Zhang L, 2017, PROC CVPR IEEE, P3010, DOI 10.1109/CVPR.2017.321
   Zhang Ningyu, 2019, LONG SHORT PAPERS, P3016, DOI [10.18653/v1/n19-1306, DOI 10.18653/V1/N19-1306]
   Zhang QH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5429
NR 72
TC 0
Z9 0
U1 5
U2 18
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2021
VL 12
IS 5
BP 741
EP 765
DI 10.3233/SW-210435
PG 25
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UK5OS
UT WOS:000692019600002
DA 2022-08-23
ER

PT J
AU Zhu, BB
   Bao, T
   Liu, L
   Han, JY
   Wang, JY
   Peng, T
AF Zhu, Beibei
   Bao, Tie
   Liu, Lu
   Han, Jiayu
   Wang, Junyi
   Peng, Tao
TI Cross-lingual knowledge graph entity alignment based on relation
   awareness and attribute involvement
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Entity alignment; Knowledge graph; Representation learning; Relation;
   Attribute; Textual information
ID WEB
AB Entity alignment is an effective means of matching entities from various knowledge graphs (KGs) that represent the equivalent real-world object. With the development of representation learning, recent entity alignment methods learn entity structure representation by embedding KGs into a low-dimensional vector space, and then entity alignment relies on the distance between entity vectors. In addition to the graph structures, relations and attributes are also critical to entity alignment. However, most existing approaches ignore the helpful features included in relations and attributes. Therefore, this paper presents a new solution RAEA (Relation Awareness and Attribute Involvement for Entity Alignment), which includes relation and attribute features. Relation representation is incorporated into entity representation by Dual-Primal Graph CNN (DPGCNN), which alternates convolution-like operations on the original graph and its dual graph. Structure representation and attribute representation are learned by graph convolutional networks (GCNs). To further enrich the entity embedding, we integrate the textual information of the entity into the entity graph embedding. Moreover, we fine-tune the entity similarity matrix by integrating fine-grained features. Experimental results on three benchmark datasets from real-world KGs show that our approach has superior performance to other representative entity alignment approaches in most cases.
C1 [Zhu, Beibei; Bao, Tie; Wang, Junyi; Peng, Tao] Jilin Univ, Coll Comp Sci & Technol, Qianjin St, Changchun 130012, Jilin, Peoples R China.
   [Liu, Lu] Jilin Univ, Coll Software, Qianjin St, Changchun 130012, Jilin, Peoples R China.
   [Liu, Lu; Peng, Tao] Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Qianjin St, Changchun 130012, Jilin, Peoples R China.
   [Han, Jiayu] Univ Washington, Dept Linguist, Seattle, WA 98195 USA.
RP Peng, T (通讯作者)，Jilin Univ, Coll Comp Sci & Technol, Qianjin St, Changchun 130012, Jilin, Peoples R China.; Peng, T (通讯作者)，Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Qianjin St, Changchun 130012, Jilin, Peoples R China.
EM zhubb20@mails.jlu.edu.cn; baotie@jlu.edu.cn; liulu@jlu.edu.cn;
   jyhan126@uw.edu; wangjy20@mails.jlu.edu.cn; tpeng@jlu.edu.cn
FU National Natural Science Foundation of China [61872163, 61806084]; Jilin
   Province Key Scientific and Technological Research and Development
   Project [20210201131GX]; Jilin Provincial Education Department project
   [JJKH20190160KJ]
FX Thanks to all the authors for their hard work. This work is supported by
   the National Natural Science Foundation of China under grant No.61872163
   and 61806084, Jilin Province Key Scientific and Technological Research
   and Development Project under grant No.20210201131GX, and Jilin
   Provincial Education Department project under grant No.JJKH20190160KJ.
CR Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bordes A., 2013, P 26 INT C NEUR INF
   Bruna J., 2014, 2 INT C LEARN REPR
   Cao YX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1452
   Chen B, 2020, LECT NOTES ARTIF INT, V12084, P845, DOI 10.1007/978-3-030-47426-3_65
   Chen L, 2021, APPL INTELL, V51, P8896, DOI 10.1007/s10489-021-02400-8
   Chen MH, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1511
   Chen W, 2021, 20 CHINA NATL C, P13
   Daggy J, 2014, STAT MED, V33, P4250, DOI 10.1002/sim.6230
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   FELLEGI IP, 1969, J AM STAT ASSOC, V64, P1183, DOI 10.2307/2286061
   Ghorbani M, 2019, PROCEEDINGS OF THE 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2019), P208, DOI 10.1145/3341161.3342942
   Guo H, 2021, NEUROCOMPUTING, V461, P598, DOI 10.1016/j.neucom.2021.03.132
   Hoffmann R., 2011, ACL
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Jia Chen, 2020, Database Systems for Advanced Applications. 25th International Conference, DASFAA 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12112), P452, DOI 10.1007/978-3-030-59410-7_31
   Jiang S, 2021, 18 INT C, P24
   Jiang TT, 2019, LECT NOTES ARTIF INT, V11670, P162, DOI 10.1007/978-3-030-29908-8_13
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kipf T.N., 2016, ARXIV PREPRINT ARXIV
   Kotnis B, 2017, ARXIV 170806816 1708
   Lacoste-Julien S, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P572
   Li Chengjiang, 2019, P 2019 C EMPIRICAL M, P2723
   Lin L, 2020, APPL INTELL, V50, P2867, DOI 10.1007/s10489-020-01673-9
   Lin XX, 2019, IEEE DATA MINING, P429, DOI 10.1109/ICDM.2019.00053
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lu GY, 2022, J TRAVEL MED, V29, DOI 10.1093/jtm/taab156
   Mahdisoltani F., 2015, CIDR 2015
   Mao X, 2021, 2021 C EMPIR METHODS
   Mishra S, 2017, APPL INTELL, V47, P197, DOI 10.1007/s10489-016-0874-z
   Monti F, 2018, ARXIV 180600770
   Moussallem D, 2018, J WEB SEMANT, V51, P1, DOI 10.1016/j.websem.2018.07.001
   Munne Rumana Ferdous, 2020, Hybrid Artificial Intelligent Systems. 15th International Conference, HAIS 2020. Proceedings. Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science (LNAI 12344), P107, DOI 10.1007/978-3-030-61705-9_10
   Navigli R, 2012, ARTIF INTELL, V193, P217, DOI 10.1016/j.artint.2012.07.001
   Ngomo ACN, 2011, 22 INT JT C ART INT
   Niu X, 2012, 21 ACM INT C INF KNO
   Ong TC, 2014, J BIOMED INFORM, V52, P43, DOI 10.1016/j.jbi.2014.01.016
   Pang N., 2019, 16 EXT SEM WEB C 201, V2377, P41
   Papadakis G, 2015, PROC VLDB ENDOW, V9, P312
   Raimond Y, 2008, WWW 2008 WORKSH LINK
   Shi Xiaofei, 2019, EMNLP IJCNLP, P813
   Song X, 2021, KNOWLEDGE SCI ENG MA
   Srivastava RK, 2015, ARXIV 150500387
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   Sun ZQ, 2020, AAAI CONF ARTIF INTE, V34, P222
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Tam NT, 2021, 37 IEEE INT C DATA E
   Tang XB, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3174
   Velickovic P., 2018, P INT C LEARN REPR A
   Volz J, 2009, LECT NOTES COMPUT SC, V5823, P650, DOI 10.1007/978-3-642-04930-9_41
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Wu Y, 2021, 30 ACM INT C INF KNO
   Wu Y, 2020, 58 ANN M ASS COMPUTA, P5
   Wu YT, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5278
   Wu Yuting, 2019, EMNLP IJCNLP, P240
   Xu K, 2020, AAAI CONF ARTIF INTE, V34, P9354
   Yang Hsiu-Wei, 2019, P 2019 C EMP METH NA, P4430
   Zhang Y, 2020, 33 ADV NEURAL INF PR
   Zhang YY, 2018, AAAI CONF ARTIF INTE, P6069
   Zhu QN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1943
   Zhu R., 2021, P PACIFIC ASIA C KNO, DOI [10.1007/978-3-030-75762-5_40, DOI 10.1007/978-3-030-75762-5_40]
   Zhu Y, 2020, ARXIV 201208128
NR 65
TC 0
Z9 0
U1 2
U2 2
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
DI 10.1007/s10489-022-03797-6
EA JUL 2022
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2R8PN
UT WOS:000821367700002
DA 2022-08-23
ER

PT J
AU Tian, H
   Chen, J
AF Tian, Hua
   Chen, Jie
TI A bibliometric analysis on global eHealth
SO DIGITAL HEALTH
LA English
DT Article
DE eHealth; bibliometrics; international cooperation; hotspots; frontier
ID IMPACT
AB Background The current coronavirus disease 2019 pandemic highlights the potential of eHealth. Drawing the knowledge map of eHealth research through data mining and visual analysis technology was helpful to systematically present the research status and future trends of global academic circles. Methods Based on the web of Science Core Collection (SCIE/SSCI) database, using bibliometric theory and visual analysis technology, this work analyzed the global eHealth research publications from 2000 to 2021, and introduced the interdisciplinary characteristics, hot topics and future trends in this field. Results A total of 10188 authors, 891 journals, 3586 institutions, 98 countries using 12 languages had conducted eHealth research in the world. The United States, the Netherlands, Australia and the United Kingdom were the main forces and international cooperation. However, the international co-operation between Eastern and Western countries was still relatively few. The frontier of global eHealth research mainly focused on #0eHealth innovation, #1physical activity, #2generalised anxiety disorder, #3lightweight authentication protocol, #4 eHealth information, #5technology readiness, #6 ehealth literacy scale, #7family carer, #8citance analysis, #9 guiding patient. Clusters #3 lightweight authentication protocol and #9 guiding patient were the latest clusters, indicating the research trend and direction of eHealth in the future. Conclusions Cooperation network framework at the regional, national and global levels and the cooperation of multidisciplinary teams with complementary backgrounds and expertise were needed to realize the in-depth popularization and application of eHealth knowledge. Interdisciplinary international cooperation should be the trend of eHealth research in the future.
C1 [Tian, Hua] Xinyang Normal Univ, Coll Life Sci, 237 Nanhu Rd, Xinyang, Peoples R China.
   [Chen, Jie] Xinyang Normal Univ, Sch Marxism, Xinyang, Peoples R China.
RP Tian, H (通讯作者)，Xinyang Normal Univ, Coll Life Sci, 237 Nanhu Rd, Xinyang, Peoples R China.
EM tianhua@xynu.edu.cn
FU Key project of the Ministry of education in the 13th 5-year plan of
   National Educational Science [DEA190361]
FX The authors disclosed receipt of the following financial support for the
   research, authorship, and/or publication of this article: This work was
   supported by the Key project of the Ministry of education in the 13th
   5-year plan of National Educational Science (grant number DEA190361).
CR Alam K, 2019, INT J MED INFORM, V131, DOI 10.1016/j.ijmedinf.2019.103960
   Ali N, 2022, J INF SCI, DOI 10.1177/01655515211068169
   Alkhaldi B, 2014, STUD HEALTH TECHNOL, V205, P875, DOI 10.3233/978-1-61499-432-9-875
   Ammenwerth E, 2020, METHOD INFORM MED, V59, pe46, DOI 10.1055/s-0040-1715796
   Bloch F, 2015, MED TEACH, V37, P500, DOI 10.3109/0142159X.2014.970993
   Bonten TN, 2020, J MED INTERNET RES, V22, DOI 10.2196/17774
   Brieux HFM, 2015, STUD HEALTH TECHNOL, V216, P434, DOI 10.3233/978-1-61499-564-7-434
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Chen HB, 2017, J CLEAN PROD, V140, P840, DOI 10.1016/j.jclepro.2015.11.085
   Cremers HP, 2021, HEALTH SERV MANAG RE, V34, P269, DOI 10.1177/0951484821994421
   Daher J, 2017, BMJ OPEN, V7, DOI 10.1136/bmjopen-2017-017604
   De Maria M., 2021, J ADV NURS, V2, DOI [10.1111/jan.15115, DOI 10.1111/JAN.15115]
   Dias D, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18082414
   Dimitri P, 2021, J MED INTERNET RES, V23, DOI 10.2196/27446
   Drosatos G, 2020, J TELEMED TELECARE, V26, P414, DOI 10.1177/1357633X19846252
   Elliott A, 2022, OBES REV, V23, DOI 10.1111/obr.13360
   Ena J, 2020, REV CLIN ESP, V220, P501, DOI 10.1016/j.rce.2020.06.002
   Eysenbach G, 2001, J MED INTERNET RES, V3, DOI 10.2196/jmir.3.2.e20
   Fanta GB, 2018, S AFR J IND ENG, V29, P132, DOI 10.7166/29-3-2055
   Hennemann S, 2017, J HEALTH COMMUN, V22, P274, DOI 10.1080/10810730.2017.1284286
   Hilberts S, 2014, ADV HEALTH SCI EDUC, V19, P115, DOI 10.1007/s10459-013-9442-z
   Kassam-Adams N, 2015, J MED INTERNET RES, V17, DOI 10.2196/jmir.3811
   Kim H, 2018, JMIR MHEALTH UHEALTH, V6, DOI 10.2196/mhealth.9988
   Knitza J, 2021, ARTHRITIS RES THER, V23, DOI 10.1186/s13075-021-02616-6
   Kontos Emily, 2014, J Med Internet Res, V16, pe172, DOI 10.2196/jmir.3117
   Kraaijkamp JJM, 2021, J MED INTERNET RES, V23, DOI 10.2196/24015
   Li J, 2020, ENVIRON HEALTH PREV, V25, DOI 10.1186/s12199-020-00896-z
   Lu CC, 2019, FRONT PUBLIC HEALTH, V7, DOI 10.3389/fpubh.2019.00384
   Lu XY, 2021, J MED INTERNET RES, V23, DOI 10.2196/14908
   [骆靖阳 Luo Jingyang], 2021, [食品科学, Food Science], V42, P278
   Mileski M, 2017, JMIR MED INF, V5, DOI 10.2196/medinform.6603
   Mousavi SM, 2021, HEALTH RES POLICY SY, V19, DOI 10.1186/s12961-021-00795-x
   Nohr C, 2018, STUD HEALTH TECHNOL, V247, P86, DOI 10.3233/978-1-61499-852-5-86
   Norman C, 2011, J MED INTERNET RES, V13, DOI 10.2196/jmir.2035
   Norman CD, 2006, J MED INTERNET RES, V8, DOI 10.2196/jmir.8.2.e9
   Oh H, 2005, J MED INTERNET RES, V7
   Pagliari Claudia, 2005, J Med Internet Res, V7, pe9, DOI 10.2196/jmir.7.1.e9
   Park H, 2016, CIN-COMPUT INFORM NU, V34, P71, DOI 10.1097/CIN.0000000000000205
   Paterson C, 2020, SEMIN ONCOL NURS, V36, DOI 10.1016/j.soncn.2020.151090
   Qi SJ, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0254988
   Scott RE, 2013, J MED INTERNET RES, V15, P11, DOI 10.2196/jmir.2250
   Sharifi A, 2021, SUSTAIN SCI, V16, P1079, DOI 10.1007/s11625-020-00853-3
   Shaw T, 2017, J MED INTERNET RES, V19, DOI 10.2196/jmir.8106
   Shen C, 2021, FOOD SCI NUTR, V9, P2316, DOI 10.1002/fsn3.2220
   Showell C, 2012, STUD HEALTH TECHNOL, V180, P881, DOI 10.3233/978-1-61499-101-4-881
   Sin J, 2019, DIGIT HEALTH, V5, DOI 10.1177/2055207619871148
   Tian Hua, 2021, Transactions of the Chinese Society of Agricultural Engineering, V37, P324, DOI 10.11975/j.issn.1002-6819.2021.05.038
   Tsai CH, 2019, STUD HEALTH TECHNOL, V264, P954, DOI 10.3233/SHTI190365
   Uribe-Toril J, 2021, HEALTHCARE-BASEL, V9, DOI 10.3390/healthcare9020108
   van Rooij T, 2016, PERS MED, V13, P57, DOI 10.2217/pme.15.40
   Verkleij M, 2021, INTERNET INTERV, V24, DOI 10.1016/j.invent.2021.100372
   Villumsen S, 2019, STUD HEALTH TECHNOL, V264, P1075, DOI 10.3233/SHTI190390
   Virtanen L, 2021, INT J MED INFORM, V149, DOI 10.1016/j.ijmedinf.2021.104432
   Vlooswijk C, 2021, CANCERS, V13, DOI 10.3390/cancers13246308
   Wang C., 2022, HEALTHCARE, P66
   Wannheden C, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13095221
   Young MD, 2021, J CONSULT CLIN PSYCH, V89, P682, DOI 10.1037/ccp0000671
   Yu CH, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0254157
   [袁媛 Yuan Yuan], 2020, [农业工程学报, Transactions of the Chinese Society of Agricultural Engineering], V36, P271
NR 60
TC 1
Z9 1
U1 23
U2 23
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 2055-2076
J9 DIGIT HEALTH
JI Digit. Health
PD APR
PY 2022
VL 8
AR 20552076221091352
DI 10.1177/20552076221091352
PG 14
WC Health Care Sciences & Services; Health Policy & Services; Public,
   Environmental & Occupational Health; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Health Care Sciences & Services; Public, Environmental & Occupational
   Health; Medical Informatics
GA 0M1ND
UT WOS:000781928000001
PM 35433016
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Noueihed, H
   Harb, H
   Tekli, J
AF Noueihed, Hamza
   Harb, Heba
   Tekli, Joe
TI Knowledge-based virtual outdoor weather event simulator using unity 3D
SO JOURNAL OF SUPERCOMPUTING
LA English
DT Article
DE Simulator modeling; Knowledge graphs; Unity 3D; Virtual sensors;
   Semantic sensor network; Weather events
AB During the past two decades, 3D simulation models have gained importance in the development of software solutions that aim to mimic real-world events and phenomena with increasing levels of accuracy and detail. In this context, knowledge representation and processing have recently shown a significant contribution to the simulation modeling domain, where knowledge graphs have been used in different fields to build knowledge representations for multiple purposes. In this paper, we introduce VOWES, a Virtual Outdoor Weather Event Simulator to replicate and measure outdoor weather events in vivid 3D visualizations. We design and implement an integrated knowledge graph (KG) representation for VOWES, by creating two constituent KGs: (i) Weather KG describing weather data and events, and (ii) Simulator KG describing 3D simulation components and properties, and connecting them with the (iii) Semantic Sensor Network (SSN) KG to form an integrated structure serving as the knowledge backbone of the VOWES simulation environment. We make use of the Unity 3D engine to build and design the simulator environment and its virtual sensors, and integrate the Mapbox SDK and the WeatherStack API for realistic real-world weather mapping. We have conducted qualitative evaluations involving 13 expert and 30 non-expert testers, to assess the quality of VOWES' KGs and its simulation environment. Results show that more than 80% of the testers gave a combined quality score >= 3 out of 4 on most evaluation criteria. We have also conducted performance evaluations to test VOWES loading, execution, and data search time, among other features. Results show that most operations require almost instantaneous or linear time, where search, refresh, and export operations share almost identical performance levels, with execution time increasing by approximately 179 mu s for every added game object. This highlights the simulation tool's time performance in running large simulation projects, and its ability to simulate complex weather environments with large numbers of sensors and weather phenomena.
C1 [Noueihed, Hamza; Harb, Heba; Tekli, Joe] Lebanese Amer Univ LAU, Sch Engn, Dept Elect & Comp Engn, Byblos 36, Lebanon.
RP Tekli, J (通讯作者)，Lebanese Amer Univ LAU, Sch Engn, Dept Elect & Comp Engn, Byblos 36, Lebanon.
EM hamza.noueihed@lau.edu; heba.harb@lau.edu; joe.tekli@lau.edu.lb
RI Tekli, Joe/AAE-4591-2020
OI Tekli, Joe/0000-0003-3441-7974
CR Abbasi M, 2011, THESIS
   Abboud R, 2020, SOFT COMPUT, V24, P9875, DOI 10.1007/s00500-019-04503-4
   Abebe MA, 2020, KNOWL-BASED SYST, V188, DOI 10.1016/j.knosys.2019.06.025
   Albrecht, 2008, T GIS, V12, P409, DOI [DOI 10.1111/J.1467-9671.2008, 10.1111/j.1467-9671.2008.01108.x]
   Angsuchotmetee C, 2020, FUTURE GENER COMP SY, V108, P1140, DOI 10.1016/j.future.2018.01.044
   Avancha S, 2004, PROCEEDINGS OF MOBIQUITOUS 2004, P194
   Buyuksalih I, 2017, ISPRS ANN PHOTO REM, V4-4, P161, DOI 10.5194/isprs-annals-IV-4-W4-161-2017
   Chen M, 2012, ANIMACIES BIOPOLITIC, P1, DOI DOI 10.1145/2407746.2407775
   Chun S, 2020, J SUPERCOMPUT, V76, P8058, DOI 10.1007/s11227-018-2672-3
   Devaraju A., 2012, INT J SENS WIREL COM, V2, P14, DOI [10.2174/2210327911202010014, DOI 10.2174/2210327911202010014]
   Durand N, 2007, PROC INT C TOOLS ART, P472, DOI 10.1109/ICTAI.2007.111
   Ebrahimi D, 2018, IEEE GLOB COMM CONF
   Ebrahimi D, 2019, IEEE INTERNET THINGS, V6, P1893, DOI 10.1109/JIOT.2018.2878834
   Fares M, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON COGNITIVE COMPUTING (IEEE ICCC 2019), P28, DOI 10.1109/ICCC.2019.00008
   Fares M, 2019, KNOWL-BASED SYST, V165, P432, DOI 10.1016/j.knosys.2018.12.017
   Fuentes S, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20133618
   Garcia-Dorado I, 2017, ACM T GRAPHIC, V36, DOI 10.1145/2999534
   Gomez M, 2008, LECT NOTES ARTIF INT, V5268, P347, DOI 10.1007/978-3-540-87696-0_30
   Google Developers, 2008, KEYH MARK LANG KML
   Hewage P, 2021, PATTERN ANAL APPL, V24, P343, DOI 10.1007/s10044-020-00898-1
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Jain Vaibhav, 2016, Applied Mechanics and Materials, V824, P740, DOI 10.4028/www.scientific.net/AMM.824.740
   Jin W, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18020629
   Li X, 2019, INT GEOSCI REMOTE SE, P5148, DOI 10.1109/IGARSS.2019.8899241
   Liangliang Sun, 2020, International Journal of Simulation and Process Modelling, V15, P213
   Maguitman A., 2005, P 14 INT C WORLD WID, P107
   Manola F., 2004, RESOURCE DESCRIPTION
   Mansour Elio, 2020, Q2SWinet '20: Proceedings of the 16th Symposium on QoS and Security for Wireless and Mobile Networks, P75, DOI 10.1145/3416013.3426451
   Mansour E, 2019, INT DATABASE ENG APP, P53, DOI 10.1145/3331076.3331102
   Mao QR, 2021, FUTURE GENER COMP SY, V115, P76, DOI 10.1016/j.future.2020.07.041
   MapBox, 2021, MOB MAPS SDK V10
   Mezaris V, 2003, 2003 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 2, PROCEEDINGS, P511
   Miller G., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235
   Moreno R, 2020, J SUPERCOMPUT, V76, P9629, DOI 10.1007/s11227-020-03219-9
   Open Geospatial Consortium, 2009, GEOGR MARK UP LANG G
   Oses N, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20216381
   Poveda-Villalon M, 2018, P 2 URSI AT RASC MAY, P1
   Rana RK, 2010, PROCEEDINGS OF THE 9TH ACM/IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS, P105, DOI 10.1145/1791212.1791226
   Raskin RG, 2005, COMPUT GEOSCI-UK, V31, P1119, DOI 10.1016/j.cageo.2004.12.004
   Richardson R., 1995, P BCS IRSG C INF RET
   Roussey C, 2020, SEMANT WEB, V11, P581, DOI 10.3233/SW-200375
   Sagar S., 2018, INT SEM WEB C ISWC 1, P163
   Salloum G, 2021, INT J HUM-COMPUT ST, V151, DOI 10.1016/j.ijhcs.2021.102610
   Sanders Brian, 2020, Virtual, Augmented and Mixed Reality. Design and Interaction. 12th International Conference, VAMR 2020 Held as Part of the 22nd HCI International Conference, HCII 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12190), P325, DOI 10.1007/978-3-030-49695-1_22
   Schlenoff Craig, 2013, 2013 IEEE International Symposium on Robotic and Sensors Environments (ROSE 2013), P96, DOI 10.1109/ROSE.2013.6698425
   Shahid A, 2020, J SUPERCOMPUT, V76, P1695, DOI 10.1007/s11227-019-03009-y
   Shimizu C, 2020, ABS200912285 CORR
   Taddesse F. G., 2009, LNCS, P44
   Tekli J, 2018, DATA KNOWL ENG, V117, P133, DOI 10.1016/j.datak.2018.07.007
   Ticona-Herrera R, 2015, LECT NOTES COMPUT SC, V9381, P261, DOI 10.1007/978-3-319-25264-3_19
   Unity, 2020, PART SYST UN DOC
   Unity, 2020, ARCH ENG CONSTR
   W3C, 2020, EXT SEM SENS NETW ON
   W3C, 2017, OWL2 XML SYNT W3C WE
   Wang RX, 2020, ADV ENG SOFTW, V148, DOI 10.1016/j.advengsoft.2020.102838
   Wang YZ, 2018, J SUPERCOMPUT, V74, P3999, DOI 10.1007/s11227-018-2406-6
   WeatherStack, 2021, REAL TIM HIST WORLD
   Xinxin Feng, 2015, Applied Mechanics and Materials, V741, P209, DOI 10.4028/www.scientific.net/AMM.741.209
   Zhang DH, 2019, IEEE ACCESS, V7, P57678, DOI 10.1109/ACCESS.2019.2912627
   Zhong, 2008, AAAI SPRING S SEM SC, P114
   Zigon B, 2018, J SUPERCOMPUT, V74, P37, DOI 10.1007/s11227-017-2103-x
NR 61
TC 1
Z9 1
U1 7
U2 9
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0920-8542
EI 1573-0484
J9 J SUPERCOMPUT
JI J. Supercomput.
PD MAY
PY 2022
VL 78
IS 8
BP 10620
EP 10655
DI 10.1007/s11227-021-04212-6
EA JAN 2022
PG 36
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1A5SO
UT WOS:000746373900006
DA 2022-08-23
ER

PT J
AU Yang, HS
   Fan, YY
   Lv, GY
   Liu, SY
   Guo, Z
AF Yang, Hansen
   Fan, Yangyu
   Lv, Guoyun
   Liu, Shiya
   Guo, Zhe
TI Exploiting emotional concepts for image emotion recognition
SO VISUAL COMPUTER
LA English
DT Article; Early Access
DE Image emotion recognition; Visual-semantic embedding; Knowledge graph;
   Emotion perception
ID CLASSIFICATION
AB With the increasing number of users express their emotions via images on social media, image emotion recognition attracts much attention of researchers. Different from conventional computer vision tasks, image emotion recognition is inherently more challenging for the ambiguity and subjectivity of emotion. Existing methods are limited to learn a direct mapping from image feature to emotion. However, emotion cognition mechanism in psychology demonstrates that human beings perceive emotion in a stepwise way. Therefore, we propose a novel image emotion recognition method that leverages emotional concepts as intermediary to bridge image and emotion. Specifically, we organize the relationship between concept and emotion in the form of knowledge graph. The relation between image and emotion is explored in the semantic embedding space where the knowledge is encoded into. Then, based on the hierarchical relation of emotions, we propose a multi-task learning deep model to recognize image emotion from visual perspective. Finally, a fusion strategy is proposed to merge the results of both visual-semantic stream and visual stream. Extensive experimental results show that our method outperforms state-of-the-art methods on two public image emotion datasets.
C1 [Yang, Hansen; Fan, Yangyu; Lv, Guoyun; Guo, Zhe] Northwestern Polytech Univ, Sch Elect & Informat, Xian, Peoples R China.
   [Liu, Shiya] Content Prod Ctr Virtual Real, Beijing, Peoples R China.
RP Lv, GY (通讯作者)，Northwestern Polytech Univ, Sch Elect & Informat, Xian, Peoples R China.
EM mikeyhs@126.com; fan_yangyu@nwpu.edu.cn; lvguoyun102@163.com;
   shiya.liu@cpcvr.org.cn; guozhe@nwpu.edu.cn
FU National Natural Science Foundation of China [62071384]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 62071384.
CR Akata Z, 2015, PROC CVPR IEEE, P2927, DOI 10.1109/CVPR.2015.7298911
   Ali AR, 2017, IEEE WINT CONF APPL, P679, DOI 10.1109/WACV.2017.81
   Argyriou A., 2006, P 20 ANN C NEUR INF, P1
   BorthD Ji R, 2013, P 21 ACM INT C MULT, P223, DOI DOI 10.1145/2502081.2502282
   Campos V, 2017, IMAGE VISION COMPUT, V65, P15, DOI 10.1016/j.imavis.2017.01.011
   Caruana R, 1997, MACH LEARN, V28, P41, DOI 10.1023/A:1007379606734
   Chen T.., 2014, COMPUT SCI
   Dellandrea E., 2010, P IEEE INT WORKSH CO, P1
   Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
   Goi MT, 2018, J MARK HIGH EDUC, V28, P90, DOI 10.1080/08841241.2018.1425231
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   He XY, 2018, NEUROCOMPUTING, V291, P187, DOI 10.1016/j.neucom.2018.02.073
   Hoffer E, 2015, LECT NOTES COMPUT SC, V9370, P84, DOI 10.1007/978-3-319-24261-3_7
   Huang Y., 2017, IEEE T PATTERN ANAL, V42
   Kao YY, 2016, SIGNAL PROCESS-IMAGE, V47, P500, DOI 10.1016/j.image.2016.05.004
   Krizhevsky Alex, 2017, Communications of the ACM, V60, P84, DOI 10.1145/3065386
   Li LD, 2019, IEEE INT CON MULTI, P430, DOI 10.1109/ICME.2019.00081
   Li ZH, 2018, MULTIMED TOOLS APPL, V77, P1115, DOI 10.1007/s11042-016-4310-5
   Lim L, 2020, IEEE IMAGE PROC, P1886, DOI 10.1109/ICIP40778.2020.9191258
   Lin, 2014, P IEEE INT C MULT EX, P1, DOI DOI 10.1109/ICME.2014.6890213
   Lin LJ, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P847
   Liu X, 2019, J VIS COMMUN IMAGE R, V58, P576, DOI 10.1016/j.jvcir.2018.12.032
   Lu Xin, 2012, Proc ACM Int Conf Multimed, V2012, P229, DOI 10.1145/2393347.2393384
   Machajdik J., 2010, P 18 ACM INT C MULT, P83
   Mehrabian A., 1974, APPROACH ENV PSYCHOL
   Mikels JA, 2005, BEHAV RES METHODS, V37, P626, DOI 10.3758/BF03192732
   Oliveira W. B. D., 2020, ACM T INF SYST, V38, P1
   Ortis A, 2021, MULTIMED TOOLS APPL, V80, P22323, DOI 10.1007/s11042-019-08312-7
   Peng KC, 2015, PROC CVPR IEEE, P860, DOI 10.1109/CVPR.2015.7298687
   Rao TR, 2020, NEURAL PROCESS LETT, V51, P2043, DOI 10.1007/s11063-019-10033-9
   Rao TR, 2016, IEEE IMAGE PROC, P634, DOI 10.1109/ICIP.2016.7532434
   Sartori A, 2015, MM'15: PROCEEDINGS OF THE 2015 ACM MULTIMEDIA CONFERENCE, P311, DOI 10.1145/2733373.2806250
   She DY, 2020, IEEE T MULTIMEDIA, V22, P1358, DOI 10.1109/TMM.2019.2939744
   Simonyan K., 2014, ARXIV
   Song KK, 2018, NEUROCOMPUTING, V312, P218, DOI 10.1016/j.neucom.2018.05.104
   Tu GY, 2020, IEEE T MULTIMEDIA, V22, P148, DOI 10.1109/TMM.2019.2922129
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Wang PC, 2018, KNOWL-BASED SYST, V158, P43, DOI 10.1016/j.knosys.2018.05.029
   Wu H.., 2019, UNIVSE ROBUST VISUAL
   Xiong HT, 2019, AAAI CONF ARTIF INTE, P363
   Yamamoto T, 2021, IEICE T INF SYST, VE104D, P1691, DOI 10.1587/transinf.2020EDP7218
   Yang JF, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3266
   Yang JF, 2018, AAAI CONF ARTIF INTE, P491
   Yang JF, 2018, IEEE T MULTIMEDIA, V20, P2513, DOI 10.1109/TMM.2018.2803520
   Yang LJ, 2017, PROC CVPR IEEE, P1978, DOI 10.1109/CVPR.2017.214
   Yanulevskaya V, 2008, IEEE IMAGE PROC, P101, DOI 10.1109/ICIP.2008.4711701
   You QZ, 2016, AAAI CONF ARTIF INTE, P308
   You QZ, 2015, AAAI CONF ARTIF INTE, P381
   Zhang J, 2020, KNOWL-BASED SYST, V191, DOI 10.1016/j.knosys.2019.105245
   Zhao SC, 2014, PROCEEDINGS OF THE 2014 ACM CONFERENCE ON MULTIMEDIA (MM'14), P47, DOI 10.1145/2647868.2654930
NR 51
TC 0
Z9 0
U1 5
U2 5
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0178-2789
EI 1432-2315
J9 VISUAL COMPUT
JI Visual Comput.
DI 10.1007/s00371-022-02472-8
EA APR 2022
PG 14
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0S9BT
UT WOS:000786565700001
DA 2022-08-23
ER

PT J
AU Sevgili, O
   Shelmanov, A
   Arkhipov, M
   Panchenko, A
   Biemann, C
AF Sevgili, Oezge
   Shelmanov, Artem
   Arkhipov, Mikhail
   Panchenko, Alexander
   Biemann, Chris
TI Neural entity linking: A survey of models based on deep learning
SO SEMANTIC WEB
LA English
DT Article
DE Entity linking; deep learning; neural networks; natural language
   processing; knowledge graphs
ID KNOWLEDGE GRAPHS; RECOGNITION; DISAMBIGUATION; CLASSIFICATION;
   EXTRACTION; FRAMEWORK
AB This survey presents a comprehensive description of recent neural entity linking (EL) systems developed since 2015 as a result of the "deep learning revolution" in natural language processing. Its goal is to systemize design features of neural entity linking systems and compare their performance to the remarkable classic methods on common benchmarks. This work distills a generic architecture of a neural EL system and discusses its components, such as candidate generation, mention-context encoding, and entity ranking, summarizing prominent methods for each of them. The vast variety of modifications of this general architecture are grouped by several common themes: joint entity mention detection and disambiguation, models for global linking, domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches. Since many neural models take advantage of entity and mention/context embeddings to represent their meaning, this work also overviews prominent entity embedding techniques. Finally, the survey touches on applications of entity linking, focusing on the recently emerged use-case of enhancing deep pre-trained masked language models based on the Transformer architecture.
C1 [Sevgili, Oezge; Biemann, Chris] Univ Hamburg, Informatikum, Language Technol Grp, Vogt Kolln Str 30, D-22527 Hamburg, Germany.
   [Shelmanov, Artem; Panchenko, Alexander] Skolkovo Inst Sci & Technol, Ctr Artificial Intelligence Technol, Bolshoy Blvd 30,Bld 1, Moscow 121205, Russia.
   [Shelmanov, Artem] Lomonosov Moscow State Univ, Res Comp Ctr, GSP 1, Moscow 119991, Russia.
   [Shelmanov, Artem] AIRI, Nizhny Susalny Lane 5 P 19, Moscow 105064, Russia.
   [Arkhipov, Mikhail] Moscow Inst Phys & Technol, Neural Networks & Deep Learning Lab, 9 Inst Skiy Per, Moscow 141701, Russia.
RP Sevgili, O (通讯作者)，Univ Hamburg, Informatikum, Language Technol Grp, Vogt Kolln Str 30, D-22527 Hamburg, Germany.; Shelmanov, A (通讯作者)，Skolkovo Inst Sci & Technol, Ctr Artificial Intelligence Technol, Bolshoy Blvd 30,Bld 1, Moscow 121205, Russia.; Shelmanov, A (通讯作者)，Lomonosov Moscow State Univ, Res Comp Ctr, GSP 1, Moscow 119991, Russia.; Shelmanov, A (通讯作者)，AIRI, Nizhny Susalny Lane 5 P 19, Moscow 105064, Russia.
EM oezge.sevgili.ergueven@studium.uni-hamburg.de; shelmanov@airi.net;
   arkhipov@yahoo.com; a.panchenko@skoltech.ru;
   christian.biemann@uni-hamburg.de
FU Deutscher Akademischer Austauschdienst (DAAD) [BI 1544/4]; Russian
   Science Foundation [20-11-20166]; joint MTS-Skoltech laboratory
FX The work was partially supported by a Deutscher Akademischer
   Austauschdienst (DAAD) doctoral stipend and the DFG-funded JOIN-T
   project BI 1544/4. The work of Artem Shelmanov in the current study
   (preparation of sections related to application of entity linking to
   neural language models, entity ranking, context-mention encoding, and
   overall harmonization of the text and results) is supported by the
   Russian Science Foundation (project 20-11-20166). Finally, this work was
   partially supported by the joint MTS-Skoltech laboratory.
CR Abel F., 2012, P INT C WORLD WID WE, P305, DOI DOI 10.1145/2187980.2188035
   Adjali Omar, 2020, Advances in Information Retrieval, 42nd European Conference on IR Research, ECIR 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12035), P463, DOI 10.1007/978-3-030-45439-5_31
   Al-Moslmi T, 2020, IEEE ACCESS, V8, P32862, DOI 10.1109/ACCESS.2020.2973928
   Aly R, 2021, 59TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 11TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1 (ACL-IJCNLP 2021), P1516
   Amiri H, 2021, ACL-IJCNLP 2021: THE 59TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 11TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 2, P1012
   [Anonymous], 2018, P 2018 C EMP METH NA, DOI DOI 10.18653/V1/D18-1270
   Aronson AR, 2010, J AM MED INFORM ASSN, V17, P229, DOI 10.1136/jamia.2009.002733
   Bahdanau D., 2014, ARXIV
   Banarescu L., 2013, P 7 LINGUISTIC ANNOT, P178
   Banerjee D, 2020, LECT NOTES COMPUT SC, V12506, P21, DOI 10.1007/978-3-030-62419-4_2
   Bansal T, 2020, AAAI CONF ARTIF INTE, V34, P7407
   Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bojanowski P., 2017, T ASSOC COMPUT LING, V5, P135, DOI [DOI 10.1162/TACL_A_00051, 10.1162/tacl_a_00051]
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Botha J.A., 2020, P 2020 C EMP METH NA, P7833, DOI [10.18653/v1/2020.emnlp-main.630, DOI 10.18653/V1/2020.EMNLP-MAIN.630]
   Broscheit Samuel, 2019, P 23 C COMPUTATIONAL, P677
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Cao N.D., 2021, P INT C LEARN REPR
   Cao Y., 2018, P INT C COMP LING, P675
   Cao YX, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1623, DOI 10.18653/v1/P17-1149
   Ceccarelli D, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P139, DOI 10.1145/2505515.2505711
   Chang AX, 2016, LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P860
   Chen LH, 2021, AAAI CONF ARTIF INTE, V35, P12657
   Chen S, 2020, AAAI CONF ARTIF INTE, V34, P7529
   Cheng X., 2013, P 2013 C EMP METH NA, P1787
   Chisholm A., 2015, T ASSOC COMPUT LING, V3, P145, DOI DOI 10.1162/tacl_a_00129
   Choi E, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P87
   Chung J., 2014, EMPIRICAL EVALUATION
   Collobert R, 2011, J MACH LEARN RES, V12, P2493
   Cucerzan S, 2007, P 2007 JOINT C EMP M, P708
   Dessi D, 2021, FUTURE GENER COMP SY, V116, P253, DOI 10.1016/j.future.2020.10.026
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Duh K., 2017, P 8 INT JOINT C NAT, P91, DOI DOI 10.18653/V1/D17-1078
   Ehrmann Maud, 2020, Experimental IR Meets Multilinguality, Multimodality, and Interaction. 11th International Conference of the CLEF Association, CLEF 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12260), P288, DOI 10.1007/978-3-030-58219-7_21
   El Vaigh CB, 2019, DOCENG'19: PROCEEDINGS OF THE ACM SYMPOSIUM ON DOCUMENT ENGINEERING 2019, DOI 10.1145/3342558.3345393
   Ellis J., 2015, P 2015 TEXT AN C TAC
   Eshel Y, 2017, P 21 C COMP NAT LANG, P58, DOI DOI 10.18653/V1/K17-1008
   Eyre H., ARXIV PREPRINT ARXIV
   Farber M, 2018, SEMANT WEB, V9
   Fang W., 2016, P 20 SIGNLL C COMP N, P260
   Fang Z, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P640, DOI 10.1145/3366423.3380146
   Fang Z, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P438, DOI 10.1145/3308558.3313517
   Fellbaum Christiane, 1998, WORDNET ELECT LEXICA
   Ferragina P., 2010, P 19 ACM INT C INF K, P1625, DOI DOI 10.1145/1871437.1871689
   Fevry Thibault, 2020, P 2020 C EMP METH NA, P4937
   Francis-Landau M., 2016, P 2016 C N AM CHAPT, P1256
   Fu X., 2020, P 2020 C EMP METH NA, P6418, DOI [10.18653/v1/2020.emnlp-main, DOI 10.18653/V1/2020.EMNLP-MAIN]
   Fumera G, 2000, PATTERN RECOGN, V33, P2099, DOI 10.1016/S0031-3203(00)00059-5
   Gabrilovich E., 2013, FACC1 FREEBASE ANNOT
   Ganea OE, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P927, DOI 10.1145/2872427.2882988
   Ganea OE, 2017, P 2017 C EMP METH NA, P2619
   Gillick Daniel, 2019, P 23 C COMP NAT LANG, P528, DOI [DOI 10.18653/V1/K19-1049, 10.18653/v1/K19-1049]
   Globerson A, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P621
   Goyal A, 2018, COMPUT SCI REV, V29, P21, DOI 10.1016/j.cosrev.2018.06.001
   Goyal P, 2018, KNOWL-BASED SYST, V151, P78, DOI 10.1016/j.knosys.2018.03.022
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Gu YJ, 2021, AAAI CONF ARTIF INTE, V35, P12920
   Guo ZC, 2018, SEMANT WEB, V9, P459, DOI 10.3233/SW-170273
   Gupta Nitish, 2017, P C EMP METH NAT LAN
   Han Xu, 2018, P 2018 C EMPIRICAL M, P4803
   HELLMAN ME, 1970, IEEE T SYST SCI CYB, VSSC6, P179, DOI 10.1109/TSSC.1970.300339
   Herbei R, 2006, CAN J STAT, V34, P709, DOI 10.1002/cjs.5550340410
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hoffart J., 2011, P EMNLP, DOI DOI 10.3115/V1/D11-1072
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   Hou Feng, 2020, P 58 ANN M ASS COMP, P6843, DOI DOI 10.18653/V1/2020.ACL-MAIN.612
   Huang H., 2015, ARXIV PREPRINT ARXIV
   Humeau S., 2020, ICLR
   Jaegle Andrew, 2021, P MACHINE LEARNING R, V139, P4651
   Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418
   Ji H., 2010, 3 TEXT AN C TAC GAIT
   Ji H., 2015, P 2015 TEXT AN C TAC, P16
   Ji H., 2011, P 49 ANN M ASS COMP, V1, P1148
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Joshi M, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1601, DOI 10.18653/v1/P17-1147
   Kar R., 2018, P AAAI C ART INT, V32
   Khare R, 2014, J BIOMED INFORM, V52, P448, DOI 10.1016/j.jbi.2014.08.004
   Kolitsas Nikolaos, 2018, P 22 C COMP NAT LANG, P519, DOI DOI 10.18653/V1/K18-1050
   Kraljevic Z, 2021, ARTIF INTELL MED, V117, DOI 10.1016/j.artmed.2021.102083
   Lazic N., 2015, T ASSOC COMPUT LING, V3, P503, DOI DOI 10.1162/tacl_a_00154
   Le P, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4081
   Lee D.-H., 2013, WORKSHOP CHALLENGES, V3, P896
   Lee J., 2020, P 1 WORKSH NLP COVID, DOI [10.18653/v1/2020.nlpcovid19-2.1, DOI 10.18653/V1/2020.NLPCOVID19-2.1]
   Lee S, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0164680
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lewis M., 2019, P 58 ANN M ASS COMP, DOI 10.18653/v1/2020.acl
   Li B. Z., 2020, EMNLP, P6433, DOI [10.18653/v1/2020.emnlp, DOI 10.18653/V1/2020.EMNLP-MAIN.522]
   Li J, 2022, IEEE T KNOWL DATA EN, V34, P50, DOI [10.1109/TKDE.2020.2981314, 10.1109/TKDE.2020.3038670]
   Ling X., 2015, T ASS COMPUT LING, V3, P315, DOI DOI 10.1162/TACL_A_00141
   Ling Xiao, 2012, P AAAI C ART INT, V12, P94
   Liu Y, 2020, P 2020 C EMP METH NA, P6442, DOI 10.18653/v1/2020.emnlp-main.523
   Liu Yinhan, 2020, RO BERT ROBUSTLY OPT
   Livni R, 2014, ADV NEUR IN, V27
   Logeswaran L, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3449
   Loureiro Daniel, 2020, Advances in Information Retrieval. 42nd European Conference on IR Research, ECIR 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12036), P230, DOI 10.1007/978-3-030-45442-5_29
   Luo G., 2015, P 2015 C EMP METH NA, P879
   Luo G., 2021, P 2021 C EMP METH NA, P6801, DOI [10.18653/v1/2021.emnlp-main. 545, DOI 10.18653/V1/2021.EMNLP-MAIN.545]
   MacAvaney S, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P1013, DOI 10.1145/3331184.3331319
   Manning CD, 2017, EMNLP, P35
   Martinez-Rodriguez JL, 2020, SEMANT WEB, V11, P255, DOI 10.3233/SW-180333
   Martins PH, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019:): STUDENT RESEARCH WORKSHOP, P190
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   Miftahutdinov Zulfat, 2021, Advances in Information Retrieval. 43rd European Conference on IR Research, ECIR 2021. Proceedings. Lecture Notes in Computer Science (LNCS 12656), P451, DOI 10.1007/978-3-030-72113-8_30
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Milne D., 2008, P 17 ACM C INF KNOWL, P509, DOI DOI 10.1145/1458082.1458150
   Moller CT, 2022, SCAND J UROL, V56, P214, DOI 10.1080/21681805.2022.2056633
   Moon S, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P2000
   Moreno JG, 2017, LECT NOTES COMPUT SC, V10249, P337, DOI 10.1007/978-3-319-58068-5_21
   Mueller D., 2018, P 2018 C EMP NAT LAN, P1024, DOI [10.18653/v1/D18-1126, DOI 10.18653/V1/D18-1126]
   Mulang IO, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P2157, DOI 10.1145/3340531.3412159
   Nadeau D, 2007, LINGUIST INVESTIG, V30, P3
   Nagrani A., 2021, ADV NEURAL INF PROCE, V34, P1
   Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355
   Nayyeri M., 2019, ARXIV PREPRINT ARXIV
   Nayyeri M, 2021, AAAI CONF ARTIF INTE, V35, P9064
   Nedelchev R., 2020, ABS200211143 CORR
   Neubig, 2019, P 2 WORKSH DEEP LEAR, P243, DOI [10.18653/v1/D19-6127, DOI 10.18653/V1/D19-6127]
   Newman-Griffis D, 2018, REPRESENTATION LEARNING FOR NLP, P195
   Nguyen D., 2016, T ASSOC COMPUT LING, V4, P215, DOI DOI 10.1162/TACL_A_00094
   Nguyen T. H., 2016, COLING 2016, P2310
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nie F, 2018, AAAI CONF ARTIF INTE, P5908
   Oliveira IL, 2021, INFORM SYST, V95, DOI 10.1016/j.is.2020.101624
   Onoe Y, 2020, AAAI CONF ARTIF INTE, V34, P8576
   Orponen P., 1994, Nordic Journal of Computing, V1, P94
   Page L., 1999, PAGERANK CITATION RA
   Pan XM, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1946, DOI 10.18653/v1/P17-1178
   Parravicini A., 2019, P 2 JOINT INT WORKSH, DOI [10.1145/ 3327964.3328499, DOI 10.1145/3327964.3328499]
   Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
   Pershina M., 2015, P 2015 C N AM CHAPT, P238, DOI [DOI 10.3115/V1/N15-1026, 10.3115/v1/N15-1026]
   Peters M.E., 2018, P 2018 C N AM CHAPT, P2227, DOI 10.18653/v1/N18-1202
   Peters M.E., 2019, PROCEEDING 9 INT JOI, P43
   Le P, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1935
   Le P, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1595
   Piccinno F., 2014, P 1 INT WORKSH ENT R, P55, DOI [10 . 1145 / 2633211.2634350, DOI 10.1145/2633211.2634350]
   Pires T, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4996
   Porner Nina, 2020, FINDINGS ASS COMPUTA, P803
   Provatorova V., 2020, CEUR WORKSHOP P, V2696
   Le Q, 2014, PR MACH LEARN RES, V32, P1188
   Raffel C, 2020, J MACH LEARN RES, V21
   Raiman J, 2018, AAAI CONF ARTIF INTE, P5406
   Rajpurkar P., 2016, P C EMP METH NAT LAN
   Ratinov L., 2011, COMPUT LINGUIST, V1, P1375
   Reimers N., 2019, PROC C EMPIRICAL MET, DOI DOI 10.18653/V1/D19-1410
   Reimers Nils, 2017, P 2017 C EMP METH NA, P338, DOI DOI 10.18653/V1/D17-1035
   Rijhwani S, 2019, AAAI CONF ARTIF INTE, P6924
   Rizzo G, 2014, LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P4593
   Roder M, 2018, SEMANT WEB, V9, P605, DOI 10.3233/SW-170286
   Ruffinelli Daniel, 2020, INT C LEARN REPR
   Sanderson M, 2010, NAT LANG ENG, V16, P100, DOI 10.1017/S1351324909005129
   Sang E.F.T.K., 2003, P N AM CHAPT ASS COM, P142, DOI [10.3115/1119176.1119195, DOI 10.3115/1119176.1119195]
   Savova GK, 2010, J AM MED INFORM ASSN, V17, P507, DOI 10.1136/jamia.2009.001560
   Sevgili O, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019:): STUDENT RESEARCH WORKSHOP, P315
   Shahbazi H., 2018, P INT C COMP LING, P2170
   Shahbazi H., 2019, ARXIV PREPRINT ARXIV
   Sharnagat Rahul, 2014, NAMED ENTITY RECOGNI
   Shen W, 2018, IEEE T KNOWL DATA EN, V30, P353, DOI 10.1109/TKDE.2017.2730862
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Shi W., 2020, ARXIV PREPRINT ARXIV
   Shnayderman I., 2019, ARXIV PREPRINT ARXIV
   Sil A, 2018, AAAI CONF ARTIF INTE, P5464
   Sima J, 2003, NEURAL COMPUT, V15, P2727, DOI 10.1162/089976603322518731
   Slonim N, 2021, NATURE, V591, P379, DOI 10.1038/s41586-021-03215-w
   Soldaini L., 2016, MEDIR WORKSH SIGIR, V2016
   Sorokin D., 2018, P 7 JOINT C LEX COMP, P65
   Sparck-Jones K, 2000, INFORM PROCESS MANAG, V36, P809, DOI 10.1016/S0306-4573(00)00016-9
   Spitkovsky VI, 2012, LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P3168
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun Y, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1333
   Sung M, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3641
   Tang H., 2021, BIDIRECTIONAL MULTIP, P13889
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Tsai C.-T., 2018, P AAAI C ART INT, V32
   Tsai C-T, 2016, P NAACL HLT
   Tutubalina E., 2020, P 28 INT C COMPUTATI, P6710
   Upadhyay S., 2018, P 2018 C EMP METH NA, P501, DOI [10.18653/v1/D18-1046, DOI 10.18653/V1/D18-1046]
   van Hulst JM, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P2197
   Vasudeva, 2018, LONG PAPERS, V1, P1844
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Vinyals O, 2015, ADV NEURAL INFORM PR, P2692
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang A, 2018, ARXIV180407461
   Wang Han, 2015, P C EMP METH NAT LAN, P695
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang XZ, 2021, T ASSOC COMPUT LING, V9, P176, DOI 10.1162/tacl_a_00360
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu JS, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P1149, DOI 10.1145/3366423.3380192
   Wu Ledell, 2020, P 2020 C EMP METH NA, P6397
   Xue MG, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5327
   Yadav V., 2018, P INT C COMP LING, P2145
   Yamada I., 2017, T ASS COMPUTATIONAL, V5, P397
   Yamada I., 2016, P 20 SIGNLL C COMP N, P250
   Yamada I., 2021, ARXIV PREPRINT ARXIV
   Yamada Ikuya, 2020, P 2020 C EMP METH NA, DOI 10.18653/v1/2020.emnlp-demos.4
   Yang B., 2015, P INT C LEARN REPR I
   Yang Xiyuan, 2019, P 2019 C EMPIRICAL M, P271
   Yao Zonghai, 2020, FIND ASS COMP LING E, P2517, DOI DOI 10.18653/V1/2020.FINDINGS-EMNLP.228
   Yih WT, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P1321
   Young T, 2018, IEEE COMPUT INTELL M, V13, P55, DOI 10.1109/MCI.2018.2840738
   Zhang S., 2018, ABS181012885 CORR
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
   Zhao Chen H, 2020, P C AS PAC CHAPT ASS, P637
   Zhou SY, 2020, T ASSOC COMPUT LING, V8, P109, DOI 10.1162/tacl_a_00303
   Zhu M, 2020, AAAI CONF ARTIF INTE, V34, P9757
   Zwicklbauer S, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P425, DOI 10.1145/2911451.2911535
   Zwicklbauer S, 2016, LECT NOTES COMPUT SC, V9678, P182, DOI 10.1007/978-3-319-34129-3_12
NR 210
TC 2
Z9 2
U1 3
U2 3
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2022
VL 13
IS 3
BP 527
EP 570
DI 10.3233/SW-222986
PG 44
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0J3YH
UT WOS:000780041500011
OA hybrid, Green Submitted
DA 2022-08-23
ER

PT J
AU Devanand, A
   Karmakar, G
   Krdzavac, N
   Farazi, F
   Lim, MQ
   Eddy, YSF
   Karimi, IA
   Kraft, M
AF Devanand, Aravind
   Karmakar, Gourab
   Krdzavac, Nenad
   Farazi, Feroz
   Lim, Mei Qi
   Eddy, Y. S. Foo
   Karimi, Iftekhar A.
   Kraft, Markus
TI ElChemo: A cross-domain interoperability between chemical and electrical
   systems in a plant
SO COMPUTERS & CHEMICAL ENGINEERING
LA English
DT Article
DE Ontology; Semantic web; Knowledge-graph; Digital twin; Industry 4; 0;
   Reasoning
ID INDUSTRY 4.0; ONTOLOGY; TECHNOLOGIES
AB The paper proposes a novel framework capable of establishing machine-to-machine (M2M) interactions between chemical and electrical systems in the industry. The framework termed as ElChemo addresses the challenges in M2M interaction of entities from different silos, such as differences in the domains' behaviour, the heterogeneities arising from different vocabularies and software. The OntoTwin ontology has been developed based on OntoPowSys and OntoEIP ontologies, which are parts of an intelligent platform called the "J-Park Simulator (JPS)". The ElChemo framework uses Description Logic (DL) and SPIN reasoning techniques to establish the interaction between the chemical and electrical systems in a plant. This paper presents a depropaniser section of a chemical plant and its corresponding electrical system as a use case scenario to demonstrate the interoperability between the two silos within the ElChemo framework. The results from the use case demonstrate, as a proof of concept, the potential of the proposed framework and can be considered as the first step towards the development of a knowledge graph based framework capable of increasing interoperability between cross-domain interactions. (c) 2021 Elsevier Ltd. All rights reserved.
C1 [Devanand, Aravind; Karimi, Iftekhar A.] Natl Univ Singapore, Dept Chem & Biomol Engn, Engn Dr 4, Singapore 117585, Singapore.
   [Devanand, Aravind; Karmakar, Gourab; Krdzavac, Nenad; Lim, Mei Qi; Eddy, Y. S. Foo; Karimi, Iftekhar A.; Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore CARES, CREATE Tower,1 Create Way, Singapore 138602, Singapore.
   [Farazi, Feroz; Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, Philippa Fawcett Dr,West Site, Cambridge CB3 0AS, England.
   [Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, 62 Nanyang Dr, Singapore 637459, Singapore.
   [Eddy, Y. S. Foo] Nanyang Technol Univ, Sch Elect & Elect Engn, 50 Nanyang Ave, Singapore 639798, Singapore.
RP Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, Philippa Fawcett Dr,West Site, Cambridge CB3 0AS, England.
EM mk306@cam.ac.uk
OI Karmakar, Gourab/0000-0002-1159-8920
FU National Research Foundation (NRF) (NRF. CARES-C4T) , Prime Ministers'
   Office, Singapore; Alexander von Humboldt foundation
FX This project is funded by the National Research Foundation (NRF.
   CARES-C4T) , Prime Ministers' Office, Singapore under its Campus for
   Research Excellence and Technological Enterprise (CRE-ATE) programme.
   Markus Kraft gratefully acknowledges the support of the Alexander von
   Humboldt foundation.
CR Authority E.M., INTRO DESCRIPTION LO
   Berners-Lee Tim, 2006, Foundations and Trends in Web Science, V1, P1, DOI 10.1561/1800000001
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bollen M. H, 2000, VOLTAGE SAGS INTERRU
   Boyer S. A., 2009, SCADA SUPERVISORY CO
   Chang Chen Chung, 1990, MODEL THEORY
   Chen BT, 2018, IEEE ACCESS, V6, P6505, DOI 10.1109/ACCESS.2017.2783682
   Devanand A, 2020, ENERGY, V1, DOI DOI 10.1016/J.EGYAI.2020.100008
   Devanand A, 2019, COMPUT CHEM ENG, V125, P339, DOI 10.1016/j.compchemeng.2019.03.024
   Eberhart A., 2002, ONTOAGENT PLATFORM D
   Eibeck A, 2019, COMPUT CHEM ENG, V131, DOI 10.1016/j.compchemeng.2019.106586
   Enterprise P. S., 1997, GPROMS
   Gikas A. J, 2017, SMART P IDS
   Hitzler P, 2012, OWL 2 WEB ONTOLOGY L, VSecond
   Horrocks I., 2006, P 10 INT C PRINC KNO, P57
   Horrocks I., 2004, W3C MEMBER SUBMISSIO, V21, P79
   Kastner CA, 2015, APPL ENERG, V155, P599, DOI 10.1016/j.apenergy.2015.05.037
   Knublauch H, 2011, W3C MEMB SUBMISS, V22
   Kraft M, 2010, PHILOS T R SOC A, V368, P3633, DOI 10.1098/rsta.2010.0124
   Lau R., 2017, P 2 INT C INT THINGS, DOI [10.1145/3018896.3025155, DOI 10.1145/3018896.3025155]
   Lee Jay, 2015, Manufacturing Letters, V3, P18, DOI 10.1016/j.mfglet.2014.12.001
   Maedche A, 2001, IEEE INTELL SYST APP, V16, P72, DOI 10.1109/5254.920602
   MATLAB, 2019, SIM MATHW VERS R2019
   Menon A, 2019, CURR OPIN CHEM ENG, V26, P33, DOI 10.1016/j.coche.2019.08.004
   Morbach J, 2007, ENG APPL ARTIF INTEL, V20, P147, DOI 10.1016/j.engappai.2006.06.010
   Mosbach S., MULTISCALE CROSS DOM
   Motik B, 2009, J ARTIF INTELL RES, V36, P165, DOI 10.1613/jair.2811
   Musulin E, 2013, COMPUT CHEM ENG, V59, P164, DOI 10.1016/j.compchemeng.2013.06.009
   Natarajan S, 2012, COMPUT CHEM ENG, V46, P124, DOI 10.1016/j.compchemeng.2012.06.009
   Pan M, 2016, APPL ENERG, V175, P305, DOI 10.1016/j.apenergy.2016.05.019
   Pan M, 2015, ENRGY PROCED, V75, P1536, DOI 10.1016/j.egypro.2015.07.313
   Parker Jr C. T., 2020, US Patent, Patent No. [10,535,003, 10535003]
   Pedrinaci C., 2010, CEUR WORKSHOP PROC, P596
   Ra Prakash K., 2018, 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). Proceedings, P1177, DOI 10.1109/ISGT-Asia.2018.8467843
   Romero DD, 2015, COMPUT-AIDED CHEM EN, V37, P965
   Rumbaugh J., 2004, UNIFIED MODELING LAN
   Russmann M., 2015, IND 40 FUTURE PRODUC, VVolume 9, P54
   Shrouf F, 2014, IN C IND ENG ENG MAN, P697, DOI 10.1109/IEEM.2014.7058728
   Sikorski JJ, 2017, APPL ENERG, V195, P234, DOI 10.1016/j.apenergy.2017.03.039
   SPGroup, 2015, COLL POW QUAL
   WINKEL ML, 1995, COMPUT CHEM ENG, V19, pS571, DOI 10.1016/0098-1354(95)00121-H
   Zhang C, 2017, APPL ENERG, V204, P1412, DOI 10.1016/j.apenergy.2017.03.130
   Zhou L, 2017, APPL ENERG, V204, P1284, DOI 10.1016/j.apenergy.2017.05.002
   Zhou XC, 2019, COMPUT CHEM ENG, V130, DOI 10.1016/j.compchemeng.2019.106577
   Ziogou C, 2017, COMPUT-AIDED CHEM EN, V40C, P2365, DOI 10.1016/B978-0-444-63965-3.50396-2
NR 45
TC 0
Z9 0
U1 2
U2 5
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0098-1354
EI 1873-4375
J9 COMPUT CHEM ENG
JI Comput. Chem. Eng.
PD JAN
PY 2022
VL 156
AR 107556
DI 10.1016/j.compchemeng.2021.107556
EA OCT 2021
PG 17
WC Computer Science, Interdisciplinary Applications; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA WI9GG
UT WOS:000708661000001
OA Bronze
DA 2022-08-23
ER

PT J
AU Wenige, L
   Ruhland, J
AF Wenige, Lisa
   Ruhland, Johannes
TI Similarity-based knowledge graph queries for recommendation retrieval
SO SEMANTIC WEB
LA English
DT Article
DE Recommender systems; linked open data; information retrieval; SPARQL;
   semantic search; SKOS
ID SYSTEMS; SEARCH
AB Current retrieval and recommendation approaches rely on hard-wired data models. This hinders personalized customizations to meet information needs of users in a more flexible manner. Therefore, the paper investigates how similarity-based retrieval strategies can be combined with graph queries to enable users or system providers to explore repositories in the Linked Open Data (LOD) cloud more thoroughly. For this purpose, we developed novel content-based recommendation approaches. They rely on concept annotations of Simple Knowledge Organization System (SKOS) vocabularies and a SPARQL-based query language that facilitates advanced and personalized requests for openly available knowledge graphs. We have comprehensively evaluated the novel search strategies in several test cases and example application domains (i.e., travel search and multimedia retrieval). The results of the web-based online experiments showed that our approaches increase the recall and diversity of recommendations or at least provide a competitive alternative strategy of resource access when conventional methods do not provide helpful suggestions. The findings may be of use for Linked Data-enabled recommender systems (LDRS) as well as for semantic search engines that can consume LOD resources.
C1 [Wenige, Lisa; Ruhland, Johannes] Friedrich Schiller Univ Jena, Chair Business Informat Syst, Jena, Germany.
RP Wenige, L (通讯作者)，Friedrich Schiller Univ Jena, Chair Business Informat Syst, Jena, Germany.
EM wenige@infai.org
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
   Adomavicius G, 2011, INFORM SYST RES, V22, P99, DOI 10.1287/isre.1100.0274
   Aggarwal C.C., 2016, RECOMMENDER SYSTEMS, DOI [10.1007/978-3-319-29659-3, DOI 10.1007/978-3-319-29659-3]
   Arenas M., 2014, ACM CIKM 2014, P939, DOI DOI 10.1145/2661829.2662027
   Ayala V., 2014, SEMANTIC WEB INFORM, DOI [10.1145/2630602.2630604, DOI 10.1145/2630602.2630604]
   Bao XB, 2010, CONTEMPORARY QUANTITATIVE FINANCE: ESSAYS IN HONOUR OF ECKHARD PLATEN, P123, DOI 10.1007/978-3-642-03479-4_7
   Beel J, 2016, USER MODEL USER-ADAP, V26, P69, DOI 10.1007/s11257-016-9174-x
   Boutilier C, 2004, J ARTIF INTELL RES, V21, P135, DOI 10.1613/jair.1234
   Castells P., 2011, NOVELTY DIVERSITY ME, DOI [10.1145/2043932.2043955, DOI 10.1145/2043932.2043955]
   Cheng G., 2008, P 17 INT C WORLD WID, P1101, DOI DOI 10.1145/1367497.1367676
   Davies J., 2004, P 37 ANN HAW INT C S, DOI [10.1002/0470858060.ch8, DOI 10.1002/0470858060.CH8]
   Di Noia T., 2012, P 8 INT C SEM SYST, P1, DOI DOI 10.1145/2362499.2362501
   Dillman DA, 1998, JOINT M AM STAT ASS
   Felfernig A, 2011, RECOMMENDER SYSTEMS HANDBOOK, P187, DOI 10.1007/978-0-387-85820-3_6
   Fernandez-Tobias I., 2011, P 2 INT WORKSH INF H, V9, P25, DOI DOI 10.1145/2039320.2039324
   Freitas A, 2011, LECT NOTES COMPUT SC, V6716, P40, DOI 10.1007/978-3-642-22327-3_5
   Grainger T., 2014, SOLR ATION
   Gunawardana Asela, 2015, RECOMMENDER SYSTEMS, P265, DOI DOI 10.1007/978-1-4899-7637-6_8
   Hahn R, 2010, LECT NOTES BUS INF P, V47, P1
   Harris S., 2013, SPARQL 1 1 QUERY LAN
   Hayes Conor, 2010, AAAI SPRING S LINK D
   Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
   Huynh D., 2009, P 18 ACM INT C WORLD
   Kabutoya Y, 2012, 2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2012), VOL 1, P625, DOI 10.1109/WI-IAT.2012.23
   Kaminskas M., 2012, P 2 INT ACM WORKSH M, P19, DOI [10.1145/2390848.2390854, DOI 10.1145/2390848.2390854]
   Khrouf H., 2013, P 7 ACM C REC SYST, P185, DOI DOI 10.1145/2507157.2507171
   Kirk P., 1982, EXPT DESIGN
   Knijnenburg BP, 2012, USER MODEL USER-ADAP, V22, P441, DOI 10.1007/s11257-011-9118-4
   Koutrika G, 2009, ACM SIGMOD/PODS 2009 CONFERENCE, P745
   Lee M, 2009, IN C IND ENG ENG MAN, P1523, DOI 10.1109/IEEM.2009.5373086
   Lehmann J, 2011, LECT NOTES COMPUT SC, V6643, P63, DOI 10.1007/978-3-642-21034-1_5
   Marie N, 2013, P 9 INT C SEM SYST, P17, DOI DOI 10.1145/2506182.2506185
   McNee S.M., 2006, P CHI 06 HUM FACT CO, P1097, DOI DOI 10.1145/1125451.1125659
   Meymandpour R, 2012, PROCEEDINGS OF THE 5TH PH.D. WORKSHOP ON INFORMATION AND KNOWLEDGE, P75
   Miles A, 2009, W3C RECOMMENDATION
   Mobasher B, 2004, LECT NOTES COMPUT SC, V3209, P57, DOI 10.1007/978-3-540-30123-3_4
   Musetti A., 2012, SEMANTIC WEB CHALLEN, DOI [10.1145/2462197.2462199, DOI 10.1145/2462197.2462199]
   Neumann T, 2010, VLDB J, V19, P91, DOI 10.1007/s00778-009-0165-y
   Oramas S, 2017, ACM T INTEL SYST TEC, V8, DOI 10.1145/2926718
   Oren E, 2006, LECT NOTES COMPUT SC, V4273, P559
   Perez J, 2006, 13TH ANNUAL IEEE INTERNATIONAL SYMPOSIUM AND WORKSHOP ON ENGINEERING OF COMPUTER BASED SYSTEMS, PROCEEDINGS, P32, DOI 10.1109/ECBS.2006.12
   Pirro G, 2015, LECT NOTES COMPUT SC, V9366, P622, DOI 10.1007/978-3-319-25007-6_36
   Policarpio S., 2012, AIMWD
   Pu P., 2011, P 5 ACM C REC SYST, P157, DOI [10.1145/2043932.2043962, DOI 10.1145/2043932.2043962]
   Rizzo G., 2011, 10 INT SEM WEB C ISW
   Romito Davide, 2012, P 6 ACM C REC SYST R, P253, DOI DOI 10.1145/2365952.2366007
   Rosati J, 2015, LECT NOTES COMPUT SC, V9415, P423, DOI 10.1007/978-3-319-26148-5_28
   Ruotsalo T, 2013, J WEB SEMANT, V20, P50, DOI 10.1016/j.websem.2013.03.001
   Ruthven I, 2003, J AM SOC INF SCI TEC, V54, P529, DOI 10.1002/asi.10240
   Sarwar B, 2001, P 10 INT C WORLD WID, P285
   Schmachtenberg M, 2014, LECT NOTES COMPUT SC, V8796, P245, DOI 10.1007/978-3-319-11964-9_16
   Schutze H., 2008, INTRO INFORM RETRIEV, V39
   Stankovic M., 2011, P 2011 IEEE WIC ACM, P219, DOI [10.1109/WI-IAT.2011.32, DOI 10.1109/WI-IAT.2011.32]
   Swearingen K., 2001, ACM SIGIR 2001 WORKS, V13
   Tunkelang D., 2009, SYNTH LECT INF CONC, P1
   Unger C., 2012, P 21 INT C WORLD WID, P639, DOI DOI 10.1145/2187836.2187923
   Varnhagen CK, 2005, ETHICS BEHAV, V15, P37, DOI 10.1207/s15327019eb1501_3
   Waitelonis J, 2009, 2009 11TH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2009), P540, DOI 10.1109/ISM.2009.111
   Wenige L., KNOWLEDGE ORG SYSTEM
   Wenige L, 2018, INT J DIGIT LIBRARIE, V19, P253, DOI 10.1007/s00799-017-0224-8
   Wenige L, 2016, LECT NOTES BUS INF P, V255, P43, DOI 10.1007/978-3-319-39426-8_4
   Zarrinkalam F., 2012, 2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE 2012), P283, DOI 10.1109/ICCKE.2012.6395393
   Ziegler Cai-Nicolas, 2005, P 14 INT C WORLD WID, P22, DOI DOI 10.1145/1060745.1060754
   Zou L, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P313, DOI 10.1145/2588555.2610525
NR 64
TC 4
Z9 4
U1 4
U2 35
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2019
VL 10
IS 6
BP 1007
EP 1037
DI 10.3233/SW-190353
PG 31
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JH5WI
UT WOS:000492839300004
DA 2022-08-23
ER

PT J
AU Wu, SB
   Yang, SJ
   Wang, MM
   Song, N
   Feng, J
   Wu, H
   Yang, AD
   Liu, CJ
   Li, YN
   Guo, F
   Qiao, JJ
AF Wu, Shengbo
   Yang, Shujuan
   Wang, Manman
   Song, Nan
   Feng, Jie
   Wu, Hao
   Yang, Aidong
   Liu, Chunjiang
   Li, Yanni
   Guo, Fei
   Qiao, Jianjun
TI Quorum sensing-based interactions among drugs, microbes, and diseases
SO SCIENCE CHINA-LIFE SCIENCES
LA English
DT Article; Early Access
DE quorum sensing; drugs; microbe-disease association; microbe-host
   interaction; gut microbiota; personalized medicine
ID SURFACE-PLASMON RESONANCE; PSEUDOMONAS-AERUGINOSA; GUT MICROBIOTA;
   DATABASE; BINDING; IDENTIFICATION; METFORMIN; DOCKING; SIGNAL;
   INHIBITORS
AB Many diseases and health conditions are closely related to various microbes, which participate in complex interactions with diverse drugs; nonetheless, the detailed targets of such drugs remain to be elucidated. Many existing studies have reported causal associations among drugs, gut microbes, or diseases, calling for a workflow to reveal their intricate interactions. In this study, we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease (QS-DMD) database (http://www.qsdmd.lbci.net/), which includes diverse interactions for more than 8,000 drugs, 163 microbes, and 42 common diseases. Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations. Furthermore, we have constructed a QS-based drug-receptor interaction network, proposed a systematic framework including various drug-receptor-microbe-disease connections, and mapped a paradigmatic circular interaction network based on the QS-DMD, which can provide the underlying QS-based mechanisms for the reported causal associations. The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases. This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.
C1 [Wu, Shengbo; Yang, Shujuan; Wang, Manman; Liu, Chunjiang; Li, Yanni; Qiao, Jianjun] Tianjin Univ, Sch Chem Engn & Technol, Tianjin 300072, Peoples R China.
   [Guo, Fei] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.
   [Wu, Shengbo; Liu, Chunjiang] Tianjin Univ, State Key Lab Chem Engn, Tianjin 300072, Peoples R China.
   [Wu, Shengbo; Liu, Chunjiang; Li, Yanni; Qiao, Jianjun] Collaborat Innovat Ctr Chem Sci & Engn Tianjin, Tianjin 300072, Peoples R China.
   [Li, Yanni; Qiao, Jianjun] Tianjin Univ, Key Lab Syst Bioengn, Minist Educ, Tianjin 300072, Peoples R China.
   [Song, Nan; Feng, Jie] Tianjin Univ, Coll Intelligence & Comp, Sch Comp Sci & Technol, Tianjin 300350, Peoples R China.
   [Yang, Aidong] Univ Oxford, Dept Engn Sci, Oxford OX1 3PJ, England.
   [Wu, Hao; Qiao, Jianjun] Tianjin Univ, Inst Shaoxing, Shaoxing 312300, Peoples R China.
RP Li, YN; Qiao, JJ (通讯作者)，Tianjin Univ, Sch Chem Engn & Technol, Tianjin 300072, Peoples R China.; Guo, F (通讯作者)，Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.; Li, YN; Qiao, JJ (通讯作者)，Collaborat Innovat Ctr Chem Sci & Engn Tianjin, Tianjin 300072, Peoples R China.; Li, YN; Qiao, JJ (通讯作者)，Tianjin Univ, Key Lab Syst Bioengn, Minist Educ, Tianjin 300072, Peoples R China.; Qiao, JJ (通讯作者)，Tianjin Univ, Inst Shaoxing, Shaoxing 312300, Peoples R China.
EM liyanni@tju.edu.cn; guofei@csu.edu.cn; jianjunq@tju.edu.cn
FU National Key Research and Development Project of China [2019YFA0905600,
   2020YFA0907900]; National Natural Science Foundation of China [31570089,
   31770076, 62172296]; Funds for Creative Research Groups of China
   [21621004]; New Century Outstanding Talent Support Program, Education
   Ministry of China
FX This work was supported by the National Key Research and Development
   Project of China (2019YFA0905600, 2020YFA0907900), the National Natural
   Science Foundation of China (31570089, 31770076, 62172296), the Funds
   for Creative Research Groups of China (21621004), and the New Century
   Outstanding Talent Support Program, Education Ministry of China.
CR Abbas HA, 2017, AFR HEALTH SCI, V17, P808, DOI 10.4314/ahs.v17i3.24
   Aggeletopoulou I, 2019, MICROB PATHOGENESIS, V137, DOI 10.1016/j.micpath.2019.103774
   Apweiler R, 2004, NUCLEIC ACIDS RES, V32, pD115, DOI 10.1093/nar/gkh131
   Backhed F, 2015, CELL HOST MICROBE, V17, P690, DOI [10.1016/j.chom.2015.04.004, 10.1016/j.chom.2015.05.012]
   Barretto SA, 2021, MICROBIOME, V9, DOI 10.1186/s40168-021-01050-9
   Berman HM, 2000, NUCLEIC ACIDS RES, V28, P235, DOI 10.1093/nar/28.1.235
   Bockova M, 2019, ANNU REV ANAL CHEM, V12, P151, DOI 10.1146/annurev-anchem-061318-115106
   Bottomley MJ, 2007, J BIOL CHEM, V282, P13592, DOI 10.1074/jbc.M700556200
   Breuninger TA, 2021, MICROBIOME, V9, DOI 10.1186/s40168-020-00969-9
   Chassaing B, 2017, GUT, V66, P1414, DOI 10.1136/gutjnl-2016-313099
   Chen GZ, 2011, MOL CELL, V42, P199, DOI 10.1016/j.molcel.2011.04.003
   Chen X, 2002, NATURE, V415, P545, DOI 10.1038/415545a
   Cheng L, 2020, NUCLEIC ACIDS RES, V48, pD554, DOI 10.1093/nar/gkz843
   Collison M, 2012, BRIEF BIOINFORM, V13, P751, DOI 10.1093/bib/bbs002
   Copeland RA, 2016, NAT REV DRUG DISCOV, V15, P87, DOI 10.1038/nrd.2015.18
   de Almeida FA, 2018, MICROB PATHOGENESIS, V121, P369, DOI 10.1016/j.micpath.2018.05.014
   Defoirdt T, 2018, TRENDS MICROBIOL, V26, P313, DOI 10.1016/j.tim.2017.10.005
   Desai MS, 2016, CELL, V167, P1339, DOI 10.1016/j.cell.2016.10.043
   Devkota S, 2012, NATURE, V487, P104, DOI 10.1038/nature11225
   Dubey BN, 2016, SCI ADV, V2, DOI 10.1126/sciadv.1600823
   Forslund K, 2015, NATURE, V528, P262, DOI 10.1038/nature15766
   Frame LA, 2020, NUTR REV, V78, P798, DOI 10.1093/nutrit/nuz106
   Gaulton A, 2017, NUCLEIC ACIDS RES, V45, pD945, DOI 10.1093/nar/gkw1074
   Hassan R, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbaa292
   Honda K, 2016, NATURE, V535, P75, DOI 10.1038/nature18848
   Ilangovan A, 2013, PLOS PATHOG, V9, DOI 10.1371/journal.ppat.1003508
   Imhann F, 2016, GUT, V65, P740, DOI 10.1136/gutjnl-2015-310376
   Irwin JJ, 2005, J CHEM INF MODEL, V45, P177, DOI 10.1021/ci049714+
   Jackson MA, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-018-05184-7
   Jackson MA, 2016, GUT, V65, P749, DOI 10.1136/gutjnl-2015-310861
   Javdan B, 2020, CELL, V181, P1661, DOI 10.1016/j.cell.2020.05.001
   Kim S, 2016, NUCLEIC ACIDS RES, V44, pD1202, DOI 10.1093/nar/gkv951
   Kim Y, 2017, PROTEINS, V85, P1831, DOI 10.1002/prot.25336
   Klunemann M, 2021, NATURE, V597, P533, DOI 10.1038/s41586-021-03891-8
   Li HL, 2006, NUCLEIC ACIDS RES, V34, pW219, DOI 10.1093/nar/gkl114
   Li LY, 2020, MICROBIOME, V8, DOI 10.1186/s40168-020-00806-z
   Lintz MJ, 2011, P NATL ACAD SCI USA, V108, P15763, DOI 10.1073/pnas.1112398108
   Liu TQ, 2007, NUCLEIC ACIDS RES, V35, pD198, DOI 10.1093/nar/gkl999
   Luo J, 2020, INT J BIOL MACROMOL, V163, P200, DOI 10.1016/j.ijbiomac.2020.06.263
   Maier L, 2018, NATURE, V555, P623, DOI 10.1038/nature25979
   Maier L, 2017, CURR OPIN MICROBIOL, V39, P128, DOI 10.1016/j.mib.2017.11.001
   Miller ST, 2004, MOL CELL, V15, P677, DOI 10.1016/j.molcel.2004.07.020
   Nguyen Y, 2015, MBIO, V6, DOI 10.1128/mBio.02429-14
   Panebianco C, 2018, MICROBIOME, V6, DOI 10.1186/s40168-018-0483-7
   Patching SG, 2014, BBA-BIOMEMBRANES, V1838, P43, DOI 10.1016/j.bbamem.2013.04.028
   Qi HJ, 2018, SCI CHINA LIFE SCI, V61, P476, DOI 10.1007/s11427-017-9270-x
   Rajamanikandan S, 2017, J BIOMOL STRUCT DYN, V35, P897, DOI 10.1080/07391102.2016.1166455
   Rajput A, 2018, NUCLEIC ACIDS RES, V46, pD894, DOI 10.1093/nar/gkx1157
   Rajput A, 2016, NUCLEIC ACIDS RES, V44, pD634, DOI 10.1093/nar/gkv1076
   Raju SC, 2020, MICROBIOME, V8, DOI 10.1186/s40168-020-00893-y
   Rathinam P, 2017, BIOFOULING, V33, P624, DOI 10.1080/08927014.2017.1350655
   Savage N, 2020, NATURE, V577, pS10, DOI 10.1038/d41586-020-00196-0
   Scarano S, 2010, BIOSENS BIOELECTRON, V25, P957, DOI 10.1016/j.bios.2009.08.039
   Sherry ST, 2001, NUCLEIC ACIDS RES, V29, P308, DOI 10.1093/nar/29.1.308
   Singh S, 2018, BIOIMPACTS, V8, P201, DOI 10.15171/bi.2018.23
   Sonnenburg ED, 2016, NATURE, V529, P212, DOI 10.1038/nature16504
   Suez J, 2014, NATURE, V514, P181, DOI 10.1038/nature13793
   Szunerits S, 2013, ANAL BIOANAL CHEM, V405, P1435, DOI 10.1007/s00216-012-6624-0
   Tap J, 2021, MICROBIOME, V9, DOI 10.1186/s40168-021-01018-9
   Trott O, 2010, J COMPUT CHEM, V31, P455, DOI 10.1002/jcc.21334
   Turnbaugh PJ, 2009, SCI TRANSL MED, V1, DOI 10.1126/scitranslmed.3000322
   Vila AV, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-019-14177-z
   Wan Y, 2019, GUT, V68, P1417, DOI 10.1136/gutjnl-2018-317609
   Weersma RK, 2020, GUT, V69, P1510, DOI 10.1136/gutjnl-2019-320204
   Wen Z., 2021, BRIEF BIOINFORM, V22, DOI [10.1093/bib/bbaa157, DOI 10.1093/BIB/BBAA157]
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Wu H, 2017, NAT MED, V23, P850, DOI 10.1038/nm.4345
   Wu SB, 2022, CHEM ENG J, V445, DOI 10.1016/j.cej.2022.136720
   Wu SB, 2021, TRENDS MICROBIOL, V29, P1130, DOI 10.1016/j.tim.2021.04.006
   Wu SB, 2021, METAB ENG, V67, P186, DOI 10.1016/j.ymben.2021.07.002
   Wu SB, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbaa218
   Wu SB, 2020, CELL MOL LIFE SCI, V77, P1319, DOI 10.1007/s00018-019-03326-8
   Wynendaele E, 2013, NUCLEIC ACIDS RES, V41, pD655, DOI 10.1093/nar/gks1137
   Xiong ZP, 2022, SCI CHINA LIFE SCI, V65, P529, DOI 10.1007/s11427-021-1946-0
   Yang FL, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbab094
   Yang L, 2009, ANTIMICROB AGENTS CH, V53, P2432, DOI 10.1128/AAC.01283-08
   Zeng X, 2021, NUCLEIC ACIDS RES, V49, pD776, DOI 10.1093/nar/gkaa924
   Zhang Qingfeng, 2020, Briefings in Bioinformatics, V22, DOI 10.1093/bib/bbaa082
   Zhang RG, 2002, NATURE, V417, P971, DOI 10.1038/nature00833
   Zhang YF, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-18414-8
   Zhao RS, 2020, GASTROENTEROLOGY, V159, P969, DOI 10.1053/j.gastro.2020.05.004
   Zhao Y., 2021, BRIEF BIOINFORM, V22, DOI [10.1093/bib/bbaa158, DOI 10.1093/BIB/BBAA158]
   Zimmermann M, 2019, NATURE, V570, P462, DOI 10.1038/s41586-019-1291-3
NR 83
TC 0
Z9 0
U1 2
U2 2
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1674-7305
EI 1869-1889
J9 SCI CHINA LIFE SCI
JI Sci. China-Life Sci.
DI 10.1007/s11427-021-2121-0
EA AUG 2022
PG 15
WC Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Life Sciences & Biomedicine - Other Topics
GA 3O3SE
UT WOS:000836754100001
PM 35933489
DA 2022-08-23
ER

PT J
AU Zhao, YQ
   Fesharaki, NJ
   Li, XH
   Patrick, TB
   Luo, JK
AF Zhao, Yiqing
   Fesharaki, Nooshin J.
   Li, Xiaohui
   Patrick, Timothy B.
   Luo, Jake
TI Semantic-Enhanced Query Expansion System for Retrieving Medical Image
   Notes
SO JOURNAL OF MEDICAL SYSTEMS
LA English
DT Article
DE Query expansion; Data visualization; Information retrieval; Knowledge
   management; Medical imaging system
ID TEXT
AB Most current image retrieval methods require constructing semantic metadata for representing image content. To manually create semantic metadata for medical images is time-consuming, yet it is a crucial component for query expansion. We proposed a new method for searching medical image notes that uses semantic metadata to improve query expansion and leverages a knowledge model developed specifically for the medical image domain to create relevant metadata. We used a syntactic parser and the Unified Medical Language System to analyze the corpus and store text information as semantic metadata in a knowledge model. Our new method has an interactive interface that allows users to provide relevance feedback and construct new queries more efficiently. Sixteen medical professionals evaluated the query expansion module, and each evaluator had prior experience searching for medical images. When using the initial query as the baseline standard, expanded queries achieved a performance boost of 22.6% in terms of the relevance score on first ten results (P-value<0.05). When using Google as another baseline, our system performed 24.6% better in terms of relevance score on the first ten results (P-value<0.05). Overall, 75% of the evaluators said the semantic-enhanced query expansion workflow is logical, easy to follow, and comfortable to use. In addition, 62% of the evaluators preferred using our system instead of Google. Evaluators who were positive about our system found the knowledge map-based visualization of candidate medical search terms helpful in refining cases from the initial search results.
C1 [Zhao, Yiqing; Fesharaki, Nooshin J.; Patrick, Timothy B.; Luo, Jake] Univ Wisconsin, Coll Hlth Sci, Ctr Biomed Data & Language Proc, Milwaukee, WI 53201 USA.
   [Li, Xiaohui] ChengDe Petr Coll, Chengde, Peoples R China.
RP Luo, JK (通讯作者)，Univ Wisconsin, Coll Hlth Sci, Ctr Biomed Data & Language Proc, Milwaukee, WI 53201 USA.
EM zhao45@uwm.edu; jafarif2@uwm.edu; xiaohui9702@163.com; tp5@uwm.edu;
   jakeluo@uwm.edu
RI Zhao, Yiqing/AEL-5533-2022
OI Zhao, Yiqing/0000-0003-2874-8136; Luo, Jake/0000-0002-3900-643X
FU GE Catalyst Grant and UWM Research Foundation [133PRJ89RA] Funding
   Source: Medline
CR ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021
   Cao G., 2008, P 31 ANN INT ACM SIG, P243, DOI DOI 10.1145/1390334.1390377
   Chen T., 2013, P 21 ACM INT C MULT, P781, DOI DOI 10.1145/2502081.2502203
   Chu W. W., 2002, COMMUNICATION I INFO, V5
   Hearst MA, 2006, COMMUN ACM, V49, P59, DOI 10.1145/1121949.1121983
   Jeon J., 2003, P 26 ANN INT ACM SIG, P119
   Jinxi Xu, 1996, SIGIR Forum, P4
   Kahn CE, 2009, J AM MED INFORM ASSN, V16, P380, DOI 10.1197/jamia.M2945
   Liu ZY, 2007, INFORM RETRIEVAL, V10, P173, DOI 10.1007/s10791-006-9020-6
   MINKER J, 1972, INFORM STORAGE RET, V8, P329, DOI 10.1016/0020-0271(72)90021-6
   Natsev A. P., 2007, P 15 ACM INT C MULT, P991, DOI DOI 10.1145/1291233.1291448
   Philbin J., 2007, COMPUT VIS PATTERN R, P1, DOI DOI 10.1109/CVPR.2007.383172
   Philbin J., 2008, P CVPR, DOI DOI 10.1109/CVPR.2008.4587635
   Rubin D. L., 2008, AAAI SPRING S SEM SC, P93
   Russell A., 2008, NITELIGHT GRAPHICAL
   Shatkay H, 2006, BIOINFORMATICS, V22, pE446, DOI 10.1093/bioinformatics/btl235
   Tong S., 2001, P 9 ACM INT C MULT, P107, DOI [DOI 10.1145/500141.500159, 10.1145/500141.500159]
   White Ryen W., 2006, COMMUN ACM, V49, P36, DOI DOI 10.1145/1121949.1121978
   Xu SH, 2008, BIOINFORMATICS, V24, P1968, DOI 10.1093/bioinformatics/btn340
   Zenz G, 2009, J WEB SEMANT, V7, P166, DOI 10.1016/j.websem.2009.07.005
   Zhao Y., BMC MED INFORM UNPUB
NR 21
TC 2
Z9 2
U1 1
U2 9
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 0148-5598
EI 1573-689X
J9 J MED SYST
JI J. Med. Syst.
PD JUN
PY 2018
VL 42
IS 6
AR 105
DI 10.1007/s10916-018-0954-1
PG 11
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA GF3FX
UT WOS:000431832100005
PM 29696548
DA 2022-08-23
ER

PT J
AU Sun, YX
   Chun, SJ
   Lee, Y
AF Sun, Yuxiang
   Chun, Seok-Ju
   Lee, Yongju
TI Learned Semantic Index Structure Using Knowledge Graph Embedding and
   Density-Based Spatial Clustering Techniques
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE semantic search; learned semantic index; knowledge graph embedding;
   linked open data; clustering techniques
AB Recently, a pragmatic approach toward achieving semantic search has made significant progress with knowledge graph embedding (KGE). Although many standards, methods, and technologies are applicable to the linked open data (LOD) cloud, there are still several ongoing problems in this area. As LOD are modeled as resource description framework (RDF) graphs, we cannot directly adopt existing solutions from database management or information retrieval systems. This study addresses the issue of efficient LOD annotation organization, retrieval, and evaluation. We propose a hybrid strategy between the index and distributed approaches based on KGE to increase join query performance. Using a learned semantic index structure for semantic search, we can efficiently discover interlinked data distributed across multiple resources. Because this approach rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. The performance of the proposed index structure is compared with some existing methods on real RDF datasets. As a result, the proposed indexing method outperforms existing methods due to its ability to prune a lot of unnecessary data scanned during semantic searching.
C1 [Sun, Yuxiang; Lee, Yongju] Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea.
   [Chun, Seok-Ju] Seoul Natl Univ Educ, Dept Comp Educ, Seoul 06639, South Korea.
RP Lee, Y (通讯作者)，Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea.
EM syx921120@gmail.com; chunsj@snue.ac.kr; yongju@knu.ac.kr
FU Basic Science Research Program through the National Research Foundation
   of Korea (NRF) - Ministry of Education [2016R1D1A1B02008553]; BK21 FOUR
   project (AI-driven Convergence Software Education Research Program) -
   Ministry of Education, School of Computer Science and Engineering,
   Kyungpook National University, Korea [4199990214394]
FX This research was supported by the Basic Science Research Program
   through the National Research Foundation of Korea (NRF) funded by
   theMinistry of Education (No. 2016R1D1A1B02008553). This study was
   supported by the BK21 FOUR project (AI-driven Convergence Software
   Education Research Program) funded by the Ministry of Education, School
   of Computer Science and Engineering, Kyungpook National University,
   Korea (4199990214394).
CR Aberger Christopher R., 2016, 2016 IEEE 32nd International Conference on Data Engineering: Workshops (ICDEW), P97, DOI 10.1109/ICDEW.2016.7495625
   aksw, LINKEDGEODATA DATASE
   Ali W, 2022, VLDB J, V31, P603, DOI 10.1007/s00778-021-00711-3
   Ameen A., 2014, COMPUT ENG INTELL SY, V5, P39
   Behning C., 2020, P INT SEMANTIC WEB C, P56
   Ben Mahria B, 2021, J BIG DATA-GER, V8, DOI 10.1186/s40537-021-00486-y
   Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chawla T., 2018, P 9 INT C COMP COMM, P1
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dingming Wu, 2020, Database Systems for Advanced Applications. 25th International Conference, DASFAA 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12113), P415, DOI 10.1007/978-3-030-59416-9_25
   Ester M., 1996, P 2 INT C KNOWL DISC, DOI DOI 10.5555/3001460.3001507
   Fernandez JD, 2013, J WEB SEMANT, V19, P22, DOI 10.1016/j.websem.2013.01.002
   Guo YB, 2005, J WEB SEMANT, V3, P158, DOI 10.1016/j.websem.2005.06.005
   Han X., 2018, P C EMP METH NAT LAN, P139
   Harth A, 2005, Third Latin American Web Congress, Proceedings, P71, DOI 10.1109/LAWEB.2005.25
   Hassan M, 2019, IEEE INT C SEMANT CO, P24, DOI 10.1109/ICOSC.2019.8665614
   Jabeen H, 2020, IEEE INT C SEMANT CO, P400, DOI 10.1109/ICSC.2020.00079
   Lashkari F, 2019, INFORM PROCESS MANAG, V56, P733, DOI 10.1016/j.ipm.2018.10.015
   Li W., 2017, P CEUR WORKSHOP P, V1963, P1
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Mikolov T., P 1 INT C LEARNING R
   Neumann T, 2010, PROC VLDB ENDOW, V3, P256, DOI 10.14778/1920841.1920877
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Quilitz B, 2008, LECT NOTES COMPUT SC, V5021, P524
   Ranichandra C, 2021, EVOL INTELL, V14, P567, DOI 10.1007/s12065-019-00315-5
   Ristoski P, 2019, SEMANT WEB, V10, P721, DOI 10.3233/SW-180317
   Sun YX, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11052405
   Thieblin E, 2020, SEMANT WEB, V11, P689, DOI 10.3233/SW-190366
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Tsatsanifos G., 2011, P 14 INT C EXT DAT T, P141, DOI [10.1145/1951365.1951384, DOI 10.1145/1951365.1951384]
   Verborgh R, 2020, SEMANT WEB, V11, P19, DOI 10.3233/SW-190372
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Yang B., 2015, P INT C LEARN REPR I
   Yongju Lee, 2014, Lecture Notes on Information Theory, V2, P243, DOI 10.12720/lnit.2.3.243-248
   Zou L, 2017, DATA SCI ENG, V2, P56, DOI 10.1007/s41019-016-0029-6
NR 37
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD JUL
PY 2022
VL 12
IS 13
AR 6713
DI 10.3390/app12136713
PG 15
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 2Y1LX
UT WOS:000825654900001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, Y
   Liu, JA
   Zhang, Z
   Wen, SP
   Xiong, WJ
AF Chen, Yao
   Liu, Jiangang
   Zhang, Zhe
   Wen, Shiping
   Xiong, Wenjun
TI MobiusE: Knowledge Graph Embedding on Mobius Ring
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Mobius ring; Torus ring; Knowledge graph; Embedding
AB In this work, we propose a novel Knowledge Graph Embedding (KGE) strategy, called MobiusE, in which the entities and relations are embedded to the surface of a Mobius ring. The proposition of such a strategy is inspired by the classic TorusE, in which the addition of two arbitrary elements is subject to a modulus operation. In this sense, TorusE naturally guarantees the critical boundedness of embedding vectors in KGE. However, the nonlinear property of addition operation on Torus ring is uniquely derived by the modulus operation, which in some extent restricts the expressiveness of TorusE. As a further generalization of TorusE, MobiusE also uses modulus operation to preserve the closeness of addition on it, but the coordinates on Mobius ring interacts with each other in the following way: any vector attaches to the surface of a Mobius ring becomes its opposite one if it moves along its parametric trace by a cycle. Hence, MobiusE assumes much more nonlinear representativeness than that of TorusE, and in turn it generates much more precise embedding results. In our experiments, MobiusE outperforms TorusE and other classic embedding strategies in several key indicators. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Chen, Yao; Liu, Jiangang; Zhang, Zhe; Xiong, Wenjun] Southwestern Univ Finance & Econ, Dept Comp Sci, Chengdu, Peoples R China.
   [Wen, Shiping] Univ Technol Sydney, Ctr Artificial Intelligence, Sydney, NSW, Australia.
RP Chen, Y (通讯作者)，Southwestern Univ Finance & Econ, Dept Comp Sci, Chengdu, Peoples R China.
EM chenyao@swufe.edu.cn
FU National Natural Science Foundation of China [61773319]; Fundamental
   Research Funds for Chinese Central Universities [JBK190502]
FX This work was supported by National Natural Science Foundation of China
   under grant No. 61773319, and also supported by the Fundamental Research
   Funds for Chinese Central Universities under grant No. JBK190502.
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   Fan M, 2014, P 28 PAC AS C LANG I, V28, P328, DOI DOI 10.3115/V1/P14-1048.URL
   Feng J, 2016, FIFTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P557
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Jia YT, 2018, ACM T WEB, V12, DOI 10.1145/3132733
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lu J, 2018, PATTERN RECOGN, V76, P228, DOI 10.1016/j.patcog.2017.11.004
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Trouillon T., 2012, P 29 INT C MACH LEAR P 29 INT C MACH LEAR, P2071
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Yang B., 2015, P 4 INT C LEARN REPR P 4 INT C LEARN REPR, P1412
   Zhang S., 2019, NEURIPS, P2731
NR 17
TC 1
Z9 1
U1 0
U2 3
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD SEP 5
PY 2021
VL 227
AR 107181
DI 10.1016/j.knosys.2021.107181
EA JUN 2021
PG 7
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TS0WT
UT WOS:000679379000024
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Filtz, E
   Kirrane, S
   Polleres, A
AF Filtz, Erwin
   Kirrane, Sabrina
   Polleres, Axel
TI The linked legal data landscape: linking legal data across different
   countries
SO ARTIFICIAL INTELLIGENCE AND LAW
LA English
DT Article
DE Linked data; Legal knowledge graph; Legal ontology; Law identifier
ID LEGISLATION; ONTOLOGY; WEB
AB The European Union is working towards harmonizing legislation across Europe, in order to improve cross-border interchange of legal information. This goal is supported for instance via standards such as the European Law Identifier (ELI) and the European Case Law Identifier (ECLI), which provide technical specifications for Web identifiers and suggestions for vocabularies to be used to describe metadata pertaining to legal documents in a machine readable format. Notably, these ECLI and ELI metadata standards adhere to the RDF data format which forms the basis of Linked Data, and therefore have the potential to form a basis for a pan-European legal Knowledge Graph. Unfortunately, to date said specifications have only been partially adopted by EU member states. In this paper we describe a methodology to transform the existing legal information system used in Austria to such a legal knowledge graph covering different steps from modeling national specific aspects, to population, and finally the integration of legal data from other countries through linked data. We demonstrate the usefulness of this approach by exemplifying practical use cases from legal information search, which are not possible in an automated fashion so far.
C1 [Filtz, Erwin; Kirrane, Sabrina; Polleres, Axel] Vienna Univ Econ & Business, Inst Data Proc & Knowledge Management, Vienna, Austria.
   [Filtz, Erwin] Siemens AG Osterreich, Vienna, Austria.
   [Polleres, Axel] Complex Sci Hub, Vienna, Austria.
RP Filtz, E (通讯作者)，Vienna Univ Econ & Business, Inst Data Proc & Knowledge Management, Vienna, Austria.; Filtz, E (通讯作者)，Siemens AG Osterreich, Vienna, Austria.
EM erwin.filtz@wu.ac.at; Sabrina.Kirrane@wu.ac.at; Axel.Polleres@wu.ac.at
OI Filtz, Erwin/0000-0003-3445-0504; Polleres, Axel/0000-0001-5670-1146;
   Kirrane, Sabrina/0000-0002-6955-7718
FU Austrian Research Promotion Agency (FFG) under the "ICT of the Future"
   program (project "AI@Work") [874111]; Federal Ministry of Digital and
   Economic Affairs of the Republic of Austria; Jubilee Fund of the City of
   Vienna; FWF Austrian Science Fund [V 759-N]; Internet Foundation Austria
   [V 759-N]; European Union's Horizon 2020 research and innovation
   programme under the Marie Sklodowska-Curie grant [860801]; Vienna
   University of Economics and Business (WU)
FX We thank the anonymous reviewers for their valuable suggestions and
   inputs. This work was partially funded by the Austrian Research
   Promotion Agency (FFG) under the "ICT of the Future" program (project
   "AI@Work", contract #874111), the Federal Ministry of Digital and
   Economic Affairs of the Republic of Austria and the Jubilee Fund of the
   City of Vienna. Sabrina Kirrane is funded by the FWF Austrian Science
   Fund and the Internet Foundation Austria under the FWF Elise Richter and
   netidee SCIENCE programmes as project number V 759-N. This project has
   received funding from the European Union's Horizon 2020 research and
   innovation programme under the Marie Sklodowska-Curie grant agreement No
   860801. Open access funding provided by Vienna University of Economics
   and Business (WU).
CR AJANI G, 2007, 11 INT C ART INT LAW, P43, DOI DOI 10.1145/1276318.1276327
   Ajani G, 2016, APPL ONTOL, V11, P325, DOI 10.3233/AO-170174
   Akbik A., 2018, P 27 INT C COMP LING, P1638
   Athan T., 2013, P 14 INT C ART INT L, P3
   Bacci L, 2019, FRONT ARTIF INTEL AP, V317, P149, DOI 10.3233/FAIA190017
   Berners-Lee T, 2006, LINKED DATA
   Boella G, 2015, P 15 INT C ART INT L, P171, DOI DOI 10.1145/2746090.2746106
   Boella G, 2019, ARTIF INTELL LAW, V27, P227, DOI 10.1007/s10506-018-9239-8
   Boer A., 2002, Legal Knowledge and Information Systems. JURIX 2002: 15th Annual Conference, P1
   Boer A, 2008, LECT NOTES ARTIF INT, V4884, P21, DOI 10.1007/978-3-540-85569-9_2
   Bosque-Gil J, 2017, P ELEX 2017 C EL LEX, P19
   Breuker J, 2009, LAW ONTOLOGIES SEMAN, V188, P3, DOI [10.3233/978-1-58603-942-4-3, DOI 10.3233/978-1-58603-942-4-3]
   Cardellino C, 2017, P 16 ED INT C ART IN, P9, DOI DOI 10.1145/3086512.3086514
   Casanovas P, 2016, SEMANT WEB, V7, P213, DOI 10.3233/SW-160224
   Chalkidis I, 2017, LECT NOTES COMPUT SC, V10249, P591, DOI 10.1007/978-3-319-58068-5_36
   Cunningham H., 1999, JAPE JAVA ANNOTATION
   Rodrigues CMD, 2019, EXPERT SYST APPL, V130, P12, DOI 10.1016/j.eswa.2019.04.009
   De Vos M, 2019, LECT NOTES COMPUT SC, V11784, P36, DOI 10.1007/978-3-030-31095-0_3
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Distinto I, 2016, SEMANT WEB, V7, P267, DOI 10.3233/SW-140151
   Dozier C, 2010, LECT NOTES ARTIF INT, V6036, P27, DOI 10.1007/978-3-642-12837-0_2
   Munoz-Soro JF, 2016, SEMANT WEB, V7, P295, DOI 10.3233/SW-150195
   Francart T, 2019, FRONT ARTIF INTEL AP, V317, P137, DOI 10.3233/FAIA190016
   Francesconi Enrico, 2015, Electronic Government and the Information Systems Perspective. 4th International Conference, EGOVIS 2015. Proceedings: LNCS 9265, P29, DOI 10.1007/978-3-319-22389-6_3
   Gangemi A., 2007, LOAIT, V321, P65
   Ghosh M. E., 2016, INT J KNOWLEDGE ENG, V2, P109
   Grishman R., 1996, COLING 1996, P466
   Hoekstra R., 2007, P LOAIT, V321, P43
   Hogan A., 2020, ARXIV PREPRINT ARXIV
   Kefali E, 2017, ON LINE PUBLICATION
   Koutraki M, 2018, 14 INT C SEM SYST CO, V2198
   Kucera J., 2015, DATESO, P52
   Lafferty J., 2001, P 18 INT C MACH LEAR
   Leitner Elena, 2019, Semantic Systems. The Power of AI and Knowledge Graphs. 15th International Conference, SEMANTiCS 2019. Proceedings: Lecture Notes in Computer Science (LNCS 11702), P272, DOI 10.1007/978-3-030-33220-4_20
   Leone V, 2020, ARTIF INTELL LAW, V28, P207, DOI 10.1007/s10506-019-09252-1
   Manning C.D., 2008, INTRO INFORM RETRIEV, DOI [10.1017/cbo9780511809071, DOI 10.1017/CBO9780511809071]
   McCrae John.P., 2017, P ELEX 2017 C, P19
   Montiel-Ponsoda E, 2017, P 1 WORKSH TECHN REG, V2049, P15
   Munoz S, 2009, J WEB SEMANT, V7, P220, DOI 10.1016/j.websem.2009.07.003
   Neumaier S, 2019, J WEB SEMANT, V55, P21, DOI 10.1016/j.websem.2018.12.007
   Oksanen A, 2019, FRONT ARTIF INTEL AP, V317, P212, DOI 10.3233/FAIA190023
   Oltramari A, 2018, SEMANT WEB, V9, P185, DOI 10.3233/SW-170283
   Palmirani Monica, 2011, Rule-Based Modeling and Computing on the Semantic Web. Proceedings 5th International Symposium, RuleML 2011 - America, P298, DOI 10.1007/978-3-642-24908-2_30
   Palmirani M, 2018, LECT NOTES COMPUT SC, V11032, P139, DOI 10.1007/978-3-319-98349-3_11
   Pandit Harshvardhan J., 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P481, DOI 10.1007/978-3-319-93417-4_31
   Presutti V, 2008, LECT NOTES COMPUT SC, V5231, P128, DOI 10.1007/978-3-540-87877-3_11
   Publications Office of the European Union, 2020, EUR LEG ID ELI
   Publications Office of the European Union, 2020, COMM DAT MOD CDM
   Rodriguez-Doncel V, 2016, SEMANT WEB, V7, P311, DOI 10.3233/SW-160215
   Sanh V., 2019, ARXIV191001108
   Santos C, 2016, STUDIES SEMANTIC WEB, V32, P69, DOI [10.3233/978-1-61499-826-6-69, DOI 10.3233/978-1-61499-826-6-69]
   Scherp A, 2013, CEUR WORKSHOP PROC, V1188
   Steyskal S., 2014, P 10 INT C SEM SYST
   Sure-Vetter Y, 2019, P POST DEM TRACK 15, V2451
   The Council of the European Union, 2011, COUNCIL CONCLUSIONS
   Uschold M, 1996, KNOWL ENG REV, V11, P93, DOI 10.1017/S0269888900007797
   van Opijnen M, 2017, 2017 7TH INTERNATIONAL CONFERENCE FOR E-DEMOCRACY AND OPEN GOVERNMENT (CEDEM), P135, DOI 10.1109/CeDEM.2017.17
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Vegetti M, 2011, ENG APPL ARTIF INTEL, V24, P1305, DOI 10.1016/j.engappai.2011.02.014
   W3C JSON for Linking Data Community Group, 2012, JAVASCRIPT OBJECT NO
   W3C Ontology-Lexica Community Group, 2016, LEX MOD ONT FIN COMM
   W3C Recommendation, 2012, DIRECT MAPPING RELAT
   Wolf T., 2019, P 2020 C EMP METH NA
NR 63
TC 2
Z9 2
U1 2
U2 10
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-8463
EI 1572-8382
J9 ARTIF INTELL LAW
JI Artif. Intell. Law
PD DEC
PY 2021
VL 29
IS 4
BP 485
EP 539
DI 10.1007/s10506-021-09282-8
EA FEB 2021
PG 55
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Law
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Government & Law
GA WJ9MK
UT WOS:000621713000001
OA hybrid
DA 2022-08-23
ER

PT J
AU Zhu, GG
   Iglesias, CA
AF Zhu, Ganggao
   Iglesias, Carlos A.
TI Computing Semantic Similarity of Concepts in Knowledge Graphs
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Semantic similarity; semantic relatedness; information content;
   knowledge graph; WordNet; DBpedia
ID REPRESENTATION; FREQUENCY; WORDNET; QUERIES
AB This paper presents a method for measuring the semantic similarity between concepts in Knowledge Graphs (KGs) such as WordNet and DBpedia. Previous work on semantic similarity methods have focused on either the structure of the semantic network between concepts (e.g., path length and depth), or only on the Information Content (IC) of concepts. We propose a semantic similarity method, namely wpath, to combine these two approaches, using IC to weight the shortest path length between concepts. Conventional corpus-based IC is computed from the distributions of concepts over textual corpus, which is required to prepare a domain corpus containing annotated concepts and has high computational cost. As instances are already extracted from textual corpus and annotated by concepts in KGs, graph-based IC is proposed to compute IC based on the distributions of concepts over instances. Through experiments performed on well known word similarity datasets, we show that the wpath semantic similarity method has produced a statistically significant improvement over other semantic similarity methods. Moreover, in a real category classification evaluation, the wpath method has shown the best performance in terms of accuracy and F score.
C1 [Zhu, Ganggao; Iglesias, Carlos A.] Univ Politecn Madrid, Escuela Tecn Super Ingn Telecomunicac, Avda Complutense 30, E-28040 Madrid, Spain.
RP Zhu, GG (通讯作者)，Univ Politecn Madrid, Escuela Tecn Super Ingn Telecomunicac, Avda Complutense 30, E-28040 Madrid, Spain.
EM gzhu@dit.upm.es; cif@gsi.dit.upm.es
RI FERNANDEZ, CARLOS ANGEL A. IGLESIAS/I-2181-2015
OI FERNANDEZ, CARLOS ANGEL A. IGLESIAS/0000-0002-1755-2712
FU Spanish Ministry of Economy and Competitiveness [TEC2015-68284-R,
   RTC-2016-5053-7]; Regional Government of Madrid - EU [P2013/ICE-3019];
   European Union through the project EUROSENTIMENT [296277]; European
   Union through MixedEmotions [141111]
FX This work is supported by the Spanish Ministry of Economy and
   Competitiveness under the R&D projects SEMOLA (TEC2015-68284-R) and
   EmoSpaces (RTC-2016-5053-7), by the Regional Government of Madrid
   through the project MOSI-AGIL-CM (grant P2013/ICE-3019, co-funded by EU
   Structural Funds FSE and FEDER), and by the European Union through the
   project EUROSENTIMENT (Grant Agreement No: 296277) and MixedEmotions
   (Grant Agreement no: 141111).
CR Agirre E, 2009, P HUM LANG TECHN 200, P19, DOI DOI 10.3115/1620754.1620758
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13
   Church K., 1999, TEXT SPEECH LANG TEC, P283
   Church K. W., 1990, Computational Linguistics, V16, P22
   Dragoni M, 2012, EXPERT SYST APPL, V39, P10376, DOI 10.1016/j.eswa.2012.01.188
   Finkelstein L, 2002, ACM T INFORM SYST, V20, P116, DOI 10.1145/503104.503110
   Francis WN., 1964, BROWN CORPUS MANUAL
   Gabrilovich E, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1606
   Gligorov R., 2007, P 16 INT C WORLD WID, P767, DOI [10.1145/1242572.1242676., DOI 10.1145/1242572.1242676]
   Han L., 2012, P 21 ACM INT C INF K, P2090
   Hill F., 2014, ARXIV14083456
   Hoffart J., 2013, P 23 INT JOINT C ART, P3161
   Hoffart J., 2012, P 21 ACM INT C INF K, P545, DOI DOI 10.1145/2396761.2396832
   Horrocks I, 2008, COMMUN ACM, V51, P58, DOI 10.1145/1409360.1409377
   Hovy E, 2013, ARTIF INTELL, V194, P2, DOI 10.1016/j.artint.2012.10.002
   Hulpus I, 2015, LECT NOTES COMPUT SC, V9366, P442, DOI 10.1007/978-3-319-25007-6_26
   Jiang J. J., 1997, P INT C RES COMP LIN
   Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211
   Leacock C, 1998, LANG SPEECH & COMMUN, P265
   Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005
   Lin D., 1998, Machine Learning. Proceedings of the Fifteenth International Conference (ICML'98), P296
   Mihalcea R., 2006, 21 NAT C ART INT 18, V1, P775
   Mikolov T., 2013, ADV NEURAL INFORM PR
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936
   Moro A., 2014, TACL, V2, P231, DOI DOI 10.1162/TACL_A_00179
   Navigli R, 2012, ARTIF INTELL, V193, P217, DOI 10.1016/j.artint.2012.07.001
   Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355
   Pedersen T., 2004, HLT NAACL 2004, P38, DOI DOI 10.3115/1614025.1614037
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Pontiki M., 2015, P 9 INT WORKSH SEM E, P486
   Pontiki M, 2014, P 10 INT WORKSH SEM, P27, DOI [10.15520/ajcsit.v4i8.9, DOI 10.18653/V1/S16-1055]
   Pound J., 2010, SIGMOD, P423
   RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528
   Resnik P, 1995, INT JOINT CONF ARTIF, P448
   Resnik P, 1999, J ARTIF INTELL RES, V11, P95, DOI 10.1613/jair.514
   RUBENSTEIN H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657
   Sanchez D, 2012, EXPERT SYST APPL, V39, P7718, DOI 10.1016/j.eswa.2012.01.082
   Schuhmacher M, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P543, DOI 10.1145/2556195.2556250
   Shekarpour S, 2015, J WEB SEMANT, V30, P39, DOI 10.1016/j.websem.2014.06.002
   Singer P, 2013, INT J SEMANT WEB INF, V9, P41, DOI 10.4018/ijswis.2013100103
   STEIGER JH, 1980, PSYCHOL BULL, V87, P245, DOI 10.1037/0033-2909.87.2.245
   Turney PD, 2010, J ARTIF INTELL RES, V37, P141, DOI 10.1613/jair.2934
   TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/h0026750
   WU ZB, 1994, 32ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P133
NR 47
TC 84
Z9 89
U1 4
U2 82
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD JAN 1
PY 2017
VL 29
IS 1
BP 72
EP 85
DI 10.1109/TKDE.2016.2610428
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA EF8CO
UT WOS:000390556100006
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Kondinski, A
   Menon, A
   Nurkowski, D
   Farazi, F
   Mosbach, S
   Akroyd, J
   Kraft, M
AF Kondinski, Aleksandar
   Menon, Angiras
   Nurkowski, Daniel
   Farazi, Feroz
   Mosbach, Sebastian
   Akroyd, Jethro
   Kraft, Markus
TI Automated Rational Design of Metal-Organic Polyhedra
SO JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
LA English
DT Article; Early Access
ID RETICULAR CHEMISTRY; KNOWLEDGE GRAPH; DATABASE; CONSTRUCTION;
   FRAMEWORKS; SCALE; CAGES
AB Metal-organic polyhedra (MOPs) are hybrid organic-inorganic nanomolecules, whose rational design depends on harmonious consideration of chemical complementarity and spatial compatibility between two or more types of chemical building units (CBUs). In this work, we apply knowledge engineering technology to automate the derivation of MOP formulations based on existing knowledge. For this purpose we have (i) curated relevant MOP and CBU data; (ii) developed an assembly model concept that embeds rules in the MOP construction; (iii) developed an OntoMOPs ontology that defines MOPs and their key properties; (iv) input agents that populate The World Avatar (TWA) knowledge graph; and (v) input agents that, using information from TWA, derive a list of new constructible MOPs. Our result provides rapid and automated instantiation of MOPs in TWA and unveils the immediate chemical space of known MOPs, thus shedding light on new MOP targets for future investigations.
C1 [Kondinski, Aleksandar; Menon, Angiras; Farazi, Feroz; Mosbach, Sebastian; Akroyd, Jethro; Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England.
   [Nurkowski, Daniel; Kraft, Markus] CMCL Innovat, Cambridge CB3 0AX, England.
   [Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore, CARES, Singapore 138602, Singapore.
   [Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore 637459, Singapore.
   [Kraft, Markus] Alan Turing Inst, London NW1 2DB, England.
RP Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England.; Kraft, M (通讯作者)，CMCL Innovat, Cambridge CB3 0AX, England.; Kraft, M (通讯作者)，Cambridge Ctr Adv Res & Educ Singapore, CARES, Singapore 138602, Singapore.; Kraft, M (通讯作者)，Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore 637459, Singapore.; Kraft, M (通讯作者)，Alan Turing Inst, London NW1 2DB, England.
EM mk306@cam.ac.uk
RI ; Kraft, Markus/D-7243-2016; Akroyd, Jethro/A-8698-2010
OI Mosbach, Sebastian/0000-0001-7018-9433; Kraft,
   Markus/0000-0002-4293-8924; Kondinski, Aleksandar/0000-0002-0559-0172;
   Farazi, Feroz/0000-0002-6786-7309; Akroyd, Jethro/0000-0002-2143-8656
FU National Research Foundation, Prime Minister's Office, Singapore under
   its Campus for Research Excellence and Technological Enterprise (CREATE)
   program; UK Engineering and Physical Sciences Research Council (EPSRC)
   [EP/R029369/1]; ARCHER; Humboldt Foundation (Berlin, Germany); Isaac
   Newtwon Trust (Cambridge, UK)
FX This research was supported by the National Research Foundation, Prime
   Minister's O ffice, Singapore under its Campus for Research Excellence
   and Technological Enterprise (CREATE) program. The authors are grateful
   to the UK Engineering and Physical Sciences Research Council (EPSRC,
   Grant Number: EP/R029369/1) and ARCHER for fi nancial and computational
   support as a part of their funding to the UK Consortium on Turbulent
   Reacting Flows ( www.ukctrf.com ) . AK and MK thank the Humboldt
   Foundation (Berlin, Germany) and the Isaac Newtwon Trust (Cambridge, UK)
   for the Feodor Lynen Fellowship. Mr. Jiaru Bai is thanked for
   discussions and feedback.
CR Addicoat MA, 2014, J PHYS CHEM A, V118, P9607, DOI 10.1021/jp507643v
   Akroyd J., 2021, DATA-CENTRIC ENG, V2, pe14
   Albalad J, 2019, CHEM COMMUN, V55, P12785, DOI 10.1039/c9cc07083d
   ALLEN FH, 1979, ACTA CRYSTALLOGR B, V35, P2331, DOI 10.1107/S0567740879009249
   [Anonymous], 2001, SCI AM
   Anyushin AV, 2020, CHEM SOC REV, V49, P382, DOI 10.1039/c8cs00854j
   Bai JR, 2022, JACS AU, V2, P292, DOI 10.1021/jacsau.1c00438
   Bai JR, 2021, J CHEM INF MODEL, V61, P1701, DOI 10.1021/acs.jcim.0c01322
   Balzani V, 2000, ANGEW CHEM INT EDIT, V39, P3348, DOI 10.1002/1521-3773(20001002)39:19<3348::AID-ANIE3348>3.0.CO;2-X
   Bird CL, 2013, CHEM SOC REV, V42, P8157, DOI 10.1039/c3cs60122f
   Castilla AM, 2014, ACCOUNTS CHEM RES, V47, P2063, DOI 10.1021/ar5000924
   Cheetham AK, 2018, ACCOUNTS CHEM RES, V51, P659, DOI 10.1021/acs.accounts.7b00497
   Chung YG, 2019, J CHEM ENG DATA, V64, P5985, DOI 10.1021/acs.jced.9b00835
   Cohen S., 2021, COMPLEX NETWORKS THE, P742
   Czaja AU, 2009, CHEM SOC REV, V38, P1284, DOI 10.1039/b804680h
   Degtyarenko K, 2008, NUCLEIC ACIDS RES, V36, pD344, DOI 10.1093/nar/gkm791
   DREXLER KE, 1981, P NATL ACAD SCI USA, V78, P5275, DOI 10.1073/pnas.78.9.5275
   Dumontheil I, 2014, DEV COGN NEUROS-NETH, V10, P57, DOI 10.1016/j.dcn.2014.07.009
   Eibeck A., 2020, DATA-CENTRIC ENG, V1
   Eibeck A, 2019, COMPUT CHEM ENG, V131, DOI 10.1016/j.compchemeng.2019.106586
   Evans JD, 2017, CHEM SOC REV, V46, P3286, DOI 10.1039/c7cs00084g
   Farazi F, 2020, COMPUT CHEM ENG, V137, DOI 10.1016/j.compchemeng.2020.106813
   Farazi F, 2020, J CHEM INF MODEL, V60, P108, DOI 10.1021/acs.jcim.9b00960
   Fernandez-Lopez M., 1997, METHONTOLOGY ONTOLOG
   Frey JG, 2013, WIRES COMPUT MOL SCI, V3, P465, DOI 10.1002/wcms.1127
   Gan HM, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-17989-6
   Giunchiglia F, 2014, KNOWL ORGAN, V41, P44, DOI 10.5771/0943-7444-2014-1-44
   Giunchiglia F, 2012, J DATA SEMANT, V1, P57, DOI 10.1007/s13740-012-0005-x
   Glimm B, 2014, J AUTOM REASONING, V53, P245, DOI 10.1007/s10817-014-9305-1
   Gosselin AJ, 2021, J AM CHEM SOC, V143, P14956, DOI 10.1021/jacs.1c05613
   Gosselin EJ, 2020, CHEM REV, V120, P8987, DOI 10.1021/acs.chemrev.9b00803
   Gruninger M, 1995, INT JOINT C ART INT
   Guillerm V, 2014, CHEM SOC REV, V43, P6141, DOI 10.1039/c4cs00135d
   Harris K, 2013, J AM CHEM SOC, V135, P12497, DOI 10.1021/ja4043609
   He SQ, 2021, J PHYS CHEM B, V125, P13229, DOI 10.1021/acs.jpcb.1c06690
   Heine T, 2014, FRONT MATER, DOI 10.3389/fmats.2014.00007
   Hoffmann R, 1998, THEOCHEM-J MOL STRUC, V424, P1, DOI 10.1016/S0166-1280(97)00219-4
   Hoffmann R, 2008, ANGEW CHEM INT EDIT, V47, P7164, DOI 10.1002/anie.200801206
   Hosono N, 2018, ACCOUNTS CHEM RES, V51, P2437, DOI 10.1021/acs.accounts.8b00361
   Inderwildi O., 2022, LECT NOTES ENERGY, V1st
   Jiang Y, 2021, CCS CHEM, V3, P1659, DOI 10.31635/ccschem.020.202000314
   Jiao JJ, 2018, J AM CHEM SOC, V140, P2251, DOI 10.1021/jacs.7b11679
   Kalmutzki MJ, 2019, INTRODUCTION TO RETICULAR CHEMISTRY: METAL-ORGANIC FRAMEWORKS AND COVALENT ORGANIC FRAMEWORKS, P453
   Kanza S, 2019, EXPERT OPIN DRUG DIS, V14, P433, DOI 10.1080/17460441.2019.1586880
   KIDO J, 1995, SCIENCE, V267, P1332, DOI 10.1126/science.267.5202.1332
   Kim S, 2006, J AM CHEM SOC, V128, P16701, DOI 10.1021/ja066376f
   Kitano H, 2021, NPJ SYST BIOL APPL, V7, DOI 10.1038/s41540-021-00189-3
   Kondinski A., 2021, CHEM MODELLING, P39
   Kondinski A, 2022, CHEM SCI, V13, P6397, DOI 10.1039/d2sc01004f
   Kondinski A, 2021, NANOSCALE, V13, P13574, DOI 10.1039/d1nr02357h
   Kondinski A, 2020, J CHEM EDUC, V97, P289, DOI 10.1021/acs.jchemed.9b00739
   Kondinski A, 2019, J CHEM EDUC, V96, P601, DOI 10.1021/acs.jchemed.8b00769
   Kraft M, 2020, CHEM-ING-TECH, V92, P967, DOI 10.1002/cite.202000002
   Krdzavac N, 2019, J CHEM INF MODEL, V59, P3154, DOI 10.1021/acs.jcim.9b00227
   Lee S, 2021, CHEM SOC REV, V50, P528, DOI 10.1039/d0cs00443j
   LEHN JM, 1993, SCIENCE, V260, P1762, DOI 10.1126/science.8511582
   Li H, 1999, NATURE, V402, P276, DOI 10.1038/46248
   Li XX, 2019, COORDIN CHEM REV, V397, P220, DOI 10.1016/j.ccr.2019.07.005
   Long JR, 2009, CHEM SOC REV, V38, P1213, DOI 10.1039/b903811f
   Lopez X, 2012, CHEM SOC REV, V41, P7537, DOI 10.1039/c2cs35168d
   Lyu H, 2020, CHEM-US, V6, P2219, DOI 10.1016/j.chempr.2020.08.008
   Mallick A, 2013, ANGEW CHEM INT EDIT, V52, P13755, DOI 10.1002/anie.201307486
   Mellot-Draznieks C, 2004, ANGEW CHEM INT EDIT, V43, P6290, DOI 10.1002/anie.200454251
   Menon A, 2019, CURR OPIN CHEM ENG, V26, P33, DOI 10.1016/j.coche.2019.08.004
   Mosbach S, 2020, J CHEM INF MODEL, V60, P6155, DOI 10.1021/acs.jcim.0c01145
   Motik B, 2009, J ARTIF INTELL RES, V36, P165, DOI 10.1613/jair.2811
   Muller A, 1998, ANGEW CHEM INT EDIT, V37, P3360, DOI 10.1002/(SICI)1521-3773(19981231)37:24<3359::AID-ANIE3359>3.0.CO;2-J
   Noy N.F., 2001, ONTOLOGY DEV
   O'Keeffe M, 2008, ACCOUNTS CHEM RES, V41, P1782, DOI 10.1021/ar800124u
   Perry JJ, 2009, CHEM SOC REV, V38, P1400, DOI 10.1039/b807086p
   Piccinno F, 2016, J CLEAN PROD, V135, P1085, DOI 10.1016/j.jclepro.2016.06.164
   Pilgrim B, 2020, CHEMPLUSCHEM, V85, P1842, DOI 10.1002/cplu.202000408
   Pinto HS, 2004, KNOWL INF SYST, V6, P441, DOI 10.1007/s10115-003-0138-1
   Piskorz TK, 2022, ACS CATAL, V12, P5806, DOI 10.1021/acscatal.2c00837
   Poole DA, 2020, CHEM SCI, V11, P12350, DOI 10.1039/d0sc03992f
   RAPPE AK, 1992, J AM CHEM SOC, V114, P10024, DOI 10.1021/ja00051a040
   Segler MHS, 2017, CHEM-EUR J, V23, P6118, DOI 10.1002/chem.201604556
   Simms C, 2020, EUR J INORG CHEM, V2020, P2559, DOI 10.1002/ejic.202000254
   Steed J. W, 2013, SUPRAMOL CHEM, P1
   Sudik AC, 2005, J AM CHEM SOC, V127, P7110, DOI 10.1021/ja042802q
   Tan CX, 2018, ANGEW CHEM INT EDIT, V57, P2085, DOI 10.1002/anie.201711310
   te Velde G, 2001, J COMPUT CHEM, V22, P931, DOI 10.1002/jcc.1056
   Tecuci G, 2016, KNOWLEDGE ENG BUILDI
   Tranchemontagne DJ, 2009, CHEM SOC REV, V38, P1257, DOI 10.1039/b817735j
   Tranchemontagne DJL, 2008, ANGEW CHEM INT EDIT, V47, P5136, DOI 10.1002/anie.200705008
   Turcani L, 2021, J CHEM PHYS, V154, DOI 10.1063/5.0049708
   USCHOLD M, 1995, WORKSH BAS ONT ISS K
   Vardhan H, 2016, COORDIN CHEM REV, V306, P171, DOI 10.1016/j.ccr.2015.05.016
   Vardhan H, 2015, ADV SYNTH CATAL, V357, P1351, DOI 10.1002/adsc.201400778
   VONHIPPEL A, 1956, SCIENCE, V123, P315, DOI 10.1126/science.123.3191.315
   Wahiduzzaman M, 2018, CHEM COMMUN, V54, P10812, DOI 10.1039/c8cc05455j
   WHITESIDES GM, 1991, SCIENCE, V254, P1312, DOI 10.1126/science.1962191
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Xing WH, 2018, J MATER CHEM A, V6, P7724, DOI 10.1039/c8ta00858b
   Young TA, 2020, J CHEM INF MODEL, V60, P3546, DOI 10.1021/acs.jcim.0c00519
   Zhang DW, 2021, NAT REV CHEM, V5, P168, DOI 10.1038/s41570-020-00246-1
   Zhang YT, 2018, J AM CHEM SOC, V140, P17365, DOI 10.1021/jacs.8b10866
   Zhang ZJ, 2014, CHEM SCI, V5, P927, DOI 10.1039/c3sc53099j
   Zhou XC, 2021, J CHEM INF MODEL, V61, P3868, DOI 10.1021/acs.jcim.1c00275
NR 99
TC 0
Z9 0
U1 15
U2 15
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0002-7863
EI 1520-5126
J9 J AM CHEM SOC
JI J. Am. Chem. Soc.
DI 10.1021/jacs.2c03402
EA JUN 2022
PG 16
WC Chemistry, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry
GA 2O7KK
UT WOS:000819233300001
PM 35731954
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Chang, SI
   Chang, LM
   Liao, JC
AF Chang, She-, I
   Chang, Li-Min
   Liao, Jhan-Cyun
TI Risk factors of enterprise internal control under the internet of things
   governance: A qualitative research approach
SO INFORMATION & MANAGEMENT
LA English
DT Article
DE Internet of things (IoT); Risk factors; Enterprise internal control;
   Qualitative research; IT governance
ID MANAGING RISKS; ACCESS-CONTROL; DELPHI METHOD; COBIT 5; MANAGEMENT;
   SECURITY; CHALLENGES; IOT; FRAMEWORK; BUSINESS
AB This study aims to (1) define the critical risk factors that influence the governance of enterprise internal control in an IoT environment, and (2) classify the risk factors and study their importance in such an environment. The study uses Gowin's Vee knowledge map as a research strategy to mitigate the limitations of qualitative research through a set of strict research procedures. In addition, the Delphi method is used to test and provide feedback to justify and revise the critical risk factors. Finally, 83 items were obtained and categorized into eight different types of critical risk factors. For emphasizing how the risk factors of enterprise internal control involve diverse stakeholders, the critical risk factors are further classified based on the three-layer DCM architecture for mapping with various perceptions. The results of this research can be used as a reference in managing risk factors under the IoT environment. In the new generation of IoT governance practice, the related factors can also be regarded as the essential measurement items for enterprises in conducing effective internal control and auditing.
C1 [Chang, She-, I; Chang, Li-Min; Liao, Jhan-Cyun] Natl Chung Cheng Univ, Dept Accounting & Informat Technol, 168 Univ Rd, Chiayi 62102, Taiwan.
RP Chang, LM (通讯作者)，Natl Chung Cheng Univ, Dept Accounting & Informat Technol, 168 Univ Rd, Chiayi 62102, Taiwan.
EM actsic@ccu.edu.tw; changclm@gmail.com; crazybearb@gmail.com
RI Chang, She-I/AAV-1985-2021
OI Chang, She-I/0000-0001-9193-4594
CR Al-Ayyoub M, 2015, CLUSTER COMPUT, V18, P919, DOI 10.1007/s10586-015-0449-5
   Al-Momani A. M, 2016, INT J APPL RES, V2, P361
   Alhawari S, 2012, INT J INFORM MANAGE, V32, P50, DOI 10.1016/j.ijinfomgt.2011.07.002
   Ali A, 2017, INT J INFORM MANAGE, V37, P639, DOI 10.1016/j.ijinfomgt.2017.05.008
   Almeida VAF, 2015, IEEE INTERNET COMPUT, V19, P56, DOI 10.1109/MIC.2015.86
   Aloini D, 2007, INFORM MANAGE-AMSTER, V44, P547, DOI 10.1016/j.im.2007.05.004
   Ammar M, 2018, J INF SECUR APPL, V38, P8, DOI 10.1016/j.jisa.2017.11.002
   [Anonymous], 2015, THE J, DOI DOI 10.4236/JSSM.2015.84056
   [Anonymous], 2016, TECHCRUNCH
   [Anonymous], 2007, ECONOMIST
   [Anonymous], 2015, GARTN SAYS 6 4 BILL
   [Anonymous], 2008, ECONOMIST
   ARM, 2017, EC INT UN IOT BUS IN
   Astuti HM, 2017, PROCEDIA COMPUT SCI, V124, P569, DOI 10.1016/j.procs.2017.12.191
   Azzali S., 2013, ACCOUNTING INFORM SY, P47, DOI DOI 10.1007/978-3-642-35761-9_3
   Babar S, 2010, COMM COM INF SC, V89, P420
   Bandyopadhyay S., 2013, J ICT STANDARDIZATIO, V1, P253
   Barnaghi P, 2013, IEEE INTELL SYST, V28, P6, DOI 10.1109/MIS.2013.142
   Beasley M., 2005, J ACCOUNT PUB POLICY, V24, P521, DOI DOI 10.1016/J.JACCPUBPOL.2005.10.001
   Benaroch M, 2006, MIS QUART, V30, P827
   Boyd D, 2012, INFORM COMMUN SOC, V15, P662, DOI 10.1080/1369118X.2012.678878
   Brender N, 2013, INT J INFORM MANAGE, V33, P726, DOI 10.1016/j.ijinfomgt.2013.05.004
   Broadbent M, 1997, SLOAN MANAGE REV, V38, P77
   Bromiley P., 2016, IESE INSIGHT, V28, P15
   Bulgurcu B, 2010, MIS QUART, V34, P523
   CAMERER C, 1992, J RISK UNCERTAINTY, V5, P325, DOI 10.1007/BF00122575
   Chang S.I., 2005, INT J MANAG ENTERP D, V3, P147
   Chen SZ, 2014, IEEE INTERNET THINGS, V1, P349, DOI 10.1109/JIOT.2014.2337336
   CHURCHILL GA, 1979, J MARKETING RES, V16, P64, DOI 10.2307/3150876
   Cloninger D.O., 1983, J BEHAV EC, V11, P33
   Cooper DW, 2001, BIOPHARM-APPL T BIO, P7
   DALKEY N, 1963, MANAGE SCI, V9, P458, DOI 10.1287/mnsc.9.3.458
   Daud W.N.W.D., 2010, INT BUSINESS EC RES, V9, P55, DOI DOI 10.5539/ijbm.v6n12p205
   Davenport TH, 1998, HARVARD BUS REV, V76, P121
   De Haes S, 2013, J INF SYST, V27, P307, DOI 10.2308/isys-50422
   Debreceny RS, 2013, J INF SYST, V27, P129, DOI 10.2308/isys-10339
   Debreceny RS, 2013, J INF SYST, V27, P157, DOI 10.2308/isys-50418
   Disparte D., 2016, SIMPLE ETHICS RULES
   Feng M, 2009, J ACCOUNT ECON, V48, P190, DOI 10.1016/j.jacceco.2009.09.004
   Fox R, 2007, J GEOGR HIGHER EDUC, V31, P269, DOI 10.1080/03098260601063735
   Fraser JRS, 2016, BUS HORIZONS, V59, P689, DOI 10.1016/j.bushor.2016.06.007
   French AM, 2016, COMMUN ASSOC INF SYS, V38, P840, DOI 10.17705/1CAIS.03840
   Gaynor M, 1995, RAND J ECON, V26, P591, DOI 10.2307/2556008
   Giannakis M, 2016, J ENTERP INF MANAG, V29, P706, DOI 10.1108/JEIM-06-2015-0050
   Gowin D. B., 1981, ED
   Grant K, 2014, INT J INFORM MANAGE, V34, P99, DOI 10.1016/j.ijinfomgt.2013.11.001
   Gubbi J, 2013, FUTURE GENER COMP SY, V29, P1645, DOI 10.1016/j.future.2013.01.010
   Hair J., 2019, MULTIVARIATE DATA AN, V8t
   Hanafin S., 2004, REV LIT DELPHI TECHN
   Hess MF, 2016, BUS HORIZONS, V59, P13, DOI 10.1016/j.bushor.2015.09.005
   HILL KQ, 1975, TECHNOL FORECAST SOC, V7, P179, DOI 10.1016/0040-1625(75)90057-8
   HOLDEN MC, 1993, ETR&D-EDUC TECH RES, V41, P5, DOI 10.1007/BF02297509
   Hsu C.C., 2007, PRACT ASSESS RES EVA, V12, P1
   Hsu CL, 2016, COMPUT HUM BEHAV, V62, P516, DOI 10.1016/j.chb.2016.04.023
   Hyun S, 2018, IEEE COMMUN MAG, V56, P171, DOI 10.1109/MCOM.2018.1700662
   IEEE, 2015, INT THINGS DEF INT T
   IEEE, 2017, P2413 IEEE
   IIA, 2013, 3 LIN DEF EFF RISK M
   ISACA, 2015, COBIT 5 WHAT IS IT
   ISO, 2018, 31000 ISO
   ISO/IEC, 2013, 270012013 ISOIEC
   Ives B., 2016, MIS Q EXEC, V15, P1
   Jin J, 2014, IEEE INTERNET THINGS, V1, P112, DOI 10.1109/JIOT.2013.2296516
   Joseph N, 2017, J GLOB INF MANAG, V25, P38, DOI 10.4018/JGIM.2017040103
   Kaplan RS, 2012, HARVARD BUS REV, V90, P48
   Kim DJ, 2008, DECIS SUPPORT SYST, V44, P544, DOI 10.1016/j.dss.2007.07.001
   Krishnan J, 2005, ACCOUNT REV, V80, P649, DOI 10.2308/accr.2005.80.2.649
   Kumar RL, 2002, INFORM MANAGE-AMSTER, V40, P63, DOI 10.1016/S0378-7206(01)00133-1
   LAWSHE CH, 1975, PERS PSYCHOL, V28, P563, DOI 10.1111/j.1744-6570.1975.tb01393.x
   Lee I, 2018, BUS HORIZONS, V61, P35, DOI 10.1016/j.bushor.2017.09.003
   Li FG, 2016, COMPUT COMMUN, V89-90, P154, DOI 10.1016/j.comcom.2016.03.007
   Madakam S, 2015, J COMPUT COMMUN, V3, P164, DOI DOI 10.4236/JCC.2015.35021
   Majeed A. A, 2017, INT J SUPPLY CHAIN M, V6, P25
   Martin K., 2014, RES ACCOUNTING REGUL, V26, P110, DOI [10.1016/j.racreg.2014.02.012, DOI 10.1016/J.RACREG.2014.02.012]
   McAfee A, 2011, HARVARD BUS REV, V89, P124
   Merhout J.W., 2015, AIS ELECT LIB, P1
   MILLER KD, 1992, J INT BUS STUD, V23, P311, DOI 10.1057/palgrave.jibs.8490270
   Moeller B., 2013, P 19 AM C INF SYST C
   Novak J., 1984, LEARNING LEARN
   Novak J. D., 1998, LEARNING CREATING US
   Novak JD, 2002, SCI EDUC, V86, P548, DOI 10.1002/sce.10032
   NOVAK JD, 1990, INSTR SCI, V19, P29, DOI 10.1007/BF00377984
   Nuijten A, 2018, INFORM MANAGE-AMSTER, V55, P80, DOI 10.1016/j.im.2017.04.002
   Okoli C, 2004, INFORM MANAGE-AMSTER, V42, P15, DOI 10.1016/j.im.2003.11.002
   Omitola T, 2018, PROCEDIA COMPUT SCI, V126, P441, DOI 10.1016/j.procs.2018.07.278
   Ouaddah A, 2017, COMPUT NETW, V112, P237, DOI 10.1016/j.comnet.2016.11.007
   Ozkan S, 2010, INT J INFORM MANAGE, V30, P567, DOI 10.1016/j.ijinfomgt.2010.08.007
   Page Michael, 2003, ACCOUNTING AUDITING, V16, P640, DOI DOI 10.1108/09513570310492335
   Pathak J, 2005, MANAG AUDIT J, V20, P569, DOI 10.1108/02686900510606065
   Perera C, 2014, IEEE COMMUN SURV TUT, V16, P414, DOI 10.1109/SURV.2013.042313.00197
   Ratnasingham P., 1998, Information Management & Computer Security, V6, P33, DOI 10.1108/09685229810207434
   Roche Nicolas, 2014, Ann Am Thorac Soc, V11 Suppl 2, pS99, DOI 10.1513/AnnalsATS.201309-300RM
   Rochette M., 2009, J RISK MANAGEMENT FI, V2, P394
   Roman R, 2013, COMPUT NETW, V57, P2266, DOI 10.1016/j.comnet.2012.12.018
   Rudner L.M., 2000, RES EVALUAT, V1, P2002
   Saleh M.S., 2011, APPL COMPUTING INFOR, V9, P107, DOI DOI 10.1016/J.ACI.2011.05.002
   Seawright J, 2008, POLIT RES QUART, V61, P294, DOI 10.1177/1065912907313077
   Sethi P, 2017, J ELECTR COMPUT ENG, V2017, DOI 10.1155/2017/9324035
   Sicari S, 2015, COMPUT NETW, V76, P146, DOI 10.1016/j.comnet.2014.11.008
   Silva MM, 2014, INT J INFORM MANAGE, V34, P733, DOI 10.1016/j.ijinfomgt.2014.07.005
   Soomro ZA, 2016, INT J INFORM MANAGE, V36, P215, DOI 10.1016/j.ijinfomgt.2015.11.009
   Sumsion T., 1998, BRIT J OCCUPATIONAL, V61, P153, DOI [DOI 10.1177/030802269806100403, DOI 10.1111/j.1532-5415.2005.53405.x]
   Sutton SG, 2008, J ASSOC INF SYST, V9, P151, DOI 10.17705/1jais.00155
   Tian F, 2015, MIS QUART, V39, P39
   Trequattrini R, 2016, BUS PROCESS MANAG J, V22, P403, DOI 10.1108/BPMJ-05-2015-0075
   Vikas B. O., 2015, INT J SCI RES SCI EN, V1, P168
   Walsh I, 2016, J STRATEGIC INF SYST, V25, P177, DOI 10.1016/j.jsis.2016.01.003
   Wang CN, 2014, J ASSOC INF SYST, V15, P111
   Wang J., 2015, MIS Q, V39, P1, DOI [10.25300/MISQ, DOI 10.25300/MISQ]
   Weber Rolf H, 2010, Computer Law and Security Report, V26, P23, DOI 10.1016/j.clsr.2009.11.008
   Weeserik BP, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10030640
   WHITMAN NI, 1990, J NURS ADMIN, V20, P30
   Wu DD, 2015, ENTERPRISE RISK MANAGEMENT IN FINANCE, P1, DOI 10.1057/9781137466297
   Xue YJ, 2008, MIS QUART, V32, P67
   Yoo Y, 2010, MIS QUART, V34, P213
   Zhang YF, 2014, COMPUT IND ENG, V76, P89, DOI 10.1016/j.cie.2014.07.011
   Zhou H, 2013, INTERNET OF THINGS IN THE CLOUD: A MIDDLEWARE PERSPECTIVE, P1
   [No title captured]
NR 118
TC 8
Z9 8
U1 7
U2 63
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0378-7206
EI 1872-7530
J9 INFORM MANAGE-AMSTER
JI Inf. Manage.
PD SEP
PY 2020
VL 57
IS 6
AR 103335
DI 10.1016/j.im.2020.103335
PG 18
WC Computer Science, Information Systems; Information Science & Library
   Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
   Economics
GA MX7AN
UT WOS:000557872900013
DA 2022-08-23
ER

PT J
AU Qiu, J
   Chai, YH
   Tian, ZH
   Du, XJ
   Guizani, M
AF Qiu, Jing
   Chai, Yuhan
   Tian, Zhihong
   Du, Xiaojiang
   Guizani, Mohsen
TI Automatic Concept Extraction Based on Semantic Graphs From Big Data in
   Smart City
SO IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
LA English
DT Article
DE Semantics; Data mining; Smart cities; Big Data; Clustering algorithms;
   Frequency-domain analysis; Detection algorithms; Knowledge discovery;
   text analysis; text mining
ID DOMAIN ONTOLOGY; COMMUNITY STRUCTURE; CONSTRUCTION; FRAMEWORK; INTERNET
AB With the rapid development of smart cities, various types of sensors can rapidly collect a large amount of data, and it becomes increasingly important to discover effective knowledge and process information from massive amounts of data. Currently, in the field of knowledge engineering, knowledge graphs, especially domain knowledge graphs, play important roles and become the infrastructure of Internet knowledge-driven intelligent applications. Domain concept extraction is critical to the construction of domain knowledge graphs. Although there have been some works that have extracted concepts, semantic information has not been fully used. However, the excellent concept extraction results can be obtained by making full use of semantic information. In this article, a novel concept extraction method, Semantic Graph-Based Concept Extraction (SGCCE), is proposed. First, the similarities between terms are calculated using the word co-occurrence, the LDA topic model and Word2Vec. Then, a semantic graph of terms is constructed based on the similarities between the terms. Finally, according to the semantic graph of the terms, community detection algorithms are used to divide the terms into different communities where each community acts as a concept. In the experiments, we compare the concept extraction results that are obtained by different community detection algorithms to analyze the different semantic graphs. The experimental results show the effectiveness of our proposed method. This method can effectively use semantic information, and the results of the concept extraction are better from domain big data in smart cities.
C1 [Qiu, Jing; Chai, Yuhan; Tian, Zhihong] Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China.
   [Qiu, Jing] Peng Cheng Lab, Cyberspace Secur Res Ctr, Shenzhen 518000, Peoples R China.
   [Du, Xiaojiang] Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA.
   [Guizani, Mohsen] Qatar Univ, Coll Engn, Doha, Qatar.
RP Tian, ZH (通讯作者)，Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China.
EM qiujing@gzhu.edu.cn; chyuhan_smile@163.com; tianzhihong@gzhu.edu.cn;
   dxj@ieee.org; mguizani@gmail.com
RI Guizani, Mohsen/AAX-4534-2021
OI Guizani, Mohsen/0000-0002-8972-8094; tian, zhihong/0000-0002-9409-5359
FU National Key Research and Development Plan [2018YFB0803504,
   2018YEB1004003]; Guangdong Province Key Research and Development Plan
   [2019B010137004]; National Natural Science Foundation of China
   [61871140, 61872100, 61572153, U1636215]; Peng Cheng Laboratory Project
   of Guangdong Province [PCL2018KP004]
FX This work was supported in part by the National Key Research and
   Development Plan under Grant 2018YFB0803504 and Grant 2018YEB1004003, in
   part by the Guangdong Province Key Research and Development Plan under
   Grant 2019B010137004, in part by the National Natural Science Foundation
   of China under Grant 61871140, Grant 61872100, Grant 61572153, and Grant
   U1636215, and in part by the Peng Cheng Laboratory Project of Guangdong
   Province under Grant PCL2018KP004.
CR [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   Anoop VS, 2016, PROCEDIA COMPUT SCI, V89, P386, DOI 10.1016/j.procs.2016.06.086
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008
   Chen RC, 2008, EXPERT SYST APPL, V34, P488, DOI 10.1016/j.eswa.2006.09.019
   Colace F, 2014, J VISUAL LANG COMPUT, V25, P818, DOI 10.1016/j.jvlc.2014.11.001
   Du XJ, 2008, IEEE WIREL COMMUN, V15, P60, DOI 10.1109/MWC.2008.4599222
   Du XJ, 2007, AD HOC NETW, V5, P24, DOI 10.1016/j.adhoc.2006.05.012
   Esserhrouchni OE, 2014, COLLOQ INF SCI TECH, P91, DOI 10.1109/CIST.2014.7016600
   Girvan M, 2002, P NATL ACAD SCI USA, V99, P7821, DOI 10.1073/pnas.122653799
   Hou X, 2011, EXPERT SYST APPL, V38, P11958, DOI 10.1016/j.eswa.2011.03.090
   Hsieh SH, 2011, ADV ENG INFORM, V25, P288, DOI 10.1016/j.aei.2010.08.004
   Jiang X, 2010, J AM SOC INF SCI TEC, V61, P150, DOI 10.1002/asi.21231
   Kang YB, 2014, EXPERT SYST APPL, V41, P4494, DOI 10.1016/j.eswa.2014.01.006
   Liu, 2010, P 23 INT C COMP LING, P13
   Liu Yuefeng, 2016, IEEE ACIS 15 INT C C, P1
   Ochoa JL, 2013, EXPERT SYST APPL, V40, P2058, DOI 10.1016/j.eswa.2012.10.017
   Mikolov T, 2013, P NAACL HLT 2013, P746
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Mikolov T., 2013, 1 INT C LEARNING REP
   Newman MEJ, 2006, P NATL ACAD SCI USA, V103, P8577, DOI 10.1073/pnas.0601602103
   Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.066133
   PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8
   Punuru J, 2012, J INTELL INF SYST, V38, P191, DOI 10.1007/s10844-011-0149-4
   Qiu J, 2018, IEEE ACCESS, V6, P74854, DOI 10.1109/ACCESS.2018.2881422
   Shen HW, 2009, PHYSICA A, V388, P1706, DOI 10.1016/j.physa.2008.12.021
   Shih CW, 2011, EXPERT SYST APPL, V38, P7544, DOI 10.1016/j.eswa.2010.12.112
   Song QX, 2014, 2014 TENTH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING (IIH-MSP 2014), P762, DOI 10.1109/IIH-MSP.2014.194
   Tan QF, 2019, IEEE INTERNET THINGS, V6, P1584, DOI 10.1109/JIOT.2018.2846624
   Tian ZH, 2019, IEEE T VEH TECHNOL, V68, P5971, DOI 10.1109/TVT.2019.2910217
   Tian ZH, 2019, INFORM SCIENCES, V491, P151, DOI 10.1016/j.ins.2019.04.011
   Tian ZH, 2019, IEEE T IND INFORM, V15, P4285, DOI 10.1109/TII.2019.2907754
   Tian ZH, 2019, FUTURE GENER COMP SY, V95, P212, DOI 10.1016/j.future.2018.12.054
   Xu YF, 2016, KNOWL-BASED SYST, V109, P238, DOI 10.1016/j.knosys.2016.07.007
   Xue H, 2016, MULTIMED TOOLS APPL, V75, P8875, DOI 10.1007/s11042-014-2303-9
   Zhao XJ, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER), P56, DOI 10.1109/SANER.2017.7884609
   Zhou Y, 2016, 2016 IEEE INTERNATIONAL CONFERENCE OF ONLINE ANALYSIS AND COMPUTING SCIENCE (ICOACS), P111, DOI 10.1109/ICOACS.2016.7563059
   Zouaq A, 2011, INFORM SYST, V36, P1064, DOI 10.1016/j.is.2011.03.005
NR 39
TC 18
Z9 19
U1 8
U2 29
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2329-924X
J9 IEEE T COMPUT SOC SY
JI IEEE Trans. Comput. Soc. Syst.
PD FEB
PY 2020
VL 7
IS 1
BP 225
EP 233
DI 10.1109/TCSS.2019.2946181
PG 9
WC Computer Science, Cybernetics; Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KU2NA
UT WOS:000519544000020
DA 2022-08-23
ER

PT J
AU Tam, NT
   Trung, HT
   Yin, HZ
   Vinh, TV
   Sakong, D
   Zheng, BL
   Hung, NQV
AF Nguyen Thanh Tam
   Huynh Thanh Trung
   Yin, Hongzhi
   Tong Van Vinh
   Sakong, Darnbi
   Zheng, Bolong
   Nguyen Quoc Viet Hung
TI Entity Alignment for Knowledge Graphs With Multi-Order Convolutional
   Networks
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Logic gates; Knowledge engineering; Data models; Biological system
   modeling; Task analysis; Correlation; Urban areas; Knowledge graph;
   entity alignment; network embedding; graph convolutional neural network
AB Knowledge graphs (KGs) have become popular structures for unifying real-world entities by modelling the relationships between them and their attributes. To support multilingual applications, a significant number of language-specific KGs have been built by different parties using various data sources. As a result, these monolingual KGs are often disconnected, causing semantic heterogeneity and detracting from the original purpose of KGs. Entity alignment - the task of identifying corresponding entities across different KGs - has attracted a great deal of attention in both academia and industry. However, existing alignment techniques often require large amounts of labelled data, are unable to encode multi-modal data simultaneously, and enforce only a few consistency constraints. In this paper, we propose an end-to-end, unsupervised entity alignment framework for cross-lingual KGs that fuses different types of information in order to fully exploit the richness of KG data. The model captures the relation-based correlation between entities by using a multi-order graph convolutional neural (GCN) model that is designed to satisfy the consistency constraints, while incorporating the attribute-based correlation via a translation machine. We adopt a late-fusion mechanism to combine all the information together, which allows these approaches to complement each other and thus enhances the final alignment result, and makes the model more robust to consistency violations. Empirical results for various scenarios on real-world and synthetic KGs show that our model is up to 22.71 percent more accurate and orders of magnitude faster than existing baselines. We also demonstrate its sensitivity to hyper-parameters, effort saving in terms of labelling, and the robustness against adversarial conditions.
C1 [Nguyen Thanh Tam] Ho Chi Minh City Univ Technol HUTECH, Fac Informat Technol, Ho Chi Minh City, Vietnam.
   [Huynh Thanh Trung; Tong Van Vinh; Sakong, Darnbi; Nguyen Quoc Viet Hung] Griffith Univ, Nathan, Qld 4111, Australia.
   [Yin, Hongzhi] Univ Queensland, St Lucia, Qld 4072, Australia.
   [Zheng, Bolong] Huazhong Univ Sci & Technol, Wuhan 430074, Hubei, Peoples R China.
RP Yin, HZ (通讯作者)，Univ Queensland, St Lucia, Qld 4072, Australia.
EM thanhtamlhp@gmail.com; thanhtrunghuynh93@gmail.com;
   db.hongzhi@gmail.com; vinhbachkhoait@gmail.com; dobbydarnbi@gmail.com;
   zblchris@gmail.com; quocviethung1@gmail.com
RI Boes, Andreas/N-4462-2019; Mitchell, Arnan/C-5017-2008
OI Boes, Andreas/0000-0001-8443-3396; Mitchell, Arnan/0000-0002-2463-2956;
   Nguyen, Thach/0000-0002-8409-5638
FU Vietnam National Foundation for Science and Technology Development
   (NAFOSTED) [102.01-2019.323]
FX This work was funded by Vietnam National Foundation for Science and
   Technology Development (NAFOSTED) under grant number 102.01-2019.323.
CR Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cao YX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1452
   Chen C., ARXIV 190700544, V2019
   Chen HX, 2019, PROC INT CONF DATA, P590, DOI 10.1109/ICDE.2019.00059
   Chen HX, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1177, DOI 10.1145/3219819.3219986
   Chen M, 2015, IEEE T KNOWL DATA EN, V27, P1465, DOI 10.1109/TKDE.2014.2382599
   Chen MH, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1511
   Dehmamy N., 2019, INT C NEURAL INF PRO, p15 387
   Dong M, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P569, DOI 10.1145/3357384.3357994
   Duong C. T., 2019, ARXIV
   Duong C. T, 2019, ARXIV
   Duong CT, 2020, PROC INT CONF DATA, P1990, DOI 10.1109/ICDE48307.2020.00222
   Goyal P, 2018, KNOWL-BASED SYST, V151, P78, DOI 10.1016/j.knosys.2018.03.022
   Hamilton Will, 2017, NEURIPS
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Trung HT, 2020, PROC INT CONF DATA, P85, DOI 10.1109/ICDE48307.2020.00015
   Trung HT, 2020, EXPERT SYST APPL, V140, DOI 10.1016/j.eswa.2019.112883
   Jayaram N, 2015, IEEE T KNOWL DATA EN, V27, P2797, DOI 10.1109/TKDE.2015.2426696
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Kollias G, 2012, IEEE T KNOWL DATA EN, V24, P2232, DOI 10.1109/TKDE.2011.174
   Koutra D, 2013, IEEE DATA MINING, P389, DOI 10.1109/ICDM.2013.152
   Lin Y., 2015, P AAAI C ART INT, P2181
   Luo ZL, 2017, IEEE T KNOWL DATA EN, V29, P2125, DOI 10.1109/TKDE.2017.2720734
   Man T., 2016, P INT JOINT C ART IN, P1823
   Nassar H, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P619, DOI 10.1145/3178876.3186128
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Phan MC, 2019, IEEE T KNOWL DATA EN, V31, P1383, DOI 10.1109/TKDE.2018.2857493
   Ren YX, 2019, PROC INT CONF DATA, P1690, DOI 10.1109/ICDE.2019.00174
   Shu K., 2017, SIGKDD EXPLOR NEWSL, V18, P5, DOI 10.1145/3068777.3068781
   Song Q, 2018, IEEE T KNOWL DATA EN, V30, P1887, DOI 10.1109/TKDE.2018.2807442
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Nguyen TT, 2021, EXPERT SYST APPL, V165, DOI 10.1016/j.eswa.2020.113857
   Huynh TT, 2019, LECT NOTES ARTIF INT, V11671, P698, DOI 10.1007/978-3-030-29911-8_54
   Wang CG, 2016, AAAI CONF ARTIF INTE, P2130
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang WQ, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P545, DOI 10.1145/3331184.3331258
   Wang Z., 2014, P 28 AAAI C ARTIFICI, P1112
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Wu YT, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5278
   Xie Q., 2020, NIPS, P1
   Xu KYL, 2018, PR MACH LEARN RES, V80
   Xu K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3156
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhang S, 2019, IEEE T KNOWL DATA EN, V31, P1680, DOI 10.1109/TKDE.2018.2866440
   Zhang S, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1345, DOI 10.1145/2939672.2939766
   Zhang YY, 2018, AAAI CONF ARTIF INTE, P6069
   Zheng WG, 2016, IEEE T KNOWL DATA EN, V28, P1805, DOI 10.1109/TKDE.2016.2530063
   Zhu H, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4258
   Zhu QN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1943
NR 52
TC 30
Z9 31
U1 2
U2 93
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD SEPT 1
PY 2022
VL 34
IS 9
BP 4201
EP 4214
DI 10.1109/TKDE.2020.3038654
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 3O1VG
UT WOS:000836626800012
DA 2022-08-23
ER

PT J
AU Masmoudi, M
   Ben Lamine, SB
   Zghal, HB
   Archimede, B
   Karray, MH
AF Masmoudi, Maroua
   Ben Lamine, Sana Ben Abdallah
   Zghal, Hajer Baazaoui
   Archimede, Bernard
   Karray, Mohamed Hedi
TI Knowledge hypergraph-based approach for data integration and querying:
   Application to Earth Observation
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Semantic data integration; Knowledge graph; Ontology; Query processing;
   Knowledge hypergraph; Earth observation
AB According to the world economic forum report, around 70% of generated data are not used. This limitation of usage is mainly due to the lack of interoperability and linking of data that resides in isolated silos. Indeed, the overwhelming amount of data has worsened heterogeneity problems, as have the types of sources generating data in heterogeneous formats and different semantics. Those data related problematics are frequent in the domain of Earth Observation (EO). Earth observed data use different terminologies, which are difficult to reconcile because they reflect overlapped disciplines. These issues lead to misunderstandings and inefficient exchange and management of data in terms of access, pricing, and data rights, which can hamper environmental phenomena understanding. Virtual Knowledge Graph (VKG), allows semantic integration of existing data sources into a wide Knowledge Graph. In this work we propose a knowledge hypergraph-based approach for data integration and querying, with an application to Earth Observation data. Our proposal takes place in two phases (1) a knowledge hypergraph-based virtual data integration and (2) a hypergraph-based query processing. The first phase allows to generate a virtual knowledge hypergraph consisting of RML mappings between an ontology and the data. The second phase consists of enhancing the user's query by extracting and consolidating a global view of data from different sources based on the generated knowledge hypergraph. The proposed approach is implemented in the Onto-KIT tool (Ontology-based Knowledge hypergraph data Integration and querying Tool) and evaluated through real use case studies. The obtained results show that our proposal enhances query processing in terms of accuracy, completeness, and semantic richness of response. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Masmoudi, Maroua; Archimede, Bernard; Karray, Mohamed Hedi] Univ Toulouse, INP ENIT, Toulouse, France.
   [Masmoudi, Maroua; Ben Lamine, Sana Ben Abdallah; Zghal, Hajer Baazaoui] Univ Manouba, Riadi Lab, Manouba, Tunisia.
RP Masmoudi, M (通讯作者)，Univ Toulouse, INP ENIT, Toulouse, France.
EM maroua.masmoudi@enit.fr
FU PHC Utique program of the French Ministry of Foreign Affairs; Tunisian
   Ministry of higher education and scientific research [17G1122/CODE CF
   37T03 NJ]
FX This research was financially supported by the PHC Utique program of the
   French Ministry of Foreign Affairs, managed by Campus France, and the
   Tunisian Ministry of higher education and scientific research, managed
   by the CMCU (project number 17G1122/CODE CF 37T03 NJ).
CR Aarnio P., 2014, P 2014 IEEE EM TECHN, P1
   Abbes H, 2018, J DATA SEMANT, V7, P1, DOI 10.1007/s13740-017-0081-z
   [Anonymous], 2018, SEMANT WEB
   [Anonymous], 2015, INT S ENV SOFTW SYST
   [Anonymous], 2012, EXTENDED SEMANTIC WE
   Arp R, 2015, BUILDING ONTOLOGIES WITH BASIC FORMAL ONTOLOGY, P1, DOI 10.7551/mitpress/9780262527811.001.0001
   Bereta K, 2019, J WEB SEMANT, V58, DOI 10.1016/j.websem.2019.100514
   Bi TT, 2018, ASIA PAC SOFWR ENG, P49, DOI 10.1109/APSEC.2018.00019
   Bretto A, 2013, HYPERGRAPH THEORY
   Buttigieg PL, 2016, J BIOMED SEMANT, V7, DOI 10.1186/s13326-016-0097-6
   Calvanese D, 2017, SEMANT WEB, V8, P471, DOI 10.3233/SW-160217
   Cyganiak R., 2012, D2RQ ACCESSING RELAT
   Das S., 2011, R2RML RDB RDF MAPPIN
   Dimou A., 2014, CEUR WORKSHOP P
   Do HH, 2003, LECT NOTES COMPUT SC, V2593, P221
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Ekaputra F., 2017, OJIS, V4, P1, DOI DOI 10.24167/SISF0RMA.V4I1.1040
   Gibert K, 2018, ENVIRON MODELL SOFTW, V106, P4, DOI 10.1016/j.envsoft.2018.04.005
   Harrison NB, 2007, LECT NOTES COMPUT SC, V4758, P263
   Kharlamov E, 2017, J WEB SEMANT, V44, P54, DOI 10.1016/j.websem.2017.02.001
   Kyzirakos K, 2018, J WEB SEMANT, V52-53, P16, DOI 10.1016/j.websem.2018.08.003
   Lenzerini M., 2002, P 21 ACM SIGMOD SIGA, V21, P233, DOI DOI 10.1145/543613.543644
   Louge T., FUTURE GENER COMPUT, V90
   Masmoudi M., 2019, 2019 IEEE ACS 16 INT, P1
   Masmoudi M, 2020, ENVIRON MODELL SOFTW, V124, DOI 10.1016/j.envsoft.2019.104581
   Masmoudi M, 2018, IEEE INT CONF SERV, P194, DOI 10.1109/SOCA.2018.00035
   Mountantonakis M, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3345551
   Patroumpas K., 2014, EDBT ICDT WORKSH, P275
   Salmen D., 2011, INTEGRATION INTELLIG
   Schoening JR, 2015, PROC SPIE, V9464, DOI 10.1117/12.2182064
   Vassiliadis P., 2009, P ACM 12 INT WORKSH, P25
   Xiao GH, 2019, DATA INTELLIGENCE, V1, P201, DOI 10.1162/dint_a_00011
   Yu L., 2011, DEV GUIDE SEMANTIC W, P241, DOI [10.1007/978-3-642-15970-1_6, DOI 10.1007/978-3-642-15970-1_6]
NR 33
TC 2
Z9 2
U1 10
U2 15
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD FEB
PY 2021
VL 115
BP 720
EP 740
DI 10.1016/j.future.2020.09.029
PG 21
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OV2EK
UT WOS:000592029600011
OA Bronze
DA 2022-08-23
ER

PT J
AU Aghaei, S
   Raad, E
   Fensel, A
AF Aghaei, Sareh
   Raad, Elie
   Fensel, Anna
TI Question Answering Over Knowledge Graphs: A Case Study in Tourism
SO IEEE ACCESS
LA English
DT Article
DE Semantics; Training data; Resource description framework; Task analysis;
   Syntactics; Question answering (information retrieval); Motion pictures;
   Knowledge graphs; question answering; semantic parsing; small and medium
   enterprises
AB Over the recent years, a large number of knowledge graphs (KGs) have been developed to store and present small and medium enterprises' data in the form of subject-predicate-object triples. The KGs are not easily accessible to end-users because they need an understanding of query languages and KGs' structures. To assist end-users in accessing these KGs, question answering over KGs targets to provide answers for natural language questions (NLQs). This paper proposes an approach to answer questions over small and medium scaled KGs based on graph isomorphism in two phases: (1) offline phase and (2) semantic parsing phase. In the offline phase, a semi-automated solution is proposed to generate NLQs and their answers, which are used to train machine learning models employed in the next phase. In the semantic parsing phase, a given input NLQ is mapped into a query pattern according to its grammatical structure. Each query pattern contains some slots that need to be filled with corresponding entities, classes and relations from the KG. While string and semantic similarity metrics are applied to identify the entities and classes, the probability distribution of the relations is used to extract the relations. The Cartesian product of the identified entities, classes and relations is utilized to fill the slots, derive SPARQL queries and finally retrieve the answers. To evaluate the proposed approach, we use SalzburgerLand KG, a real KG describing touristic entities of the region of Salzburg, Austria. Our results show that the approach improves the end-to-end user experience in terms of interactive question answering and performance.
C1 [Aghaei, Sareh; Fensel, Anna] Univ Innsbruck, Semant Technol Inst STI Innsbruck, Dept Comp Sci, A-6020 Innsbruck, Austria.
   [Raad, Elie] WordLift, I-00186 Rome, Italy.
   [Fensel, Anna] Wageningen Univ & Res, Wageningen Data Competence Ctr, NL-6708 PB Wageningen, Netherlands.
   [Fensel, Anna] Wageningen Univ & Res, Chair Grp Consumpt & Hlth Lifestyles, NL-6706 KN Wageningen, Netherlands.
RP Aghaei, S (通讯作者)，Univ Innsbruck, Semant Technol Inst STI Innsbruck, Dept Comp Sci, A-6020 Innsbruck, Austria.
EM sareh.aghaei@sti2.at
OI Fensel, Anna/0000-0002-1391-7104
FU Project WordLiftNG within the Eureka, Eurostars Program; Austrian
   Research Promotion Agency (FFG) [877857]; Project KI-NET within the
   Interreg Osterreich-Bayern 2014-2020 Program [AB 292]
FX This work was supported in part by the Project WordLiftNG within the
   Eureka, Eurostars Program with the Austrian Research Promotion Agency
   (FFG) under Grant 877857; and in part by the Project KI-NET within the
   Interreg Osterreich-Bayern 2014-2020 Program under Grant AB 292.
CR Abu-Salih B, 2021, J NETW COMPUT APPL, V185, DOI 10.1016/j.jnca.2021.103076
   Aghaei S., 2022, PROC 22 INT C WEB EN
   Ait-Mlouk A, 2020, IEEE ACCESS, V8, P149220, DOI 10.1109/ACCESS.2020.3016142
   Akbar Zaenal, 2018, On the Move to Meaningful Internet Systems. OTM 2018 Conferences. Confederated International Conferences: CoopIS, C&TC, and ODBASE 2018. Proceedings: Lecture Notes in Computer Science (LNCS 11230), P481, DOI 10.1007/978-3-030-02671-4_29
   [Anonymous], WORDLIFT NEW GENERAT
   Arnaout H, 2018, J WEB SEMANT, V48, P66, DOI 10.1016/j.websem.2017.12.001
   Athreya RG, 2021, IEEE INT C SEMANT CO, P195, DOI 10.1109/ICSC50631.2021.00041
   Bao J, 2016, COLING
   Bast H., 2015, P 24 ACM INT C INF K, P1431, DOI [10.1145/2806416.2806472, DOI 10.1145/2806416.2806472]
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
   Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
   Chen Y., 2019, ARXIV
   Cui W., 2019, ARXIV
   Devlin J., 2018, ARXIV
   Diefenbach D., 2018, THESIS U LYON FRANCE
   Dileep AK, 2021, IEEE INT C SEMANT CO, P443, DOI 10.1109/ICSC50631.2021.00079
   Dinghe Xiao, 2020, Chinese Computational Linguistics. 19th China National Conference, CCL 2020. Proceedings. Lecture Notes in Artificial Intelligence. Subseries of Lecture Notes in Computer Science (LNAI 12522), P159, DOI 10.1007/978-3-030-63031-7_12
   Dubey M, 2019, LECT NOTES COMPUT SC, V11779, P69, DOI 10.1007/978-3-030-30796-7_5
   Fensel A, 2020, INFORMATION, V11, DOI 10.3390/info11050253
   Fu B., 2020, ARXIV
   Gao HN, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3030
   He GL, 2021, WSDM '21: PROCEEDINGS OF THE 14TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P535, DOI 10.1145/3437963.3441753
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Kalaycinodot Elem Guzel, 2020, The Semantic Web - ISWC 2020. 19th International Semantic Web Conference. Lecture Notes in Computer Science (LNCS 12507), P464, DOI 10.1007/978-3-030-62466-8_29
   Karle E, 2018, LECT NOTES COMPUT SC, V11153, P260, DOI 10.1007/978-3-030-03056-8_25
   Klyne G., 2004, RESOURCE DESCRIPTION
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Levenshtein V.I., 1966, SOV PHYS DOKL, V10, P707
   Li YJ, 2007, IEEE T PATTERN ANAL, V29, P1091, DOI 10.1109/TPAMI.2007.1070
   Lu C, 2016, LECT NOTES COMPUT SC, V10024, P416, DOI 10.1007/978-3-319-49004-5_27
   Mans~eld M., 2021, PROC 11 KNOWL CAPTUR, P129
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   Ojokoh B, 2018, J WEB ENG, V17, P717, DOI 10.13052/jwe1540-9589.1785
   Ou S., 2008, P FLAIRS C, P183
   Qiu YQ, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P474, DOI 10.1145/3336191.3371812
   Reimers N., 2019, ARXIV
   Sun H., 2019, ARXIV
   Sun H., 2018, ARXIV
   Talmor A., 2018, ARXIV
   Trivedi P, 2017, LECT NOTES COMPUT SC, V10588, P210, DOI 10.1007/978-3-319-68204-4_22
   Unger C., 2012, P 21 INT C WORLD WID, P639, DOI DOI 10.1145/2187836.2187923
   Vollmers D., 2021, PROC 17 INT C SEMANT, V53, P103
   Xu K., 2018, ARXIV
   Xu K., 2018, ARXIV
   Xuchao Liang, 2020, 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), P828, DOI 10.1109/ICPICS50287.2020.9202197
   Yasunaga M., 2021, ARXIV
   Yih WT, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2, P201
   Zhang W., 2018, PROC INT C PIONEERIN, P213
   Zheng WG, 2018, PROC VLDB ENDOW, V11, P1373, DOI 10.14778/3236187.3236192
   Zheng WG, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1809, DOI 10.1145/2723372.2747648
NR 52
TC 0
Z9 0
U1 0
U2 0
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 69788
EP 69801
DI 10.1109/ACCESS.2022.3187178
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3Q7TK
UT WOS:000838428600001
OA gold
DA 2022-08-23
ER

PT J
AU Butcher, KR
   Bhushan, S
   Sumner, T
AF Butcher, KR
   Bhushan, S
   Sumner, T
TI Multimedia displays for conceptual discovery: information seeking with
   strand maps
SO MULTIMEDIA SYSTEMS
LA English
DT Article
DE information seeking; strand maps; educational digital libraries; science
   learning; educational technology
ID KNOWLEDGE MAPS; SEARCH PROCESS; DESIGN; USERS
AB This article explores the use of a multimedia search interface for digital libraries based on strand maps developed by the American Association for the Advancement of Science. As semantic-spatial displays, strand maps provide a visual organization of relevant conceptual information that may promote the use of science content during digital library use. A study was conducted to compare users' cognitive processes during information seeking tasks when using a multimedia strand maps interface, versus the textual search interface currently implemented in the Digital Library for Earth System Education. Quantitative and qualitative data from think-aloud protocols revealed that students were more likely to engage with science content (e.g., analyzing the relevance of science concepts with regard to task needs) during search when using the strand maps interface compared to those using textual searching. In contrast, students using a textual search interface engaged more frequently with surface-level information (e.g., the type of a resource regardless of its science content) during search and retrieval. As a multimedia search interface for digital libraries, strand maps appear to be promising tools to promote conceptual discovery and learning through content-based processes that promote learner engagement with relevant science knowledge.
C1 Univ Corp Atmospher Res, Digital Lib Earth Syst Educ Program Ctr, Boulder, CO 80307 USA.
   Univ Colorado, Dept Comp Sci, Boulder, CO 80309 USA.
RP Butcher, KR (通讯作者)，Univ Corp Atmospher Res, Digital Lib Earth Syst Educ Program Ctr, POB 3000, Boulder, CO 80307 USA.
EM kbutcher@ucar.edu; sonal@ucar.edu; sumner@colorado.edu
RI Butcher, Kirsten/AAU-4881-2020
OI Butcher, Kirsten/0000-0002-7433-0972
CR AHMAD F, 2004, ACM IEEE JOINT C DIG
   American Association for the Advancement of Science (AAAS), 1993, 2061 AAAS
   American Association for the Advancement of Science (AAAS), 1989, SCI ALL AM
   American Association for the Advancement of Science (AAAS), 2001, DES SCI LIT
   American Association for the Advancement of Science (AAAS), 2001, ATL SCI LIT
   ARMS W, 2002, DLIB MAGAZINE, V8
   BINDING C, 2004, J DIGIT INF, V4
   Carney RN, 2002, EDUC PSYCHOL REV, V14, P5, DOI 10.1023/A:1013176309260
   CHI MTH, 1994, COGNITIVE SCI, V18, P439, DOI 10.1016/0364-0213(94)90016-7
   Chmielewski TL, 1998, J EDUC PSYCHOL, V90, P407, DOI 10.1037/0022-0663.90.3.407
   DENIMAN D, 2003, J DIGIT INF, V4
   Ericsson K. A., 1993, PROTOCOL ANAL VERBAL
   FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212
   Goldman SR, 2003, LEARN INSTR, V13, P239, DOI 10.1016/S0959-4752(02)00023-3
   GU Q, 2004, 3 ANN C SCAL VECT GR
   Kintsch W., 1998, COMPREHENSION PARADI
   KUHLTHAU CC, 1988, LIBR INFORM SCI RES, V10, P257
   Kuhlthau CC, 2001, J DOC, V57, P25, DOI 10.1108/EUM0000000007076
   MACDONALD J, 1998, OPEN LEARNING, V13, P38
   MARCHIONINI G, 1995, INFORMATION SEEKING
   MARLINO M, 2001, DIGITAL LIBR EARLY S, P80
   Mayer R.E., 2001, MULTIMEDIA LEARNING
   Mayer RE, 2003, LEARN INSTR, V13, P125, DOI 10.1016/S0959-4752(02)00016-6
   Narayanan NH, 2002, INT J HUM-COMPUT ST, V57, P279, DOI 10.1006/ijhc.1019
   National Research Council, 1996, NAT SCI ED STAND
   North C, 2000, INT J HUM-COMPUT ST, V53, P715, DOI 10.1006/ijhc.2000.0418
   *NSDL, 2003, NAT SCI DIG LIBR PRO
   O'Donnell AM, 2002, EDUC PSYCHOL REV, V14, P71, DOI 10.1023/A:1013132527007
   *OAI, 2001, OP ARCH IN PROT MET
   REWEY KL, 1989, J EDUC PSYCHOL, V81, P604, DOI 10.1037/0022-0663.81.4.604
   SUMNER T, 2004, DLIB MAGAZINE, P10
   SUMNER T, 2004, ACM IEEE JOINT C DIG
   Sumner W, 2005, BMC PUBLIC HEALTH, V5, DOI 10.1186/1471-2458-5-18
   Vakkari P, 2001, J DOC, V57, P44, DOI 10.1108/EUM0000000007075
   *W3C, 2003, SCAL VECT GRAPH SVG
NR 35
TC 5
Z9 5
U1 1
U2 12
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0942-4962
EI 1432-1882
J9 MULTIMEDIA SYST
JI Multimedia Syst.
PD MAR
PY 2006
VL 11
IS 3
BP 236
EP 248
DI 10.1007/s00530-005-0004-y
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 022TK
UT WOS:000236078600005
DA 2022-08-23
ER

PT J
AU Xiao, GH
   Corman, J
AF Xiao, Guohui
   Corman, Julien
TI Ontology-Mediated SPARQL Query Answering over Knowledge Graphs
SO BIG DATA RESEARCH
LA English
DT Article
ID EPISTEMIC NEGATION
AB Ontology-Mediated Query Answering (OMQA) is a well-established framework to answer queries over Knowledge Graphs (KGs), enriched with RDFS or owl. ontologies. OMQA was originally designed for Unions of Conjunctive Queries (UCQs), and based on certain answers. More recently, OMQA has been extended to SPARQL queries, but to our knowledge, none of the efforts made in this direction (either in the literature, or the so-called W3C SPARQL entailment regimes) is able to capture both certain answers for UCQs and the standard interpretation of SPARQL over a plain graph. We formalize these as requirements to be met by any semantics that aims at conciliating certain answers and SPARQL answers, and extend these with three additional requirements. Then we define two semantics that satisfies all requirements for SPARQL queries with SELECT, UNION, JOIN, and OPTIONAL. Finally, we investigate the combined complexity of query answering under these semantics over a KG enriched with a DL-Lite(R) ontology, showing that for several fragments of SPARQL, known upper-bounds for query answering over a plain KG are matched. (C) 2020 Elsevier Inc. All rights reserved.
C1 [Xiao, Guohui; Corman, Julien] Free Univ Bozen Bolzano, Fac Comp Sci, Bolzano, Italy.
RP Xiao, GH (通讯作者)，Free Univ Bozen Bolzano, Fac Comp Sci, Bolzano, Italy.
EM xiao@inf.unibz; corman@inf.unibz
OI Xiao, Guohui/0000-0002-5115-4769
FU EU H2020 project INODE [863410]; Italian PRIN project HOPE [2017MMJJRE];
   European Regional Development Fund (ERDF) Investment for Growth and Jobs
   Programme 2014-2020 through the project IDEE [FESR1133]; Free University
   of Bozen-Bolzano through the project KGID; Free University of
   Bozen-Bolzano through the project GeoVKG; National Natural Science
   Foundation of China [61972455]
FX This research has been partially supported by the EU H2020 project INODE
   (863410), by the Italian PRIN project HOPE (2017MMJJRE), by the European
   Regional Development Fund (ERDF) Investment for Growth and Jobs
   Programme 2014-2020 through the project IDEE (FESR1133), by the Free
   University of Bozen-Bolzano through the projects KGID and GeoVKG, and by
   the National Natural Science Foundation of China (61972455).
CR Ahmetaj S, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P23, DOI 10.1145/2736277.2741636
   Arenas Marcelo, 2011, P 30 ACM SIGMOD SIGA, P305, DOI DOI 10.1145/1989284.1989312
   Artale A, 2009, J ARTIF INTELL RES, V36, P1, DOI 10.1613/jair.2820
   Baader F., 2003, DESCRIPTION LOGIC HD
   Bavirisetti DP, 2018, 2018 21ST INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), P1511, DOI 10.23919/ICIF.2018.8455767
   Bienvenu M, 2013, IJCAI
   Calvanese D, 2007, J AUTOM REASONING, V39, P385, DOI 10.1007/s10817-007-9078-x
   Calvanese D, 2017, SEMANT WEB, V8, P471, DOI 10.3233/SW-160217
   Corman J., 2019, 9 JOINT INT SEM TECH
   Costantini S, 2019, THEOR PRACT LOG PROG, V19, P790, DOI 10.1017/S147106841900019X
   Eiter T., 2012, P 26 AAAI C ART INT
   Gelfond M., 1991, AAAI-91. Proceedings Ninth National Conference on Artificial Intelligence, P386
   Glimm B., 2013, OGBUJI SPARQL 1 1 EN
   Gutierrez C., 2016, AMW
   Harris S., 2013, SPARQL 1 1 QUERY LAN
   Hernandez D, 2018, LECT NOTES COMPUT SC, V11136, P337, DOI 10.1007/978-3-030-00671-6_20
   Kontchakov R, 2014, LECT NOTES COMPUT SC, V8796, P552, DOI 10.1007/978-3-319-11964-9_35
   Kostylev EV, 2014, LECT NOTES COMPUT SC, V8797, P374, DOI 10.1007/978-3-319-11915-1_24
   Lanti D, 2019, SEMANT WEB, V10, P413, DOI 10.3233/SW-180336
   Letelier A, 2013, ACM T DATABASE SYST, V38, DOI 10.1145/2500130
   LIFSCHITZ V, 1991, P 12 INT JOINT C ART, P381
   Mengel S., 2017, ARXIV171208939
   Motik B, 2010, J ACM, V57, DOI 10.1145/1754399.1754403
   Perez J, 2009, ACM T DATABASE SYST, V34, DOI 10.1145/1567274.1567278
   Polleres Axel, 2013, Journal of Applied Non-Classical Logic, V23, P159, DOI 10.1080/11663081.2013.798992
   Schmidt Michael, 2010, P 13 INT C DAT THEOR, P4, DOI DOI 10.1145/1804669.1804675
   Shen YD, 2016, ARTIF INTELL, V237, P115, DOI 10.1016/j.artint.2016.04.004
   Wang X, 2020, WORLD WIDE WEB, V23, P1465, DOI 10.1007/s11280-019-00739-0
   Xiao G., 2020, ISWC, P2020
   Xiao GH, 2019, DATA INTELLIGENCE, V1, P201, DOI 10.1162/dint_a_00011
   Xu Q, 2019, IEEE ACCESS, V7, P116453, DOI 10.1109/ACCESS.2019.2936465
   Zhang X., 2016, J ARTIF INTELL RES, V56, P403, DOI DOI 10.1613/JAIR.5028
NR 32
TC 0
Z9 0
U1 0
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-5796
J9 BIG DATA RES
JI Big Data Res.
PD FEB 15
PY 2021
VL 23
AR 100177
DI 10.1016/j.bdr.2020.100177
EA JAN 2021
PG 25
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PU2WL
UT WOS:000609166100012
DA 2022-08-23
ER

PT J
AU Yu, SK
   Li, X
   Zhao, XY
   Zhang, ZF
   Wu, F
   Wang, JD
   Zhuang, YT
   Li, XL
AF Yu, Shengkang
   Li, Xi
   Zhao, Xueyi
   Zhang, Zhongfei
   Wu, Fei
   Wang, Jingdong
   Zhuang, Yueting
   Li, Xuelong
TI A Bilinear Ranking SVM for Knowledge Based Relation Prediction and
   Classification
SO IEEE TRANSACTIONS ON BIG DATA
LA English
DT Article
DE Relation prediction; knowledge embeddings; ranking SVM; bilinear
AB As an important and challenging problem, knowledge representation and inference are typically carried out in a knowledge embedding framework over a multi-relational knowledge graph, and thus have a wide range of applications such as semantic retrieval and question answering. In this paper, we propose a bilinear learning framework which performs cross-entity knowledge relation analysis in the continuous vector space (derived from knowledge embedding). In the framework, we effectively model the intrinsic correlations among different types of knowledge relations within a max-margin multi-relational ranking scheme, which jointly optimizes the tasks of entity embedding and cross-entity relation prediction in terms of multi-relational structures of the knowledge graph. Specifically, we devise a bilinear scoring function that aims to evaluate the confidence degree of semantic relation prediction for entity pairs through a multi-relational learning-to-rank pipeline. In essence, the pipeline formulates the problem of relation prediction for entity pairs as that of learning relation-specific ranking functions by max-margin optimization. Experimental results demonstrate the effectiveness of the proposed framework on two common benchmark datasets.
C1 [Yu, Shengkang; Zhao, Xueyi; Zhang, Zhongfei] Zhejiang Univ, Dept Informat Sci & Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China.
   [Yu, Shengkang] CETC, Key Lab IntelliSense Technol, Nanjing 210013, Jiangsu, Peoples R China.
   [Yu, Shengkang] Nanjing Res Inst Elect Technol, Nanjing 210013, Jiangsu, Peoples R China.
   [Li, Xi; Wu, Fei; Zhuang, Yueting] Zhejiang Univ, Coll Comp Sci & Technologu, Hangzhou 310027, Zhejiang, Peoples R China.
   [Wang, Jingdong] Microsoft Res Asia, Visual Comp Grp, Beijing 100080, Peoples R China.
   [Li, Xuelong] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China.
RP Li, X (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technologu, Hangzhou 310027, Zhejiang, Peoples R China.
EM shengkangyu@zju.edu.cn; xilizju@zju.edu.cn; xueyizhao@zju.edu.cn;
   zhongfei@zju.edu.cn; wufei@cs.zju.edu.cn; jingdw@microsoft.com;
   yzhuang@cs.zju.edu.cn; xuelong_li@opt.ac.cn
RI Li, Xi/L-1234-2013; Wang, Jingdong/E-9920-2017; Li,
   Xuelong/ABF-3381-2020
OI Li, Xi/0000-0003-3023-1662; Wang, Jingdong/0000-0002-4888-4445; 
FU National Key Research and Development Program of China [2018YFB1107400];
   National Natural Science Foundation of China [U1509206, 61472353,
   61751209]; National Basic Research Program of China [2015CB352302]
FX This work was in part by the National Key Research and Development
   Program of China (2018YFB1107400), in part supported by the National
   Natural Science Foundation of China under Grants (U1509206, 61472353,
   and 61751209), and in part by the National Basic Research Program of
   China under Grant 2015CB352302. Correspondence should be addressed to
   Prof. Xi Li.
CR Akata Z, 2013, PROC CVPR IEEE, P819, DOI 10.1109/CVPR.2013.111
   Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Bassiliades N, 2000, IEEE T KNOWL DATA EN, V12, P824, DOI 10.1109/69.877511
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2012, P 15 INT C ART INT S, P127
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Bordes Antoine, 2011, AAAI
   Burges C., 2005, P 22 INT C MACH LEAR, P89, DOI DOI 10.1145/1102351.1102363
   Chang K W, 2013, P 2013 C EMP METH NA, P1602
   Deng Dan, 2018, ARXIV180101315
   Elisseeff A, 2002, ADV NEUR IN, V14, P681
   Getoor L., 2011, P ACM SIGMOD INT C M, P1195, DOI DOI 10.1145/1989323.1989451
   Grant J, 2000, SYNTHESE, V125, P121, DOI 10.1023/A:1005231601157
   Graupmann J., 2005, P 31 INT C VER LARG, P529
   Gu K., 2015, ARXIV150601094
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Hu D, 2006, INT C COMMUN CIRCUIT, P1605, DOI 10.1109/ICCCAS.2006.284980
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Joachims T., 1998, TEXT CATEGORIZATION
   Kolda TG, 2009, SIAM REV, V51, P455, DOI 10.1137/07070111X
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Menon AK, 2011, LECT NOTES ARTIF INT, V6912, P437, DOI 10.1007/978-3-642-23783-6_28
   Mikolov T, 2013, P NAACL HLT 2013, P746
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mirzazadeh F, 2014, AAAI CONF ARTIF INTE, P1989
   Nadeau D, 2007, LINGUIST INVESTIG, V30, P3
   Ng V, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P104
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Paccanaro A, 2001, IEEE T KNOWL DATA EN, V13, P232, DOI 10.1109/69.917563
   Radev D, 2009, WIREL TELECOMM SYMP, P10
   Ritter A., 2011, P 2011 C EMP METH NA, P1524
   Singhal A., 2012, INTRO KNOWLEDGE GRAP
   Snow R, 2004, ADV NEURAL INFORM PR, V17, P1297
   Socher R., 2012, P 2012 JOINT C EMP M, P1201, DOI [10.5555/2390948.2391084, DOI 10.1162/153244303322533223]
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Sutskever I., 2009, NIPS
   Wang HQ, 2018, IEEE T NEUR NET LEAR, V29, P718, DOI 10.1109/TNNLS.2016.2626379
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Waters R., 2012, FINANC TIMES, V16, P1
   Weimer Markus, 2007, ADV NEURAL INFORM PR, P1
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xiaoming X., 2018, SCI CHINA INFORM SCI, V61
   Yazdani M., 2013, P 11 WORKSH MIN LEAR
   Zhai H., 2009, DTIC DOCUMENT
NR 47
TC 1
Z9 1
U1 2
U2 3
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2332-7790
J9 IEEE T BIG DATA
JI IEEE Trans. Big Data
PD DEC
PY 2019
VL 5
IS 4
BP 588
EP 600
DI 10.1109/TBDATA.2018.2843766
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JT9LA
UT WOS:000501301600015
DA 2022-08-23
ER

PT J
AU Manian, V
   Orozco-Sandoval, J
   Diaz-Martinez, V
   Janwa, H
   Agrinsoni, C
AF Manian, Vidya
   Orozco-Sandoval, Jairo
   Diaz-Martinez, Victor
   Janwa, Heeralal
   Agrinsoni, Carlos
TI Detection of Target Genes for Drug Repurposing to Treat Skeletal Muscle
   Atrophy in Mice Flown in Spaceflight
SO GENES
LA English
DT Article
DE machine learning; skeletal muscle atrophy; graph convolutional neural
   networks; graph neural network; random forest; gradient boosting method;
   knowledge graphs; node embeddings; random walk; diseases; drugs; link
   prediction
ID MODEL
AB Skeletal muscle atrophy is a common condition in aging, diabetes, and in long duration spaceflights due to microgravity. This article investigates multi-modal gene disease and disease drug networks via link prediction algorithms to select drugs for repurposing to treat skeletal muscle atrophy. Key target genes that cause muscle atrophy in the left and right extensor digitorum longus muscle tissue, gastrocnemius, quadriceps, and the left and right soleus muscles are detected using graph theoretic network analysis, by mining the transcriptomic datasets collected from mice flown in spaceflight made available by GeneLab. We identified the top muscle atrophy gene regulators by the Pearson correlation and Bayesian Markov blanket method. The gene disease knowledge graph was constructed using the scalable precision medicine knowledge engine. We computed node embeddings, random walk measures from the networks. Graph convolutional networks, graph neural networks, random forest, and gradient boosting methods were trained using the embeddings, network features for predicting links and ranking top gene-disease associations for skeletal muscle atrophy. Drugs were selected and a disease drug knowledge graph was constructed. Link prediction methods were applied to the disease drug networks to identify top ranked drugs for therapeutic treatment of skeletal muscle atrophy. The graph convolution network performs best in link prediction based on receiver operating characteristic curves and prediction accuracies. The key genes involved in skeletal muscle atrophy are associated with metabolic and neurodegenerative diseases. The drugs selected for repurposing using the graph convolution network method were nutrients, corticosteroids, anti-inflammatory medications, and others related to insulin.
C1 [Manian, Vidya; Orozco-Sandoval, Jairo; Diaz-Martinez, Victor] Univ Puerto Rico, Dept Elect & Comp Engn, Mayaguez, PR 00681 USA.
   [Manian, Vidya] Univ Puerto Rico, Biomed Engn, Mayaguez, PR 00681 USA.
   [Janwa, Heeralal; Agrinsoni, Carlos] Univ Puerto Rico, Dept Math, Rio Piedras, PR 00925 USA.
RP Manian, V (通讯作者)，Univ Puerto Rico, Dept Elect & Comp Engn, Mayaguez, PR 00681 USA.; Manian, V (通讯作者)，Univ Puerto Rico, Biomed Engn, Mayaguez, PR 00681 USA.
EM vidya.manian@upr.edu; jairo.orozco@upr.edu; victor.diaz16@upr.edu;
   heeralal.janwa@upr.edu; carlos.agrinsoni@upr.edu
OI Orozco, Jairo/0000-0001-5438-1177; Manian, Vidya/0000-0003-3834-8857
FU NASA EPSCoR [80NSSC20M0132]
FX This research was funded by NASA EPSCoR, grant 80NSSC20M0132. The APC is
   funded by 80NSSC20M0132. Opinions, findings, conclusions, or
   recommendations expressed in this material are those of the authors and
   do not necessarily reflect the views of NASA.
CR Abbas K, 2021, BMC BIOINFORMATICS, V22, DOI 10.1186/s12859-021-04082-y
   Allen DL, 2009, J APPL PHYSIOL, V106, P582, DOI 10.1152/japplphysiol.90780.2008
   Anderson DM, 2016, P NATL ACAD SCI USA, V113, pE4494, DOI 10.1073/pnas.1608423113
   Barabasi A.L., 2017, NETWORK MED COMPLEX
   Barabasi A.-L., 2014, NETW SCI, V1st
   Beehler BC, 2006, EXP BIOL MED, V231, P335
   Biggs N., 1993, ALGEBRAIC GRAPH THEO
   Cadena SM, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-45821-9
   Cahill T, 2021, INT J MOL SCI, V22, DOI 10.3390/ijms22179470
   Calura E, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-630
   Campins L, 2017, PHARMACOLOGY, V99, P1, DOI 10.1159/000448247
   Chiu HC, 2018, J CACHEXIA SARCOPENI, V9, P585, DOI 10.1002/jcsm.12289
   Comfort P, 2021, SPORTS MED, V51, P2097, DOI 10.1007/s40279-021-01496-9
   Costantini A, 2016, NEURAL REGEN RES, V11, P1487, DOI 10.4103/1673-5374.191225
   Geister KA, 2019, DIS MODEL MECH, V12, DOI 10.1242/dmm.037176
   Gomes-Duarte A, 2018, RNA BIOL, V15, P26, DOI 10.1080/15476286.2017.1391437
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Guasconi V, 2008, CURR OPIN CLIN NUTR, V11, P233, DOI 10.1097/MCO.0b013e3282fa1810
   Harding C., 2016, NASA SP GRANT CONSOR, P1
   Heras G, 2019, J MOL CELL BIOL, V11, P356, DOI 10.1093/jmcb/mjy036
   Hunt LC, 2021, CELL REP, V37, DOI 10.1016/j.celrep.2021.109971
   ILYINAKAKUEVA EI, 1977, AVIAT SPACE ENVIR MD, V48, P115
   Janwa H, 2019, FRONT GENET, V10, DOI 10.3389/fgene.2019.00240
   Juhl OJ, 2021, NPJ MICROGRAVITY, V7, DOI 10.1038/s41526-021-00158-4
   Kan JB, 2021, BIOENGINEERED, V12, P3485, DOI 10.1080/21655979.2021.1948951
   Kenny HC, 2020, J PROTEOME RES, V19, P3438, DOI 10.1021/acs.jproteome.0c00256
   Kim HJ, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0252135
   Kumar R, 2022, IBRO NEUROSCI REP, V12, P25, DOI 10.1016/j.ibneur.2021.12.002
   Kwok AT, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-90010-2
   Lang T, 2017, NPJ MICROGRAVITY, V3, DOI 10.1038/s41526-017-0013-0
   LeCun Y, 1989, NEURAL COMPUT, V1, P541, DOI 10.1162/neco.1989.1.4.541
   Li Y, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.01016
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Loscalzo J., 2016, NETW SCI, V3rd
   Manian V, 2021, FRONT CELL DEV BIOL, V9, DOI 10.3389/fcell.2021.732370
   Manian V, 2021, GENES-BASEL, V12, DOI 10.3390/genes12060938
   Manian V, 2021, COMPUTERS, V10, DOI 10.3390/computers10020018
   Marchetti N, 2019, AM J RESP CRIT CARE, V199, P256, DOI 10.1164/rccm.201807-1327ED
   Massart IS, 2020, CANCERS, V12, DOI 10.3390/cancers12113221
   Montesano A, 2015, OXID MED CELL LONGEV, V2015, DOI 10.1155/2015/646171
   NASA, GEN LAB DAT REP
   Nath SR, 2020, ACTA NEUROPATHOL, V140, P63, DOI 10.1007/s00401-020-02156-4
   Nelson CA, 2021, LIFE-BASEL, V11, DOI 10.3390/life11010042
   Pereira CV, 2020, EMBO MOL MED, V12, DOI 10.15252/emmm.201910674
   Ram R, 2011, IEEE ACM T COMPUT BI, V8, P353, DOI 10.1109/TCBB.2009.70
   Romanick M, 2013, BBA-MOL BASIS DIS, V1832, P1410, DOI 10.1016/j.bbadis.2013.03.011
   Salanova M, 2015, SCI REP-UK, V5, DOI 10.1038/srep17027
   Sandri M, 2013, INT J BIOCHEM CELL B, V45, P2121, DOI 10.1016/j.biocel.2013.04.023
   Serre J-P., 1980, TREES-STRUCT FUNCT, DOI 10.1007/978-3-642-61856-7
   So HK, 2021, FRONT PHYSIOL, V12, DOI 10.3389/fphys.2021.712389
   Sung M, 2013, J MUSCULOSKEL NEURON, V13, P405
   Thulasiraman K.K.T., 2016, HDB GRAPH THEORY COM
   Wang DT, 2015, OXID MED CELL LONGEV, V2015, DOI 10.1155/2015/684965
   Zatyka M, 2020, J MOL BIOL, V432, P2735, DOI [10.1016/jmb.2020.02.012, 10.1016/j.jmb.2020.02.012]
NR 54
TC 1
Z9 1
U1 4
U2 4
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-4425
J9 GENES-BASEL
JI Genes
PD MAR
PY 2022
VL 13
IS 3
AR 473
DI 10.3390/genes13030473
PG 20
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA 0C4SN
UT WOS:000775305100001
PM 35328027
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Abedini, F
   Keyvanpour, MR
   Menhaj, MB
AF Abedini, Farhad
   Keyvanpour, Mohammad Reza
   Menhaj, Mohammad Bagher
TI Correction Tower: A General Embedding Method of the Error Recognition
   for the Knowledge Graph Correction
SO INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Correction tower; erroneous relations; inconsistency; knowledge graph
   correction; error recognition; outliers
ID QUALITY; WEB
AB Today, knowledge graphs (KGs) are growing by enrichment and refinement methods. The enrichment and refinement can be gained using the correction and completion of the KG. The studies of the KG completion are rich, but less attention has been paid to the methods of the KG error correction. The correction methods are divided into embedding and nonembedding methods. Embedding correction methods have been recently introduced in which a KG is embedded into a vector space. Also, existing correction approaches focused on the recognition of the three types of errors, the outliers, inconsistencies and erroneous relations. One of the challenges is that most outlier correction methods can recognize only numeric outlier entities by nonembedding methods. On the other hand, inconsistency errors are recognized during the knowledge extraction step and existing methods of this field do not pay attention to the recognition of these errors as post-correction by embedding methods. Also, to correct erroneous relations, new embedding techniques have not been used. Since the errors of a KG are variant and there is no method to cover all of them, a new general correction method is proposed in this paper. This method is called correction tower in which these three error types are corrected in three trays. In this correction tower, a new configuration will be suggested to solve the above challenges. For this aim, a new embedding method is proposed for each tray. Finally, the evaluation results show that the proposed correction tower can improve the KG error correction methods and proposed configuration can outperform previous results.
C1 [Abedini, Farhad] Islamic Azad Univ, Qazvin Branch, Fac Comp & Informat Technol Engn, Qazvin, Iran.
   [Keyvanpour, Mohammad Reza] Alzahra Univ, Dept Comp Engn, Tehran, Iran.
   [Menhaj, Mohammad Bagher] Amirkabir Univ Technol, Elect Engn Dept, Ctr Excellence Control & Robot, 424 Hafez Ave, Tehran, Iran.
RP Keyvanpour, MR (通讯作者)，Alzahra Univ, Dept Comp Engn, Tehran, Iran.
EM Abedini@raiau.ac.ir; keyvanpour@alzahra.ac.ir; menhaj@aut.ac.ir
RI Abedini, Farhad/ABG-9635-2021; Keyvanpour, Mohammad Reza/AAL-5574-2020
OI Abedini, Farhad/0000-0002-1475-9427; Keyvanpour, Mohammad
   Reza/0000-0003-2115-9099
CR Abedini Farhad, 2013, International Journal of Computer Theory and Engineering, V5, P432, DOI 10.7763/IJCTE.2013.V5.724
   Abedini F, 2011, INT J MACH LEARN COM, V1, P113, DOI [10.7763/IJMLC.2011.V1.17, DOI 10.7763/IJMLC.2011.V1.17]
   Abedini F., 2012, INT J MACHINE LEARNI, V2, P178
   Abedini F., 2011, 4 INT C ADV COMP THE
   Abedini F., 2012, INT J MACH LEARN COM, V2, P113
   Abedini F, 2019, APPL ARTIF INTELL, V33, P656, DOI 10.1080/08839514.2019.1602317
   Abedini F, 2019, NEURAL COMPUT APPL, V31, P1135, DOI 10.1007/s00521-017-3053-1
   Achichi M, 2019, J WEB SEMANT, V55, P108, DOI 10.1016/j.websem.2018.12.003
   Aggarwal C. C., 2017, OUTLIER DETECTION GR, P369
   Almeida A., 2018, AUTOMATIC POI MATCHI
   Arenas M, 2016, J WEB SEMANT, V37-38, P55, DOI 10.1016/j.websem.2015.12.002
   Arnaout H, 2018, J WEB SEMANT, V48, P66, DOI 10.1016/j.websem.2017.12.001
   Azevedo MSP, 2012, BENEF MICROBES, V3, P33, DOI 10.3920/BM2011.0041
   Balazevic I., ARXIV180807018
   Benbernou S, 2017, IEEE INT CONF BIG DA, P74, DOI 10.1109/BigData.2017.8257913
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Breunig MM, 2000, SIGMOD REC, V29, P93, DOI 10.1145/335191.335388
   Chang L, 2017, IEEE ACCESS, V5, P20898, DOI 10.1109/ACCESS.2017.2759139
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Dylla M., 2011, BTW, P474
   Fan WF, 2018, INT CONF MANAGE DATA, P381, DOI 10.1145/3183713.3183753
   Fang Jun, 2010, Tsinghua Science and Technology, V15, P687, DOI 10.1016/S1007-0214(10)70116-4
   Fleischhacker D., 2015, THESIS
   Fleischhacker D, 2014, LECT NOTES COMPUT SC, V8796, P357, DOI 10.1007/978-3-319-11964-9_23
   Flesca S, 2010, ACM T DATABASE SYST, V35, DOI 10.1145/1735886.1735893
   Gao H, 2018, IEEE ACCESS, V6, P58978, DOI 10.1109/ACCESS.2018.2875066
   Goyal P, 2018, KNOWL-BASED SYST, V151, P78, DOI 10.1016/j.knosys.2018.03.022
   Guan NN, 2019, KNOWL-BASED SYST, V164, P38, DOI 10.1016/j.knosys.2018.10.008
   Han X, 2018, IEEE ACCESS, V6, P76606, DOI 10.1109/ACCESS.2018.2884012
   He LR, 2018, COGN COMPUT, V10, P1075, DOI 10.1007/s12559-018-9565-x
   Hsu YP, 2011, IEEE INT SYMP INFO, P791, DOI 10.1109/ISIT.2011.6034243
   Huiying Li, 2015, Database and Expert Systems Applications. 26th International Conference, DEXA 2015. Proceedings: LNCS 9261, P61, DOI 10.1007/978-3-319-22849-5_5
   Idoudi R, 2019, KNOWL MAN RES PRACT, V17, P151, DOI 10.1080/14778238.2018.1538599
   Kazemi S. M., ABS180204868 CORR
   Kim H, 2017, KNOWL MAN RES PRACT, V15, P305, DOI 10.1057/s41275-017-0056-8
   Kristiadi A., ARXIV180200934
   Lao N, 2010, MACH LEARN, V81, P53, DOI 10.1007/s10994-010-5205-8
   Lassila O., 1999, W3C
   Lin, 2018, ARXIV180803752
   Lin X, 2018, INT J COMPUT COMMUN, V13, P71, DOI 10.15837/ijccc.2018.1.3104
   Lin XX, 2019, NEURAL COMPUT APPL, V31, P5629, DOI 10.1007/s00521-018-3384-6
   Liu H., ARXIV170502426
   Ma Y., 2014, CCIS, V480, P29, DOI DOI 10.1007/978-3-662-45495-4
   Ma YP, 2019, J WEB SEMANT, V59, DOI 10.1016/j.websem.2018.12.008
   McCabe WL, 1993, UNIT OPERATIONS CHEM, V1130
   Melo A., 2016, P IEEE INT EN C EXH, P1
   Melo A., 2017, P KNOWL CAPT C, P1
   Melo A., 2017, 1 INT WORKSH QUAL EN, P1
   Melo A.T., 2018, THESIS
   Melo A, 2017, INT J ARTIF INTELL T, V26, DOI 10.1142/S0218213017600119
   Miklosik A, 2019, KNOWL MAN RES PRACT, V17, P172, DOI 10.1080/14778238.2018.1538602
   Miller G. A., 1995, COMMUNICATIONS ACM
   Ndapandula N., 2011, P 4 ACM INT C WEB SE, P227
   Nickel M., 2013, TENSOR FACTORIZATION
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Padia A, 2019, J WEB SEMANT, V59, DOI 10.1016/j.websem.2019.01.004
   PARZEN E, 1962, ANN MATH STAT, V33, P1065, DOI 10.1214/aoms/1177704472
   Paulheim H., 2018, REASONING WEB INT SU
   Paulheim H., 2014, WORKSH DEB ONT ONT M, P27
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Paulheim H, 2014, INT J SEMANT WEB INF, V10, P63, DOI 10.4018/ijswis.2014040104
   Petlyuk F.B., 2004, DISTILLATION THEORY
   Petrova A., 2017, ENTITY COMPARISON RD
   Ringsquandl M., 2018, EVENT ENHANCED LEARN
   Ringsquandl M, 2017, IEEE INT CONF BIG DA, P1676, DOI 10.1109/BigData.2017.8258105
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9981, P498, DOI 10.1007/978-3-319-46523-4_30
   Ristoski P, 2016, J WEB SEMANT, V36, P1, DOI 10.1016/j.websem.2016.01.001
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Suchanek F. M., 2009, P 18 INT C WORLD WID, P631
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Suchanek FM, 2008, THESIS
   Toepper G., 2012, I SEMANTICS, P33
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang CY, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P307, DOI 10.1145/2983323.2983705
   Wang M., 2018, EMBEDDING KNOWLEDGE
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Weikum G., 2016, DATA ENG, P1
   Wienand Dominik, 2014, The Semantic Web: Trends and Challenges. 11th International Conference (ESWC 2014). Proceedings: LNCS 8465, P504, DOI 10.1007/978-3-319-07443-6_34
   Wilcke WX, 2019, J WEB SEMANT, V59, DOI 10.1016/j.websem.2018.12.004
   Zhang D, 2015, PROCEEDINGS OF 2015 IEEE 14TH INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS & COGNITIVE COMPUTING (ICCI*CC), P141, DOI 10.1109/ICCI-CC.2015.7259378
   Zhang M., 2018, DISCRIMINATIVE PATH
   Zhao LH, 2017, LECT NOTES COMPUT SC, V10675, P164, DOI 10.1007/978-3-319-70682-5_11
   Zhaohua Sheng, 2012, Web Information Systems and Mining. Proceedings International Conference, WISM 2012, P480, DOI 10.1007/978-3-642-33469-6_60
   Zhu JZ, 2018, J COMPUT SCI TECH-CH, V33, P323, DOI 10.1007/s11390-018-1821-8
   Zhu QN, 2019, J COMPUT SCI-NETH, V30, P108, DOI 10.1016/j.jocs.2018.11.004
NR 87
TC 1
Z9 1
U1 2
U2 31
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-0014
EI 1793-6381
J9 INT J PATTERN RECOGN
JI Int. J. Pattern Recognit. Artif. Intell.
PD SEP
PY 2020
VL 34
IS 10
AR 2059034
DI 10.1142/S021800142059034X
PG 38
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NR6IC
UT WOS:000571664900015
DA 2022-08-23
ER

PT J
AU Yang, MY
   Chen, KH
   Sun, SQ
   Han, ZY
   Kong, LL
   Meng, QY
AF Yang, Muyun
   Chen, Kehai
   Sun, Shuqi
   Han, Zhongyuan
   Kong, Leilei
   Meng, Qingye
TI A Pattern Driven Graph Ranking Approach to Attribute Extraction for
   Knowledge Graph
SO IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
LA English
DT Article
DE Data mining; Semantics; Postal services; Informatics; Manuals; Web
   search; Urban areas; Distributional similarity; graph-based ranking;
   pattern driven; semantic class attributes; Web search queries
ID CONSTRUCTION
AB Attribution extraction refers to find the attributes for the instances of a given semantic class, which is essential to enhance the schema of a knowledge graph. To facilitate the attribution extraction from the query log, this article proposes a pattern driven graph ranking approach to jointly employ the pattern and context distribution information. First, a simple pattern on query text is applied to automatically acquire seed attributes. Then, a graph-based weight propagation is designed to rank the patterns by context distribution algorithm information. Experimental results show that, on a Chinese query log collected by Baidu, the automatically acquired seeds are more representative than the classical manually assembled seeds, achieving an improvement of 11.6% in MAP as compared to the baseline approach. And the graph-based ranking algorithm manipulates the two types of evidence more effectively, outperforming both the distributional similarity based baseline and the HITS algorithm by 29.2% and 11.3%, respectively.
C1 [Yang, Muyun; Chen, Kehai; Meng, Qingye] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China.
   [Sun, Shuqi] Baidu Inc, Beijing 100085, Peoples R China.
   [Han, Zhongyuan; Kong, Leilei] Foshan Univ, Foshan 528011, Peoples R China.
RP Chen, KH (通讯作者)，Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China.
EM yangmuyun@hit.edu.cn; chenkehai@gmail.com; sunshuqi01@baidu.com;
   hanzhongyuan@gmail.com; kongleilei@fosu.edu.cn; mqy1997@hotmail.com
OI meng, qingye/0000-0002-4980-4785
FU National Social Science Fund of China [18BYY125]
FX This work was supported by the National Social Science Fund of China
   under Grant 18BYY125. Paper no. TII-21-0057.
CR Alfonseca E, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P58
   Collarana D, 2017, IEEE INT C SEMANT CO, P109, DOI 10.1109/ICSC.2017.85
   Khandelwal S, 2020, ACM INT CONF PR SER, P204, DOI 10.1145/3371158.3371187
   Kozareva Z., 2011, EMNLP, P118
   Langville A., 2008, MATH INTELL, V30, P68, DOI DOI 10.1007/BF02985759
   Lin C. -Y, 2011, P 49 ANN M ASS COMP, P1159
   Martinez-Rodriguez JL, 2020, SEMANT WEB, V11, P255, DOI 10.3233/SW-180333
   Martinez-Rodriguez JL, 2018, EXPERT SYST APPL, V113, P339, DOI 10.1016/j.eswa.2018.07.017
   Nastase V, 2015, NAT LANG ENG, V21, P665, DOI 10.1017/S1351324915000340
   Pasca M., 2008, ACL, P19
   Pasca M, 2010, LECT NOTES COMPUT SC, V5993, P62, DOI 10.1007/978-3-642-12275-0_9
   Pasca Marius, 2007, P 16 INT C WORLD WID, P101, DOI DOI 10.1145/1242572.1242587
   Sahu SK, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4309
   Teneva N, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2, P530, DOI 10.18653/v1/P17-2084
   Vyas V., 2009, P 18 ACM C INF KNOWL, P225
   Wang CB, 2018, COMPUT GEOSCI-UK, V112, P112, DOI 10.1016/j.cageo.2017.12.007
   Wang D, 2020, P 58 ANN M ASS COMP, P6209, DOI DOI 10.18653/V1/2020.ACL-MAIN.553
   Xiao ZJ, 2020, ACM T WEB, V14, DOI 10.1145/3386042
   Xue MG, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5327
   Yang L, 2021, IEEE T IND INFORM, V17, P1039, DOI 10.1109/TII.2020.2990953
   Yin YJ, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5387
   Yin YY, 2021, IEEE T IND INFORM, V17, P6153, DOI 10.1109/TII.2020.3039500
   Zhang YX, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1349, DOI 10.1145/3132847.3132956
NR 23
TC 1
Z9 1
U1 8
U2 18
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1551-3203
EI 1941-0050
J9 IEEE T IND INFORM
JI IEEE Trans. Ind. Inform.
PD FEB
PY 2022
VL 18
IS 2
BP 1250
EP 1259
DI 10.1109/TII.2021.3073726
PG 10
WC Automation & Control Systems; Computer Science, Interdisciplinary
   Applications; Engineering, Industrial
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science; Engineering
GA WO6LW
UT WOS:000712564700055
DA 2022-08-23
ER

PT J
AU Li, WD
   Peng, R
   Wang, YQ
   Yan, ZH
AF Li, Weidong
   Peng, Rong
   Wang, Yaqian
   Yan, Zhihuan
TI Knowledge graph based natural language generation with adapted
   pointer-generator networks
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph; Natural language generation; Supervised attention;
   Seq2Seq neural networks; Coverage loss
AB Pointer-generator networks have recently shown superior performance in Natural Language Generation (NLG) tasks, such as automatic generating descriptions for entities in Knowledge Graph (KG). In the absence of the introductory description about an entity we intend to know, the natural language text automatically generated by neural network model can greatly help people to better understand the entity. Entities in KG always have multiple property fields and corresponding values. The target generated description sentences should consist of these slot types and slot values in a consistent and high coverage manner. In order to cover the facts in an input KG, pointer-generator networks always copy certain segments from the input sequences via softmax pointing while generating novel words through the generator. But the challenges that when and where to integrate the copied information with the generated one, how to prevent duplicate generation and information loss are hard to conquer. In this paper, the KG2TEXT model based on adapted pointer-generator networks is proposed. In it, a varied coverage loss function is devised to cover attribute-value pairs as many as possible when generating natural language descriptions for entities in KG. Secondly, an attention mechanism named supervised attention mechanism is added to the model which aims to guide the soft switch transformation process (to generate or to copy). With empirical experiments on two kinds of realistic datasets, the KG2TEXT model achieves promising results and outperforms the state-of-the-art approaches. (C) 2019 Published by Elsevier B.V.
C1 [Li, Weidong; Peng, Rong; Wang, Yaqian; Yan, Zhihuan] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
RP Peng, R (通讯作者)，Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
EM weidonghappy@whu.edu.cn; rongpeng@whu.edu.cn
OI Peng, Rong/0000-0003-2330-9409
FU National Key Research and Development Plan of China [2017YFB0503702,
   2016YFB0501801]; National Natural Science Foundation of China [61170026]
FX This work is supported by the National Key Research and Development Plan
   of China under Grant No. 2017YFB0503702, 2016YFB0501801, and National
   Natural Science Foundation of China under Grant No. 61170026.
CR Angeli G, 2010, P 2010 C EMP METH NA, P502
   [Anonymous], [No title captured]
   [Anonymous], 2012, P 50 ANN M ASS COMP
   [Anonymous], 2018, P 11 INT C NAT LANG
   [Anonymous], [No title captured]
   [Anonymous], 2016, P 54 ANN M ASS COMP
   [Anonymous], 2006, P 11 C EUR CHAPT ASS
   Bahdanau Dzmitry, 2015, 3 INT C LEARN REPR I
   Bast H, 2016, FOUND TRENDS INF RET, V10, P120, DOI 10.1561/1500000032
   Belz Anja, 2008, Natural Language Engineering, P431, DOI 10.1017/S1351324907004664
   Cawsey AJ, 1997, J AM MED INFORM ASSN, V4, P473, DOI 10.1136/jamia.1997.0040473
   Cho K., 2014, ARXIV14061078, P1724
   Cui WY, 2017, PROC VLDB ENDOW, V10, P565, DOI 10.14778/3055540.3055549
   Duchi J, 2011, J MACH LEARN RES, V12, P2121
   Duma D., 2013, P 10 INT C COMP SEM, P83
   Galanis D., 2007, P 11 EUR WORKSH NAT, P143
   Green N., 2006, P 4 INT NAT LANG GEN, P114
   Konstas I, 2012, P 2012 C N AM CHAPT, P752
   Kukich K., 1983, 21st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, P145
   Kumar V, 2008, INT CONF ELECTRO INF, P128, DOI 10.1109/EIT.2008.4554280
   Kumar MVP, 2016, INT SYM WIRELESS COM, P143, DOI 10.1109/ISWCS.2016.7600890
   Lebret Remi, 2016, ARXIV160307771
   Lin C.Y., 2004, P M ASS COMP LING AC, P74
   Liu TY, 2018, AAAI CONF ARTIF INTE, P4881
   Mei Hongyuan, 2016, P 2016 C N AM CHAPT, P720
   Mi H., 2016, P 2016 C EMP METH NA, P2283, DOI DOI 10.18653/V1/D16-1249
   Mohammed S., 2018, P 2018 C N AM CHAPT, V2, P291, DOI [10.18653/v1/N18-2047, DOI 10.18653/V1/N18-2047]
   Nguyen M., 2018, P 27 INT C COMP LING, P2277
   Palumbo E, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P32, DOI 10.1145/3109859.3109889
   Papineni K, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P311, DOI 10.3115/1073083.1073135
   See A, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1073, DOI 10.18653/v1/P17-1099
   Song LF, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1616
   Sutskever I., 2011, P 28 ANN INT C MACH, V28, P1017
   Tu ZP, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P76
   Vedantam R, 2015, PROC CVPR IEEE, P4566, DOI 10.1109/CVPR.2015.7299087
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wen T.H., 2015, C P EMNLP 2015 C EMP, P1711, DOI 10.18653/v1/D15-1199
   WHO, 2012, WORLD MALARIA REPORT 2012, P1
   Wiseman Sam, 2017, ARXIV170708052
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
NR 40
TC 10
Z9 10
U1 4
U2 24
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD MAR 21
PY 2020
VL 382
BP 174
EP 187
DI 10.1016/j.neucom.2019.11.079
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KK6WW
UT WOS:000512881200018
DA 2022-08-23
ER

PT J
AU Nelson, CA
   Acuna, AU
   Paul, AM
   Scott, RT
   Butte, AJ
   Cekanaviciute, E
   Baranzini, SE
   Costes, SV
AF Nelson, Charlotte A.
   Acuna, Ana Uriarte
   Paul, Amber M.
   Scott, Ryan T.
   Butte, Atul J.
   Cekanaviciute, Egle
   Baranzini, Sergio E.
   Costes, Sylvain V.
TI Knowledge Network Embedding of Transcriptomic Data from Spaceflown Mice
   Uncovers Signs and Symptoms Associated with Terrestrial Diseases
SO LIFE-BASEL
LA English
DT Article
DE spaceflight; knowledge graph; transcriptomics
ID STRESS
AB There has long been an interest in understanding how the hazards from spaceflight may trigger or exacerbate human diseases. With the goal of advancing our knowledge on physiological changes during space travel, NASA GeneLab provides an open-source repository of multi-omics data from real and simulated spaceflight studies. Alone, this data enables identification of biological changes during spaceflight, but cannot infer how that may impact an astronaut at the phenotypic level. To bridge this gap, Scalable Precision Medicine Oriented Knowledge Engine (SPOKE), a heterogeneous knowledge graph connecting biological and clinical data from over 30 databases, was used in combination with GeneLab transcriptomic data from six studies. This integration identified critical symptoms and physiological changes incurred during spaceflight.
C1 [Nelson, Charlotte A.; Baranzini, Sergio E.] Univ Calif San Francisco, Integrated Program Quantitat Biol, San Francisco, CA 94143 USA.
   [Acuna, Ana Uriarte; Paul, Amber M.; Scott, Ryan T.; Cekanaviciute, Egle; Costes, Sylvain V.] NASA, Space Biosci Div, Ames Res Ctr, Moffett Field, CA 94035 USA.
   [Acuna, Ana Uriarte; Scott, Ryan T.] NASA, KBR, Ames Res Ctr, Moffett Field, CA 94035 USA.
   [Paul, Amber M.] NASA, Postdoctoral Program, Univ Space Res Assoc USRA, Mountain View, CA 94043 USA.
   [Butte, Atul J.; Baranzini, Sergio E.] Univ Calif San Francisco, Bakar Computat Hlth Sci Inst, San Francisco, CA 94143 USA.
   [Butte, Atul J.] Univ Calif San Francisco, Dept Pediat, San Francisco, CA 94143 USA.
   [Baranzini, Sergio E.] Univ Calif San Francisco, Dept Neurol, Weill Inst Neurosci, San Francisco, CA 94143 USA.
RP Baranzini, SE (通讯作者)，Univ Calif San Francisco, Integrated Program Quantitat Biol, San Francisco, CA 94143 USA.; Costes, SV (通讯作者)，NASA, Space Biosci Div, Ames Res Ctr, Moffett Field, CA 94035 USA.; Baranzini, SE (通讯作者)，Univ Calif San Francisco, Bakar Computat Hlth Sci Inst, San Francisco, CA 94143 USA.; Baranzini, SE (通讯作者)，Univ Calif San Francisco, Dept Neurol, Weill Inst Neurosci, San Francisco, CA 94143 USA.
EM Charlotte.Nelson@ucsf.edu; ana.e.uriarteacuna@nasa.gov;
   amber.m.paul@nasa.gov; ryan.t.scott@nasa.gov; atul.butte@ucsf.edu;
   egle.cekanaviciute@nasa.gov; Sergio.Baranzini@ucsf.edu;
   sylvain.v.costes@nasa.gov
OI Baranzini, Sergio/0000-0003-0067-194X; Scott, Ryan/0000-0003-0654-5661;
   Paul, Amber/0000-0002-1657-3618; Cekanaviciute,
   Egle/0000-0003-3306-1806; Nelson, Charlotte/0000-0002-3687-1102
FU National Science Foundation [NSF_2033569]
FX This research was funded by [National Science Foundation] grant number
   [NSF_2033569]. S.E.B. is the Distinguished Professor in Neurology at
   UCSF and holds the Heidrich Friends and Family endowed chair in
   Neurology at UCSF. GeneLab Project at NASA Ames Research Center, through
   NASA's Space Biology Program in the Division of Biological and Physical
   Sciences (BPS) of the Science Mission Directorate. Any use of trade
   names is for descriptive purposes only and does not imply endorsement by
   the US Government.
CR Afshinnekoo E, 2020, CELL, V183, P1162, DOI 10.1016/j.cell.2020.10.050
   Agarwala R, 2018, NUCLEIC ACIDS RES, V46, pD8, DOI [10.1093/nar/gkr1184, 10.1093/nar/gks1189, 10.1093/nar/gkx1095, 10.1093/nar/gky1069, 10.1093/nar/gkv1290]
   Andrews S., 2010, FASTQC QUALITY CONTR
   Antonsen E., 2020, RISK MAPPING INTERAC
   Antonsen R.D, 2019, HOUST J HLTH LAW POL, V19, P1
   Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Barabasi AL, 2011, NAT REV GENET, V12, P56, DOI 10.1038/nrg2918
   Berrios DC, 2021, NUCLEIC ACIDS RES, V49, pD1515, DOI 10.1093/nar/gkaa887
   Carbon S, 2019, NUCLEIC ACIDS RES, V47, pD330, DOI 10.1093/nar/gky1055
   da Silveira WA, 2020, CELL, V183, P1185, DOI 10.1016/j.cell.2020.11.002
   Dobin A, 2013, BIOINFORMATICS, V29, P15, DOI 10.1093/bioinformatics/bts635
   Ewels P, 2016, BIOINFORMATICS, V32, P3047, DOI 10.1093/bioinformatics/btw354
   Goh KI, 2007, P NATL ACAD SCI USA, V104, P8685, DOI 10.1073/pnas.0701361104
   Haveliwala T.H., 2002, P 11 INT C WORLD WID, P517, DOI DOI 10.1145/511446.511513
   Himmelstein DS, 2015, PLOS COMPUT BIOL, V11, DOI 10.1371/journal.pcbi.1004259
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Krueger F., 2015, TRIM GALORE WRAPPER
   Lebsack TW, 2010, J CELL BIOCHEM, V110, P372, DOI 10.1002/jcb.22547
   Li B, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-323
   Love MI, 2014, GENOME BIOL, V15, DOI 10.1186/s13059-014-0550-8
   Lukacs-Kornek V, 2016, FRONT IMMUNOL, V7, DOI 10.3389/fimmu.2016.00548
   Malkani Sherina, 2020, Cell Rep, V33, P108448, DOI 10.1016/j.celrep.2020.108448
   MANN HB, 1947, ANN MATH STAT, V18, P50, DOI 10.1214/aoms/1177730491
   Mi HY, 2019, NUCLEIC ACIDS RES, V47, pD419, DOI 10.1093/nar/gky1038
   Munro SA, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms6125
   NASA GeneLab, 2010, MICROARRAY ANAL SPAC, V4th ed., DOI [10.25966/qq9p-pc28, DOI 10.25966/QQ9P-PC28]
   Nelson CA, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-11069-0
   Nicholson DN, 2020, COMPUT STRUCT BIOTEC, V18, P1414, DOI 10.1016/j.csbj.2020.05.017
   Page L., 1999, PAGERANK CITATION RA
   R Development Core Team, 2016, R LANG ENV STAT COMP
   Reynolds RJ, 2020, LEO HUMAN HLTH ISSUE
   Rutter L, 2020, PATTERNS, V1, DOI 10.1016/j.patter.2020.100148
   Scott RT, 2020, CELL REP, V33, DOI 10.1016/j.celrep.2020.108441
   Sievert C., 2020, INTERACTIVE WEB BASE, DOI DOI 10.1201/9780429447273
   Stouffer S. A., 1949, AM SOLDIER ADJUSTMEN, V1
   Wang LL, 2015, BIOINFORMATICS, V31, P262, DOI 10.1093/bioinformatics/btu644
   Zhou XZ, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms5212
NR 37
TC 3
Z9 3
U1 3
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-1729
J9 LIFE-BASEL
JI Life-Basel
PD JAN
PY 2021
VL 11
IS 1
AR 42
DI 10.3390/life11010042
PG 14
WC Biology; Microbiology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Life Sciences & Biomedicine - Other Topics; Microbiology
GA PW1AF
UT WOS:000610407800001
PM 33445483
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Carvalho, R
   Morgado, AC
   Andrade, C
   Nedelcu, T
   Carreiro, A
   Vasconcelos, MJM
AF Carvalho, Rafaela
   Morgado, Ana C.
   Andrade, Catarina
   Nedelcu, Tudor
   Carreiro, Andre
   Vasconcelos, Maria Joao M.
TI Integrating Domain Knowledge into Deep Learning for Skin Lesion Risk
   Prioritization to Assist Teledermatology Referral
SO DIAGNOSTICS
LA English
DT Article
DE teledermatology; risk prioritization; skin lesion classification; domain
   knowledge; hierarchical learning; curriculum learning
ID SEGMENTATION
AB Teledermatology has developed rapidly in recent years and is nowadays an essential tool for early diagnosis. In this work, we aim to improve existing Teledermatology processes for skin lesion diagnosis by developing a deep learning approach for risk prioritization with a dataset of retrospective data from referral requests of the Portuguese National Health System. Given the high complexity of this task, we propose a new prioritization pipeline guided and inspired by domain knowledge. We explored automatic lesion segmentation and tested different learning schemes, namely hierarchical classification and curriculum learning approaches, optionally including additional patient metadata. The final priority level prediction can then be obtained by combining predicted diagnosis and a baseline priority level accounting for explicit expert knowledge. In both the differential diagnosis and prioritization branches, lesion segmentation with 30% tolerance for contextual information was shown to improve classification when compared with a flat baseline model trained on original images; furthermore, the addition of patient information was not beneficial for most experiments. Curriculum learning delivered better results than a flat or hierarchical approach. The combination of diagnosis information and a knowledge map, created in collaboration with dermatologists, together with the priority achieved interesting results (best macro F1 of 43.93% for a validated test set), paving the way for new data-centric and knowledge-driven approaches.
C1 [Carvalho, Rafaela; Morgado, Ana C.; Andrade, Catarina; Nedelcu, Tudor; Carreiro, Andre; Vasconcelos, Maria Joao M.] Fraunhofer Portugal AICOS, Rua Alfredo Allen, P-4200135 Porto, Portugal.
RP Vasconcelos, MJM (通讯作者)，Fraunhofer Portugal AICOS, Rua Alfredo Allen, P-4200135 Porto, Portugal.
EM rafaela.carvalho@fraunhofer.pt; ana.morgado@fraunhofer.pt;
   catarina.andrade@fraunhofer.pt; nedelcu_tudor_ionut@yahoo.com;
   andre.carreiro@fraunhofer.pt; maria.vasconcelos@fraunhofer.pt
OI Morgado, Ana C./0000-0002-7654-1642; Vasconcelos,
   Maria/0000-0002-0634-7852; Carvalho, Rafaela/0000-0002-6936-3286;
   Carreiro, Andre/0000-0002-4234-5336
CR An GZ, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-83503-7
   Andrade C, 2021, J IMAGING, V7, DOI 10.3390/jimaging7010002
   Apalla Z, 2017, DERMATOLOGY THER, V7, pS5, DOI 10.1007/s13555-016-0165-y
   Argenziano G., 2002, DERMOSCOPY TUTORIAL
   Ballerini L., 2013, COLOR MED IMAGE ANAL, P63, DOI [10.1007/978-94-007-5389-1_4, DOI 10.1007/978-94-007-5389-1_4]
   Baltrusaitis T, 2019, IEEE T PATTERN ANAL, V41, P423, DOI 10.1109/TPAMI.2018.2798607
   Barata C, 2019, I S BIOMED IMAGING, P841, DOI 10.1109/ISBI.2019.8759561
   Bengio Y, 2009, FOUND TRENDS MACH LE, V2, P1, DOI 10.1561/2200000006
   Burdick J, 2018, J DIGIT IMAGING, V31, P435, DOI 10.1007/s10278-017-0026-y
   Chan S, 2020, DERMATOLOGY THER, V10, P365, DOI 10.1007/s13555-020-00372-0
   Chatfield K, 2011, PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE 2011, DOI 10.5244/C.25.76
   Chen LCE, 2018, LECT NOTES COMPUT SC, V11211, P833, DOI 10.1007/978-3-030-01234-2_49
   Codella NCF, 2018, I S BIOMED IMAGING, P168, DOI 10.1109/ISBI.2018.8363547
   Dai DW, 2022, MED IMAGE ANAL, V75, DOI 10.1016/j.media.2021.102293
   Dash T., 2021, ARXIV210300180
   Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
   Esteva A, 2017, NATURE, V542, P115, DOI 10.1038/nature21056
   EyePACS, DIABETIC RETINOPATHY
   Fisher R.B., 2019, MED IMAGE UNDERSTAND, V1065, P86
   Gutman D., 2016, EPRINT ARXIV16050139
   Hoehn J, 2021, J MED INTERNET RES, V23, DOI 10.2196/20708
   Kassem MA, 2021, DIAGNOSTICS, V11, DOI 10.3390/diagnostics11081390
   Kawahara J, 2019, IEEE J BIOMED HEALTH, V23, P538, DOI 10.1109/JBHI.2018.2824327
   Kawahara J, 2016, I S BIOMED IMAGING, P1397, DOI 10.1109/ISBI.2016.7493528
   Kharazmi P, 2018, SKIN RES TECHNOL, V24, P256, DOI 10.1111/srt.12422
   Krizhevsky A., 2012, ADV NEURAL INF PROCE, V25, P1097, DOI [10.1145/3065386, DOI 10.1145/3065386]
   Li R, 2020, J MED INTERNET RES, V22, DOI 10.2196/20645
   Lin BS, 2017, 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), P117
   Lin M., 2013, 13124400 ARXIV
   Lopez AR, 2017, 2017 13TH IASTED INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING (BIOMED), P49, DOI 10.2316/P.2017.852-053
   Mahbod A, 2019, COMPUT MED IMAG GRAP, V71, P19, DOI 10.1016/j.compmedimag.2018.10.007
   Maicas G, 2018, LECT NOTES COMPUT SC, V11070, P546, DOI 10.1007/978-3-030-00928-1_62
   Menegola A, 2017, I S BIOMED IMAGING, P297, DOI 10.1109/ISBI.2017.7950523
   Mirikharaji Z, 2021, IEEE COMPUT SOC CONF, P1837, DOI 10.1109/CVPRW53098.2021.00203
   Moreira D, 2021, HEALTHINF: PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES - VOL. 5: HEALTHINF, P122, DOI 10.5220/0010234201220132
   Muralidhar N, 2018, IEEE INT CONF BIG DA, P36, DOI 10.1109/BigData.2018.8621955
   Nedelcu T., 2021, P HEALTHINFO 2021 6, P23
   Nedelcu T., 2020, P 6 WORLD C EL ENG C, P1
   Ozturk S, 2020, J DIGIT IMAGING, V33, P958, DOI 10.1007/s10278-020-00343-z
   Puig S., 2019, ARXIV190802288
   Ramachandram D, 2017, IEEE SIGNAL PROC MAG, V34, P96, DOI 10.1109/MSP.2017.2738401
   Ronneberger O, 2015, LECT NOTES COMPUT SC, V9351, P234, DOI 10.1007/978-3-319-24574-4_28
   Salakhutdinov R, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P3176, DOI 10.1145/3292500.3340416
   Sali R., 2020, P CINC, P1
   Sandler M, 2018, PROC CVPR IEEE, P4510, DOI 10.1109/CVPR.2018.00474
   Sarker Md Mostafa Kamal, 2018, Medical Image Computing and Computer Assisted Intervention - MICCAI 2018. 21st International Conference. Proceedings: Lecture Notes in Computer Science (LNCS 11071), P21, DOI 10.1007/978-3-030-00934-2_3
   Selvaraju RR, 2017, IEEE I CONF COMP VIS, P618, DOI 10.1109/ICCV.2017.74
   Shermin T, 2019, LECT NOTES COMPUT SC, V11854, P142, DOI 10.1007/978-3-030-34879-3_12
   Szegedy C, 2016, PROC CVPR IEEE, P2818, DOI 10.1109/CVPR.2016.308
   Tan MX, 2019, PR MACH LEARN RES, V97
   Tang P, 2021, APPL SOFT COMPUT, V110, DOI 10.1016/j.asoc.2021.107656
   Tang P, 2020, IEEE J BIOMED HEALTH, V24, P2870, DOI 10.1109/JBHI.2020.2977013
   Tang P, 2019, COMPUT METH PROG BIO, V178, P289, DOI 10.1016/j.cmpb.2019.07.005
   Tang YX, 2018, LECT NOTES COMPUT SC, V11046, P249, DOI 10.1007/978-3-030-00919-9_29
   Tschandl P, 2018, SCI DATA, V5, DOI 10.1038/sdata.2018.161
   Vasconcelos MJM, 2014, LECT NOTES COMPUT SC, V8888, P21, DOI 10.1007/978-3-319-14364-4_3
   Xie XZ, 2021, MED IMAGE ANAL, V69, DOI 10.1016/j.media.2021.101985
   Yan ZC, 2015, IEEE I CONF COMP VIS, P2740, DOI 10.1109/ICCV.2015.314
   Yap J, 2018, EXP DERMATOL, V27, P1261, DOI 10.1111/exd.13777
   Yu LQ, 2017, IEEE T MED IMAGING, V36, P994, DOI 10.1109/TMI.2016.2642839
   Zhu JY, 2017, IEEE I CONF COMP VIS, P2242, DOI 10.1109/ICCV.2017.244
   Zhu X., 2017, 170909890 ARXIV
NR 62
TC 0
Z9 0
U1 1
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-4418
J9 DIAGNOSTICS
JI Diagnostics
PD JAN
PY 2022
VL 12
IS 1
AR 36
DI 10.3390/diagnostics12010036
PG 26
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA YL9BZ
UT WOS:000746182300001
PM 35054203
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Uddin, S
   Choudhury, N
   Hossain, ME
AF Uddin, Shahadat
   Choudhury, Nazim
   Hossain, Md Ekramul
TI A research framework to explore knowledge evolution and scholarly
   quantification of collaborative research
SO SCIENTOMETRICS
LA English
DT Article
DE Knowledge evolution; Scholarly quantification; Knowledge map;
   Collaborative research; Project management
ID DYNAMICS; IMPACT
AB Most of the present research problems require the participation of scientists who can bring complementary skills. For this reason, research collaboration among scientists from different disciplines have become very common in interdisciplinary science. Understanding knowledge evolution and scholarly impact of collaborative research is very important to explore its contribution to the overall progress of science over the time. With the advent of modern technologies, scientific collaboration in different research areas has been expanded not only to various sectors and industries but also across regional and international boundaries. This eventually yields scholarly competence and contributes towards knowledge evolution. Both the economic imperative demands and enrichment of individual's command of resources and techniques to address increasingly interdisciplinary research issues have triggered an upsurge in intra- and inter-country collaborative research trends. There are many bibliometric methods available in the literature for exploring various aspects of such collaborative efforts. To date, however, there is a lack of a conceptualized framework that can be followed to explore knowledge evolution and scholarly quantification for any research domain. Using metadata from scholarly publications and bibliometric methods, this study first proposed a research framework for exploring knowledge evolution and scholarly quantification of collaborative research. To understand topical knowledge evolution, collaboration dynamics and their impact on research outcomes, the proposed framework was then employed to the project management research.
C1 [Uddin, Shahadat; Hossain, Md Ekramul] Univ Sydney, Complex Syst Res Grp, Darlington, NSW 2008, Australia.
   [Choudhury, Nazim] Univ S Florida, Dept Comp Sci & Engn, Distributed Syst Grp, Tampa, FL 33620 USA.
RP Uddin, S (通讯作者)，Univ Sydney, Complex Syst Res Grp, Darlington, NSW 2008, Australia.
EM shahadat.uddin@sydney.edu.au; nachoudhury@usf.edu;
   ekramul.hossain@sydney.edu.au
RI Uddin, Shahadat/E-1963-2011; Choudhury, Nazim/AAR-4944-2021
OI Uddin, Shahadat/0000-0003-0091-6919; 
CR Abbasi A, 2011, SCIENTOMETRICS, V89, P687, DOI 10.1007/s11192-011-0463-1
   Adams J., 2006, INT PARTNERSHIPS RES
   Adams J, 2012, NATURE, V490, P335, DOI 10.1038/490335a
   [Anonymous], 2004, GUID PROJ MAN BOD KN
   Ronda-Pupo GA, 2012, STRATEGIC MANAGE J, V33, P162, DOI 10.1002/smj.948
   Banerjee S, 2017, 1 COMPL SYST DIG CAM
   Choudhury N, 2016, SCIENTOMETRICS, V108, P745, DOI 10.1007/s11192-016-2003-5
   Davenport S, 1999, TECHNOVATION, V19, P31
   Edler J., 2009, 2 DG RES
   Falagas ME, 2008, FASEB J, V22, P338, DOI 10.1096/fj.07-9492LSF
   FINE MA, 1993, AM PSYCHOL, V48, P1141, DOI 10.1037/0003-066X.48.11.1141
   Guan JC, 2016, RES POLICY, V45, P770, DOI 10.1016/j.respol.2016.01.003
   He TW, 2009, SCIENTOMETRICS, V80, P571, DOI 10.1007/s11192-007-2043-y
   Heimerl F, 2016, IEEE T VIS COMPUT GR, V22, P190, DOI 10.1109/TVCG.2015.2467621
   Ioannidis JPA, 2014, PLOS MED, V11, DOI 10.1371/journal.pmed.1001747
   Katz JS, 1997, RES POLICY, V26, P1, DOI 10.1016/S0048-7333(96)00917-1
   Kaur J, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0043235
   Khasseh AA, 2017, INFORM PROCESS MANAG, V53, P705, DOI 10.1016/j.ipm.2017.02.001
   Kim KW, 2006, SCIENTOMETRICS, V66, P231, DOI 10.1007/s11192-006-0017-0
   Lau Chuen-Yen, 2014, J AIDS Clin Res, V5
   Leahey E., 2008, AM SOCIOL, V39, P290, DOI DOI 10.1007/S12108-008-9042-1
   Low WY, 2014, SCIENTOMETRICS, V98, P1521, DOI 10.1007/s11192-013-1121-6
   Mantel N, 2002, CANC RES 1, V59, P377
   Matthew U, 2016, PROCD SOC BEHV, V226, P27, DOI 10.1016/j.sbspro.2016.06.158
   Newman MEJ, 2001, P NATL ACAD SCI USA, V98, P404, DOI 10.1073/pnas.021544898
   Peng DX, 2014, PROD OPER MANAG, V23, P1421, DOI 10.1111/j.1937-5956.2012.01383.x
   Rousseau R, 2014, NATURE, V510, P218, DOI 10.1038/510218e
   Soderlund J., 2004, International Journal of Project Management, V22, P183, DOI 10.1016/S0263-7863(03)00070-X
   SUBRAMANYAM K, 1983, J INFORM SCI, V6, P33, DOI 10.1177/016555158300600105
   Uddin S, 2016, J INFORMETR, V10, P1166, DOI 10.1016/j.joi.2016.10.004
   Uddin S, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0123537
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vanclay JK, 2013, J INFORMETR, V7, P265, DOI 10.1016/j.joi.2012.11.009
   Walker DHT, 2008, INT J MANAG PROJ BUS, V1, P168, DOI 10.1108/17538370810866313
   Wasserman S., 2003, SOCIAL NETWORK ANAL
NR 35
TC 4
Z9 4
U1 12
U2 54
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAY
PY 2019
VL 119
IS 2
BP 789
EP 803
DI 10.1007/s11192-019-03057-4
PG 15
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA HT9ON
UT WOS:000464901100011
DA 2022-08-23
ER

PT J
AU Ayala, D
   Hernandez, I
   Ruiz, D
   Rahm, E
AF Ayala, Daniel
   Hernandez, Inma
   Ruiz, David
   Rahm, Erhard
TI LEAPME: Learning-based Property Matching with Embeddings
SO DATA & KNOWLEDGE ENGINEERING
LA English
DT Article
DE Data integration; Machine learning; Knowledge graphs
ID TAPON
AB Data integration tasks such as the creation and extension of knowledge graphs involve the fusion of heterogeneous entities from many sources. Matching and fusion of such entities require to also match and combine their properties (attributes). However, previous schema matching approaches mostly focus on two sources only and often rely on simple similarity measurements. They thus face problems in challenging use cases such as the integration of heterogeneous product entities from many sources. We therefore present a new machine learning-based property matching approach called LEAPME (LEArning-based Property Matching with Embeddings) that utilizes numerous features of both property names and instance values. The approach heavily makes use of word embeddings to better utilize the domain-specific semantics of both property names and instance values. The use of supervised machine learning helps exploit the predictive power of word embeddings. Our comparative evaluation against five baselines for several multi-source datasets with real-world data shows the high effectiveness of LEAPME. We also show that our approach is even effective when training data from another domain (transfer learning) is used.
C1 [Ayala, Daniel; Hernandez, Inma; Ruiz, David] Univ Seville, ETSII, Avda Reina Mercedes S-N, Seville, Spain.
   [Rahm, Erhard] Univ Leipzig, Inst Informat, D-04109 Leipzig, Germany.
RP Ayala, D (通讯作者)，Univ Seville, ETSII, Avda Reina Mercedes S-N, Seville, Spain.
EM dayala1@us.es; inmahernandez@us.es; druiz@us.es;
   rahm@informatik.uni-leipzig.de
RI ; Ayala, Daniel/X-1748-2018
OI Ruiz, David/0000-0003-4460-5493; Ayala, Daniel/0000-0003-2095-1009
FU Spanish RDI programme [TIN2016-75394-R, PID2019-105471RB-I00,
   P18-RT-1060]
FX Our work was supported by the Spanish R&D&I programme with grants
   TIN2016-75394-R, PID2019-105471RB-I00, and P18-RT-1060.
CR Arnold P, 2014, DATA KNOWL ENG, V93, P1, DOI 10.1016/j.datak.2014.07.001
   Ayala D, 2019, LECT NOTES COMPUT SC, V11503, P397, DOI 10.1007/978-3-030-21348-0_26
   Ayala D, 2019, INFORM SYST, V83, P57, DOI 10.1016/j.is.2018.12.006
   Ayala D, 2019, KNOWL-BASED SYST, V163, P931, DOI 10.1016/j.knosys.2018.10.017
   Bellahsene Z, 2011, DATA CENTRIC SYST AP, P1, DOI 10.1007/978-3-642-16518-4
   Bernstein PA, 2011, PROC VLDB ENDOW, V4, P695
   Borrego A, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P115, DOI 10.1145/3360901.3364418
   Chang F., 2019, OM ISWC, V2536, P138
   Curino C, 2007, DEXA 2007: 18TH INTERNATIONAL CONFERENCE ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS, P424, DOI 10.1109/DEXA.2007.16
   Djeddi Warith Eddine, 2013, International Journal of Metadata, Semantics and Ontologies, V8, P75
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Eckert K, 2009, LECT NOTES COMPUT SC, V5554, P158, DOI 10.1007/978-3-642-02121-3_15
   Euzenat J., 2007, ONTOLOGY MATCHING, V18
   Faria D., 2019, CEUR WORKSHOP PROC, V2536, P123
   Fernandez RC, 2018, PROC INT CONF DATA, P989, DOI 10.1109/ICDE.2018.00093
   Gruetze T., 2012, LDOW, V937
   Hassanzadeh O., 2009, PROC VLDB ENDOW, V2, P1282, DOI DOI 10.14778/1687627.1687771
   Hertling S., 2020, CEUR WORK PROC, V2788, P60
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Ichise R, 2008, 7TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE IN CONJUNCTION WITH 2ND IEEE/ACIS INTERNATIONAL WORKSHOP ON E-ACTIVITY, PROCEEDINGS, P340, DOI 10.1109/ICIS.2008.51
   Kolyvakis  P., 2018, P 2018 C N AM CHAPT, V1, P787, DOI DOI 10.18653/V1/N18-1072
   Lin FY, 2008, INT FED INFO PROC, V276, P341
   Madhavan J, 2005, PROC INT CONF DATA, P57
   Marie A, 2008, LECT NOTES COMPUT SC, V5331, P283
   Megdiche I, 2016, LECT NOTES COMPUT SC, V9981, P393, DOI 10.1007/978-3-319-46523-4_24
   Nezhadi Azadeh Haratian, 2011, International Journal of Computer Science & Information Technology, V3, P139, DOI 10.5121/ijcsit.2011.3210
   Obraczka D., 2019, P DI2KG
   Otero-Cerdeira L, 2015, EXPERT SYST APPL, V42, P949, DOI 10.1016/j.eswa.2014.08.032
   Pennington J., 2014, EMNLP, P1532
   Qin H, 2007, LECT NOTES COMPUT SC, V4803, P832
   Rahm E, 2001, VLDB J, V10, P334, DOI 10.1007/s007780100057
   Rahm Erhard, 2016, Advances in Databases and Information Systems. 20th East European Conference, ADBIS 2016. Proceedings: LNCS 9809, P11, DOI 10.1007/978-3-319-44039-2_2
   Rahm E, 2011, DATA CENTRIC SYST AP, P3, DOI 10.1007/978-3-642-16518-4_1
   Rivero CR, 2020, PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20), P907, DOI 10.1145/3341105.3373882
   Roldan J.C., 2019, KNOWL-BASED SYST
   Roussille P., 2018, CEUR WS WORKSHOP P
   Saeedi Alieh, 2020, Semantic Web 17th International Conference, ESWC 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12123), P393, DOI 10.1007/978-3-030-49461-2_23
   Saeedi Alieh, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P576, DOI 10.1007/978-3-319-93417-4_37
   Shenoy K.M., 2012, INT C ADV INF TECHN, P122
   Shi LX, 2017, BIOMED RES INT, V2017, DOI 10.1155/2017/2858423
   Songyun Duan, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P49, DOI 10.1007/978-3-642-35176-1_4
   Spohr D, 2011, LECT NOTES COMPUT SC, V7031, P665, DOI 10.1007/978-3-642-25073-6_42
   Szekely P, 2015, LECT NOTES COMPUT SC, V9367, P205, DOI 10.1007/978-3-319-25010-6_12
   Web Data Commons, 2020, GOLD STAND PROD MATC
   Xu K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3156
   Zheng WG, 2018, PROC VLDB ENDOW, V11, P1373, DOI 10.14778/3236187.3236192
NR 46
TC 1
Z9 1
U1 4
U2 6
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0169-023X
EI 1872-6933
J9 DATA KNOWL ENG
JI Data Knowl. Eng.
PD JAN
PY 2022
VL 137
AR 101943
DI 10.1016/j.datak.2021.101943
EA NOV 2021
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XH8AC
UT WOS:000725649600001
OA Green Submitted, hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Dimitrova, M
   Senderov, VE
   Georgiev, T
   Zhelezov, G
   Penev, L
AF Dimitrova, Mariya
   Senderov, Viktor E.
   Georgiev, Teodor
   Zhelezov, Georgi
   Penev, Lyubomir
TI Infrastructure and Population of the OpenBiodiv Biodiversity Knowledge
   Graph
SO BIODIVERSITY DATA JOURNAL
LA English
DT Article
AB Background
   OpenBiodiv is a biodiversity knowledge graph containing a synthetic linked open dataset, OpenBiodiv-LOD, which combines knowledge extracted from academic literature with the taxonomic backbone used by the Global Biodiversity Information Facility. The linked open data is modelled according to the OpenBiodiv-O ontology integrating semantic resource types from recognised biodiversity and publishing ontologies with OpenBiodiv-O resource types, introduced to capture the semantics of resources not modelled before.
   New information
   We introduce the new release of the OpenBiodiv-LOD attained through information extraction and modelling of additional biodiversity entities. It was achieved by further developments to OpenBiodiv-O, the data storage infrastructure and the workflow and accompanying R software packages used for transformation of academic literature into Resource Description Framework (RDF). We discuss how to utilise the LOD in biodiversity informatics and give examples by providing solutions to several competency questions. We investigate performance issues that arise due to the large amount of inferred statements in the graph and conclude that OWL-full inference is impractical for the project and that unnecessary inference should be avoided.
C1 [Dimitrova, Mariya] Bulgarian Acad Sci, Inst Informat & Commun Technol, Sofia, Bulgaria.
   [Dimitrova, Mariya; Georgiev, Teodor; Zhelezov, Georgi; Penev, Lyubomir] Pensoft Publishers, Sofia, Bulgaria.
   [Senderov, Viktor E.] Swedish Museum Nat Hist, Dept Bioinformat & Genet, Stockholm, Sweden.
   [Penev, Lyubomir] Bulgarian Acad Sci, Inst Biodivers & Ecosyst Res, Sofia, Bulgaria.
RP Dimitrova, M (通讯作者)，Bulgarian Acad Sci, Inst Informat & Commun Technol, Sofia, Bulgaria.; Dimitrova, M (通讯作者)，Pensoft Publishers, Sofia, Bulgaria.
EM m.dimitrova@pensoft.net
OI Senderov, Viktor/0000-0003-3340-5963; Georgiev,
   Teodor/0000-0001-8558-6845; Zhelezov, Georgi/0000-0002-6159-0097;
   Dimitrova, Mariya/0000-0002-8083-6048
FU European Union [642241, 764840]
FX This research received funding from the European Union's Horizon 2020
   Research and Innovation Programme under the Marie Sklodowska-Curie grant
   agreements BIG4 (No 642241) and IGNITE (No 764840) .
CR [Anonymous], 2001, SCI AM
   BERENDSOHN WG, 1995, TAXON, V44, P207, DOI 10.2307/1222443
   Catapano T, 2010, J ARTICLE TAG SUITE, DOI DOI 10.5281/ZENODO.3484285
   Dimitrova M, 2019, BIODIVERSITY INFORM, V3, DOI [10.3897/biss.3.35089, DOI 10.3897/BISS.3.35089]
   Dimitrova M, 2019, BIODIVERSITY INFORM, V3, DOI [10.3897/biss.3.35773, DOI 10.3897/BISS.3.35773]
   Dimitrova M., 2020, BISS, V4, DOI [10.3897/biss.4.59042, DOI 10.3897/BISS.4.59042]
   GBIF Secretariat, 2021, WHAT IS GBIF
   GBIF Secretariat, 2017, GBIF BACKBONE TAXONO, DOI [10.15468/39omei, DOI 10.15468/39OMEI]
   Gray AG, 2020, NANOPUBLICATION GUID
   Groth Paul, 2010, Information Services & Use, V30, P51, DOI 10.3233/ISU-2010-0613
   Heath T., 2011, SYNTHESIS LECT SEMAN, DOI [DOI 10.2200/S00334ED1V01Y201102WBE001, 10.2200/s00334ed1v01y201102wbe001]
   Hobern D, 2019, BIODIVERS DATA J, V7, DOI 10.3897/BDJ.7.e33679
   Kuhn T, 2016, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.78
   Miles A, 2008, SKOS SIMPLE KNOWLEDG
   MongoDB Inc, 2021, MONGODB 4 0 20
   Ontotext, 2020, GRAPHDB 9 4 0
   Ontotext, 2021, GRAPHDB EE DOC REL 9
   Ooms J, 2020, **DATA OBJECT**
   Page R, 2020, BIODIVERSITY INFORM, V4, DOI [10.3897/biss.4.59126, DOI 10.3897/BISS.4.59126]
   Page R., 2016, RIO, V2, P8767, DOI [10.3897/rio.2.e8767, DOI 10.3897/RIO.2.E8767, DOI 10.3897/RI0.2.E8767]
   Page RDM, 2019, PEERJ, V7, DOI 10.7717/peerj.6739
   Penev L, 2019, PUBLICATIONS, V7, DOI 10.3390/publications7020038
   Penev L, 2011, ZOOKEYS, P89, DOI 10.3897/zookeys.150.2213
   Peroni S, 2018, LECT NOTES COMPUT SC, V11137, P119, DOI 10.1007/978-3-030-00668-6_8
   Sarkar IN, 2007, BRIEF BIOINFORM, V8, P347, DOI 10.1093/bib/bbm037
   Senderov V, 2016, RES IDEAS OUTCOMES, V2, P7757, DOI DOI 10.3897/RIO.2.E7757
   Senderov V, 2018, J BIOMED SEMANT, V9, DOI 10.1186/s13326-017-0174-5
   The Apache Software Foundation, 2013, AP LUC QUER PARS SYN
   Urbanek S, 2020, **DATA OBJECT**
   Wieczorek J, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0029715
NR 30
TC 1
Z9 1
U1 2
U2 4
PU PENSOFT PUBLISHERS
PI SOFIA
PA 12 PROF GEORGI ZLATARSKI ST, SOFIA, 1700, BULGARIA
SN 1314-2836
EI 1314-2828
J9 BIODIVERS DATA J
JI Biodiver. Data J.
PD SEP 24
PY 2021
VL 9
AR e67671
DI 10.3897/BDJ.9.e67671
PG 31
WC Biodiversity Conservation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biodiversity & Conservation
GA WD6PY
UT WOS:000705062500002
PM 34690512
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Liu, Y
   Hua, W
   Qu, JF
   Xin, KX
   Zhou, XF
AF Liu, Yu
   Hua, Wen
   Qu, Jianfeng
   Xin, Kexuan
   Zhou, Xiaofang
TI Temporal knowledge completion with context-aware embeddings
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph embedding; Temporal consistency; Contextual consistency;
   Context-aware embedding
AB Temporal knowledge graph embedding can be used to improve the coverage of temporal KGs via link predictions. Most existing works only concentrate on the target facts themselves, regardless of the rich and informative interactions between the target facts and their highly-related contexts. In this paper, we propose a novel approach to take advantage of useful contextual interactions from two aspects, namely temporal consistency and contextual consistency. More specifically, temporal consistency measures how well the target fact interacts with its surrounding contexts in the temporal dimension, while contextual consistency treats all facts as a whole integrity and captures the semantic interactions between multiple contexts. Additionally, considering the existence of useless and misleading context information, we design a crafted context selection strategy to pick out the most useful contexts with reference to the target facts, and then encode them using deep neural networks to capture the temporal and semantic interactions. Experimental results on real world datasets verify the effectiveness of our proposals comparing with competitive KGE methods and temporal KGE methods.
C1 [Liu, Yu; Hua, Wen; Xin, Kexuan] Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld, Australia.
   [Qu, Jianfeng] Soochow Univ, Suzhou, Jiangsu, Peoples R China.
   [Zhou, Xiaofang] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China.
RP Hua, W (通讯作者)，Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld, Australia.
EM yu.liu@uq.edu.au; w.hua@uq.edu.au; jfqu@suda.edu.cn; k.xin@uq.edu.au;
   zxf@cse.ust.hk
CR Dasgupta SS, 2018, P 2018 C EMPIRICAL M, P2001
   Esteban C, 2016, 2016 19TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), P98
   Garcia-Duran Alberto, 2018, P 2018 C EMP METH NA, P4816
   Gardner M., 2015, P 2015 C EMP METH NA, P1488, DOI DOI 10.18653/V1/D15-1173
   Glorot X., 2010, JMLR WRKSHOP C P, P249
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Hua W, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1761, DOI 10.1145/2723372.2751522
   Hua W, 2015, PROC INT CONF DATA, P495, DOI 10.1109/ICDE.2015.7113309
   Jiang T., 2016, P 2016 C EMP METH NA, P2350, DOI DOI 10.18653/V1/D16-1260
   Jiang T., 2016, P 26 INT C COMP LING, P1715
   Kingma D. P., 2017, ARXIV
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Leblay J, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1771, DOI 10.1145/3184558.3191639
   Lin Y., 2015, ARXIV 150600379
   LIN Y, 2015, P 29 AAAI C ART INT, P1
   Liu Y, 2021, WORLD WIDE WEB, V24, P135, DOI 10.1007/s11280-020-00836-5
   Liu Y, 2019, LECT NOTES COMPUT SC, V11881, P583, DOI 10.1007/978-3-030-34223-4_37
   Luo K., 2018, P 2018 C EMP METH NA, P2185
   Mahdisoltani F, 2013, CIDR
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   Min B., 2013, P 2013 C N AM CHAPT, P777, DOI DOI 10.1186/S40537-016-0043-6
   Oh B, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P257, DOI 10.1145/3269206.3271769
   Shi BX, 2018, AAAI CONF ARTIF INTE, P1957
   Sun Z., 2019, 7 INT C LEARN REPR
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Trivedi R, 2017, PR MACH LEARN RES, V70
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Yafang, 2011, P 20 ACM INT C INF K, P837, DOI DOI 10.1145/2063576.2063698
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Yang B, 2014, ARXIV 14126575
   Yih WT, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P1321
   Yu Liu, 2019, Databases Theory and Applications. 30th Australasian Database Conference, ADC 2019. Proceedings: Lecture Notes in Computer Science (LNCS 11393), P17, DOI 10.1007/978-3-030-12079-5_2
   Zeng D, 2015, P 2015 C EMP METH NA, P1753, DOI DOI 10.18653/V1/D15-1203
NR 36
TC 1
Z9 1
U1 3
U2 11
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
PD MAR
PY 2021
VL 24
IS 2
BP 675
EP 695
DI 10.1007/s11280-021-00867-6
EA MAR 2021
PG 21
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QZ6YU
UT WOS:000625618100001
DA 2022-08-23
ER

PT J
AU Gaur, M
   Faldu, K
   Sheth, A
AF Gaur, Manas
   Faldu, Keyur
   Sheth, Amit
TI Semantics of the Black-Box: Can Knowledge Graphs Help Make Deep Learning
   Systems More Interpretable and Explainable?
SO IEEE INTERNET COMPUTING
LA English
DT Article
DE Deep learning; Computational modeling; Semantics; Education; Natural
   language processing; Artificial intelligence; Task analysis
AB The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, and human-computer interactions. However, DL's black-box nature and over-reliance on massive amounts of data condensed into labels and dense representations pose challenges for interpretability and explainability. Furthermore, DLs have not proven their ability to effectively utilize relevant domain knowledge critical to human understanding. This aspect was missing in early data-focused approaches and necessitated knowledge-infused learning (K-iL) to incorporate computational knowledge. This article demonstrates how knowledge, provided as a knowledge graph, is incorporated into DL using K-iL. Through examples from natural language processing applications in healthcare and education, we discuss the utility of K-iL towards interpretability and explainability.
C1 [Gaur, Manas; Sheth, Amit] Univ South Carolina, Artificial Intelligence Inst, Columbia, SC 29208 USA.
   [Faldu, Keyur] Embibe, Bengaluru 560095, India.
RP Gaur, M (通讯作者)，Univ South Carolina, Artificial Intelligence Inst, Columbia, SC 29208 USA.
EM mgaur@email.sc.edu; k@embibe.com; amit@sc.edu
RI Gaur, Manas/AAD-6704-2022
OI Gaur, Manas/0000-0002-5411-2230; Sheth, Amit/0000-0002-0021-5293
CR Arachie C, 2020, AAAI CONF ARTIF INTE, V34, P354
   Bhatt S, 2018, 2018 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2018), P293, DOI 10.1109/WI.2018.00-77
   Dhavala S, P 13 INT C ED DAT MI, V2020, P730
   Faldu K., 2018, US Patent, Patent No. [US 2018/0090023A1, 20180090023]
   Gaur Manas, 2020, HT '20: Proceedings of the 31st ACM Conference on Hypertext and Social Media, P309, DOI 10.1145/3372923.3404862
   Gilpin LH, 2018, PR INT CONF DATA SC, P80, DOI 10.1109/DSAA.2018.00018
   Park DH, 2018, PROC CVPR IEEE, P8779, DOI 10.1109/CVPR.2018.00915
   Purohit H, 2020, IEEE INTERNET COMPUT, V24, P48, DOI 10.1109/MIC.2020.3013683
   Ren H., 2019, P INT C LEARN REPR
   Ribeiro M., P ACM, P2020
   Rudin C, 2019, NAT MACH INTELL, V1, P206, DOI 10.1038/s42256-019-0048-x
   Samek W., 2019, LNCS, P5, DOI [DOI 10.1007/978-3-030-28954-6_1, 10.1007/978-3-030-28954-6]
   Sheth A, 2019, IEEE INTERNET COMPUT, V23, P54, DOI 10.1109/MIC.2019.2960071
   Wallach H., 2018, ADV NEURAL INFORM PR, V31, P9539
   Wang ZW, 2019, LECT NOTES ARTIF INT, V11626, P303, DOI 10.1007/978-3-030-23207-8_56
NR 15
TC 16
Z9 16
U1 8
U2 26
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1089-7801
EI 1941-0131
J9 IEEE INTERNET COMPUT
JI IEEE Internet Comput.
PD JAN 1
PY 2021
VL 25
IS 1
BP 51
EP 59
DI 10.1109/MIC.2020.3031769
PG 9
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QL9LC
UT WOS:000621401300006
OA Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Harrando, I
   Troncy, R
AF Harrando, Ismail
   Troncy, Raphael
TI Combining semantic and linguistic representations for media
   recommendation
SO MULTIMEDIA SYSTEMS
LA English
DT Article; Early Access
DE Media recommendation; Content representation; Knowledge graph;
   Content-based recommendation; Automatic annotation; Textual embeddings
AB Content-based recommendation systems offer the possibility of promoting media (e.g., posts, videos, podcasts) to users based solely on a representation of the content (i.e., without using any user-related data such as views or interactions between users and items). In this work, we study the potential of using different textual representations (based on the content of the media) and semantic representations (created from a knowledge graph of media metadata). We also show that using off-the-shelf automatic annotation tools from the Information Extraction literature, we can improve recommendation performance, without any extra cost of training, data collection or annotation. We first evaluate multiple textual content representations on two tasks of recommendation: user-specific, which is performed by suggesting new items to the user given a history of interactions, and item-based, which is based solely on content relatedness, and is rarely investigated in the literature of recommender systems. We compare how using automatically extracted content (via ASR) compares to using human-written summaries. We then derive a semantic content representation by combining manually created metadata and automatically extracted annotations and we show that Knowledge Graphs, through their embeddings, constitute a great modality to seamlessly integrate extracted knowledge to legacy metadata and can be used to provide good content recommendations. We finally study how combining both semantic and textual representations can lead to superior performance on both recommendation tasks. Our code is available at https://github.com/D2KLab/ka-recsys to support experiment reproducibility.
C1 [Harrando, Ismail; Troncy, Raphael] EURECOM, Data Sci Dept, Sophia Antipolis, France.
RP Harrando, I (通讯作者)，EURECOM, Data Sci Dept, Sophia Antipolis, France.
EM ismail.harrando@eurecom.fr; raphael.troncy@eurecom.fr
FU CHISTERA within the CIMPLE project [CHIST-ERA-19-XAI-003]
FX This work has been partially supported by CHISTERA within the CIMPLE
   project (CHIST-ERA-19-XAI-003) and by raisin.ai within the
   MyLittleEngine project.
CR Bianchi F, 2021, ACL-IJCNLP 2021: THE 59TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 11TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 2, P759
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bojanowski P., 2017, T ASSOC COMPUT LING, V5, P135, DOI [DOI 10.1162/TACL_A_00051, 10.1162/tacl_a_00051]
   Bordes A., 2013, ADV NEURAL INFORM PR, V26
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Cao Y., 2019, WORLD WIDE WEB C WWW
   Chicaiza J, 2021, INFORMATION, V12, DOI 10.3390/info12060232
   De Gemmis M., 2015, RECOMMENDER SYSTEMS, P119, DOI DOI 10.1007/978-1-4899-7637-6_4
   Devlin J., 2018, P NAACL HLT 2019 2 7
   EFRON B, 1979, ANN STAT, V7, P1, DOI 10.1214/aos/1176344552
   Grootendorst M., 2020, **DATA OBJECT**, DOI 10.5281/zenodo.4461265
   Guo Q., 2020, ARXIV
   Harrando I, 2021, RANLP, V260, P488
   Harrando I., 2021, CEUR WORKSHOP PROC, V5th
   Honnibal M., 2020, **DATA OBJECT**, DOI 10.5281/zenodo.1212303
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kotkov D, 2016, KNOWL-BASED SYST, V111, P180, DOI 10.1016/j.knosys.2016.08.014
   Kunaver M, 2017, KNOWL-BASED SYST, V123, P154, DOI 10.1016/j.knosys.2017.02.009
   Lops P, 2019, USER MODEL USER-ADAP, V29, P239, DOI 10.1007/s11257-019-09231-w
   Merchant A, 2017, PROCEEDINGS OF THE FOURTH ACM IKDD CONFERENCES ON DATA SCIENCES (CODS '17), DOI 10.1145/3041823.3041829
   Mikolov T., 2013, ADV NEURAL INFORM PR
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mishra A, 2015, INT CONF COMPUT INTE, P772, DOI 10.1109/CICN.2015.157
   Pappas N, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P773
   Pappas N, 2015, MULTIMED TOOLS APPL, V74, P1175, DOI 10.1007/s11042-013-1840-y
   Pappas N, 2013, INT WORK CONTENT MUL, P47, DOI 10.1109/CBMI.2013.6576551
   Pennington J., 2014, EMNLP, P1532
   Polignano M., 2021, TOGETHER IS BETTER H, P187
   Le Q, 2014, PR MACH LEARN RES, V32, P1188
   Reimers N., 2019, PROC C EMPIRICAL MET, DOI DOI 10.18653/V1/D19-1410
   Rendle Steffen, 2010, Proceedings 2010 10th IEEE International Conference on Data Mining (ICDM 2010), P995, DOI 10.1109/ICDM.2010.127
   Schafer J. B., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P291
   Sun R, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P1405, DOI 10.1145/3340531.3411947
   Tian T, 2019, PROCEDIA COMPUT SCI, V151, P1134, DOI 10.1016/j.procs.2019.04.161
   Yi X, 2009, LECT NOTES COMPUT SC, V5478, P29
   Yu S.Y., 2019, ARXIV
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhang YF, 2020, FOUND TRENDS INF RET, V14, P1, DOI 10.1561/1500000066
NR 38
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0942-4962
EI 1432-1882
J9 MULTIMEDIA SYST
JI Multimedia Syst.
DI 10.1007/s00530-022-00968-w
EA JUL 2022
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Y5BU
UT WOS:000825913200001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Chhetri, TR
   Kurteva, A
   DeLong, RJ
   Hilscher, R
   Korte, K
   Fensel, A
AF Chhetri, Tek Raj
   Kurteva, Anelia
   DeLong, Rance J.
   Hilscher, Rainer
   Korte, Kai
   Fensel, Anna
TI Data Protection by Design Tool for Automated GDPR Compliance
   Verification Based on Semantically Modeled Informed Consent
SO SENSORS
LA English
DT Article
DE GDPR; privacy; compliance verification; informed consent; standard data
   protection model; data sharing; data protection by design; knowledge
   graph; distributed systems
ID INTERNET
AB The enforcement of the GDPR in May 2018 has led to a paradigm shift in data protection. Organizations face significant challenges, such as demonstrating compliance (or auditability) and automated compliance verification due to the complex and dynamic nature of consent, as well as the scale at which compliance verification must be performed. Furthermore, the GDPR's promotion of data protection by design and industrial interoperability requirements has created new technical challenges, as they require significant changes in the design and implementation of systems that handle personal data. We present a scalable data protection by design tool for automated compliance verification and auditability based on informed consent that is modeled with a knowledge graph. Automated compliance verification is made possible by implementing a regulation-to-code process that translates GDPR regulations into well-defined technical and organizational measures and, ultimately, software code. We demonstrate the effectiveness of the tool in the insurance and smart cities domains. We highlight ways in which our tool can be adapted to other domains.
C1 [Chhetri, Tek Raj; Kurteva, Anelia; Hilscher, Rainer; Fensel, Anna] Univ Innsbruck, Semant Technol Inst STI, Dept Comp Sci, A-6020 Innsbruck, Austria.
   [DeLong, Rance J.] Open Grp, Reading RG1 LAX, Berks, England.
   [Hilscher, Rainer] RTI Int, Ctr Data Sci, Res Triangle Pk, NC 27709 USA.
   [Korte, Kai] Leibniz Univ Hannover, Inst Rechtsinformat IRI, D-30167 Hannover, Germany.
   [Fensel, Anna] Wageningen Univ & Res, Wageningen Data Competence Ctr, NL-6708 PB Wageningen, Netherlands.
   [Fensel, Anna] Wageningen Univ & Res, Consumpt & Hlth Lifestyles Chair Grp, NL-6706 KN Wageningen, Netherlands.
RP Chhetri, TR (通讯作者)，Univ Innsbruck, Semant Technol Inst STI, Dept Comp Sci, A-6020 Innsbruck, Austria.
EM tekraj.chhetri@sti2.at; anelia.kurteva@sti2.at; r.delong@opengroup.org;
   rainer.hilscher@sti2.at; kai.wendt@iri.uni-hannover.de;
   anna.fensel@wur.nl
OI Hilscher, Rainer/0000-0002-5004-4584; Fensel, Anna/0000-0002-1391-7104;
   Kurteva, Anelia/0000-0003-4512-5969; Chhetri, Tek
   Raj/0000-0002-3905-7878
FU European Union [871477]
FX This work was supported by the European Union's Horizon 2020 project
   smashHit [grant number 871477].
CR Advanced Encryption Standard, 2001, 197 FIPS
   Angele K., 2020, KNOWLEDGE GRAPHS MET
   [Anonymous], 2020, 5652020 INCITS
   [Anonymous], 2020, C IND DAT PROT SUP A
   Arfelt E, 2019, LECT NOTES COMPUT SC, V11735, P681, DOI 10.1007/978-3-030-29959-0_33
   Barati M, 2022, IEEE T SERV COMPUT, V15, P1498, DOI 10.1109/TSC.2020.2999559
   Barati M, 2020, COMM COM INF SC, V1156, P322, DOI 10.1007/978-981-15-2777-7_26
   Barati M, 2020, IEEE ACCESS, V8, P119697, DOI 10.1109/ACCESS.2020.3005509
   Basin D., 2011, INT C RUNT VER, P360
   Basin D, 2018, LECT NOTES COMPUT SC, V10957, P20, DOI 10.1007/978-3-662-58387-6_2
   Basin D, 2015, J ACM, V62, DOI 10.1145/2699444
   BECHHOFER S, OWL WEB ONTOLOGY LAN
   Becker M., 2010, MSRTR201032
   Bellare M., 2003, IACR EPRINT ARCHIVE
   Bless C., 2021, FURTHER KNOWLEDGE GR, V53, P44, DOI [10.3233/SSW210034, DOI 10.3233/SSW210034]
   Bonatti PA, 2020, KUNSTL INTELL, V34, P303, DOI 10.1007/s13218-020-00677-4
   Bonatti PA, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1746
   Brodin M., 2019, EUROPEAN J SECURITY, V4, P243, DOI [10.1007/s41125-019-00042-z, DOI 10.1007/S41125-019-00042-Z]
   Camilo J, 2019, OPEN IDENTITY SUMMIT, V2019, P165
   Chhetri TR, 2021, LECT NOTES COMPUT SC, V12739, P165, DOI 10.1007/978-3-030-80418-3_29
   Council of the European Union and European Parliament, 2016, OFFICIAL J EUROPEA L, VL119
   Dalpiaz F., 2016, SECURITY REQUIREMENT
   Daoudagh S, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21217154
   Data and Knowledge Group, U OXF HERMIT OWL REA
   Data Protection Commissioner, SELF ASSESSMENT CHEC
   Data Protection Vocabularies and Controls W3C Community Group, PRIV VOC DPV
   Davari M, 2019, IEEE INT CONF BIG DA, P4017, DOI 10.1109/BigData47090.2019.9006455
   De Lauretis L, 2019, IEEE INT SYMP SOFTW, P93, DOI 10.1109/ISSREW.2019.00050
   Dehury CK, 2020, FUTURE GENER COMP SY, V109, P1, DOI 10.1016/j.future.2020.03.029
   Dragoni N., 2017, PRESENT ULTERIOR SOF, P195, DOI DOI 10.1007/978-3-319-67425-4_12
   Eismann S, 2021, IEEE SOFTWARE, V38, P32, DOI 10.1109/MS.2020.3023302
   Garg N., 2014, J COMPUT SCI MOB COM, V3, P1190
   GDPR, DATA PROTECTION IMPA
   Hamdani R.E., 2021, P 18 INT C ART INT L, P40
   Hasselbring Wilhelm, 2017, 2017 IEEE International Conference on Software Architecture: Workshops (ICSAW), P243, DOI 10.1109/ICSAW.2017.11
   IAPP, IAPP FTI CONS PRIV G
   Kakkar Alpana, 2020, Innovations in Computer Science and Engineering. Proceedings of 7th ICICSE. Lecture Notes in Networks and Systems (LNNS 103), P145, DOI 10.1007/978-981-15-2043-3_18
   Kirrane S., 2020, ABS200109461
   Koc C.K., 2021, PARTIALLY HOMOMORPHI, P37
   Kurteva A., 2021, SEMANT WEB, P1
   Kurteva A, 2021, LECT NOTES COMPUT SC, V12739, P155, DOI 10.1007/978-3-030-80418-3_28
   Lebo T., 2013, PROV O PROV ONTOLOGY
   Lin Zou, 2020, Frontier Computing. Theory, Technologies and Applications (FC 2019). Lecture Notes in Electrical Engineering (LNEE 551), P541, DOI 10.1007/978-981-15-3250-4_68
   Lindqvist J, 2018, INT J LAW INFORM TEC, V26, P45, DOI 10.1093/ijlit/eax024
   Mahindrakar A, 2020, 2020 IEEE 6TH INT CONFERENCE ON BIG DATA SECURITY ON CLOUD (BIGDATASECURITY) / 6TH IEEE INT CONFERENCE ON HIGH PERFORMANCE AND SMART COMPUTING, (HPSC) / 5TH IEEE INT CONFERENCE ON INTELLIGENT DATA AND SECURITY (IDS), P86, DOI 10.1109/BigDataSecurity-HPSC-IDS49724.2020.00026
   Mangini V., 2020, P 15 INT C AV REL SE, P1, DOI [10.1145/3407023.3407080, DOI 10.1145/3407023.3407080]
   Merlec MM, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21237994
   Chung MT, 2016, 2016 IEEE SIXTH INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND ELECTRONICS (ICCE), P52, DOI 10.1109/CCE.2016.7562612
   Nadeem A., 2005, INF COMM TECHN 2005, P84, DOI DOI 10.1109/ICICT.2005.1598556
   Truong NB, 2020, IEEE T INF FOREN SEC, V15, P1746, DOI 10.1109/TIFS.2019.2948287
   Padmavathi B., 2013, INT, V2, P170, DOI DOI 10.3844/AJASSP.2014.1472.1479
   Piras L, 2019, LECT NOTES COMPUT SC, V11711, P78, DOI 10.1007/978-3-030-27813-7_6
   Ranise S., 2017, COMPUTER SAFETY RELI, P361, DOI DOI 10.1007/978-3-319-66284-8_30
   Rao TR, 2019, KNOWL INF SYST, V60, P1165, DOI 10.1007/s10115-018-1248-0
   Rhahla M., 2019, P INT C RISKS SEC IN, P211, DOI [10.1007/978-3-030-41568-6_14, DOI 10.1007/978-3-030-41568-6_14]
   Robol M., IFIP WORK C PRACT EN
   Ryan P., 2020, INT C ENTERPRISE INF, P905
   Ryan P, 2020, PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS (ICEIS), VOL 2, P787, DOI 10.5220/0009464507870795
   Selent D., 2010, RIVIER ACAD J, V6, P1, DOI DOI 10.1016/S1353-4858(10)70006-4
   Subramanian H., 2019, HANDS ON RESTFUL API
   Tokas S, 2022, J LOG ALGEBR METHODS, V125, DOI 10.1016/j.jlamp.2021.100733
   van Rossum G., PEP 8 STYLE GUIDE PY
   Westphal P., 2018, P 14 INT C SEM SYST
   Wilson S, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1330
NR 64
TC 0
Z9 0
U1 7
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD APR
PY 2022
VL 22
IS 7
AR 2763
DI 10.3390/s22072763
PG 35
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA 0N9NA
UT WOS:000783155200001
PM 35408377
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wang, X
   Wang, SM
   Xin, YQ
   Yang, YJ
   Li, JX
   Wang, XF
AF Wang, Xin
   Wang, Simiao
   Xin, Yueqi
   Yang, Yajun
   Li, Jianxin
   Wang, Xiaofei
TI Distributed Pregel-based provenance-aware regular path query processing
   on RDF knowledge graphs
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Regular path query; Provenance-aware; RDF graph; Pregel
AB With the proliferation of knowledge graphs, massive RDF graphs have been published on the Web. As an essential type of queries for RDF graphs, Regular Path Queries (RPQs) have been attracting increasing research efforts. However, the existing query processing approaches mainly focus on RPQs under the standard semantics, which cannot provide the provenance of the answer sets. We propose a distributed Pregel-based approach DP2RPQ to evaluating provenance-aware RPQs over big RDF graphs. Our method employs Glushkov automata to keep track of matching processes of RPQs in parallel. Meanwhile, three optimization strategies are devised according to the cost model, including vertex-computation optimization, message-communication reduction, and counting-paths alleviation, which can reduce the intermediate results of the basic DP2RPQ algorithm dramatically and overcome the counting-paths problem to some extent. The proposed algorithms are verified by extensive experiments on both synthetic and real-world datasets, which show that our approach can efficiently answer the provenance-aware RPQs over large RDF graphs. Furthermore, the RPQ semantics of DP2RPQ is richer than that of RDFPath, and the performance of DP2RPQ is still far better than that of RDFPath.
C1 [Wang, Xin; Wang, Simiao; Xin, Yueqi; Yang, Yajun; Wang, Xiaofei] Tianjin Univ, Coll Intelligence & Comp, Peiyang Pk Campus, Tianjin, Peoples R China.
   [Wang, Xin; Wang, Simiao; Xin, Yueqi; Yang, Yajun; Wang, Xiaofei] Tianjin Key Lab Cognit Comp & Applicat, Tianjin, Peoples R China.
   [Li, Jianxin] Deakin Univ, Sch Informat Technol, Geelong, Vic, Australia.
RP Wang, XF (通讯作者)，Tianjin Univ, Coll Intelligence & Comp, Peiyang Pk Campus, Tianjin, Peoples R China.; Wang, XF (通讯作者)，Tianjin Key Lab Cognit Comp & Applicat, Tianjin, Peoples R China.
EM xiaofeiwang@tju.edu.cn
OI Li, Jianxin/0000-0002-9059-330X
CR Arenas M, 2012, P 21 INT C WORLD WID, P629, DOI DOI 10.1145/2187836.2187922
   Avery Ching, 2011, P HAD SUMM SANT CLAR
   Bai Y., 2014, IEEE 4 INT C BIG DAT
   Bai YY, 2017, WORLD WIDE WEB, V20, P515, DOI 10.1007/s11280-016-0401-5
   Bai Y, 2013, INT CONF MEASURE, P333, DOI 10.1109/MIC.2013.6757977
   Barcelo P, 2012, ACM T DATABASE SYST, V37, DOI 10.1145/2389241.2389250
   BRUGGEMANNKLEIN A, 1993, THEOR COMPUT SCI, V120, P197, DOI 10.1016/0304-3975(93)90287-4
   BRZOZOWSKI JA, 1964, J ACM, V11, P481, DOI 10.1145/321239.321249
   Calvanese D, 2000, SEVENTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-2001) / TWELFTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE (IAAI-2000), P386
   Dean J, 2004, USENIX ASSOCIATION PROCEEDINGS OF THE SIXTH SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION (OSDE '04), P137
   Dey S, 2013, IEEE NANOTECHNOL MAT, P21
   GERBESSIOTIS AV, 1994, J PARALLEL DISTR COM, V22, P251, DOI 10.1006/jpdc.1994.1085
   Harris S., 2013, W3C RECOMM, V21, P1
   Jupp S, 2014, BIOINFORMATICS, V30, P1338, DOI 10.1093/bioinformatics/btt765
   Koschmieder A, 2012, LECT NOTES COMPUT SC, V7338, P177, DOI 10.1007/978-3-642-31235-9_12
   Kostylev EV, 2015, LECT NOTES COMPUT SC, V9366, P3, DOI 10.1007/978-3-319-25007-6_1
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Libkin L., 2013, P 16 INT C DAT THEOR, P129, DOI DOI 10.1145/2448496.2448513
   Malewicz G., 2010, P 2010 ACM SIGMOD IN, V2010, P135, DOI [DOI 10.1145/1807167.1807184, 10.1145/1807167.1807184]
   Nole M., 2016, P 28 INT C SCI STAT, P13
   Nole M., 2016, EDBT ICDT WORKSH
   Przyjaciel-Zablocki M, 2012, LECT NOTES COMPUT SC, V7117, P50, DOI 10.1007/978-3-642-25953-1_5
   Tong YX, 2016, WORLD WIDE WEB, V19, P1151, DOI 10.1007/s11280-015-0377-6
   Wang M, 2017, LECT NOTES COMPUT SC, V10588, P219, DOI 10.1007/978-3-319-68204-4_23
   Wang X, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1933, DOI 10.1145/2983323.2983877
   Wang X, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P395, DOI 10.1145/2567948.2577284
   Wang X, 2016, LECT NOTES COMPUT SC, V9877, P480, DOI 10.1007/978-3-319-46922-5_44
NR 27
TC 8
Z9 8
U1 0
U2 4
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
PD MAY
PY 2020
VL 23
IS 3
SI SI
BP 1465
EP 1496
DI 10.1007/s11280-019-00739-0
PG 32
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LS0YD
UT WOS:000536118500001
DA 2022-08-23
ER

PT J
AU Lawrynowicz, A
AF Lawrynowicz, Agnieszka
TI Creative AI: A new avenue for the Semantic Web?
SO SEMANTIC WEB
LA English
DT Article
DE Computational creativity; artificial intelligence; Semantic Web;
   knowledge graph; ontology
ID COMPUTERS; FRAMEWORK
AB Computational Creativity (or artificial creativity) is a multidisciplinary field, researching how to construct computer programs that model, simulate, exhibit or enhance creative behaviour. This vision paper explores a potential of the Semantic Web and its technologies for creative AI. Possible uses of the Semantic Web and semantic technologies are discussed, regarding three types of creativity: i) exploratory creativity, ii) combinational creativity, and iii) transformational creativity and relevant research questions. For exploratory creativity, how can we explore the limits of what is possible, while remaining bound by a set of existing domain axioms, templates, and rules, expressed with semantic technologies? To achieve a combinational creativity, how can we combine or blend existing concepts, frames, ontology design patterns, and other constructs, and benefit from cross-fertilization? Ultimately, can we use ontologies and knowledge graphs, which describe an existing domain with its constraints and, applying a meta-rule for transformational creativity, start dropping constraints and adding new constraints to produce novel artifacts? Together with these new challenges, the paper also provides pointers to emerging and growing application domains of Semantic Web related to computational creativity: from recipe generation to scientific discovery and creative design.
C1 [Lawrynowicz, Agnieszka] Poznan Univ Tech, Fac Comp, Poznan, Poland.
   [Lawrynowicz, Agnieszka] Poznan Univ Tech, Ctr Artificial Intelligence & Machine Learning CA, Poznan, Poland.
RP Lawrynowicz, A (通讯作者)，Poznan Univ Tech, Fac Comp, Poznan, Poland.; Lawrynowicz, A (通讯作者)，Poznan Univ Tech, Ctr Artificial Intelligence & Machine Learning CA, Poznan, Poland.
EM alawrynowicz@cs.put.poznan.pl
OI Lawrynowicz, Agnieszka/0000-0002-2442-345X
CR Berners-Lee T, 2001, SCI AM, V284, P34, DOI 10.1038/scientificamerican0501-34
   Blomqvist E, 2016, SEMANT WEB, V7, P1, DOI 10.3233/SW-150202
   Blomqvist E, 2007, LECT NOTES COMPUT SC, V4803, P971
   Boden M.A., 2004, CREATIVE MIND MYTHS, V2nd ed., DOI 10.4324/9780203508527
   BODEN MA, 1992, J CREATIVE BEHAV, V26, P213, DOI 10.1002/j.2162-6057.1992.tb01178.x
   Boden MA, 2009, AI MAG, V30, P23, DOI 10.1609/aimag.v30i3.2254
   Colton S., 2011, P 2 INT C COMP CREAT, P90
   Colton S, 2012, FRONT ARTIF INTEL AP, V242, P21, DOI 10.3233/978-1-61499-098-7-21
   Confalonieri R, 2018, ANN MATH ARTIF INTEL, V82, P69, DOI 10.1007/s10472-016-9524-8
   d'Amato C, 2010, SEMANT WEB, V1, P53, DOI 10.3233/SW-2010-0007
   De Mantaras RL, 2005, KNOWL ENG REV, V20, P215, DOI 10.1017/S0269888906000646
   Du Sautoy M., 2019, CREATIVITY CODE ART
   Dubitzky Werner, 2012, Bisociative Knowledge Discovery. An Introduction to Concept, Algorithms, Tools, and Applications: LNCS 7250, P11, DOI 10.1007/978-3-642-31830-6_2
   Duch W, 2007, IEEE COMPUT INTELL M, V2, P40, DOI 10.1109/MCI.2007.385365
   Eppe M, 2018, ARTIF INTELL, V256, P105, DOI 10.1016/j.artint.2017.11.005
   Fanizzi N., 2007, P ACM INT C KNOWL MA, P51, DOI DOI 10.1145/1321440.1321450
   Fanizzi N, 2008, LECT NOTES ARTIF INT, V5194, P107, DOI 10.1007/978-3-540-85928-4_12
   Fauconnier Gilles, 2002, WAY WE THINK CONCEPT
   Fayyad U, 1996, AI MAG, V17, P37
   Finke R.A., 1992, CREATIVE COGNITION T
   Goguen JA, 2010, STRUCTUE OF STYLE: ALGORITHMIC APPROACHES TO UNDERSTANDING MANNER AND MEANING, P291, DOI 10.1007/978-3-642-12337-5_12
   Hitzler P, 2012, OWL 2 WEB ONTOLOGY L, VSecond
   Jennings KE, 2010, MIND MACH, V20, P489, DOI 10.1007/s11023-010-9206-y
   Jursic Matjaz, 2012, Bisociative Knowledge Discovery. An Introduction to Concept, Algorithms, Tools, and Applications: LNCS 7250, P66, DOI 10.1007/978-3-642-31830-6_6
   King RD, 2009, SCIENCE, V324, P85, DOI 10.1126/science.1165620
   Koestler A, 1964, ACT CREATION
   Kotter Tobias, 2012, Bisociative Knowledge Discovery. An Introduction to Concept, Algorithms, Tools, and Applications: LNCS 7250, P33, DOI 10.1007/978-3-642-31830-6_3
   Krawiec K, 2016, STUD COMPUT INTELL, V618, P1, DOI 10.1007/978-3-319-27565-9
   Kutz O., 2015, COMPUTATIONAL CREATI, P167, DOI [10.2991/978-94-6239-085-0_9, DOI 10.2991/978-94-6239-085-0_9]
   Lawrynowicz A., 2017, STUDIES SEMANTIC WEB, V29, DOI [10.3233/978-1-61499-746-7-i, DOI 10.3233/978-1-61499-746-7-I]
   Lawrynowicz A, 2011, LECT NOTES COMPUT SC, V6804, P428, DOI 10.1007/978-3-642-21916-0_46
   Lehmann J, 2007, LECT NOTES ARTIF INT, V4571, P883
   Lehmann J, 2010, MACH LEARN, V78, P203, DOI 10.1007/s10994-009-5146-2
   Liapis A, 2013, INT C COMP CREAT, P56
   Martins P, 2019, IBM J RES DEV, V63, DOI 10.1147/JRD.2019.2898417
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Pereira F.C., 2007, CREATIVITY CONCEPTUA
   Pereira FC, 2006, KNOWL-BASED SYST, V19, P459, DOI 10.1016/j.knosys.2006.04.008
   Potoniec J, 2015, AAAI CONF ARTIF INTE, P4198
   QUILLIAN MR, 1967, BEHAV SCI, V12, P410, DOI 10.1002/bs.3830120511
   Ritchie G, 2007, MIND MACH, V17, P67, DOI 10.1007/s11023-007-9066-2
   Robinson A, 2019, SCIENCE, V364, P842, DOI 10.1126/science.aax8954
   Roth-Berghofer T, 2010, LECT NOTES ARTIF INT, V6176, P451
   Toivanen JM, 2019, CONNECT SCI, V31, P4, DOI 10.1080/09540091.2018.1443320
   Toivonen H., TRANSFORMATIONAL CRE
   Toivonen H, 2015, WIRES DATA MIN KNOWL, V5, P265, DOI 10.1002/widm.1170
   Varshney LR, 2019, IBM J RES DEV, V63, DOI 10.1147/JRD.2019.2893905
   Veale T, 2000, COGN LINGUIST, V11, P253
   Veale T., 2012, P 3 INT C COMP CREAT, P1
   Vidal ME, 2014, SEMANT WEB, V5, P23, DOI 10.3233/SW-130101
   Wiggins GA, 2006, KNOWL-BASED SYST, V19, P449, DOI 10.1016/j.knosys.2006.04.009
   Wood D., 2014, RDF 1 1 CONCEPTS ABS
   Xiao P, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3186729
   You JX, 2018, PR MACH LEARN RES, V80
   2016, STUDIES SEMANTIC WEB, V25, P1
NR 55
TC 3
Z9 3
U1 5
U2 17
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2020
VL 11
IS 1
BP 69
EP 78
DI 10.3233/SW-190377
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KJ8IH
UT WOS:000512298900009
DA 2022-08-23
ER

PT J
AU Liang, JC
   Xie, JC
   Wang, X
   Wang, SJ
   Yu, MY
AF Liang, Jichao
   Xie, Jiancang
   Wang, Xue
   Wang, Shaojiu
   Yu, Mengyu
TI Visualization of Multi Scenario Water Resources Regulation Based on a
   Dualistic Water Cycle Framework
SO WATER
LA English
DT Article
DE dualistic water cycle; comprehensive integration platform; system
   simulation; visual description; water resources regulation
ID SIMULATION; QUANTITY; SYSTEM
AB Under the influences of global environmental change, the water cycle exhibits a characteristic "natural-social" duality. The theoretical framework of this dualistic water cycle model has become relatively mature and the frameworks for the natural and social water cycles of the process description are now relatively clear. Although many studies in this field focus on further improvement of the model, it is difficult to apply it to the multi-scenario regulation of water resources. To address this gap, based on the comprehensive integrated platform, this paper uses visual knowledge map technology and component technology to visualize the theoretical framework of the dualistic water cycle, and establishes a framework system for the visualization of the dualistic water cycle process. Three different water resource regulation scenarios were established in the system and example applications of water resources regulation using the system were realized. The simulation results of the system show that the system intends to assist the business function of water resources regulation, and it is able to set up a number of dynamic scenarios that can be controlled by users and assist the application of regional water resources regulation. The system's regulatory process is visual, trustworthy, and operational, and it realizes the simulation application of water resources regulation under the framework of the dualistic water cycle.
C1 [Liang, Jichao; Xie, Jiancang; Wang, Xue; Wang, Shaojiu; Yu, Mengyu] Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg China, Xian 710048, Peoples R China.
   [Wang, Xue] Coordinat Ctr Water Resources Dev Survey, Xian 710004, Peoples R China.
   [Wang, Xue] Hanjiang Weihe River Project Shaanxi Prov, Xian 710004, Peoples R China.
   [Yu, Mengyu] Xian Aerosp Macro Informat Technol Co Ltd, Xian 710100, Peoples R China.
RP Xie, JC (通讯作者)，Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg China, Xian 710048, Peoples R China.
EM xutljchao@163.com; jcxie@xaut.edu.cn; doraemon2129@126.com;
   shaojiuwang01@163.com; mengyuyu2021@163.com
OI Liang, Jichao/0000-0002-5743-0584
FU Chinese Academy of Water Sciences - National Key Research and
   Development Program of China [2016YFC0401409]; Natural Science Basic
   Research Program of Shaanxi Province [2019JLZ-16]; Science and
   Technology Program of Shaanxi Province [2019slkj-13, 2020slkj-16]
FX The authors thank anonymous reviewers for their constructive comments
   and suggestions, and we are grateful for the assistance of Hao Wang and
   Yong Zhao from the Chinese Academy of Water Sciences. We would like to
   thank Shannon Elliot at Michigan State University for his assistance
   with English language and grammatical editing.; This research was funded
   by The National Key Research and Development Program of China, grant No.
   2016YFC0401409, Natural Science Basic Research Program of Shaanxi
   Province, grant No. 2019JLZ-16, and Science and Technology Program of
   Shaanxi Province, grant No. 2019slkj-13, 2020slkj-16.
CR Pena-Guzman CA, 2017, WATER-SUI, V9, DOI 10.3390/w9040285
   Bederson Benjamin B, 2003, CRAFT INFORM VISUALI
   Cai Y, 2020, WATER CONSERV INFORM, V1, P1
   [蔡阳 Cai Yang], 2020, [水利学报, Journal of Hydraulic Engineering], V51, P685
   Cannata M, 2018, SPAT INF RES, V26, P241, DOI 10.1007/s41324-017-0140-4
   Cheng J.Q., 2002, WATER RESOURCE, P25
   Delfs JO, 2009, ADV WATER RESOUR, V32, P1386, DOI 10.1016/j.advwatres.2009.06.005
   Dong ZCA, 2021, WATER RESOUR MANAG, V35, P3411, DOI 10.1007/s11269-021-02901-8
   Moniz PF, 2020, WATER INT, V45, P660, DOI 10.1080/02508060.2020.1765130
   HOWARD RA, 1989, MANAGE SCI, V35, P903, DOI 10.1287/mnsc.35.8.903
   Hughes JD, 2016, HYDROL PROCESS, V30, P1955, DOI 10.1002/hyp.10752
   [贾仰文 JIA Yang-wen], 2010, [水科学进展, Advances in Water Science], V21, P1
   Jiang SM, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16152717
   Kang MS, 2016, WATER-SUI, V8, DOI 10.3390/w8080351
   Kralj AK, 2012, J IND ENG CHEM, V18, P1320, DOI 10.1016/j.jiec.2012.01.033
   Li J.X., 2011, WATER CONSERV INFORM, V4, P1
   Liu H, 2018, WATER POLICY, V20, P510, DOI 10.2166/wp.2017.062
   Liu JJ, 2019, J HYDROL, V578, DOI 10.1016/j.jhydrol.2019.124096
   Lu SB, 2016, RENEW SUST ENERG REV, V63, P132, DOI 10.1016/j.rser.2016.04.071
   Mosiichuk YB, 2019, J WATER CHEM TECHNO+, V41, P261, DOI 10.3103/S1063455X1904009X
   Olaru V, 2010, ENVIRON ENG MANAG J, V9, P423
   [裴源生 Pei Yuansheng], 2020, [水利学报, Journal of Hydraulic Engineering], V51, P1473
   Peng JY, 2019, ENVIRON SCI POLLUT R, V26, P16085, DOI 10.1007/s11356-019-04565-9
   Qin DY, 2014, CHINESE SCI BULL, V59, P810, DOI 10.1007/s11434-013-0096-2
   Shao Q, 2016, SOIL SCI, V181, P45, DOI 10.1097/SS.0000000000000136
   Shrestha RR, 2009, J HYDROL ENG, V14, P1309, DOI 10.1061/(ASCE)HE.1943-5584.0000123
   Valiantzas JD, 2009, J IRRIG DRAIN ENG, V135, P450, DOI 10.1061/(ASCE)IR.1943-4774.0000011
   Vimal S, 2022, HYDROL EARTH SYST SC, V26, P445, DOI 10.5194/hess-26-445-2022
   Wang H, 2004, SCI CHINA SER E, V47, P51, DOI 10.1360/04ez0005
   Wang H., 2004, CHINA WATER RESOURCE, V22, P43
   [王浩 Wang Hao], 2020, [水利学报, Journal of Hydraulic Engineering], V51, P1009
   [王浩 Wang Hao], 2016, [水利学报, Journal of Hydraulic Engineering], V47, P1219
   Wang H, 2013, CHINESE SCI BULL, V58, P3297, DOI 10.1007/s11434-012-5371-0
   [王浩 WANG Hao], 2011, [水利学报, Journal of Hydraulic Engineering], V42, P379
   Wang J.H., 2014, PRINCIPLE REGULATION
   Wang J, 2019, HYDROL RES, V50, P886, DOI 10.2166/nh.2019.117
   Wang K, 2020, J HYDROL, V580, DOI 10.1016/j.jhydrol.2019.124243
   Wang XQ, 2007, J GEOGR SCI, V17, P304, DOI 10.1007/s11442-007-0304-6
   Wei N., 2015, HYDRAUL HYDROELECTR, V46, P2
   Wen T.F., 2020, HYDROLOGY-BASEL, V40, P7
   Xie J.C, 2015, WATER CONSERV INFORM, V6, P18
   Xie J.C., 2010, WATER CONSERV INFORM, V5, P18, DOI [10.3969/j.issn.1674-9405.2010.05.004, DOI 10.19364/J.1674-9405.2010.05.004]
   Xifeng W., 2012, HKIE T HONG KONG I E, V19, P31, DOI [10.1080/1023697X.2012.10668256, DOI 10.1080/1023697X.2012.10668256]
   Zhang F., 2017, PEOPLES PEARL RIVER, V38, P105
   Zhang SH, 2017, J HYDROL, V551, P352, DOI 10.1016/j.jhydrol.2017.06.013
   Zhang SP, 2015, POL J ENVIRON STUD, V24, P829
   Zhou H, 2020, WATER CONSERV INFORM, V6, P7
   [周晋军 Zhou Jinjun], 2020, [水利学报, Journal of Hydraulic Engineering], V51, P1325
   Zhou XY, 2021, WATER-SUI, V13, DOI 10.3390/w13050671
   Zhou ZH, 2018, J WATER RES PLAN MAN, V144, DOI [10.1061/(ASCE)WR.1943-5452.0000990, 10.1061/(asce)wr.1943-5452.0000990]
   Zhu G.J., 2014, WATER CONSERV INFORM, V1, P54
   刘家宏, 2010, [科学通报, Chinese Science Bulletin], V55, P512
NR 52
TC 0
Z9 0
U1 13
U2 13
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-4441
J9 WATER-SUI
JI Water
PD APR
PY 2022
VL 14
IS 7
AR 1128
DI 10.3390/w14071128
PG 21
WC Environmental Sciences; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Water Resources
GA 0M3OC
UT WOS:000782066800001
OA gold
DA 2022-08-23
ER

PT J
AU Ji, B
   Long, RY
AF Ji, Bin
   Long, Ruyin
TI International Research Progress and Evolution Trend of Interpersonal
   Trust-Prospects under COVID-19 Pandemic
SO SUSTAINABILITY
LA English
DT Article
DE interpersonal trust; evolution trend; co-occurrence analysis; cluster
   analysis
ID PERFORMANCE; LEADERSHIP; STATES
AB Retrospecting articles on interpersonal trust is of great importance for understanding its current status and future development in the context of the COVID-19 pandemic, especially, with the widespread use of Big Data and Blockchain. In total, 1532 articles related to interpersonal trust were collected as research database to draw keyword co-occurrence mapping and timeline mapping by VOSviewer and CiteSpace. On this basis, the research content and evolution trend of interpersonal trust were systematically analyzed. The results show that: (1) Data cleaning by code was first integrated with Knowledge Mapping and then used to review the research of interpersonal trust; (2) Developed countries have contributed the most to the research of interpersonal trust; (3) Social capital, knowledge sharing, job and organizational performance, Chinese Guanxi are the research hotspots of interpersonal trust; (4) The research hotspots on interpersonal trust evolve from the level of individual psychology and behavior to the level of social stability and development and then to the level of organization operation and management; (5) At present, the research on interpersonal trust is in the outbreak period; fMRI technology and Big Data and Blockchain technology gradually become vital research tools of interpersonal trust, which provides significant prospects for the following research of interpersonal trust under the COVID-19 pandemic.
C1 [Ji, Bin] China Univ Min & Technol, Sch Econ & Management, Xuzhou 221116, Jiangsu, Peoples R China.
   [Long, Ruyin] Jiangnan Univ, Inst Jiangnan Culture, Wuxi 214122, Jiangsu, Peoples R China.
   [Long, Ruyin] Jiangnan Univ, Sch Business, Wuxi 214122, Jiangsu, Peoples R China.
RP Long, RY (通讯作者)，Jiangnan Univ, Inst Jiangnan Culture, Wuxi 214122, Jiangsu, Peoples R China.; Long, RY (通讯作者)，Jiangnan Univ, Sch Business, Wuxi 214122, Jiangsu, Peoples R China.
EM binji199411l@163.com; longruyin@163.com
CR Almakaeva A, 2018, SOC INDIC RES, V139, P923, DOI 10.1007/s11205-017-1724-z
   Brown H, 2021, J ROY ANTHROPOL INST, V27, P9, DOI 10.1111/1467-9655.13426
   Bulinska-Stangrecka H, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11164423
   Burt RS, 2018, SOC NETWORKS, V54, P12, DOI 10.1016/j.socnet.2017.12.001
   BUTLER JK, 1991, J MANAGE, V17, P643, DOI 10.1177/014920639101700307
   Cassar A, 2017, WORLD DEV, V94, P90, DOI 10.1016/j.worlddev.2016.12.042
   Chan RKH, 2021, CURR SOCIOL, V69, P547, DOI 10.1177/0011392121990026
   Chen Y, 2018, INT J HUM-COMPUT INT, V34, P356, DOI 10.1080/10447318.2018.1427826
   Coleman S.J, 1994, FDN SOCIAL THEORY
   Deutsch M., 1958, J CONFLICT RESOLUT, V2, P265
   Dinesen PT, 2020, ANNU REV POLIT SCI, V23, P441, DOI 10.1146/annurev-polisci-052918-020708
   Dirks KT, 2002, J APPL PSYCHOL, V87, P611, DOI 10.1037/0021-9010.87.4.611
   Dubey R, 2020, INT J PROD RES, V58, P3381, DOI 10.1080/00207543.2020.1722860
   Dubey R, 2019, INT J PROD ECON, V210, P120, DOI 10.1016/j.ijpe.2019.01.023
   Engelmann JB, 2019, SCI ADV, V5, DOI 10.1126/sciadv.aau3413
   Bianchini GE, 2019, DADOS-REV CIENC SOC, V62, DOI 10.1590/001152582019186
   Fischer S, 2020, AUST J MANAGE, V45, P662, DOI 10.1177/0312896219899450
   Fukuyama F., 1995, TRUST SOCIAL VIRTUES
   Gefen D., 2002, Data Base for Advances in Information Systems, V33, P38, DOI 10.1145/569905.569910
   Gundelach B, 2014, POLIT STUD-LONDON, V62, P596, DOI 10.1111/1467-9248.12064
   HARDIN R, 1993, POLIT SOC, V21, P505, DOI 10.1177/0032329293021004006
   Ishiyama J, 2018, SOC SCI QUART, V99, P872, DOI 10.1111/ssqu.12474
   Mavi RK, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13041932
   Kong DT, 2014, ACAD MANAGE J, V57, P1235, DOI 10.5465/amj.2012.0461
   Kramer RM, 1999, ANNU REV PSYCHOL, V50, P569, DOI 10.1146/annurev.psych.50.1.569
   Kramer RM, 2010, ACAD MANAG ANN, V4, P245, DOI 10.1080/19416520.2010.487403
   Lankton NK, 2011, DATA BASE ADV INF SY, V42, P32, DOI 10.1145/1989098.1989101
   Lewicki RJ., 1996, TRUST ORG FRONTIERS, P114, DOI DOI 10.4135/9781452243610.N7
   LEWIS JD, 1985, SOC FORCES, V63, P967, DOI 10.2307/2578601
   Lins KV, 2017, J FINANC, V72, P1785, DOI 10.1111/jofi.12505
   Luhmann N., 1979, TRUST POWER
   MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
   MCALLISTER DJ, 1995, ACAD MANAGE J, V38, P24, DOI 10.2307/256727
   McKnight DH, 2002, INFORM SYST RES, V13, P334, DOI 10.1287/isre.13.3.334.81
   Nudelman G, 2017, PERS INDIV DIFFER, V116, P191, DOI 10.1016/j.paid.2017.04.048
   Olaisen J, 2017, INT J INFORM MANAGE, V37, P1441, DOI 10.1016/j.ijinfomgt.2016.10.002
   Ommen O, 2011, INT J PUBLIC HEALTH, V56, P319, DOI 10.1007/s00038-010-0212-x
   Park S, 2018, J KNOWL MANAG, V22, P1408, DOI 10.1108/JKM-10-2017-0467
   Putnam R.D., 1994, CONTEMP SOCIOL, V26, P306, DOI 10.2307/j.ctt7s8r7
   ROTTER JB, 1967, J PERS, V35, P651, DOI 10.1111/j.1467-6494.1967.tb01454.x
   Rousseau DM, 1998, ACAD MANAGE REV, V23, P393, DOI 10.5465/AMR.1998.926617
   Tong C.K., 2014, CHINESE BUSINESS, P41
   Ward PR, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0095555
   WILLIAMSON OE, 1993, J LAW ECON, V36, P453, DOI 10.1086/467284
   YAMAGISHI T, 1994, MOTIV EMOTION, V18, P129, DOI 10.1007/BF02249397
   Ye Fei, 2013, Journal of the China Society for Scientific and Technical Information, V32, P1196, DOI 10.3772/j.issn.1000-0135.2013.11.008
   Zaheer A, 1998, ORGAN SCI, V9, P141, DOI 10.1287/orsc.9.2.141
   Zhang JZ, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12135348
   Zhao DH, 2017, INT REV ADM SCI, V83, P358, DOI 10.1177/0020852315582136
NR 49
TC 0
Z9 0
U1 19
U2 19
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JAN
PY 2022
VL 14
IS 2
AR 987
DI 10.3390/su14020987
PG 15
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA ZB7YU
UT WOS:000757053900001
OA gold
DA 2022-08-23
ER

PT J
AU Guo, B
   Feng, T
AF Guo, Bin
   Feng, Tao
TI Mapping Knowledge Domains of Integration in BIM-Based Construction
   Networks: A Systematic Mixed-Method Review
SO ADVANCES IN CIVIL ENGINEERING
LA English
DT Review
ID LIFE
AB Building information modeling-based construction networks (BbCNs) are teams from several professional organizations working together to assume building information modeling- (BIM-) related assignments on BIM-enabled projects. With a view to achieving a better understanding of the knowledge domains on integration in BbCNs, a systematic mixed-method review of the relevant studies published from 2008 to 2018 is conducted in this study. An integration pentagon made up of context, process, organization, task, and actor is used as a theoretical lens to identify and construct knowledge maps describing the integration in BbCNs. The study conducts a comprehensive review upon a bibliometric analysis based on 1019 researches into BIM and a qualitative analysis of 42 carefully selected researches into integration in BbCNs. The findings confirm that the solutions provided by these researches to support integration in BbCNs are altogether technology oriented. The sociotechnical dimensions including context, organization, task, and actor show limitations. More importantly, the major academic contributions of the study lie in offering an objective and systematic analysis of previous researches, revealing the gaps on integration in BbCNs, and advising researchers in future studies regarding the integration pentagon as an all-inclusive analysis tool. These results highlight the status quo of BbCNs knowledge and serve as a dynamic platform to allow other scholars to perform further developments of integration in BbCNs.
C1 [Guo, Bin] Xian Univ Architecture & Technol, Inst Management, Sch Publ Adm, Fac Engn Econ & Management, Xian 710000, Shaanxi, Peoples R China.
   [Feng, Tao] Xian Univ Architecture & Technol, Inst Management, Xian 710000, Shaanxi, Peoples R China.
RP Feng, T (通讯作者)，Xian Univ Architecture & Technol, Inst Management, Xian 710000, Shaanxi, Peoples R China.
EM 249640039@qq.com; 756783706@qq.com
OI FENG, TAO/0000-0002-2872-1729
CR Aarseth W, 2017, INT J PROJ MANAG, V35, P1071, DOI 10.1016/j.ijproman.2016.11.006
   Abbas A, 2016, PROCEDIA ENGINEER, V145, P151, DOI 10.1016/j.proeng.2016.04.034
   Ahola T, 2017, INT J PROJ MANAG, V35, P1006, DOI 10.1016/j.ijproman.2017.05.002
   Ajam M, 2010, AUTOMAT CONSTR, V19, P762, DOI 10.1016/j.autcon.2010.04.001
   Akintola A, 2017, J CONSTR ENG M, V143, DOI 10.1061/(ASCE)CO.1943-7862.0001366
   AL Mousli MH, 2016, ARCHIT ENG DES MANAG, V12, P353, DOI 10.1080/17452007.2016.1187111
   Alreshidi E, 2018, REQUIR ENG, V23, P1, DOI 10.1007/s00766-016-0254-6
   Alter S., 2002, COMMUNICATIONS ASS I, V9, DOI [10.17705/1cais.00906, DOI 10.17705/1CAIS.00906]
   Amann J, 2016, J COMPUT CIVIL ENG, V30, DOI 10.1061/(ASCE)CP.1943-5487.0000592
   Arayici Y, 2018, AUTOMAT CONSTR, V85, P167, DOI 10.1016/j.autcon.2017.10.018
   Azhar S., 2011, LEADERSH MANAGE ENG, V11, P241, DOI [10.1061/(ASCE)LM.1943-5630.0000127, DOI 10.1061/(ASCE)LM.1943-5630.0000127]
   Babic NC, 2010, AUTOMAT CONSTR, V19, P539, DOI 10.1016/j.autcon.2009.11.005
   Bargstadt HJ, 2015, PROCEDIA ENGINEER, V117, P52, DOI 10.1016/j.proeng.2015.08.123
   Berteaux F, 2015, J MANAGE ENG, V31, DOI 10.1061/(ASCE)ME.1943-5479.0000366
   Bosch-Sijtsema PM, 2019, AUTOMAT CONSTR, V97, P44, DOI 10.1016/j.autcon.2018.10.024
   Bostrom RP, 2009, J MANAGE INFORM SYST, V26, P17, DOI 10.2753/MIS0742-1222260102
   Browning TR, 2014, INT J PROJ MANAG, V32, P229, DOI 10.1016/j.ijproman.2013.05.008
   Butt A, 2016, INT J PROJ MANAG, V34, P1579, DOI 10.1016/j.ijproman.2016.08.010
   Cao DP, 2017, INT J PROJ MANAG, V35, P658, DOI 10.1016/j.ijproman.2016.02.002
   Chen K, 2015, INT J PROJ MANAG, V33, P1405, DOI 10.1016/j.ijproman.2015.03.006
   Chen SM, 2013, AUTOMAT CONSTR, V35, P89, DOI 10.1016/j.autcon.2013.04.002
   Dave B, 2018, AUTOMAT CONSTR, V95, P35, DOI 10.1016/j.autcon.2018.07.022
   Davis K, 2014, INT J PROJ MANAG, V32, P189, DOI 10.1016/j.ijproman.2013.02.006
   Demirkesen S, 2017, INT J PROJ MANAG, V35, P1639, DOI 10.1016/j.ijproman.2017.09.008
   Ding LY, 2014, AUTOMAT CONSTR, V46, P82, DOI 10.1016/j.autcon.2014.04.009
   Dowsett RM, 2019, CONSTR MANAG ECON, V37, P551, DOI 10.1080/01446193.2018.1476728
   Eadie R, 2015, BUILT ENVIRON PROJ A, V5, P4, DOI 10.1108/BEPAM-07-2013-0023
   Eskerod P, 2018, INT J PROJ MANAG, V36, P161, DOI 10.1016/j.ijproman.2017.05.003
   FADEYI MO, 2017, VALUE INT J SUSTAINA, V0006, P00711
   Forgues E. C., 2016, P INT C COOP DES SYD
   Gemunden HG, 2018, INT J PROJ MANAG, V36, P147, DOI 10.1016/j.ijproman.2017.07.009
   Gibbert M, 2008, STRATEGIC MANAGE J, V29, P1465, DOI 10.1002/smj.722
   Gough D, 2015, SYST REV, V4, DOI 10.1186/s13643-015-0151-y
   Grilo A, 2013, INT J COMPUT INTEG M, V26, P152, DOI 10.1080/0951192X.2012.681918
   Grilo A, 2013, ADV ENG INFORM, V27, P160, DOI 10.1016/j.aei.2012.10.004
   Grilo A, 2010, AUTOMAT CONSTR, V19, P522, DOI 10.1016/j.autcon.2009.11.003
   Habibi S, 2017, ENERG BUILDINGS, V153, P525, DOI 10.1016/j.enbuild.2017.08.009
   He QH, 2017, INT J PROJ MANAG, V35, P670, DOI 10.1016/j.ijproman.2016.08.001
   Hosseini MR, 2016, CONSTR ECON BUILD, V16, P71, DOI 10.5130/AJCEB.v16i3.5159
   Ilhan B, 2016, AUTOMAT CONSTR, V70, P26, DOI 10.1016/j.autcon.2016.05.001
   Jacobsson M, 2010, CONSTR MANAG ECON, V28, P13, DOI 10.1080/01446190903406154
   Jiao Y, 2013, ADV ENG INFORM, V27, P173, DOI 10.1016/j.aei.2012.11.006
   Jin RY, 2017, J CONSTR ENG M, V143, DOI 10.1061/(ASCE)CO.1943-7862.0001408
   Leavitt H. J., 1964, EXPT BRAIN RES, V45, P1
   Lee SK, 2012, AUTOMAT CONSTR, V25, P82, DOI 10.1016/j.autcon.2012.04.015
   Linderoth HCJ, 2010, AUTOMAT CONSTR, V19, P66, DOI 10.1016/j.autcon.2009.09.003
   Liu Y, 2017, INT J PROJ MANAG, V35, P686, DOI 10.1016/j.ijproman.2016.06.007
   London K, 2013, ARCHIT ENG DES MANAG, V9, P135, DOI 10.1080/17452007.2012.684451
   Lowstedt M, 2018, INT J PROJ MANAG, V36, P889, DOI 10.1016/j.ijproman.2018.05.002
   Lyytinen K, 2008, EUR J INFORM SYST, V17, P589, DOI 10.1057/ejis.2008.50
   Mahamadu A. M., 2013, J ENDOCRINOLOGICAL I, V28, P425
   Merschbrock C, 2018, J MANAGE ENG, V34, DOI 10.1061/(ASCE)ME.1943-5479.0000605
   Mignone G, 2016, ARCHIT ENG DES MANAG, V12, P333, DOI 10.1080/17452007.2016.1169987
   Oraee M, 2017, INT J PROJ MANAG, V35, P1288, DOI 10.1016/j.ijproman.2017.07.001
   Oti AH, 2016, AUTOMAT CONSTR, V69, P44, DOI 10.1016/j.autcon.2016.05.019
   Ozorhon B, 2014, J MANAGE ENG, V30, P256, DOI 10.1061/(ASCE)ME.1943-5479.0000204
   Papadonikolaki E, 2017, BUILD RES INF, V45, P649, DOI 10.1080/09613218.2017.1301718
   Papadonikolaki E, 2016, ARCHIT ENG DES MANAG, V12, P476, DOI 10.1080/17452007.2016.1212693
   Poirier E, 2016, CONSTR MANAG ECON, V34, P769, DOI 10.1080/01446193.2016.1206660
   Poirier EA, 2017, BUILD RES INF, V45, P681, DOI 10.1080/09613218.2017.1324724
   Porwal A, 2013, AUTOMAT CONSTR, V31, P204, DOI 10.1016/j.autcon.2012.12.004
   Rahman RA, 2016, PROCEDIA ENGINEER, V145, P812, DOI 10.1016/j.proeng.2016.04.106
   Riaz Z, 2014, AUTOMAT CONSTR, V45, P96, DOI 10.1016/j.autcon.2014.05.010
   Sackey E, 2015, J MANAGE ENG, V31, DOI 10.1061/(ASCE)ME.1943-5479.0000303
   Salem O, 2006, J MANAGE ENG, V22, P168, DOI 10.1061/(ASCE)0742-597X(2006)22:4(168)
   Sanguinetti P, 2012, ADV ENG INFORM, V26, P317, DOI 10.1016/j.aei.2011.12.001
   Sebastian R, 2011, ENG CONSTR ARCHIT MA, V18, P176, DOI 10.1108/09699981111111148
   Shafiq MT, 2013, J INF TECHNOL CONSTR, V18, P148
   Shao JT, 2018, INT J PROJ MANAG, V36, P108, DOI 10.1016/j.ijproman.2017.05.004
   Shin TS, 2017, INT J STEEL STRUCT, V17, P205, DOI 10.1007/s13296-016-0190-9
   Singh V, 2011, AUTOMAT CONSTR, V20, P134, DOI 10.1016/j.autcon.2010.09.011
   Solihin W, 2016, ADV ENG INFORM, V30, P168, DOI 10.1016/j.aei.2016.02.007
   Son H, 2015, AUTOMAT CONSTR, V49, P92, DOI 10.1016/j.autcon.2014.10.012
   Succar B, 2009, AUTOMAT CONSTR, V18, P357, DOI 10.1016/j.autcon.2008.10.003
   Takim R, 2013, PROCD SOC BEHV, V101, P23, DOI 10.1016/j.sbspro.2013.07.175
   Tarandi V, 2015, PROC ECON FINANC, V21, P383, DOI 10.1016/S2212-5671(15)00190-2
   Uhm M, 2017, AUTOMAT CONSTR, V81, P67, DOI 10.1016/j.autcon.2017.06.002
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   van Gassel FJM, 2014, AUTOMAT CONSTR, V39, P85, DOI 10.1016/j.autcon.2013.12.001
   Wang WC, 2014, AUTOMAT CONSTR, V37, P68, DOI 10.1016/j.autcon.2013.10.009
   Wen Q, 2016, AUTOMAT CONSTR, V72, P309, DOI 10.1016/j.autcon.2016.06.002
   Whyte J, 2016, INT J PROJ MANAG, V34, P339, DOI 10.1016/j.ijproman.2015.02.006
   Whyte JK, 2017, BUILD RES INF, V45, P591, DOI 10.1080/09613218.2017.1324726
   Xue XL, 2010, J MANAGE ENG, V26, P196, DOI 10.1061/(ASCE)ME.1943-5479.0000025
   Zhang JP, 2017, AUTOMAT CONSTR, V81, P180, DOI 10.1016/j.autcon.2017.06.021
NR 85
TC 2
Z9 2
U1 5
U2 24
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-8086
EI 1687-8094
J9 ADV CIV ENG
JI Adv. Civ. Eng.
PY 2019
VL 2019
AR 5161579
DI 10.1155/2019/5161579
PG 12
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA HZ9LP
UT WOS:000469178200001
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Arsintescu, B
   Deo, S
   Harris, W
AF Arsintescu, Bogdan
   Deo, Shardul
   Harris, Warren
TI PathQuery Pregel: high-performance graph query with bulk synchronous
   processing
SO PATTERN ANALYSIS AND APPLICATIONS
LA English
DT Article
DE Distributed graph compute; Pregel; Graph query; Bulk synchronous
   parallel computing; Graph database
AB High-performance graph query systems are a scalable way to mine information in Knowledge Graphs, especially when the queries benefit from a high-level expressive query language. This paper presents techniques to algorithmically compile queries expressed in a high-level language (e.g., Datalog) into a directed acyclic graph query plan and details how these queries can be run on a Pregel graph vertex-centric compute system. Our solution, called PathQuery Pregel, creates plans for any conjunctive or disjunctive queries with aggregation and negation; we describe how the query execution extracts graph results optimally while avoiding many join operations where parallel map execution is permitted. We provide details of how we scaled this system out to execute large set of queries in parallel over the Google Knowledge Graph, a graph of 70B edges, or facts; we describe our production experience with PathQuery Pregel.
C1 [Arsintescu, Bogdan; Deo, Shardul; Harris, Warren] Google Inc, 1600 Amphiteatre Pkwy, Mountain View, CA 94043 USA.
RP Arsintescu, B (通讯作者)，Google Inc, 1600 Amphiteatre Pkwy, Mountain View, CA 94043 USA.
EM sdeo@google.com; warrenharris@google.com
OI Arsintescu, Bogdan/0000-0003-1063-0084
CR Aberger CR, 2017, ACM T DATABASE SYST, V42, DOI 10.1145/3129246
   Angles R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1322432.1322433
   Bast H, 2016, FOUND TRENDS INF RET, V10, P120, DOI 10.1561/1500000032
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Ceri S., 1989, IEEE Transactions on Knowledge and Data Engineering, V1, P146, DOI 10.1109/69.43410
   Chassel RJ, MAPCAR FUNCTION
   Chavarria-Miranda D, 2016, IEEE SYM PARA DISTR, P1453, DOI 10.1109/IPDPSW.2016.216
   COLMERAUER A, 1993, SIGPLAN NOTICES, V28, P37
   Cuzzocrea A, 2016, ALGORITHMS, V9, DOI 10.3390/a9010007
   Dean J, 2004, USENIX ASSOCIATION PROCEEDINGS OF THE SIXTH SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION (OSDE '04), P137
   Fan JC, 2015, ADV INTEL SYS RES, V123, P4
   HILLIS WD, 1989, CONNECTION MACHINE
   Junghanns M., 2017, HDB BIG DATA TECHNOL, P457, DOI DOI 10.1007/978-3-319-49340-4_14
   Kim H, 2014, INT SOC DESIGN CONF, P15, DOI 10.1109/ISOCC.2014.7087562
   Kim K, 2014, EXPERT SYST APPL, V41, P4596, DOI 10.1016/j.eswa.2014.01.027
   LinkedIn, 2018, LINKEDIN EC GRAPH
   LIPSETT R, 1989, VHDL HARDWARE DESCRI
   Low Y, 2012, PROC VLDB ENDOW, V5, P716, DOI 10.14778/2212351.2212354
   Malewicz G., 2010, P 2010 ACM SIGMOD IN, V2010, P135, DOI [DOI 10.1145/1807167.1807184, 10.1145/1807167.1807184]
   Marcus G, 2012, WEB GETS SMARTER
   Moustafa WE, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), P56, DOI 10.1109/BigData.2016.7840589
   Raval Mihir, 2012, BMC Res Notes, V5, P594, DOI 10.1186/1756-0500-5-594
   Ribeiro-Neto, 1999, MODERN INFORM RETRIE, VVolume 463
   Rohloff K, 2010, PROGRAMMING SUPPORT, P4
   Seo J, 2013, PROC VLDB ENDOW, V6, P1906, DOI 10.14778/2556549.2556572
   Steele G., 1986, LISP FUNCTIONAL PROG, P279
   VALIANT LG, 1990, COMMUN ACM, V33, P103, DOI 10.1145/79173.79181
   Vincent J, 2018, APPLE BOASTS SALES G
   Wang JJ, 2015, PROC VLDB ENDOW, V8, P1542
   Yan D, 2014, PROC VLDB ENDOW, V7, P1821, DOI 10.14778/2733085.2733089
   Yan D, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1307, DOI 10.1145/2736277.2741096
   Yan Zongfu, 1996, Journal of Computer Science and Technology (English Language Edition), V11, P562, DOI 10.1007/BF02951619
   Zhang QZ, 2016, SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P2189, DOI 10.1145/2882903.2899398
NR 33
TC 1
Z9 1
U1 0
U2 3
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1433-7541
EI 1433-755X
J9 PATTERN ANAL APPL
JI Pattern Anal. Appl.
PD AUG
PY 2020
VL 23
IS 3
BP 1493
EP 1504
DI 10.1007/s10044-019-00841-z
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LY5ZK
UT WOS:000540607300030
DA 2022-08-23
ER

PT J
AU Yang, X
   Wu, CK
   Nenadic, G
   Wang, W
   Lu, K
AF Yang, Xi
   Wu, Chengkun
   Nenadic, Goran
   Wang, Wei
   Lu, Kai
TI Mining a stroke knowledge graph from literature
SO BMC BIOINFORMATICS
LA English
DT Article
DE Stroke; Knowledge graph; Biomedical text mining; Traditional Chinese
   Medicine
ID ENTITY; PREVENTION; SYSTEM; CORPUS
AB Background: Stroke has an acute onset and a high mortality rate, making it one of the most fatal diseases worldwide. Its underlying biology and treatments have been widely studied both in the "Western" biomedicine and the Traditional Chinese Medicine (TCM). However, these two approaches are often studied and reported in insolation, both in the literature and associated databases.
   Results: To aid research in finding effective prevention methods and treatments, we integrated knowledge from the literature and a number of databases (e.g. CID, TCMID, ETCM). We employed a suite of biomedical text mining (i.e. named-entity) approaches to identify mentions of genes, diseases, drugs, chemicals, symptoms, Chinese herbs and patent medicines, etc. in a large set of stroke papers from both biomedical and TCM domains. Then, using a combination of a rule-based approach with a pre-trained BioBERT model, we extracted and classified links and relationships among stroke-related entities as expressed in the literature. We construct StrokeKG, a knowledge graph includes almost 46 k nodes of nine types, and 157 k links of 30 types, connecting diseases, genes, symptoms, drugs, pathways, herbs, chemical, ingredients and patent medicine.
   Conclusions: Our Stroke-KG can provide practical and reliable stroke-related knowledge to help with stroke-related research like exploring new directions for stroke research and ideas for drug repurposing and discovery. We make StrokeKG freely available at (Please click "Connect" directly) and the source structured data for stroke at https://github.com/yangxi1016/Stroke
C1 [Yang, Xi; Wang, Wei; Lu, Kai] Natl Univ Def Technol, Coll Comp, Changsha 410073, Peoples R China.
   [Yang, Xi; Wu, Chengkun] Natl Univ Def Technol, State Key Lab High Performance Comp, Changsha 410073, Peoples R China.
   [Yang, Xi; Nenadic, Goran] Univ Manchester, Dept Comp Sci, Manchester M13 9PL, Lancs, England.
RP Nenadic, G (通讯作者)，Univ Manchester, Dept Comp Sci, Manchester M13 9PL, Lancs, England.
EM g.nenadic@manchester.ac.uk
RI Wu, Chengkun/B-4923-2009
OI Wu, Chengkun/0000-0002-9688-5311
FU National High-level Personnel for Defense Technology Program
   [2017-JCJQZQ-013]; NSF [61902405]; National Key R&D project by Ministry
   of Science and Technology of China [2018YFB1003203]; State Key
   Laboratory of High Performance Computing [201901-11]; National Science
   Foundation of China [U1811462]
FX Computing resources are supported by National High-level Personnel for
   Defense Technology Program (2017-JCJQZQ-013), and NSF 61902405.
   Publication costs are funded by the National Key R&D project by Ministry
   of Science and Technology of China (2018YFB1003203), the open fund from
   the State Key Laboratory of High Performance Computing (No. 201901-11),
   and National Science Foundation of China (U1811462). The funder CW and
   KL took part in the formulation and development of methodology, and
   provided financial support for this study.
CR [Anonymous], 2020, SSNAP
   [Anonymous], 2006, INT STROKE DATABASE
   [Anonymous], 2015, OSR ACUTE STROKE DAT
   Bean DM, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-16674-x
   Blake JA, 2015, NUCLEIC ACIDS RES, V43, pD1049, DOI 10.1093/nar/gku1179
   Brainin M, 2020, LANCET NEUROL, V19, P487, DOI 10.1016/S1474-4422(20)30141-1
   ChunYang Liu, 2013, Advanced Data Mining and Applications. 9th International Conference, ADMA 2013. Proceedings: LNCS 8347, P231, DOI 10.1007/978-3-642-53917-6_21
   Cohen AM, 2005, BRIEF BIOINFORM, V6, P57, DOI 10.1093/bib/6.1.57
   Ernst P, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0549-5
   Fauqueur J, ARXIV PREPRINT ARXIV
   Gerner M, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-85
   Gregory JK, 2009, MED TEACH, V31, P855, DOI 10.1080/01421590903183795
   Gyrard A., 2018, PERSONALIZED HLTH KN
   Herrero-Zazo M, 2013, J BIOMED INFORM, V46, P914, DOI 10.1016/j.jbi.2013.07.011
   Huang L, 2018, NUCLEIC ACIDS RES, V46, pD1117, DOI 10.1093/nar/gkx1028
   Ji S, ARXIV PREPRINT ARXIV
   Junge A, 2020, BIOINFORMATICS, V36, P264, DOI 10.1093/bioinformatics/btz490
   Katan M, 2018, SEMIN NEUROL, V38, P208, DOI 10.1055/s-0038-1649503
   Kim B, 2019, PLOS ONE, V14
   Kim J-D, 2009, P NAT LANG PROC BIOM P NAT LANG PROC BIOM
   Krauthammer M, 2004, J BIOMED INFORM, V37, P512, DOI 10.1016/j.jbi.2004.08.004
   Lau AYL, 2012, J CLIN NEUROSCI, V19, P1383, DOI 10.1016/j.jocn.2012.01.027
   Leaman R, 2015, J CHEMINFORMATICS, V7, DOI 10.1186/1758-2946-7-S1-S3
   Leaman R, 2013, BIOINFORMATICS, V29, P2909, DOI 10.1093/bioinformatics/btt474
   Lee K, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2029-1
   Li J, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw068
   Loper E, 2002, ARXIV PREPRINT ARXIV
   Luo L, 2018, BIOINFORMATICS, V34, P1381, DOI 10.1093/bioinformatics/btx761
   Mattingly CJ, 2006, J EXP ZOOL PART A, V305A, P689, DOI 10.1002/jez.a.307
   Mohamed SK, 2020, BIOINFORMATICS, V36, P603, DOI 10.1093/bioinformatics/btz600
   Peisker T, 2017, TRENDS CARDIOVAS MED, V27, P59, DOI 10.1016/j.tcm.2016.06.009
   Perera D, 2016, NATURE, V532, P259, DOI 10.1038/nature17437
   Petry NM, 2005, ARCH GEN PSYCHIAT, V62, P1148, DOI 10.1001/archpsyc.62.10.1148
   Ravikumar KE, 2017, DATABASE-OXFORD, DOI 10.1093/database/baw156
   Rocktaschel T, 2012, BIOINFORMATICS, V28, P1633, DOI 10.1093/bioinformatics/bts183
   Roux KJ, 2018, CURR PROTOC PROT SCI, V91
   Ru JL, 2014, J CHEMINFORMATICS, V6, DOI 10.1186/1758-2946-6-13
   Sang ST, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2167-5
   Sarhan I, 2016, PROC INT C TOOLS ART, P177, DOI [10.1109/ICTAI.2016.33, 10.1109/ICTAI.2016.0036]
   Schriml LM, 2012, NUCLEIC ACIDS RES, V40, pD940, DOI 10.1093/nar/gkr972
   Song M, 2015, J BIOMED INFORM, V57, P320, DOI 10.1016/j.jbi.2015.08.008
   Stebbing J, 2020, LANCET INFECT DIS, V20, P400, DOI 10.1016/S1473-3099(20)30132-8
   Sun M, 2019, BRIEF INFORM
   Taboureau O, 2011, NUCLEIC ACIDS RES, V39, pD367, DOI 10.1093/nar/gkq906
   Teschke R, 2015, ANN HEPATOL, V14, P7, DOI 10.1016/S1665-2681(19)30796-3
   Dang TH, 2018, BIOINFORMATICS, V34, P3539, DOI 10.1093/bioinformatics/bty356
   Ton G, 2019, J ALTERN COMPLEM MED, V25, P946, DOI 10.1089/acm.2018.0496
   Trifiro G, 2011, STUD HEALTH TECHNOL, V166, P25, DOI 10.3233/978-1-60750-740-6-25
   van Mulligen EM, 2012, J BIOMED INFORM, V45, P879, DOI 10.1016/j.jbi.2012.04.004
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang W, 2017, BMC BIOINFORMATICS, V18, DOI 10.1186/s12859-017-1962-8
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wei CH, 2015, BIOMED RES INT, V2015, DOI 10.1155/2015/918710
   Wei CH, 2013, NUCLEIC ACIDS RES, V41, pW518, DOI 10.1093/nar/gkt441
   Wu C, 2015, BMC SYST BIOL, V9, DOI 10.1186/1752-0509-9-S6-S5
   Wu SM, 2019, LANCET NEUROL, V18, P394, DOI 10.1016/S1474-4422(18)30500-3
   Xi E, 2017, ARXIV PREPRINT ARXIV
   Xu HY, 2019, NUCLEIC ACIDS RES, V47, pD976, DOI 10.1093/nar/gky987
   Yadav V., 2019, ARXIV PREPRINT ARXIV
   Yu X, 2019 10 INT C INF TE 2019 10 INT C INF TE, P49
   Yuan JB, 2020, KNOWL INF SYST, V62, P317, DOI 10.1007/s10115-019-01351-4
   Yuanzhuo W., 2014, COMMUN CHIN COMPUT F, V10, P30
   Zhang D., 2015, ARXIV PREPRINT ARXIV
   Zhang RZ, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-03039-7
   Zhang YJ, 2018, J BIOMED INFORM, V81, P83, DOI 10.1016/j.jbi.2018.03.011
   Zitnik M, 2018, BIOINFORMATICS, V34, P457, DOI 10.1093/bioinformatics/bty294
NR 66
TC 2
Z9 2
U1 12
U2 24
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD JUL 29
PY 2021
VL 22
IS SUPPL 10
SU 10
SI SI
AR 387
DI 10.1186/s12859-021-04292-4
PG 18
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
GA TS1RR
UT WOS:000679435700001
PM 34325669
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Li, QJ
   Guo, XX
   Zhang, LH
AF Li, Qingjun
   Guo, Xiaoxia
   Zhang, Lianhe
TI Bibliometric Analysis of Water Resource Management
SO JOURNAL OF COASTAL RESEARCH
LA English
DT Article
DE Water resource management; knowledge graph; research hot spots analysis;
   literature bibliometric
ID SYSTEM
AB Based on the Web of Science database, this paper systematically reveals a general trend of publication, country or region, and organization distribution; source publication distribution; frequently cited papers; co-occurring keywords; and time zone analysis of keywords in the field of water resource management (WRM) from multiple perspectives by using bibliometric analysis and visualization software. A corresponding visualization knowledge graph is obtained that clearly and intuitively shows the current situation and research hot spots of WRM research from a specific perspective. The results show that in the past 21 years, research in WRM has been increasing continuously. China's research on WRM is in the leading position, and "water resource management," "management," and "integrated water resource management" are the top 3 high-frequency keywords. These findings provide a knowledge basis for further research on WRM.
C1 [Li, Qingjun] Weifang Univ, Sch Econ & Management, Weifang 261061, Peoples R China.
   [Guo, Xiaoxia] Weifang Univ, Media & Commun Coll, Weifang 261061, Peoples R China.
   [Zhang, Lianhe] Shandong Acad Sci, Jinan 250014, Peoples R China.
RP Guo, XX (通讯作者)，Weifang Univ, Media & Commun Coll, Weifang 261061, Peoples R China.
EM 33104731@qq.com
FU Soft Science of Shandong Province for Major Project [2019RZB01020];
   Social Science Foundation of Shandong [19CGLJ26]
FX This work was supported by Soft Science of Shandong Province for Major
   Project (grant 2019RZB01020) and the Social Science Foundation of
   Shandong (grant 19CGLJ26).
CR Allan J.A., 1993, FORTUNATELY THERE AR
   [Anonymous], 2014, WATER RESOURCES MANA, DOI DOI 10.1016/J.DEVCEL.2014.04.002
   Bellin A, 2016, ENVIRON MODELL SOFTW, V75, P176, DOI 10.1016/j.envsoft.2015.10.013
   Dou M, 2017, WATER SCI TECH-W SUP, V17, P238, DOI 10.2166/ws.2016.130
   Kim J, 2008, J ADVERTISING, V37, P99, DOI 10.2753/JOA0091-3367370108
   Momblanch A, 2014, J HYDROL, V519, P3369, DOI 10.1016/j.jhydrol.2014.10.002
   Safavi HR, 2015, J HYDROL, V528, P773, DOI 10.1016/j.jhydrol.2015.07.014
   Tzabiras J, 2016, WATER RESOUR MANAG, V30, P5819, DOI 10.1007/s11269-016-1536-y
   Wang H, 2011, CHINA WATER RESOURCE, V6, P28
   Yoder J, 2017, J AM WATER RESOUR AS, V53, P456, DOI 10.1111/1752-1688.12507
   Zhou YL, 2015, J HYDROL, V531, P964, DOI 10.1016/j.jhydrol.2015.10.007
NR 11
TC 1
Z9 1
U1 5
U2 20
PU COASTAL EDUCATION & RESEARCH FOUNDATION
PI COCONUT CREEK
PA 5130 NW 54TH STREET, COCONUT CREEK, FL 33073 USA
SN 0749-0208
EI 1551-5036
J9 J COASTAL RES
JI J. Coast. Res.
PD WIN
PY 2020
SI 105
BP 210
EP 214
DI 10.2112/JCR-SI105-044.1
PG 5
WC Environmental Sciences; Geography, Physical; Geosciences,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Physical Geography; Geology
GA PN9MB
UT WOS:000604796700044
DA 2022-08-23
ER

PT J
AU Ciampaglia, GL
   Shiralkar, P
   Rocha, LM
   Bollen, J
   Menczer, F
   Flammini, A
AF Ciampaglia, Giovanni Luca
   Shiralkar, Prashant
   Rocha, Luis M.
   Bollen, Johan
   Menczer, Filippo
   Flammini, Alessandro
TI Computational Fact Checking from Knowledge Networks
SO PLOS ONE
LA English
DT Article
ID MISINFORMATION
AB Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation.
C1 [Ciampaglia, Giovanni Luca; Shiralkar, Prashant; Rocha, Luis M.; Bollen, Johan; Menczer, Filippo; Flammini, Alessandro] Indiana Univ, Ctr Complex Networks & Syst Res, Bloomington, IN 47405 USA.
   [Rocha, Luis M.] Inst Gulbenkian Ciencias, Oeiras, Portugal.
RP Ciampaglia, GL (通讯作者)，Indiana Univ, Ctr Complex Networks & Syst Res, Bloomington, IN 47405 USA.
EM gciampag@indiana.edu
RI Shiralkar, Prashant/U-3056-2019
FU Swiss National Science Foundation [142353]; Lilly Endowment; James S.
   McDonnell Foundation; National Science Foundation [CCF-1101743];
   Department of Defense [W911NF-12-1-0037]; Division of Computing and
   Communication Foundations [1101743] Funding Source: National Science
   Foundation
FX This work was supported in part by the Swiss National Science Foundation
   (fellowship 142353, http://snf.ch), the Lilly Endowment
   (http://www.lillyendowment.org/), the James S. McDonnell Foundation
   (https://www.jsmf.org/), the National Science Foundation (grant
   CCF-1101743, http://www.nsf.gov/), and Department of Defense (grant
   W911NF-12-1-0037, http://www.darpa.mil/). The funders had no role in
   study design, data collection and analysis, decision to publish, or
   preparation of the manuscript.
CR Aiello LM, 2012, ACM T WEB, V6, DOI 10.1145/2180861.2180866
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Berners-Lee T, 2001, SCI AM, V284, P34, DOI 10.1038/scientificamerican0501-34
   Bishop, 2006, PATTERN RECOGNITION
   BREIMAN L, 2001, MACH LEARN, V0045
   Capitan JA, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0043694
   Castillo Carlos, 2011, P 20 INT C WORLD WID, P675, DOI 10.1145/1963405.1963500
   Cohen S, 2011, COMMUN ACM, V54, P66, DOI 10.1145/2001269.2001288
   Conover MD, 2011, NETWORKS, V133, P89
   Cranor LF, 1998, COMMUN ACM, V41, P74, DOI 10.1145/280324.280336
   DeDeo S, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0075818
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Etzioni O, 2008, COMMUN ACM, V51, P68, DOI 10.1145/1409360.1409378
   Fawcett T, 2006, PATTERN RECOGN LETT, V27, P861, DOI 10.1016/j.patrec.2005.10.010
   Finn S, 2014, P COMP JOURN C
   Flanagin AJ, 2000, JOURNALISM MASS COMM, V77, P515, DOI 10.1177/107769900007700304
   Friggeri A., 2014, 8 INT AAAI C WEBL SO
   Giles J, 2005, NATURE, V438, P900, DOI 10.1038/438900a
   Glance N., 2005, P 3 INT WORKSHOP LIN, P36, DOI DOI 10.1145/1134271.1134277
   Gupta A, 2014, P 6 INT C SOC INF SC
   Jagatic TN, 2007, COMMUN ACM, V50, P94, DOI 10.1145/1290958.1290968
   KAMADA T, 1989, INFORM PROCESS LETT, V31, P7, DOI 10.1016/0020-0190(89)90102-6
   Kata A, 2010, VACCINE, V28, P1709, DOI 10.1016/j.vaccine.2009.12.022
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Lewandowsky S, 2012, PSYCHOL SCI PUBL INT, V13, P106, DOI 10.1177/1529100612451018
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Luper S, 2012, STANFORD ENCY PHILOS
   Markines B, 2009, 20TH ACM CONFERENCE ON HYPERTEXT AND HYPERMEDIA (HYPERTEXT 2009), P347
   Mendoza M., 2010, P 1 WORKSH SOC MED A, V10, P71, DOI DOI 10.1145/1964858.1964869
   Nickel M., 2015, ABS150300759 CORR
   Niu F, 2012, INT J SEMANT WEB INF, V8, P42, DOI 10.4018/jswis.2012070103
   Nyhan B, 2013, MED CARE, V51, P127, DOI 10.1097/MLR.0b013e318279486b
   Orr D, 2013, 50 000 LESSONS ON HO
   Masucci AP, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0017333
   Poole KT, 2007, IDEOLOGY C POLITICAL
   Priedhorsky R, 2007, GROUP'07: PROCEEDINGS OF THE 2007 INTERNATIONAL ACM CONFERENCE ON SUPPORTING GROUP WORK, P259
   Ratkiewicz J, 2011, P 20 INT C COMP WORL, V11, P297
   Resnick P, 2014, P COMP JOURN C
   Rieh SY, 2007, ANNU REV INFORM SCI, V41, P307, DOI 10.1002/aris.2007.1440410114
   Simas T, 2014, NETWORK SCI IN PRESS
   Steyvers M, 2005, COGNITIVE SCI, V29, P41, DOI 10.1207/s15516709cog2901_3
   Wilner T., 2014, COLUMBIA JOURNALISM
   Wu Y, 2014, P VLDB ENDOWMENT, V7
NR 43
TC 109
Z9 112
U1 3
U2 35
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1932-6203
J9 PLOS ONE
JI PLoS One
PD JUN 17
PY 2015
VL 10
IS 6
AR e0128193
DI 10.1371/journal.pone.0128193
PG 13
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA CK9NS
UT WOS:000356567400033
PM 26083336
OA gold, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Styliadis, T
   Angelopoulos, J
   Leonardou, P
   Pallis, P
AF Styliadis, Theodore
   Angelopoulos, Jason
   Leonardou, Panagiota
   Pallis, Petros
TI Promoting Sustainability through Assessment and Measurement of Port
   Externalities: A Systematic Literature Review and Future Research Paths
SO SUSTAINABILITY
LA English
DT Review
DE port externalities; sustainability; SLR; port indicators and metrics;
   meta-analysis
ID ENVIRONMENTAL PERFORMANCE INDICATORS; MAJOR PORTS; ECONOMIC-IMPACT;
   EMPLOYMENT EVIDENCE; AIR-POLLUTION; GREEN PORT; SEAPORTS; MANAGEMENT;
   IDENTIFICATION; EMISSIONS
AB In an attempt to mitigate and balance economic, environmental, and societal externalities that ports exert, port sustainability is becoming increasingly important as a key priority. This work embarks on an effort to explore the boundaries, provide a holistic view, and establish a knowledge map of contemporary research in the field of port externalities and impacts, by conducting a systematic literature review (SLR) by carrying out a three-stage procedure (planning, execution, and reporting), enhancing objectivity, while limiting errors and bias. The literature under study pertains to research domains focusing on: (a) identifying port externalities and impacts, (b) utilizing metrics and indicators to measure and quantify the positive or negative impact of port externalities, and (c) promoting an inclusive framework for sustainable port development. Our results include, among others, the classification of methodologies and particularly, of indicators established per type of externality to measure and monitor sustainable port performance, as well as identification of trends and gaps within the contemporary literature. We find considerable room for exploring new concepts and research paths within the domain of port impacts and externalities, both within individual dimensions of sustainability (economic, social, environmental), and on a multidisciplinary level.
C1 [Styliadis, Theodore; Angelopoulos, Jason; Pallis, Petros] Univ Piraeus, Dept Maritime Studies, Piraeus 18534, Greece.
   [Leonardou, Panagiota] OASA Transport Athens, Athens 10682, Greece.
RP Styliadis, T (通讯作者)，Univ Piraeus, Dept Maritime Studies, Piraeus 18534, Greece.
EM theostil@unipi.gr; jason.angelopoulos@gmail.com; pleonardou@oasa.gr;
   ppallis@unipi.gr
FU Hellenic Foundation of Research and Innovation [173]
FX This research was funded by the "Hellenic Foundation of Research and
   Innovation", Grant No. 173, 2nd Postdoc Call.
CR Acciaro M, 2014, ENERG POLICY, V71, P4, DOI 10.1016/j.enpol.2014.04.013
   Acciaro M, 2008, INT J TRANSP ECON, V35, P295
   Angelopoulos J., 2021, MARIT TRANSP RES, V2, P100021, DOI [10.1016/j.martra.2021.100021, DOI 10.1016/J.MARTRA.2021.100021]
   Angelopoulos J, 2017, MARIT ECON LOGIST, V19, P126, DOI 10.1057/s41278-016-0050-8
   Anne O, 2015, ENVIRON SCI POLLUT R, V22, P1072, DOI 10.1007/s11356-014-3410-x
   Antao P, 2016, SAFETY SCI, V85, P266, DOI 10.1016/j.ssci.2015.12.031
   Artal-Tur A, 2016, MARIT ECON LOGIST, V18, P371, DOI 10.1057/mel.2015.24
   Asgari N, 2015, TRANSPORT RES E-LOG, V78, P19, DOI 10.1016/j.tre.2015.01.014
   Axel B.N., 2011, ENV IMPACTS INT SHIP, VVolume 2011, DOI [10.1787/9789264097339-en, DOI 10.1787/9789264097339-EN]
   Ballini F, 2015, RES TRANSP BUS MANAG, V17, P92, DOI 10.1016/j.rtbm.2015.10.007
   Berechman J, 2012, TRANSPORT RES D-TR E, V17, P35, DOI 10.1016/j.trd.2011.09.009
   Beskovnik B, 2015, PROBL EKOROZW, V10, P99
   Bottasso A, 2013, TRANSPORT POLICY, V27, P32, DOI 10.1016/j.tranpol.2012.12.001
   Bowen RE, 2003, OCEAN COAST MANAGE, V46, P299, DOI 10.1016/S0964-5691(03)00008-5
   Braun B. M., 2002, Tourism Economics, V8, P281
   Chang YT, 2014, TRANSPORT POLICY, V35, P333, DOI 10.1016/j.tranpol.2014.04.006
   Chatzinikolaou SD, 2015, TRANSPORT RES D-TR E, V40, P155, DOI 10.1016/j.trd.2015.08.010
   Chen C, 2018, MARIT POLICY MANAG, V45, P944, DOI 10.1080/03088839.2018.1450528
   Chen Z, 2017, MARIT POLICY MANAG, V44, P537, DOI 10.1080/03088839.2017.1327726
   Cheon S, 2017, SUSTAIN DEV, V25, P50, DOI 10.1002/sd.1641
   Cheon S, 2017, MARIT POLICY MANAG, V44, P227, DOI 10.1080/03088839.2016.1275860
   Chiu RH, 2014, MATH PROBL ENG, V2014, DOI 10.1155/2014/802976
   Chlomoudis C., 2012, J SHIPP OCEAN ENG, V2, P45, DOI [10.1007/s12544-010-0043-0, DOI 10.1007/S12544-010-0043-0]
   Chlomoudis C, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14031634
   Danielis R, 2013, MARIT ECON LOGIST, V15, P222, DOI 10.1057/mel.2013.1
   Darbra RM, 2009, J ENVIRON MANAGE, V90, P1396, DOI 10.1016/j.jenvman.2008.08.010
   Darbra RM, 2005, MAR POLLUT BULL, V50, P866, DOI 10.1016/j.marpolbul.2005.04.037
   Davarzani H, 2016, TRANSPORT RES D-TR E, V48, P473, DOI 10.1016/j.trd.2015.07.007
   Rodriguez GD, 2017, WMU J MARIT AFF, V16, P405, DOI 10.1007/s13437-016-0122-8
   del Saz-Salazar S, 2016, MARIT POLICY MANAG, V43, P59, DOI 10.1080/03088839.2015.1032379
   Delphine, 2019, OCEAN COAST MANAGE, V171, P119, DOI 10.1016/j.ocecoaman.2019.01.021
   Di Vaio A, 2018, ENERG POLICY, V122, P229, DOI 10.1016/j.enpol.2018.07.046
   Donnelly A, 2007, ENVIRON IMPACT ASSES, V27, P161, DOI 10.1016/j.eiar.2006.10.006
   Dooms M, 2015, MARIT POLICY MANAG, V42, P459, DOI 10.1080/03088839.2014.944238
   Elkington J., 2004, ENTER TRIPLE BOTTOM, P1, DOI DOI 10.4324/9781849773348
   Ferrari C, 2010, INT J TRANSP ECON, V37, P9
   Fink A., 2005, CONDUCTING RES LIT R, V2nd ed
   Frank H, 2014, HOW TO GET PUBLISHED IN THE BEST ENTREPRENEURSHIP JOURNALS: A GUIDE TO STEER YOUR ACADEMIC CAREER, P94
   Gobbi GP, 2020, SCI TOTAL ENVIRON, V719, DOI 10.1016/j.scitotenv.2019.134984
   Laxe FG, 2017, MAR POLLUT BULL, V119, P220, DOI 10.1016/j.marpolbul.2017.03.064
   Laxe FG, 2016, MAR POLLUT BULL, V113, P232, DOI 10.1016/j.marpolbul.2016.09.022
   Gu Q, 2009, SERV ORIENTED COMPUT, V3, P171, DOI 10.1007/s11761-009-0046-7
   Hakam M.H., 2015, J SERV MANAG, V8, P14, DOI [10.4236/jssm.2015.81002, DOI 10.4236/JSSM.2015.81002]
   Han FY, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11164316
   Jiang B, 2020, SOFT COMPUT, V24, P2503, DOI 10.1007/s00500-018-3559-1
   Kang D, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9122333
   Kim S, 2017, J KOREA TRADE, V21, P125, DOI 10.1108/JKT-03-2017-0025
   Kraus S, 2020, INT ENTREP MANAG J, V16, P1023, DOI 10.1007/s11365-020-00635-4
   Lam J.S.L., 2012, INT FORUM SHIPPING P
   Lam JSL, 2014, TRANSPORT REV, V34, P169, DOI 10.1080/01441647.2014.891162
   Lee T, 2014, TRANSPORT POLICY, V33, P82, DOI 10.1016/j.tranpol.2014.02.009
   Lim S, 2019, TRANSPORT RES D-TR E, V72, P47, DOI 10.1016/j.trd.2019.04.009
   Lirn TC, 2013, INT J PHYS DISTR LOG, V43, P427, DOI 10.1108/IJPDLM-04-2012-0134
   Lu CS, 2016, MARIT BUS REV, V1, P90, DOI 10.1108/MABR-05-2016-0009
   Lu CS, 2016, MARIT POLICY MANAG, V43, P909, DOI 10.1080/03088839.2016.1199918
   Maragkogianni A, 2015, TRANSPORT RES D-TR E, V36, P10, DOI 10.1016/j.trd.2015.02.014
   Marner Torsten, 2020, International Journal of Computer Aided Engineering and Technology, V12, P480
   Mileski J, 2016, OCEAN YEARB, V30, P541, DOI 10.1163/9789004321595_020
   Morrissey K, 2019, MAR POLICY, V99, P324, DOI 10.1016/j.marpol.2018.10.047
   Muangpan T, 2019, COGENT BUS MANAG, V6, DOI 10.1080/23311975.2019.1603275
   Munim Z.H., 2018, J SHIPPING TRADE, V3, P1, DOI [10.1186/s41072-018-0027-0, DOI 10.1186/S41072-018-0027-0]
   Munim ZH, 2020, MARIT POLICY MANAG, V47, P577, DOI 10.1080/03088839.2020.1788731
   Musso E., 2000, MARIT ECON LOGIST, V2, P283, DOI [10.1057/ijme.2000.23, DOI 10.1057/IJME.2000.23]
   Nunes RAO, 2019, J ENVIRON MANAGE, V235, P62, DOI 10.1016/j.jenvman.2019.01.039
   Oh H, 2018, OCEAN COAST MANAGE, V161, P50, DOI 10.1016/j.ocecoaman.2018.04.028
   Papaefthimiou S, 2017, MARIT POLICY MANAG, V44, P81, DOI 10.1080/03088839.2016.1224943
   Park JY, 2012, ASIAN J SHIPPING LOG, V28, P67
   Park JS, 2019, RES TRANSP ECON, V78, DOI 10.1016/j.retrec.2019.100765
   Park JS, 2016, TRANSPORT RES E-LOG, V85, P107, DOI 10.1016/j.tre.2015.11.009
   Parola F, 2017, TRANSPORT REV, V37, P116, DOI 10.1080/01441647.2016.1231232
   Peris-Mora E, 2005, MAR POLLUT BULL, V50, P1649, DOI 10.1016/j.marpolbul.2005.06.048
   Petticrew M, 2006, SYSTEMATIC REVIEWS IN THE SOCIAL SCIENCES: A PRACTICAL GUIDE, P1, DOI 10.1002/9780470754887
   PIANC, 2014, SUST PORTS GUID PORT
   Puig M, 2017, OCEAN COAST MANAGE, V140, P34, DOI 10.1016/j.ocecoaman.2017.02.017
   Puig M, 2015, OCEAN COAST MANAGE, V113, P8, DOI 10.1016/j.ocecoaman.2015.05.007
   Puig M, 2014, MAR POLLUT BULL, V81, P124, DOI 10.1016/j.marpolbul.2014.02.006
   Quynh LX, 2011, PHYS CHEM EARTH, V36, P141, DOI 10.1016/j.pce.2010.05.004
   Rodrigues V, 2021, OCEAN COAST MANAGE, V202, DOI 10.1016/j.ocecoaman.2020.105514
   Roh S, 2016, ASIAN J SHIPPING LOG, V32, P107, DOI 10.1016/j.ajsl.2016.05.004
   Roos EC, 2017, MAR POLLUT BULL, V115, P211, DOI 10.1016/j.marpolbul.2016.12.015
   Saengsupavanich C, 2009, J CLEAN PROD, V17, P154, DOI 10.1016/j.jclepro.2008.04.001
   Samanes T., 2019, WORLD SCI NEWS, V132, P16
   Santos AMP, 2018, MARIT ECON LOGIST, V20, P169, DOI 10.1057/s41278-017-0081-9
   Santos AMP, 2018, MARIT POLICY MANAG, V45, P684, DOI 10.1080/03088839.2018.1471536
   Schipper CA, 2017, TRANSPORT RES D-TR E, V57, P84, DOI 10.1016/j.trd.2017.08.017
   Seo YJ, 2018, REG STUD, V52, P80, DOI 10.1080/00343404.2016.1262014
   Shan J, 2014, TRANSPORT RES E-LOG, V69, P41, DOI 10.1016/j.tre.2014.05.010
   Shiau TA, 2015, MARIT POLICY MANAG, V42, P26, DOI 10.1080/03088839.2013.863436
   Sislian L, 2016, RES TRANSP BUS MANAG, V19, P19, DOI 10.1016/j.rtbm.2016.03.005
   Spengler T, 2021, J ENVIRON MANAGE, V279, DOI 10.1016/j.jenvman.2020.111807
   Stankovic JJ, 2021, J MAR SCI ENG, V9, DOI 10.3390/jmse9010074
   Talley W.K., 2009, PORT EC, P232, DOI [10.4324/9780203880067, DOI 10.4324/9780203880067]
   Tawfik GM, 2019, TROP MED HEALTH, V47, DOI 10.1186/s41182-019-0165-6
   Teerawattana R, 2019, ASIAN J SHIPPING LOG, V35, P63, DOI 10.1016/j.ajsl.2019.03.009
   Tichavska M, 2015, TRANSPORT RES E-LOG, V83, P126, DOI 10.1016/j.tre.2015.09.002
   Tranfield D, 2003, BRIT J MANAGE, V14, P207, DOI 10.1111/1467-8551.00375
   Van Eck N.J., 2013, VOSVIEWER MANUAL, V1, P1
   Wan CP, 2018, TRANSPORT RES D-TR E, V61, P431, DOI 10.1016/j.trd.2017.06.021
   Wang CX, 2021, INT J SHIP TRANS LOG, V13, P205, DOI 10.1504/IJSTL.2021.112924
   Wang YX, 2019, TRANSPORT POLICY, V78, P1, DOI 10.1016/j.tranpol.2019.03.007
   Wiegmans BW, 2011, J TRANSP GEOGR, V19, P575, DOI 10.1016/j.jtrangeo.2010.06.007
   Yap WY, 2013, OCEAN COAST MANAGE, V71, P13, DOI 10.1016/j.ocecoaman.2012.10.011
   Yogeswari K., 2019, J GREEN ENG, V9, P224
NR 103
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUL
PY 2022
VL 14
IS 14
AR 8403
DI 10.3390/su14148403
PG 20
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA 3G9WT
UT WOS:000831697100001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, WH
   Lu, C
AF Wang, Weihong
   Lu, Chang
TI Visualization analysis of big data research based on Citespace
SO SOFT COMPUTING
LA English
DT Article
DE Big data; Citespace; Visualization analysis; Knowledge maps
ID BIBLIOMETRIC ANALYSIS
AB In recent years, with the massive growth of data, the world today has entered the era of big data. Big data has brought tremendous value to all fields of today's society, and it has also brought enormous challenges, which has attracted great attention from all walks of life. Analyze and forecast the research hotspots and future development trends in the field of big data, and understand the development changes and priorities in the field of big data research, which will play a significant role in promoting the development of social development and scientific research. In the era of big data, how to extract information from huge amounts of complex data and present complex information more clearly and clearly, the most effective way is to use visualization technology. The article uses the information visualization software Citespace to study the data related to big data in the Web of Science and CNKI database from 2008 to 2017 for 10 years, from macro to micro to the representative countries of the literature, keywords and co-cited documents. Through visualization analysis, the article clarifies the key research directions, key documents and hot spot frontiers in the field of big data research, forecasts the future development trends in this field, and compares the research situation at home and abroad, in order to provide readers and other researchers with certain reference and help.
C1 [Wang, Weihong; Lu, Chang] Zhejiang Univ Technol, Sch Comp Sci & Technol, Hangzhou 310023, Peoples R China.
RP Lu, C (通讯作者)，Zhejiang Univ Technol, Sch Comp Sci & Technol, Hangzhou 310023, Peoples R China.
EM 827070981@qq.com
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bian Y, 2011, VACCINE, V29, P1041, DOI 10.1016/j.vaccine.2010.11.062
   Boyd D., 2012, INFORM COMMUNICATION, V15, P1
   Brandes U, 2001, J MATH SOCIOL, V25, P163, DOI 10.1080/0022250X.2001.9990249
   Chen CLP, 2014, INFORM SCIENCES, V275, P314, DOI 10.1016/j.ins.2014.01.015
   Chen HC, 2012, MIS QUART, V36, P1165
   Chen K, 2010, J SOFTW, V20, P1337
   Chen M, 2014, MOBILE NETW APPL, V19, P171, DOI 10.1007/s11036-013-0489-0
   Chen X, 2017, DISCOVERING RECENT R, P507
   Chen XL, 2017, LECT NOTES COMPUT SC, V10594, P89, DOI 10.1007/978-3-319-69182-4_10
   [陈悦 Chen Yue], 2008, [科学学研究, Studies in Science of Science], V26, P449
   Cheng Xueqi, 2014, J SOFTWARE, V09, P1889
   Dai S, 2014, ENG ENG INTERDISCIP, V6, P275
   Danasingh AA, 2016, INT J RECENT INNOV T, V4, P273
   Dean J, 2004, USENIX ASSOCIATION PROCEEDINGS OF THE SIXTH SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION (OSDE '04), P137
   ESR, 2011, INSIGHTS IMAGING, V2, P93, DOI 10.1007/s13244-011-0066-7
   Fu HZ, 2010, WASTE MANAGE, V30, P2410, DOI 10.1016/j.wasman.2010.06.008
   Gantz J., 2011, IDC IVIEW, P1
   Guan S., 2015, J INTELL-BASEL, V34, P98
   Guojie L, 2012, B CHIN ACAD SCI, V27, P647
   Hao TY, 2018, SOFT COMPUT, V22, P7875, DOI 10.1007/s00500-018-3511-4
   Hou J, 2019, J MOD INF, V33, P99
   Lazer D, 2014, SCIENCE, V343, P1203, DOI 10.1126/science.1248506
   Li W, 2015, ENVIRON IMPACT ASSES, V50, P158, DOI 10.1016/j.eiar.2014.09.012
   Li ZH, 2017, IEEE T KNOWL DATA EN, V29, P2100, DOI 10.1109/TKDE.2017.2728531
   Liu H., 2016, INT J DATA SCI ANAL, V1, P137, DOI [10.1007/s41060-016-0023-0, DOI 10.1007/S41060-016-0023-0]
   [刘峤 Liu Qiao], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P582
   Luo MN, 2017, COMPUT VIS IMAGE UND, V163, P67, DOI 10.1016/j.cviu.2017.07.001
   Manyika J., 2011, BIG DATA NEXT FRONTI
   Marcos-Pablos S., 2018, SOFT COMPUT, P1
   McAfee A, 2012, HARVARD BUS REV, V90, P60
   Meng Xiaofeng, 2013, J COMPUTER RES DEV, V01, P146
   PRICE DJD, 1965, SCIENCE, V149, P510
   Qin C.J., 2009, J ACAD LIB, V27, P30
   Shi Y, 2014, CHIN J COMPUT, V36, P209
   Shneider AM, 2009, TRENDS BIOCHEM SCI, V34, P217, DOI 10.1016/j.tibs.2009.02.002
   Tan HL, 2018, PATTERN RECOGN, V76, P434, DOI 10.1016/j.patcog.2017.11.020
   Tan HL, 2017, IEEE ACCESS, V5, P15001, DOI 10.1109/ACCESS.2017.2733718
   Taylor RC, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-S12-S1
   Walker S, 2014, INT J ADVERT, V33, P181, DOI 10.2501/IJA-33-1-181-183
   Wang Shan, 2011, Chinese Journal of Computers, V34, P1741, DOI 10.3724/SP.J.1016.2011.01741
   [王元卓 Wang Yuanzhuo], 2013, [计算机学报, Chinese Journal of Computers], V36, P1125
   Wu XD, 2014, IEEE T KNOWL DATA EN, V26, P97, DOI 10.1109/TKDE.2013.109
   Wu Y, 2016, INT J MOL MED, V37, P74, DOI 10.3892/ijmm.2015.2394
   Zhang SH, 2017, IEEE ACCESS, V5, P16904, DOI 10.1109/ACCESS.2017.2741221
   Zhang Y., 2013, J COMPUT RES DEV, V50, P216
   Zheng L, 2013, INF CONSTR, V1, P10
NR 47
TC 27
Z9 28
U1 54
U2 224
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1432-7643
EI 1433-7479
J9 SOFT COMPUT
JI Soft Comput.
PD JUN
PY 2020
VL 24
IS 11
SI SI
BP 8173
EP 8186
DI 10.1007/s00500-019-04384-7
PG 14
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA LK0KT
UT WOS:000530547900029
DA 2022-08-23
ER

PT J
AU Chen, MK
   Wei, X
   Chen, JX
   Wang, L
   Zhou, L
AF Chen, Mingkai
   Wei, Xin
   Chen, Jianxin
   Wang, Lei
   Zhou, Liang
TI Integration and Provision for City Public Service in Smart City Cloud
   Union: Architecture and Analysis
SO IEEE WIRELESS COMMUNICATIONS
LA English
DT Article
DE Cloud computing; Smart cities; Computer architecture; Servers; Security;
   Genetic algorithms
ID CONTENT DELIVERY; NETWORKS
AB City public service is recognized as a foundation to satisfy users' daily life in smart city. However, in the existing framework of smart city, different service providers are separated from each other and are incapable of providing communications with high reliability and low latency. On the other hand, data onto a smart city needs to be integrated, providing superb services for all of these users. As such, this article proposes the concept of "city cloud union" to embrace the whole city public service and raise the users' satisfaction. In the city cloud union, city public service can be divided into two main categories, individual service and subscription service. Then, a city public service relevance and sharing architecture of the city cloud union are constructed. In this architecture, knowledge mapping for information integration and cooperative sharing scheme are designed for performing individual service and accelerating the subscription service, respectively. Accordingly, for the quality of service in the city cloud union, an optimal distribution of genetic algorithm (ODGA) is proposed to complete the cache placement in the cooperative sharing scheme and obtain the ultra low latency. Simulation results illustrate that the proposed algorithm reveals the relationship between average latency and some key parameters in the smart city and it can reduce the latency effectively.
C1 [Chen, Mingkai; Wei, Xin; Chen, Jianxin; Wang, Lei; Zhou, Liang] Nanjing Univ Posts & Telecommun, Natl Engn Res Ctr Commun & Network Technol, Nanjing, Peoples R China.
RP Wei, X (通讯作者)，Nanjing Univ Posts & Telecommun, Natl Engn Res Ctr Commun & Network Technol, Nanjing, Peoples R China.
FU National Natural Science Foundation of China [61571240, 61671253];
   Natural Science Foundation of the Jiangsu Higher Educations Institutions
   [16KJA510004]
FX This work is partly supported by the National Natural Science Foundation
   of China (61571240, 61671253), and the major projects of the Natural
   Science Foundation of the Jiangsu Higher Educations Institutions
   (16KJA510004).
CR Chen M, 2018, IEEE WIREL COMMUN, V25, P70, DOI 10.1109/MWC.2018.1700216
   Chen MK, 2019, IEEE NETWORK, V33, P89, DOI 10.1109/MNET.2019.1800253
   Chen MK, 2018, IEEE ACCESS, V6, P44803, DOI 10.1109/ACCESS.2018.2856539
   Chen SZ, 2017, IEEE T VEH TECHNOL, V66, P3185, DOI 10.1109/TVT.2016.2596438
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Santos J, 2018, IEEE COMMUN MAG, V56, P177, DOI 10.1109/MCOM.2018.1701322
   Moreno MV, 2017, IEEE T IND INFORM, V13, P800, DOI 10.1109/TII.2016.2605581
   Wang JJ, 2018, IEEE WIREL COMMUN, V25, P122, DOI 10.1109/MWC.2017.1600275
   Wang XF, 2018, IEEE WIREL COMMUN, V25, P32, DOI 10.1109/MWC.2018.1700215
   Wei LL, 2014, IEEE COMMUN MAG, V52, P90, DOI 10.1109/MCOM.2014.6829950
   Zhang HJ, 2017, IEEE WIREL COMMUN, V24, P120, DOI 10.1109/MWC.2017.1700007
   Zhou L, 2019, IEEE J SEL AREA COMM, V37, P1656, DOI 10.1109/JSAC.2019.2916452
   Zhou L, 2018, IEEE INTERNET THINGS, V5, P1657, DOI 10.1109/JIOT.2017.2785624
   Zhou L, 2018, IEEE T IND INFORM, V14, P1626, DOI 10.1109/TII.2017.2784100
   Zhou L, 2017, IEEE COMMUN MAG, V55, P91, DOI 10.1109/MCOM.2017.1700481
NR 15
TC 12
Z9 12
U1 3
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1536-1284
EI 1558-0687
J9 IEEE WIREL COMMUN
JI IEEE Wirel. Commun.
PD APR
PY 2020
VL 27
IS 2
BP 148
EP 154
DI 10.1109/MWC.001.1900264
PG 7
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LM4ZI
UT WOS:000532257900020
DA 2022-08-23
ER

PT J
AU Geng, YS
   Xiao, X
   Sun, XB
   Zhu, Y
AF Geng, Yishuai
   Xiao, Xiao
   Sun, Xiaobing
   Zhu, Yi
TI Representation Learning: Recommendation With Knowledge Graph via
   Triple-Autoencoder
SO FRONTIERS IN GENETICS
LA English
DT Article
DE personalized recommendation; autoencoder; semi-autoencoder;
   representation learning; collaborative filtering
ID ALGORITHMS; MODEL
AB The last decades have witnessed a vast amount of interest and research in feature representation learning from multiple disciplines, such as biology and bioinformatics. Among all the real-world application scenarios, feature extraction from knowledge graph (KG) for personalized recommendation has achieved substantial performance for addressing the problem of information overload. However, the rating matrix of recommendations is usually sparse, which may result in significant performance degradation. The crucial problem is how to extract and extend features from additional side information. To address these issues, we propose a novel feature representation learning method for the recommendation in this paper that extends item features with knowledge graph via triple-autoencoder. More specifically, the comment information between users and items is first encoded as sentiment classification. These features are then applied as the input to the autoencoder for generating the auxiliary information of items. Second, the item-based rating, the side information, and the generated comment representations are incorporated into the semi-autoencoder for reconstructed output. The low-dimensional representations of this extended information are learned with the semi-autoencoder. Finally, the reconstructed output generated by the semi-autoencoder is input into a third autoencoder. A serial connection between the semi-autoencoder and the autoencoder is designed here to learn more abstract and higher-level feature representations for personalized recommendation. Extensive experiments conducted on several real-world datasets validate the effectiveness of the proposed method compared to several state-of-the-art models.
C1 [Geng, Yishuai; Sun, Xiaobing; Zhu, Yi] Yangzhou Univ, Sch Informat Engn, Yangzhou, Peoples R China.
   [Xiao, Xiao] Yangzhou Univ, Dept Ultrasound, Affiliated Hosp, Yangzhou, Peoples R China.
RP Sun, XB (通讯作者)，Yangzhou Univ, Sch Informat Engn, Yangzhou, Peoples R China.; Xiao, X (通讯作者)，Yangzhou Univ, Dept Ultrasound, Affiliated Hosp, Yangzhou, Peoples R China.
EM 092298@yzu.edu.cn; xbsun@yzu.edu.cn
FU National Natural Science Foundation of China [61906060, 62076217];
   Yangzhou University Interdisciplinary Research Foundation for Animal
   Husbandry Discipline of Targeted Support [yzuxk202015]; Opening
   Foundation of the Key Laboratory of Huizhou Architecture in Anhui
   Province [HPJZ-2020-02]; Open Project Program of the Joint International
   Research Laboratory of Agriculture and Agri-Product Safety
   [JILAR-KF202104]
FX This research was partially supported by the National Natural Science
   Foundation of China (61906060 and 62076217), Yangzhou University
   Interdisciplinary Research Foundation for Animal Husbandry Discipline of
   Targeted Support (yzuxk202015), the Opening Foundation of the Key
   Laboratory of Huizhou Architecture in Anhui Province under grant
   HPJZ-2020-02, and the Open Project Program of the Joint International
   Research Laboratory of Agriculture and Agri-Product Safety
   (JILAR-KF202104).
CR Alshahrani M, 2017, BIOINFORMATICS, V33, P2723, DOI 10.1093/bioinformatics/btx275
   Batmaz Z, 2019, ARTIF INTELL REV, V52, P1, DOI 10.1007/s10462-018-9654-y
   Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
   Bengio Y, 2009, FOUND TRENDS MACH LE, V2, P1, DOI 10.1561/2200000006
   Botangen KA, 2020, EXPERT SYST APPL, V151, DOI 10.1016/j.eswa.2020.113347
   Chae DK, 2019, KNOWL-BASED SYST, V176, P110, DOI 10.1016/j.knosys.2019.03.026
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dong BB, 2021, NEUROCOMPUTING, V442, P307, DOI 10.1016/j.neucom.2021.01.129
   Geng X, 2015, IEEE I CONF COMP VIS, P4274, DOI 10.1109/ICCV.2015.486
   Georgiev K., 2013, P 30 INT C MACH LEAR, P1148
   Gupta U, 2020, INT S HIGH PERF COMP, P488, DOI 10.1109/HPCA47549.2020.00047
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   HOHENBERG P, 1964, PHYS REV B, V136, pB864, DOI 10.1103/PhysRev.136.B864
   Kim DH, 2019, IEEE T AFFECT COMPUT, V10, P223, DOI 10.1109/TAFFC.2017.2695999
   Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Lee DD, 2001, ADV NEUR IN, V13, P556
   Lee H, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1073, DOI 10.1145/3292500.3330859
   Lee J, 2012, J MACH LEARN RES, V13, P2699
   Li PY, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbaa266
   Li ZH, 2017, ADV MECH ENG, V9, DOI 10.1177/1687814017693541
   Liu H, 2019, AAAI CONF ARTIF INTE, P1036
   [刘知远 Liu Zhiyuan], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P247
   Locatello F., 2019, INT C ADV MACH LEARN, P4114
   Lops P, 2019, USER MODEL USER-ADAP, V29, P239, DOI 10.1007/s11257-019-09231-w
   Lu YF, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1563, DOI 10.1145/3394486.3403207
   Ma YF, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2291, DOI 10.1145/3394486.3403278
   Ni J, 2022, INFORM SCIENCES, V582, P22, DOI 10.1016/j.ins.2021.09.006
   Ni J, 2021, INFORM SCIENCES, V542, P324, DOI 10.1016/j.ins.2020.07.038
   Niu GL, 2020, AAAI CONF ARTIF INTE, V34, P2950
   Qian XM, 2014, IEEE T KNOWL DATA EN, V26, P1763, DOI 10.1109/TKDE.2013.168
   Qiang JP, 2022, IEEE T KNOWL DATA EN, V34, P1427, DOI 10.1109/TKDE.2020.2992485
   Ramakrishnan R, 2015, J CHEM THEORY COMPUT, V11, P2087, DOI 10.1021/acs.jctc.5b00099
   Rashed A, 2019, RECSYS 2019: 13TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, P314, DOI 10.1145/3298689.3346999
   Salakhutdinov R, 2007, ADV NEURAL INFORM PR, P1257
   Salakhutdinov R., 2007, P 24 INT C MACHINE L, P791
   Salakhutdinov R., 2008, P INT C MACH LEARN, P880, DOI [10.1145/1390156., DOI 10.1145/1390156.1390267]
   Schuster M, 2012, INT CONF ACOUST SPEE, P5149, DOI 10.1109/ICASSP.2012.6289079
   Sedhain S, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P111, DOI 10.1145/2740908.2742726
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   Su Xiaoyuan, 2009, ADV ARTIFICIAL INTEL, DOI DOI 10.1155/2009/421425
   Wang H, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1235, DOI 10.1145/2783258.2783273
   Wang JD, 2021, IEEE T PATTERN ANAL, V43, P3349, DOI 10.1109/TPAMI.2020.2983686
   Wei LY, 2019, BIOINFORMATICS, V35, P4272, DOI 10.1093/bioinformatics/btz246
   Xu KYL, 2018, PR MACH LEARN RES, V80
   Yang Y, 2022, APPL INTELL, V52, P6196, DOI 10.1007/s10489-021-02647-1
   Yu ZP, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4213
   Zhang S, 2017, LECT NOTES COMPUT SC, V10634, P185, DOI 10.1007/978-3-319-70087-8_20
   Zhang YF, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1449, DOI 10.1145/3132847.3132892
   Zhong ST, 2022, IEEE T CYBERNETICS, V52, P5229, DOI 10.1109/TCYB.2020.3029002
   Zhu Y, 2021, EXPERT SYST APPL, V186, DOI 10.1016/j.eswa.2021.115825
   Zhu Y, 2018, KNOWL-BASED SYST, V152, P100, DOI 10.1016/j.knosys.2018.04.010
   Zhuang FZ, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P567, DOI 10.1145/3018661.3018720
   Zhuang FZ, 2018, ACM T INTEL SYST TEC, V9, DOI 10.1145/3108257
   Zhuang FZ, 2017, NEURAL NETWORKS, V90, P83, DOI 10.1016/j.neunet.2017.03.009
NR 55
TC 0
Z9 0
U1 4
U2 4
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1664-8021
J9 FRONT GENET
JI Front. Genet.
PD JUN 3
PY 2022
VL 13
AR 891265
DI 10.3389/fgene.2022.891265
PG 13
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA 2F4GL
UT WOS:000812869900001
PM 35719384
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Sosa, DN
   Altman, RB
AF Sosa, Daniel N.
   Altman, Russ B.
TI Contexts and contradictions: a roadmap for computational drug
   repurposing with knowledge inference
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Review
DE drug repurposing; knowledge graphs; natural language processing;
   metascience
ID NETWORKS; BIOLOGY; GENES
AB The cost of drug development continues to rise and may be prohibitive in cases of unmet clinical need, particularly for rare diseases. Artificial intelligence-based methods are promising in their potential to discover new treatment options. The task of drug repurposing hypothesis generation is well-posed as a link prediction problem in a knowledge graph (KG) of interacting of drugs, proteins, genes and disease phenotypes. KGs derived from biomedical literature are semantically rich and up-to-date representations of scientific knowledge. Inference methods on scientific KGs can be confounded by unspecified contexts and contradictions. Extracting context enables incorporation of relevant pharmacokinetic and pharmacodynamic detail, such as tissue specificity of interactions. Contradictions in biomedical KGs may arise when contexts are omitted or due to contradicting research claims. In this review, we describe challenges to creating literature-scale representations of pharmacological knowledge and survey current approaches toward incorporating context and resolving contradictions.
C1 [Sosa, Daniel N.] Stanford Univ, Biomed Informat Program, Stanford, CA 94305 USA.
   [Altman, Russ B.] Stanford Univ, Bioengn Genet Med Biomed Data Sci & By Courtesy C, Stanford, CA 94305 USA.
RP Altman, RB (通讯作者)，Stanford Univ, Dept Bioengn, Stanford, CA 94305 USA.; Altman, RB (通讯作者)，Stanford Univ, Dept Genet, Stanford, CA 94305 USA.; Altman, RB (通讯作者)，Stanford Univ, Dept Med, Stanford, CA 94305 USA.
EM russ.altman@stanford.edu
FU National Institutes of Health [GM102365, LM007033]; Stanford Data
   Science Scholars Program
FX National Institutes of Health (grant numbers GM102365 and LM007033);
   Stanford Data Science Scholars Program to D.S.
CR Abu-Salih B, 2021, J NETW COMPUT APPL, V185, DOI 10.1016/j.jnca.2021.103076
   Achakulvisut T., 2020, CS
   Alamri A., 2016, THESIS U SHEFFIELD
   Alamri A., 2015, P BIONLP, P141
   Alamri A, 2016, J BIOMED SEMANT, V7, DOI 10.1186/s13326-016-0083-z
   Ali M., 2021, IEEE T PATTERN ANAL, V1
   Ali M, 2019, BIOINFORMATICS, V35, P3538, DOI 10.1093/bioinformatics/btz117
   Alshahrani M, 2021, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.341
   Amberger JS, 2015, NUCLEIC ACIDS RES, V43, pD789, DOI 10.1093/nar/gku1205
   Ananiadou Sophia, 2013, Computational Linguistics and Intelligent Text Processing. 14th International Conference (CICLing 2013). Proceedings, P318, DOI 10.1007/978-3-642-37256-8_27
   Ananiadou S, 2015, BRIEF FUNCT GENOMICS, V14, P213, DOI 10.1093/bfgp/elu015
   [Anonymous], 2021, MEDLINE CITATION COU
   Bach S., 2017, J MACH LEARN RES, V18, P1
   Benfey PN, 2008, SCIENCE, V320, P495, DOI 10.1126/science.1153716
   Blomqvist E., 2021, PREPRINT
   Bommasani R., 2021, PREPRINT
   Bonner S., 2021, PREPRINT
   Bowman S, 2015, P 2015 C EMP METH NA, P632, DOI DOI 10.18653/V1/D15-1075
   Cen YK, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1358, DOI 10.1145/3292500.3330964
   Chapman WW, 2001, J BIOMED INFORM, V34, P301, DOI 10.1006/jbin.2001.1029
   Cheerkoot-Jalim S, 2021, HEALTH TECHNOL-GER, V11, P1205, DOI 10.1007/s12553-021-00605-y
   CHEN X, 2019, PROC AAAI C ARTIF IN, V33, P3363
   Clark T, 2014, J BIOMED SEMANT, V5, DOI 10.1186/2041-1480-5-28
   Cong Q, 2018, IEEE INT C BIOINFORM, P1628, DOI 10.1109/BIBM.2018.8621568
   COOPER GF, 1992, MACH LEARN, V9, P309, DOI 10.1007/BF00994110
   Dagenais S., 2000, NEW ENGL J MED, V342, P154
   Dahl GE., 2014, PREPRINT
   De Franchi E, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0012214
   DiMasi JA, 2016, J HEALTH ECON, V47, P20, DOI 10.1016/j.jhealeco.2016.01.012
   Doench JG, 2018, NAT REV GENET, V19, P67, DOI 10.1038/nrg.2017.97
   Domingo-Fernandez D, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-2863-9
   Dong XL, 2014, PROC VLDB ENDOW, V7, P881, DOI 10.14778/2732951.2732962
   Farber M, 2018, SEMANT WEB, V9
   Fanelli D, 2012, SCIENTOMETRICS, V90, P891, DOI 10.1007/s11192-011-0494-7
   Fluck J, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw113
   Fraser N, 2021, PLOS BIOL, V19, DOI 10.1371/journal.pbio.3000959
   Glicksberg BS, 2015, BIOCOMPUT-PAC SYM, P407
   Gysi D. M., 2020, ARXIV
   Hamilton WL, 2018, ADV NEUR IN, V31
   Harabagiu S., 2006, AAAI, V6, P755
   Harrison RK, 2016, NAT REV DRUG DISCOV, V15, P817, DOI 10.1038/nrd.2016.184
   Hur A., 2021, ARXIV211008012 CS
   Ioannidis JPA, 2005, J CLIN EPIDEMIOL, V58, P543, DOI 10.1016/j.jclinepi.2004.10.019
   Jin GX, 2012, CANCER RES, V72, P33, DOI 10.1158/0008-5472.CAN-11-2333
   Kantarjian HM, 2018, CANCER-AM CANCER SOC, V124, P2881, DOI 10.1002/cncr.31522
   Keiser MJ, 2009, NATURE, V462, P175, DOI 10.1038/nature08506
   Kilicoglu H, 2020, BMC BIOINFORMATICS, V21, DOI 10.1186/s12859-020-3517-7
   Kilicoglu H, 2012, BIOINFORMATICS, V28, P3158, DOI 10.1093/bioinformatics/bts591
   Kim JJ, 2006, BIOINFORMATICS, V22, P597, DOI 10.1093/bioinformatics/btk016
   Koch U, 2014, BBA-PROTEINS PROTEOM, V1844, P156, DOI 10.1016/j.bbapap.2013.05.010
   Kramer A, 2021, BMC BIOINFORMATICS, V22, DOI 10.1186/s12859-021-04148-x
   Kuhn M, 2008, NUCLEIC ACIDS RES, V36, pD684, DOI 10.1093/nar/gkm795
   Lamurias A, 2017, FRONT IMMUNOL, V8, DOI 10.3389/fimmu.2017.01656
   Lever J., 2017, BIONLP, P176, DOI [DOI 10.18653/V1/W17-2322, 10.18653/v1/w17-2322]
   Liakata M, 2012, BIOINFORMATICS, V28, P991, DOI 10.1093/bioinformatics/bts071
   Liu Y., PREPRINT
   Lu ZY, 2011, DATABASE-OXFORD, DOI 10.1093/database/baq036
   Luciano DC., 2013, CLAUSIE CLAUSE BASED, P355, DOI DOI 10.1145/2488388.2488420
   Ma XC, 2014, J CARDIOVASC PHARM T, V19, P97, DOI 10.1177/1074248413509026
   Marneffe Marie-Catherine, 2008, ACL, P1039
   Matuszek C., 2006, AAAI SPRING S FORM C, P44
   Mayr A, 2016, FRONT ENV SCI-SWITZ, V3, DOI 10.3389/fenvs.2015.00080
   Menche J, 2015, SCIENCE, V347, DOI 10.1126/science.1257601
   Milsted RAV, 2007, ADV CANCER RES, V96, P371, DOI 10.1016/S0065-230X(06)96012-6
   Minsky M., 306 MITAI LAB
   Nadkarni R., 2022, SCI LANGUAGE MODELS, V2021
   Nelson W, 2019, FRONT GENET, V10, DOI 10.3389/fgene.2019.00381
   Noriega-Atala E, 2020, IEEE ACM T COMPUT BI, V17, P1895, DOI 10.1109/TCBB.2019.2904231
   Nosengo N, 2016, NATURE, V534, P314, DOI 10.1038/534314a
   Paolini GV, 2006, NAT BIOTECHNOL, V24, P805, DOI 10.1038/nbt1228
   Papageorgiou DC., 2017, 28ES JOURN ES FRANCO, V28, P50
   Papaleo L, 2014, LECT NOTES ARTIF INT, V8876, P373, DOI 10.1007/978-3-319-13704-9_29
   Percha B, 2018, BIOINFORMATICS, V34, P2614, DOI 10.1093/bioinformatics/bty114
   Percha B, 2015, PLOS COMPUT BIOL, V11, DOI 10.1371/journal.pcbi.1004216
   Pham MQN., 2013, P INT JOINT C NAT LA, P1017
   Piro RM, 2012, HUM GENET, V131, P1811, DOI 10.1007/s00439-012-1206-y
   Pujara J., 2017, P 2017 C EMP METH NA, P1751, DOI DOI 10.18653/V1/D17-1184
   Quan CQ, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0102039
   Raad J, 2017, K-CAP 2017: PROCEEDINGS OF THE KNOWLEDGE CAPTURE CONFERENCE, DOI 10.1145/3148011.3148032
   Rastegar-Mojarad M, 2015, IEEE INT C BIOINFORM, P669, DOI 10.1109/BIBM.2015.7359766
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Ritter A., 2008, P C EMP METH NAT LAN, P11
   Romanov A., 2018, EMNLP
   Rosemblat G, 2019, J BIOMED INFORM, V98, DOI 10.1016/j.jbi.2019.103275
   Sa?s F., 2019, THESIS U PARIS SUD
   Sanchez-Graillet O, 2007, BIOINFORMATICS, V23, pI424, DOI 10.1093/bioinformatics/btm184
   Sarafraz F., 2012, THESIS U MANCHESTER
   Sergeeva Elena, 2019, AMIA Jt Summits Transl Sci Proc, V2019, P212
   Shardlow M, 2018, BMC MED INFORM DECIS, V18, DOI 10.1186/s12911-018-0639-1
   Sosa DN, 2020, PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, P463
   Stephens NG, 1996, LANCET, V347, P781, DOI 10.1016/S0140-6736(96)90866-1
   Strong DM, 1997, COMMUN ACM, V40, P103, DOI 10.1145/253769.253804
   Studer R, 1998, DATA KNOWL ENG, V25, P161, DOI 10.1016/S0169-023X(97)00056-6
   Su C, 2020, BRIEF BIOINFORM, V21, P182, DOI 10.1093/bib/bby117
   Subramanian A, 2017, CELL, V171, P1437, DOI 10.1016/j.cell.2017.10.049
   Suchanek FM., 2007, SEMANT WEB, V10
   Suyuan Zhang, 2019, Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019. Proceedings. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST 294), P361, DOI 10.1007/978-3-030-32388-2_31
   SWANSON DR, 1986, LIBR QUART, V56, P103, DOI 10.1086/601720
   Tatsioni A, 2007, JAMA-J AM MED ASSOC, V298, P2517, DOI 10.1001/jama.298.21.2517
   Thorn Caroline F, 2013, Methods Mol Biol, V1015, P311, DOI 10.1007/978-1-62703-435-7_20
   Torgersen H, 2009, SCI CULT-UK, V18, P65, DOI 10.1080/09505430802603829
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Williams A., NAACL, V1, P1112
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD608, DOI 10.1093/nar/gkx1089
   Wong AK, 2018, NUCLEIC ACIDS RES, V46, pW65, DOI 10.1093/nar/gky408
   Wu SY, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0084732
   Yang HT, 2017, BRIEF BIOINFORM, V18, P488, DOI 10.1093/bib/bbw030
   Zaveri A, 2016, SEMANT WEB, V7, P63, DOI 10.3233/SW-150175
   Zhang R, 2021, J BIOMED INFORM, V115, DOI 10.1016/j.jbi.2021.103696
   Zhang R, 2014, CANCER INFORM, V13, P103, DOI 10.4137/CIN.S13889
   Zitnik M, 2017, BIOINFORMATICS, V33, pI190, DOI 10.1093/bioinformatics/btx252
NR 112
TC 0
Z9 0
U1 4
U2 4
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1467-5463
EI 1477-4054
J9 BRIEF BIOINFORM
JI Brief. Bioinform.
PD JUL 18
PY 2022
VL 23
IS 4
DI 10.1093/bib/bbac268
EA JUL 2022
PG 13
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA 3M0JJ
UT WOS:000822897200001
PM 35817308
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Khan, MJ
   Breslin, JG
   Curry, E
AF Khan, Muhammad Jaleed
   Breslin, John G.
   Curry, Edward
TI Common Sense Knowledge Infusion for Visual Understanding and Reasoning:
   Approaches, Challenges, and Applications
SO IEEE INTERNET COMPUTING
LA English
DT Article
DE Visualization; Semantics; Commonsense reasoning; Task analysis;
   Internet; Training; Feature extraction
AB Visual understanding involves detecting objects in a scene and investigating rich semantic relationships between the objects, which is required for downstream visual reasoning tasks. The scene graph is widely used for structured scene representation, however, the performance of the scene graph generation for visual reasoning is limited due to challenges posed by imbalanced datasets and insufficient attention toward common sense knowledge infusion. Most of the existing approaches use statistical or language priors for knowledge infusion. Common Sense knowledge infusion using heterogeneous knowledge graphs can help in improving the accuracy, robustness, and generalizability of the scene graph generation and enable explainable higher level reasoning by providing rich and diverse background and factual knowledge about the concepts in visual scenes. In this article, we present the background and applications of the scene graph generation and the initial approaches and key challenges in common sense knowledge infusion using heterogeneous knowledge graphs for visual understanding and reasoning.
C1 [Khan, Muhammad Jaleed; Breslin, John G.; Curry, Edward] Natl Univ Ireland, Data Sci Inst, Galway H91 TK33, Ireland.
RP Khan, MJ (通讯作者)，Natl Univ Ireland, Data Sci Inst, Galway H91 TK33, Ireland.
EM m.khan12@nuigalway.ie; john.breslin@nuigalway.ie;
   edward.curry@nuigalway.ie
RI Breslin, John/B-7025-2015
OI Breslin, John/0000-0001-5790-050X
FU Science Foundation Ireland [18/CRT/6223, 12/RC/2289_P2]
FX This work was supported by the Science Foundation Ireland under Grant
   18/CRT/6223 and Grant 12/RC/2289_P2. For the purpose of Open Access, the
   author has applied a CCBY public copyright licence to any author
   accepted manuscript version arising from this submission.
CR Chen TS, 2019, PROC CVPR IEEE, P6156, DOI 10.1109/CVPR.2019.00632
   Curry E, 2022, IEEE INTERNET THINGS, V9, P13705, DOI 10.1109/JIOT.2022.3143171
   Dai B, 2017, PROC CVPR IEEE, P3298, DOI 10.1109/CVPR.2017.352
   Faldu K., 2021, ARXIV PREPRINT ARXIV
   Gu JX, 2019, PROC CVPR IEEE, P1969, DOI 10.1109/CVPR.2019.00207
   Ilievski F, 2021, LECT NOTES COMPUT SC, V12731, P680, DOI 10.1007/978-3-030-77385-4_41
   Johnson J, 2018, PROC CVPR IEEE, P1219, DOI 10.1109/CVPR.2018.00133
   Khan M. J., 2020, PROC ACM INT C INF K
   Kursuncu U., 2019, ARXIV
   Lecue F, 2020, SEMANT WEB, V11, P41, DOI 10.3233/SW-190374
   Liang XD, 2017, PROC CVPR IEEE, P4408, DOI 10.1109/CVPR.2017.469
   Lu CW, 2016, LECT NOTES COMPUT SC, V9905, P852, DOI 10.1007/978-3-319-46448-0_51
   Schroeder B., 2020, P IEEE CVF C COMP VI, P178
   Shizhe Chen, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P9959, DOI 10.1109/CVPR42600.2020.00998
   Wickramarachchi R, 2021, FRONT BIG DATA, V4, DOI 10.3389/fdata.2021.759110
   Yuyu Guo, 2020, MM '20: Proceedings of the 28th ACM International Conference on Multimedia, P3090, DOI 10.1145/3394171.3414025
   Zareian Alireza, 2020, Computer Vision - ECCV 2020. 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12368), P606, DOI 10.1007/978-3-030-58592-1_36
   Zellers R, 2018, PROC CVPR IEEE, P5831, DOI 10.1109/CVPR.2018.00611
   Zhu G., 2022, ARXIV
   Ziaeefard M., 2020, PROC 28 INT C COMPUT, P1863
NR 20
TC 0
Z9 0
U1 0
U2 0
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1089-7801
EI 1941-0131
J9 IEEE INTERNET COMPUT
JI IEEE Internet Comput.
PD JUL-AUG
PY 2022
VL 26
IS 4
BP 21
EP 27
DI 10.1109/MIC.2022.3176500
PG 7
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3G1OG
UT WOS:000831124400010
OA hybrid
DA 2022-08-23
ER

PT J
AU Yao, JR
   Guo, XL
   Wang, L
   Jiang, H
AF Yao, Jianrong
   Guo, Xiangliang
   Wang, Lu
   Jiang, Hui
TI Understanding Green Consumption: A Literature Review Based on Factor
   Analysis and Bibliometric Method
SO SUSTAINABILITY
LA English
DT Review
DE literature review; green consumption; factor analysis; bibliometric
   analysis; knowledge mapping; principal component analysis
ID CHINA
AB In recent years, research on green consumption has grown at an exponential rate. Because this field of study has seen such rapid growth, research hotspots have been constantly changing, making it difficult for scholars to keep track of the most current hotspots and trends related to the topic of green consumption. In this study, we employed Citespace, COOC1.9, and SPSS 20 to map knowledge in the field of green consumption and to identify current research preferences, cooperative networks among countries and institutions, and collaborative networks among authors. A total of 2194 papers from the period of 2016-2022, sourced from the Web of Science, were collected as our data sample. The results show that the topic of green consumption has caught the attention of researchers around the world, particularly in some countries with high levels of economic development, for instance, in China, USA, and England. In addition, although there are numerous scholars who have focused on the study of green consumption, currently, there are few efficient and productive authors. Collaborative networks among authors, and cooperative networks among institutions and countries, are all still immature and need to be further strengthened. A principal component analysis (PCA) showed that the existing literature focuses on the following three topics: (1) consumer green behavior, (2) corporate green production, and (3) green marketing in social media. In addition, we conducted a multidimensional scaling (MDS) analysis to verify our results. Finally, we offer some suggestions intended to inform and enrich the field for future researchers.
C1 [Yao, Jianrong; Guo, Xiangliang; Wang, Lu; Jiang, Hui] Zhejiang Univ Finance & Econ, Sch Informat Management & Artificial Intelligence, Hangzhou 310018, Peoples R China.
RP Jiang, H (通讯作者)，Zhejiang Univ Finance & Econ, Sch Informat Management & Artificial Intelligence, Hangzhou 310018, Peoples R China.
EM yjr@zufe.edu.cn; 17857690607@163.com; wanglu_hit@126.com;
   fidojianghui@hotmail.com
OI Jiang, Hui/0000-0001-7054-9654
FU National Natural Science Foundation of China [71704153, 71701180,
   71704020, 71904092]; China Postdoctoral Science Foundation [2018M642472]
FX This research received National Natural Science Foundation of China:
   71704153, 71701180, 71704020, 71904092; China Postdoctoral Science
   Foundation: 2018M642472.
CR BEM DJ, 1995, PSYCHOL BULL, V118, P172, DOI 10.1037/0033-2909.118.2.172
   Bojanowska A, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12218925
   Chen CM, 2014, EXPERT OPIN BIOL TH, V14, P1295, DOI 10.1517/14712598.2014.920813
   Chen PT, 2010, INT J CONTEMP HOSP M, V22, P535, DOI 10.1108/09596111011042730
   Cheng QK, 2020, SCIENTOMETRICS, V124, P1923, DOI 10.1007/s11192-020-03576-5
   Chiang CT, 2020, INT J CONTEMP HOSP M, V32, P3091, DOI 10.1108/IJCHM-03-2020-0230
   Dang Q, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13179865
   Dedeoglu BB, 2020, TOURISM MANAGE, V76, DOI 10.1016/j.tourman.2019.103954
   Demirel P, 2019, BUS STRATEG ENVIRON, V28, P847, DOI 10.1002/bse.2286
   Di Stefano G, 2012, RES POLICY, V41, P1283, DOI 10.1016/j.respol.2012.03.021
   Ding ZH, 2017, ENERG POLICY, V100, P252, DOI 10.1016/j.enpol.2016.10.013
   Duong QH, 2022, INT J PROD ECON, V243, DOI 10.1016/j.ijpe.2021.108340
   Gooch D, 2017, J ASSOC INF SCI TECH, V68, P378, DOI 10.1002/asi.23658
   Gurtu A, 2015, MANAG RES REV, V38, P166, DOI 10.1108/MRR-06-2013-0157
   Hajli N, 2018, J BUS ETHICS, V149, P799, DOI 10.1007/s10551-016-3036-7
   Keramatfar A, 2019, J INF SCI, V45, P3, DOI 10.1177/0165551518761013
   Kim JJ, 2019, J TRAVEL TOUR MARK, V36, P76, DOI 10.1080/10548408.2018.1494086
   Kim W, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8070631
   Kyvik S, 2017, SCIENTOMETRICS, V113, P951, DOI 10.1007/s11192-017-2497-5
   Lee D, 2018, MANAGE SCI, V64, P5105, DOI 10.1287/mnsc.2017.2902
   Leeuw J.D., 2005, J STAT SOFTW, V14, P1, DOI [10.18637/jss.v014.b04, DOI 10.18637/JSS.V014.B04]
   Lin SF, 2017, J CLEAN PROD, V166, P952, DOI 10.1016/j.jclepro.2017.08.107
   Liobikien G, 2021, ENERGY, V231, DOI 10.1016/j.energy.2021.120817
   Machova R, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14031364
   Magie W., 1931, SCIENCE, V74, P18, DOI [10.1126/science.74.1905.18, DOI 10.1126/SCIENCE.74.1905.18]
   MCCAIN KW, 1990, J AM SOC INFORM SCI, V41, P433, DOI 10.1002/(SICI)1097-4571(199009)41:6<433::AID-ASI11>3.0.CO;2-Q
   Moriuchi E, 2022, ASIA PAC J MARKET LO, DOI 10.1108/APJML-08-2021-0631
   Park SM, 2022, IEEE ACCESS, V10, P4209, DOI 10.1109/ACCESS.2021.3140175
   Park S, 2020, ICT EXPRESS, V6, P57, DOI 10.1016/j.icte.2019.09.004
   Qiu J.P., 2019, J MOD INF, V39, P26
   Rojas-Lamorena AJ, 2022, J BUS RES, V139, P1067, DOI 10.1016/j.jbusres.2021.10.025
   Rowlands I, 1999, SCIENTOMETRICS, V44, P533, DOI 10.1007/BF02458494
   Saeed N, 2018, ACM COMPUT SURV, V51, DOI 10.1145/3178155
   Shlens Jonathon, 2014, ARXIV PREPRINT ARXIV
   Spielmann N, 2021, J BUS ETHICS, V173, P759, DOI 10.1007/s10551-020-04493-6
   Sun HP, 2018, ENERGY, V153, P685, DOI 10.1016/j.energy.2018.04.077
   Sun Y, 2020, ASIA PAC J MARKET LO, V32, P860, DOI 10.1108/APJML-03-2019-0178
   Torgerson WS, 1952, PSYCHOMETRIKA, V17, P401
   Wei JP, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12030812
   Weismayer C, 2017, SCIENTOMETRICS, V113, P1757, DOI 10.1007/s11192-017-2555-z
   White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-W
   Zhang W., 2020, J CLEAN PROD, V262, P121168, DOI [DOI 10.1016/j.jclepro.2020.121168, 10.1016/j.jclepro.2020.121168]
   Zhang Y.R., 2018, FORUM SCI TECHNOL CH, V9, P113
   Zhang YW, 2018, FILOMAT, V32, P1499, DOI 10.2298/FIL1805499Z
   Zhao GM, 2020, ENVIRON SCI POLLUT R, V27, P44937, DOI 10.1007/s11356-020-11029-y
NR 45
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUL
PY 2022
VL 14
IS 14
AR 8324
DI 10.3390/su14148324
PG 13
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA 3J9CM
UT WOS:000833686800001
OA gold
DA 2022-08-23
ER

PT J
AU Azra, MN
   Noor, MIM
   Sung, YY
   Lawrence, ER
   Abd Ghaffar, M
AF Azra, Mohamad Nor
   Noor, Mohd Iqbal Mohd
   Sung, Yeong Yik
   Lawrence, Elizabeth R.
   Abd Ghaffar, Mazlan
TI What evidence exists on the impact of climate change on some of the
   worst invasive fish and shellfish? A systematic map protocol
SO ENVIRONMENTAL EVIDENCE
LA English
DT Article
DE Biodiversity loss; Exotic; Introduced; Environmental monitoring;
   Non-native; 100 world's worst; Climate change
AB Background: The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has estimated that invasive alien species (IAS) might cause billions of dollars of losses every year across the world. One example is South-East Asia, where IAS have caused an estimated loss of 33.5 billion USD, affecting the environment, human health, and agricultural production. Factors associated with climate change, such as increased carbon dioxide (CO2), heavy precipitation, and elevated temperatures is expected to facilitate biological invasion, leading only to further financial and public health loss. Thus, further study is needed to identify, collate and categorise what evidence exists on the impacts of climate change on fish and shellfish species that contribute to the list of"One Hundred of the World's Worst Invasive Alien Species"as identified by the International Union for Conservation of Nature's (IUCN). Such mapping will identify regions more at risk of biological invasion as climate change progresses.
   Methods: We outline a systematic mapping review protocol that follows the Guideline and Standards for Evidence Synthesis in Environmental Management and RepOrting standards for Systematic Evidence Syntheses (ROSES). We describe how peer-reviewed articles will be collected from Web of Science and Scopus, and then analyzed to create knowledge maps on the impact climate change has on invasive species. Finally, we speculate on how our results will aid future management of invasive species in the light of climate change.
C1 [Azra, Mohamad Nor] Univ Malaysia Terengganu UMT, Inst Marine Biotechnol, Terengganu 21030, Malaysia.
   [Azra, Mohamad Nor; Sung, Yeong Yik; Abd Ghaffar, Mazlan] Univ Malaysia Terengganu UMT, Inst Marine Biotechnol IMB, Climate Change Adaptat Lab, Terengganu 21030, Malaysia.
   [Noor, Mohd Iqbal Mohd] Univ Teknol MARA UiTM Pahang, Fac Business Management, Raub 27600, Pahang, Malaysia.
   [Noor, Mohd Iqbal Mohd] Univ Teknol MARA UiTM, Inst Biodivers & Sustainable Dev, Shah Alam 40450, Selangor, Malaysia.
   [Lawrence, Elizabeth R.] Concordia Univ, Quebec Ctr Biodivers Sci, 1455 Blvd Maison Neuve O, Montreal, PQ H3G 1M8, Canada.
   [Abd Ghaffar, Mazlan] Univ Malaysia Terengganu, Fac Sci & Marine Environm, Terengganu 21030, Malaysia.
   [Abd Ghaffar, Mazlan] Univ Malaysia Terengganu, Inst Trop Aquaculture & Fisheries AKUATROP, Higher Inst Ctr Excellence HICoE, Terengganu 21030, Malaysia.
RP Azra, MN (通讯作者)，Univ Malaysia Terengganu UMT, Inst Marine Biotechnol, Terengganu 21030, Malaysia.; Azra, MN; Abd Ghaffar, M (通讯作者)，Univ Malaysia Terengganu UMT, Inst Marine Biotechnol IMB, Climate Change Adaptat Lab, Terengganu 21030, Malaysia.; Abd Ghaffar, M (通讯作者)，Univ Malaysia Terengganu, Fac Sci & Marine Environm, Terengganu 21030, Malaysia.; Abd Ghaffar, M (通讯作者)，Univ Malaysia Terengganu, Inst Trop Aquaculture & Fisheries AKUATROP, Higher Inst Ctr Excellence HICoE, Terengganu 21030, Malaysia.
EM azramn@umt.edu.my; mag@umt.edu.my
RI Azra, Mohamad N/T-6226-2018; Mohd Noor, Mohd Iqbal/GNM-7479-2022;
   GHAFFAR, MAZLAN ABD/O-3604-2016
OI Azra, Mohamad N/0000-0001-9333-9270; GHAFFAR, MAZLAN
   ABD/0000-0001-9248-5284
FU Department of Higher Education, Ministry of Higher Education Malaysia
   [LRGS/1/2020/UMT/01/1, 56040]
FX This systematic map is a part of preliminary references on the project
   of 'Charting the effects of climate change and acidification through
   marine organisms' physiological responses', under the Long-Term Research
   Grant Scheme (LRGS) funded by the Department of Higher Education,
   Ministry of Higher Education Malaysia (LRGS/1/2020/UMT/01/1; LRGS UMT
   Vot No. 56040) (2020-2024). The funding body did not participate in the
   design of the project, collection, analysis, or interpretation of data,
   or writing of the manuscript.
CR [Anonymous], 2021, GUIDELINES STANDARDS
   Azzeri A, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17051533
   Casey J. P., 2022, CABI Agriculture and Bioscience, V3, DOI 10.1186/s43170-022-00077-8
   Folt CL, 1999, LIMNOL OCEANOGR, V44, P864, DOI 10.4319/lo.1999.44.3_part_2.0864
   GISD, 2021, GLOBAL INVASIVE SPEC
   Haddaway NR, 2018, ENVIRON EVID, V7, DOI [10.1186/s13750-018-0140-4, 10.1186/s13750-018-0121-7]
   Hesselschwerdt J, 2018, SCI TOTAL ENVIRON, V645, P44, DOI 10.1016/j.scitotenv.2018.07.078
   IPBES, 2018, SUMMARY POLICYMAKERS
   IPBES, SCI OUTC INT SCI POL
   Lowe S.J., 2000, 100 WORLDS WORST INV
   Martin PA, 2020, ENVIRON EVID, V9, DOI 10.1186/s13750-020-0186-y
   NAP-IAS, 2021, 20212025 NAPIAS MIN, P84
   Perez J, 2020, J SYST SOFTWARE, V168, DOI 10.1016/j.jss.2020.110657
   Price CJ, 2019, ENVIRON EVID, V8, DOI 10.1186/s13750-019-0156-4
   Rilov G, 2012, ENVIRON EVID, V1, DOI 10.1186/2047-2382-1-10
   Roberts PD, 2013, ENVIRON EVID, V2, DOI 10.1186/2047-2382-2-5
   Rolls RJ, 2017, ECOL EVOL, V7, P4109, DOI 10.1002/ece3.2982
   Saletta JM, 2019, INT J ORAL MAX SURG, V48, P364, DOI 10.1016/j.ijom.2018.07.014
   Schindler S, 2016, ENVIRON EVID, V5, DOI 10.1186/s13750-016-0062-y
   Son Seung Hun, 2021, Journal of Ecology and Environment, V45, P26, DOI 10.1186/s41610-021-00204-y
   Stephens KL, 2019, ECOSPHERE, V10, DOI 10.1002/ecs2.2680
NR 21
TC 0
Z9 0
U1 5
U2 5
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 2047-2382
J9 ENVIRON EVID
JI Environ. Evid.
PD MAY 21
PY 2022
VL 11
IS 1
AR 19
DI 10.1186/s13750-022-00273-z
PG 7
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 1M1PD
UT WOS:000799747100001
OA gold
DA 2022-08-23
ER

PT J
AU Lu, CC
   Liu, M
   Shang, WR
   Yuan, Y
   Li, MX
   Deng, XX
   Li, HJ
   Yang, KH
AF Lu, Cuncun
   Liu, Ming
   Shang, Wenru
   Yuan, Yuan
   Li, Meixuan
   Deng, Xiuxiu
   Li, Huijuan
   Yang, Kehu
TI Knowledge Mapping of Angelica sinensis (Oliv.) Diels (Danggui) Research:
   A Scientometric Study
SO FRONTIERS IN PHARMACOLOGY
LA English
DT Article
DE traditional Chinese medicine; Angelica sinensis (Oliv; ) Diels;
   scientometric; HistCite; CiteSpace
ID CHINESE HERBAL DECOCTION; N-BUTYLIDENEPHTHALIDE; BIOLOGICAL ASSESSMENT;
   RADIX ASTRAGALI; Z-LIGUSTILIDE; BUXUE-TANG; MEDICINE; POLYSACCHARIDE;
   QUANTIFICATION; IDENTIFICATION
AB Background Traditional Chinese medicine (TCM) has been widely accepted and applied worldwide, and many publications related to Angelica sinensis (Oliv.) Diels (AS, Chinese name is "Danggui") have been published. However, to date, there has not been a scientometric study to systematically analyze the intellectual landscape and emerging research trends regarding AS. Therefore, we performed a scientometric study to address this gap. Methods Publications related to AS published from 2009 to 2018 were identified and selected from the Web of Science (WoS) Core Collection on May 30, 2019 using relevant keywords. HistCite, CiteSpace, and Excel 2016 software tools were used to conduct this scientometric study. Results Seven hundred and sixty-seven articles (including 717 primary articles and 60 review articles) and their cited references were included and analyzed. The majority of publications (N = 565, 73.7%) were published in mainland China, with Nanjing University of Chinese Medicine contributing the most publications (N = 42, 5.5%). The first core journal was Journal of Ethnopharmacology (N = 58, 7.6%; impact factor = 3.414). The identification and assessment of active components (like ferulic acid) of AS and their pharmacological actions (such as immunomodulatory effects) are the current research foci for AS research. Conclusion The present scientometric study provides an overview of the development of AS research over the previous decade using quantitative and qualitative methods, and this overview can provide references for researchers focusing on AS.
C1 [Lu, Cuncun; Liu, Ming; Li, Meixuan; Li, Huijuan; Yang, Kehu] Lanzhou Univ, Evidence Based Med Ctr, Sch Basic Med Sci, Lanzhou, Peoples R China.
   [Lu, Cuncun; Liu, Ming; Li, Meixuan; Li, Huijuan; Yang, Kehu] Lanzhou Univ, Evidence Based Social Sci Ctr, Sch Publ Hlth, Lanzhou, Peoples R China.
   [Shang, Wenru] Fudan Univ, Sch Publ Hlth, Shanghai, Peoples R China.
   [Yuan, Yuan] Gansu Univ Tradit Chinese Med, Clin Coll Chinese Med, Lanzhou, Peoples R China.
   [Deng, Xiuxiu] Hosp Chengdu Univ Tradit Chinese Med, Cent Lab, Chengdu, Peoples R China.
RP Yang, KH (通讯作者)，Lanzhou Univ, Evidence Based Med Ctr, Sch Basic Med Sci, Lanzhou, Peoples R China.; Yang, KH (通讯作者)，Lanzhou Univ, Evidence Based Social Sci Ctr, Sch Publ Hlth, Lanzhou, Peoples R China.
EM kehuyangebm2006@126.com
RI Lu, cuncun/AAK-2842-2020
OI Lu, cuncun/0000-0001-9541-9733
CR Bunel V, 2015, TOXICOL IN VITRO, V29, P458, DOI 10.1016/j.tiv.2014.12.017
   Cai YY, 2019, FRONT PHARMACOL, V10, DOI 10.3389/fphar.2019.00734
   Chao WW, 2011, CHIN MED-UK, V6, DOI 10.1186/1749-8546-6-29
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen YL, 2018, CHIN J INTEGR MED, V24, P163, DOI 10.1007/s11655-017-2795-2
   Chen YL, 2018, BMJ-BRIT MED J, V360, DOI 10.1136/bmj.j5158
   Chen YB, 2019, EVID-BASED COMPL ALT, V2019, DOI 10.1155/2019/3961395
   Deng SX, 2006, J NAT PROD, V69, P536, DOI 10.1021/np050301s
   Dong TTX, 2006, J AGR FOOD CHEM, V54, P2767, DOI 10.1021/jf053163l
   DUBOIS M, 1956, ANAL CHEM, V28, P350, DOI 10.1021/ac60111a017
   Feng L. N., 2019, J PRACT TRADIT CHIN, V33, P7, DOI [10.13729/j.issn.1671-7813.z20190112, DOI 10.13729/J.ISSN.1671-7813.Z20190112]
   Flower A, 2014, EUR J INTEGR MED, V6, P186, DOI 10.1016/j.eujim.2013.07.011
   Gao QT, 2007, FEBS LETT, V581, P233, DOI 10.1016/j.febslet.2006.12.018
   Garfield E., 2006, INFORM WISSENSCHAFT, V57, P391
   Hu Q., 2017, J TRADIT CHIN MED, V36, P819
   Kuang X, 2006, BRAIN RES, V1102, P145, DOI 10.1016/j.brainres.2006.04.110
   Lao SC, 2004, ANAL CHIM ACTA, V526, P131, DOI 10.1016/j.aca.2004.09.050
   Li WW, 2017, FRONT PHARMACOL, V8, P15, DOI 10.3389/fphar.2017.00074
   Li WX, 2012, MOLECULES, V17, P10614, DOI 10.3390/molecules170910614
   Li XX, 2015, HEALTH POLICY, V119, P503, DOI 10.1016/j.healthpol.2014.09.002
   Lin HQ, 2017, J ETHNOPHARMACOL, V199, P205, DOI 10.1016/j.jep.2017.01.044
   Liu EH, 2010, COMB CHEM HIGH T SCR, V13, P869, DOI 10.2174/138620710793360301
   Liu JH, 2001, J AGR FOOD CHEM, V49, P2472, DOI 10.1021/jf0014157
   Liu PJ, 2010, EXP HEMATOL, V38, P437, DOI 10.1016/j.exphem.2010.03.012
   Liu WS, 2011, PHYTOTHER RES, V25, P1494, DOI 10.1002/ptr.3377
   Lu CC, 2019, EVID-BASED COMPL ALT, V2019, DOI 10.1155/2019/1704816
   Lu JL, 2009, BIOCHEM SYST ECOL, V37, P405, DOI 10.1016/j.bse.2009.04.007
   Luo H, 2012, J ETHNOPHARMACOL, V140, P555, DOI 10.1016/j.jep.2012.01.031
   Miao Y, 2018, PEERJ, V6, DOI 10.7717/peerj.5477
   Ooi VEC, 2000, CURR MED CHEM, V7, P715
   Pan B, 2018, INT J BEHAV NUTR PHY, V15, DOI 10.1186/s12966-018-0703-3
   Song ZH, 2004, PLANTA MED, V70, P1222, DOI 10.1055/s-2004-835855
   Su SL, 2013, CHIN MED-UK, V8, DOI 10.1186/1749-8546-8-5
   Tang YP, 2010, MOLECULES, V15, P341, DOI 10.3390/molecules15010341
   Tian SY, 2017, J FOOD DRUG ANAL, V25, P766, DOI 10.1016/j.jfda.2016.08.012
   Tsai NM, 2006, J NEUROCHEM, V99, P1251, DOI 10.1111/j.1471-4159.2006.04151.x
   Wang JG, 2018, EXPERT REV MOL MED, V20, DOI 10.1017/erm.2018.3
   Wei HP, 2019, PHYTOMEDICINE, V59, DOI 10.1016/j.phymed.2018.11.027
   Wei WL, 2016, J ETHNOPHARMACOL, V190, P116, DOI 10.1016/j.jep.2016.05.023
   Xiao FJ, 2017, FRONT CHEM, V5, DOI 10.3389/fchem.2017.00067
   Xie GG, 2018, BIOMED RES INT, V2018, DOI 10.1155/2018/3951783
   Xu QH, 2013, BMC COMPLEM ALTERN M, V13, DOI 10.1186/1472-6882-13-132
   Yan R, 2008, DRUG METAB DISPOS, V36, P400, DOI 10.1124/dmd.107.017707
   Yang TH, 2006, INT J BIOL MACROMOL, V39, P179, DOI 10.1016/j.ijbiomac.2006.02.013
   Yeh JC, 2011, ANGIOGENESIS, V14, P187, DOI 10.1007/s10456-011-9202-8
   Yi LZ, 2009, J CHROMATOGR A, V1216, P1991, DOI 10.1016/j.chroma.2008.07.033
   Zhang KJ, 2019, FRONT PHARMACOL, V10, DOI 10.3389/fphar.2019.00844
   Zhang WJ, 2019, FRONT PHARMACOL, V10, DOI 10.3389/fphar.2019.00743
   Zhao KJ, 2003, J AGR FOOD CHEM, V51, P2576, DOI 10.1021/jf026178h
   Zhao MM, 2017, ANN TRANSL MED, V5, DOI 10.21037/atm.2017.01.17
   Zhou KC, 2015, NEUROSCI LETT, V597, P26, DOI 10.1016/j.neulet.2015.04.030
   Zhu JF, 2019, EVID-BASED COMPL ALT, V2019, DOI 10.1155/2019/1056708
   Zou J, 2019, CHEM COMMUN, V55, P6221, DOI 10.1039/c9cc02681a
NR 53
TC 12
Z9 13
U1 15
U2 48
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1663-9812
J9 FRONT PHARMACOL
JI Front. Pharmacol.
PD MAR 13
PY 2020
VL 11
AR 294
DI 10.3389/fphar.2020.00294
PG 10
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA LC8VO
UT WOS:000525610800001
PM 32231572
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhang, J
   Pei, ZM
   Luo, ZK
AF Zhang, Jie
   Pei, Zhongmin
   Luo, Zhangkai
TI Reasoning for Local Graph Over Knowledge Graph With a Multi-Policy Agent
SO IEEE ACCESS
LA English
DT Article
DE Cognition; Trajectory; Magnetic heads; Training; Search problems; Markov
   processes; Task analysis; Knowledge graph completion; local graph
   reasoning; Markov decision process; reinforcement learning; searching
   window
AB The reinforcement learning framework for multi-hop relational paths is one of the effective methods for solving knowledge graph incompletion. However, these models are associated with limited performances attributed to delayed rewards and far-fetched search trajectories. To overcome these challenges, we propose the searching window and multi-policy agent. The searching window provides a large action space, so that the agent can backtrack based on the newly obtained information and establish a local graph instead of a path chain. Based on the searching window, a double long short-term memory (DBL-LSTM) policy network is introduced to encode the local graph and relation sequence, after which the encoding information is used by the agent to select a correct entity to grow the local graph. Furthermore, multi-policy agent separately infers the local graph through three different policy networks, then, all local graphs are integrated into an information-rich local graph. Experiments using the WN18RR dataset revealed that local graph reasoning with searching window had greater rewards than path reasoning, the proposed DBL-LSTM policy network improved all HITS@N(N = 1,3,5,10) compared to prior works, and that the multi-policy agent achieved higher hit rates than single-policy agent.
C1 [Zhang, Jie; Pei, Zhongmin; Luo, Zhangkai] Space Engn Univ, Sci & Technol Complex Elect Syst Simulat Lab, Beijing 101416, Peoples R China.
RP Zhang, J (通讯作者)，Space Engn Univ, Sci & Technol Complex Elect Syst Simulat Lab, Beijing 101416, Peoples R China.
EM misite_J@163.com
OI Luo, Zhangkai/0000-0002-1683-5745
FU Science and Technology on Complex Electronic System Simulation
   Laboratory [DXZT-JC-ZZ-2018-007, DXZT-JC-ZZ-2019-003]; Science and
   Technology on Information Security Laboratory [2020XXAQ05]
FX This work was supported in part by the Science and Technology on Complex
   Electronic System Simulation Laboratory under Grant DXZT-JC-ZZ-2018-007
   and Grant DXZT-JC-ZZ-2019-003, and in part by the Science and Technology
   on Information Security Laboratory under Grant 2020XXAQ05.
CR [Anonymous], 2017, P EMNLP
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen W., 2018, ARXIV180306581
   Das R., 2017, ARXIV171105851
   Das R, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P132
   Deng Zhi-Hong, 2019, ICLR
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Drumond L., 2012, P 27 ANN ACM S APPL, P326, DOI DOI 10.1145/2245276.2245341
   Fu C., 2019, ARXIV190900230
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Ioffe S, 2015, PR MACH LEARN RES, V37, P448
   Lin X.V., 2018, C EMPIRICAL METHODS, P3243
   Marra G., 2019, ARXIV190104195
   McCarthy J., 1959, P TEDDINGTON C MECHA, P75
   Nguyen D.Q., 2017, ARXIV170308098, V1703
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   NILSSON NJ, 1991, ARTIF INTELL, V47, P31, DOI 10.1016/0004-3702(91)90049-P
   Qiu YQ, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P474, DOI 10.1145/3336191.3371812
   Rocktaschel Tim, 2016, P 5 WORKSH AUT KNOWL, P45
   Shen Y., 2018, P ADV NEUR INF PROC, V31
   Trouillon T, 2017, J MACH LEARN RES, V18
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vaswani A, 2017, ADV NEUR IN, V30
   Velickovic P., 2018, P INT C LEARN REPR A
   Wang Heng, 2019, P 2019 C EMP METH NA, P2623
   Wang Q, 2020, KNOWL-BASED SYST, V197, DOI 10.1016/j.knosys.2020.105910
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Yang F., 2017, ARXIV170208367
   Zhang Y., 2017, ARXIV170904071, P6069
   Zhou M., 2018, P ACL, P2010
NR 31
TC 1
Z9 1
U1 4
U2 11
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2021
VL 9
BP 78452
EP 78462
DI 10.1109/ACCESS.2021.3083794
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA TJ9RH
UT WOS:000673808800001
OA gold
DA 2022-08-23
ER

PT J
AU Cui, XT
   Guo, XY
   Wang, YD
   Wang, XL
   Zhu, WH
   Shi, JH
   Lin, CY
   Gao, X
AF Cui, Xintong
   Guo, Xiaoyu
   Wang, Yidi
   Wang, Xuelei
   Zhu, Weihong
   Shi, Jianghong
   Lin, Chunye
   Gao, Xiang
TI Application of remote sensing to water environmental processes under a
   changing climate
SO JOURNAL OF HYDROLOGY
LA English
DT Review
DE Watershed model; Hydrological process; CiteSpace; Climate change;
   Bibliometrics
ID LAND-SURFACE TEMPERATURE; SOIL-MOISTURE RETRIEVAL; SNOW-COVER;
   EVAPOTRANSPIRATION; MODIS; RESOURCES; DYNAMICS; IMPACTS; IMPROVEMENTS;
   ASSIMILATION
AB Remote sensing, as a crucial method to obtain information on water environmental processes, has become a major source of data, particularly of water environment and water resources, which are sensitive to global climate change. The bibliometric analysis provided here shows the research characteristics and developments of remote sensing-based observations of water environmental processes under a changing climate from 2000 to 2018. Visualized knowledge mapping is introduced to investigate the development status, scientific collaboration, involved disciplines, research hotspots and emerging trends of this field. The breadth and depth of remote sensing application in water environmental process studies have improved significantly as the number of related publications rose at an average annual growth rate of 15.97% in the 21st century. The United States and China were the leading contributors with the largest number of publications and all of the top 15 most active institutions. In addition, this field is a highly interdisciplinary field that covers a wide range of interests, from water resources to environmental science, geology, engineering, ecology, and agriculture. The application of remote sensing technology has significantly promoted the estimation of evapotranspiration and soil moisture, thereby offering a more complete perspective to the understanding of the water cycle. Additionally, climate change and its complex interactions with water environmental processes, including the occurrence of drought events, are of great significance and require special attention.
C1 [Cui, Xintong; Wang, Yidi; Lin, Chunye; Gao, Xiang] Beijing Normal Univ, Sch Environm, State Key Lab Water Environm Simulat, Beijing 100875, Peoples R China.
   [Cui, Xintong; Guo, Xiaoyu] Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China.
   [Wang, Xuelei] Minist Ecol & Environm, Satellite Environm Ctr, Beijing 100094, Peoples R China.
   [Zhu, Weihong] Yanbian Univ, Coll Sci, Changbai Mt Key Lab Biol Resources & Funct Mol, Yanji 133003, Jilin, Peoples R China.
   [Shi, Jianghong] Southern Univ Sci & Technol, Sch Environm Sci & Engn, Guangdong Prov Key Lab Soil & Groundwater Pollut, Shenzhen 518055, Peoples R China.
RP Wang, YD (通讯作者)，Beijing Normal Univ, Sch Environm, State Key Lab Water Environm Simulat, Beijing 100875, Peoples R China.
EM wangyidi_1992@163.com
OI Wang, Yidi/0000-0002-0169-5081
FU National Natural Science Foundation of China [41871346, U1706217,
   41830643]; Natural Science Foundation of Beijing [19JH0009]; Open Fund
   of the Key Laboratory of Nonpoint Source Pollution Control, Ministry of
   Agriculture [1610132016005]
FX The research was financially supported by the National Natural Science
   Foundation of China (41871346, U1706217, 41830643), the Natural Science
   Foundation of Beijing (19JH0009) and Open Fund of the Key Laboratory of
   Nonpoint Source Pollution Control, Ministry of Agriculture (No.
   1610132016005).
CR ABBOTT MB, 1986, J HYDROL, V87, P45, DOI 10.1016/0022-1694(86)90114-9
   Alcamo J, 2007, HYDROLOG SCI J, V52, P247, DOI 10.1623/hysj.52.2.247
   Allen MR, 2002, NATURE, V419, P224, DOI 10.1038/nature01092
   Barnett TP, 2005, NATURE, V438, P303, DOI 10.1038/nature04141
   Beven K.J., 1979, HYDROL SCI J, V24, P43, DOI [10.1080/02626667909491834, DOI 10.1080/02626667909491834]
   Bolch T, 2012, SCIENCE, V336, P310, DOI 10.1126/science.1215828
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Chen SY, 2013, SCI TOTAL ENVIRON, V456, P24, DOI 10.1016/j.scitotenv.2013.03.059
   Collischonn B, 2008, J HYDROL, V360, P207, DOI 10.1016/j.jhydrol.2008.07.032
   Crow WT, 2008, REMOTE SENS ENVIRON, V112, P1268, DOI 10.1016/j.rse.2006.11.033
   Donat MG, 2016, NAT CLIM CHANGE, V6, P508, DOI [10.1038/nclimate2941, 10.1038/NCLIMATE2941]
   Easterling DR, 2000, SCIENCE, V289, P2068, DOI 10.1126/science.289.5487.2068
   Entekhabi D, 2010, P IEEE, V98, P704, DOI 10.1109/JPROC.2010.2043918
   Gardner AS, 2013, SCIENCE, V340, P852, DOI 10.1126/science.1234532
   Glenn EP, 2007, CRIT REV PLANT SCI, V26, P139, DOI 10.1080/07352680701402503
   Hagemann S, 2013, EARTH SYST DYNAM, V4, P129, DOI 10.5194/esd-4-129-2013
   Hall DK, 2002, REMOTE SENS ENVIRON, V83, P181, DOI 10.1016/S0034-4257(02)00095-0
   Hao FH, 2013, J HYDROL, V486, P412, DOI 10.1016/j.jhydrol.2013.02.008
   Herman MR, 2018, J HYDROL, V556, P39, DOI 10.1016/j.jhydrol.2017.11.009
   Hou AY, 2014, B AM METEOROL SOC, V95, P701, DOI 10.1175/BAMS-D-13-00164.1
   Houborg R, 2012, WATER RESOUR RES, V48, DOI 10.1029/2011WR011291
   Huete A, 2002, REMOTE SENS ENVIRON, V83, P195, DOI 10.1016/S0034-4257(02)00096-2
   Huntington TG, 2006, J HYDROL, V319, P83, DOI 10.1016/j.jhydrol.2005.07.003
   Immerzeel WW, 2008, J HYDROL, V349, P411, DOI 10.1016/j.jhydrol.2007.11.017
   Immerzeel WW, 2009, REMOTE SENS ENVIRON, V113, P40, DOI 10.1016/j.rse.2008.08.010
   Jiao W, 2014, J HAZARD MATER, V280, P252, DOI 10.1016/j.jhazmat.2014.07.081
   Karnieli A, 2010, J CLIMATE, V23, P618, DOI 10.1175/2009JCLI2900.1
   Kerr YH, 2012, IEEE T GEOSCI REMOTE, V50, P1384, DOI 10.1109/TGRS.2012.2184548
   Kerr YH, 2010, P IEEE, V98, P666, DOI 10.1109/JPROC.2010.2043032
   Kidd C, 2017, B AM METEOROL SOC, V98, P69, DOI 10.1175/BAMS-D-14-00283.1
   Kornelsen KC, 2013, J HYDROL, V476, P460, DOI 10.1016/j.jhydrol.2012.10.044
   Kundzewicz ZW, 2008, HYDROLOG SCI J, V53, P3, DOI 10.1623/hysj.53.1.3
   Landerer FW, 2012, WATER RESOUR RES, V48, DOI 10.1029/2011WR011453
   Li BL, 2012, J HYDROL, V446, P103, DOI 10.1016/j.jhydrol.2012.04.035
   Liu YQ, 2007, WATER RESOUR RES, V43, DOI 10.1029/2006WR005756
   Long D, 2014, WATER RESOUR RES, V50, P1131, DOI 10.1002/2013WR014581
   Lutz AF, 2014, NAT CLIM CHANGE, V4, P587, DOI [10.1038/nclimate2237, 10.1038/NCLIMATE2237]
   Matthews HD, 2004, CLIM DYNAM, V22, P461, DOI 10.1007/s00382-004-0392-2
   Mendoza M, 2002, PROG PHYS GEOG, V26, P319, DOI 10.1191/0309133302pp335ra
   Merigo JM, 2017, OMEGA-INT J MANAGE S, V73, P37, DOI 10.1016/j.omega.2016.12.004
   Mu Q, 2007, REMOTE SENS ENVIRON, V111, P519, DOI 10.1016/j.rse.2007.04.015
   Mu QZ, 2011, REMOTE SENS ENVIRON, V115, P1781, DOI 10.1016/j.rse.2011.02.019
   Njoku EG, 2003, IEEE T GEOSCI REMOTE, V41, P215, DOI 10.1109/TGRS.2002.808243
   Oki T, 2006, SCIENCE, V313, P1068, DOI 10.1126/science.1128845
   Ouyang W, 2016, CLIM DYNAM, V47, P651, DOI 10.1007/s00382-015-2861-1
   Ouyang W, 2016, J HYDROL, V537, P96, DOI 10.1016/j.jhydrol.2016.03.034
   Ouyang W, 2015, J HYDROL, V523, P555, DOI 10.1016/j.jhydrol.2015.01.050
   Ouyang W, 2012, ECOL INDIC, V18, P163, DOI 10.1016/j.ecolind.2011.11.013
   Ouyang W, 2010, SCI TOTAL ENVIRON, V409, P396, DOI 10.1016/j.scitotenv.2010.10.020
   Pan M, 2016, GEOPHYS RES LETT, V43, P9662, DOI 10.1002/2016GL069964
   Parajka J, 2008, J HYDROL, V358, P240, DOI 10.1016/j.jhydrol.2008.06.006
   Piao SL, 2010, NATURE, V467, P43, DOI 10.1038/nature09364
   Reichle RH, 2008, ADV WATER RESOUR, V31, P1411, DOI 10.1016/j.advwatres.2008.01.001
   Schewe J, 2014, P NATL ACAD SCI USA, V111, P3245, DOI 10.1073/pnas.1222460110
   Schmugge TJ, 2002, ADV WATER RESOUR, V25, P1367, DOI 10.1016/S0309-1708(02)00065-9
   SOBRINO JA, 1994, IEEE T GEOSCI REMOTE, V32, P243, DOI 10.1109/36.295038
   Sun DL, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002422
   Teuling AJ, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL036584
   Trenberth KE, 2011, CLIM RES, V47, P123, DOI 10.3354/cr00953
   Van Loon AF, 2015, WIRES WATER, V2, P359, DOI 10.1002/wat2.1085
   Venturim V, 2008, REMOTE SENS ENVIRON, V112, P132, DOI 10.1016/j.rse.2007.04.014
   Vorosmarty CJ, 2000, SCIENCE, V289, P284, DOI 10.1126/science.289.5477.284
   Wada Y, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL044571
   Wang H, 2016, INFORM FUSION, V29, P98, DOI 10.1016/j.inffus.2015.08.009
   Wang KC, 2012, REV GEOPHYS, V50, DOI 10.1029/2011RG000373
   WEXLER H, 1960, SCIENCE, V131, P1708, DOI 10.1126/science.131.3415.1708
   Wu YY, 2018, J HYDROL, V556, P438, DOI 10.1016/j.jhydrol.2017.11.037
   Yadav M, 2007, ADV WATER RESOUR, V30, P1756, DOI 10.1016/j.advwatres.2007.01.005
   Yang JF, 2000, HYDROL PROCESS, V14, P1887, DOI 10.1002/1099-1085(200007)14:10&lt;1887::AID-HYP69&gt;3.0.CO;2-B
   Yang S, 2018, ENVIRON SCI POLLUT R, V25, P12284, DOI 10.1007/s11356-018-1723-x
   Zhu ZC, 2016, NAT CLIM CHANGE, V6, P791, DOI [10.1038/NCLIMATE3004, 10.1038/nclimate3004]
NR 72
TC 23
Z9 28
U1 28
U2 154
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0022-1694
EI 1879-2707
J9 J HYDROL
JI J. Hydrol.
PD JUL
PY 2019
VL 574
BP 892
EP 902
DI 10.1016/j.jhydrol.2019.04.078
PG 11
WC Engineering, Civil; Geosciences, Multidisciplinary; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Geology; Water Resources
GA IL0CD
UT WOS:000476962800069
DA 2022-08-23
ER

PT J
AU Martins, BF
   Gil, LJS
   Roman, JFR
   Panach, JI
   Pastor, O
   Hadad, M
   Rochwerger, B
AF Franco Martins, Beatriz
   Serrano Gil, Lenin Javier
   Reyes Roman, Jose Fabian
   Ignacio Panach, Jose
   Pastor, Oscar
   Hadad, Moshe
   Rochwerger, Benny
TI A framework for conceptual characterization of ontologies and its
   application in the cybersecurity domain
SO SOFTWARE AND SYSTEMS MODELING
LA English
DT Article; Early Access
DE Conceptual modeling; Ontology classification; Cybersecurity ontology;
   Ontology
ID FORMAL ONTOLOGY; PRINCIPLES; LANGUAGES
AB Organizations are actively seeking efficient solutions for the management and protection of their assets. However, Cybersecurity is a vast and complex domain, especially for large enterprises because it requires an interdisciplinary approach. Knowledge Graphs are one of the mechanisms that organizations use to explore security among assets and possible attacks. The grounding of concepts is fundamental to implementing Knowledge Graphs, and it is one of the most relevant ontology applications. Therefore, Cybersecurity Ontologies have emerged as an important research subject. The first contribution of this paper is a search for previously existing works that have defined Cybersecurity Ontologies. We found twenty-eight ontologies in this search. Based on this result, we propose a Cybersecurity Terminological Validation and a Framework for Classifying Ontologies. Then, we provide a cross-analysis of these two proposals and present a proposal of best practices for improving the ontological approach in the cybersecurity domain. We also discuss the impact of this proposal with regard to the Ontology Engineering process. Our goal is to provide a solution that meets the organization's needs in terms of Cybersecurity and to contribute to Ontology Engineering research.
C1 [Franco Martins, Beatriz; Serrano Gil, Lenin Javier; Reyes Roman, Jose Fabian; Pastor, Oscar] Univ Politecn Valencia, Valencian Res Inst Artificial Intelligence VRAIN, Camino Vera S-N, Valencia 46022, Spain.
   [Serrano Gil, Lenin Javier] Univ Pontificia Bolivariana, Ingn Sistemas & Informat, Km 7 Via Bucaramanga, Santander, Colombia.
   [Ignacio Panach, Jose] Univ Valencia, Escola Tecn Super dEngn, Avinguda Univ, Valencia 46100, Spain.
   [Hadad, Moshe; Rochwerger, Benny] Accenture Israel Cyber R&D Lab, Tel Aviv, Israel.
RP Martins, BF; Pastor, O (通讯作者)，Univ Politecn Valencia, Valencian Res Inst Artificial Intelligence VRAIN, Camino Vera S-N, Valencia 46022, Spain.
EM bmartins@pros.upv.es; lserrano@pros.upv.es; jreyes@pros.upv.es;
   joigpana@uv.es; opastor@dsic.upv.es; moshe.hadad@accenture.com;
   benny.rochwerger@accenture.com
RI ; Pastor Lopez, Oscar/S-4216-2016
OI Franco Martins, Beatriz/0000-0001-9190-1047; Pastor Lopez,
   Oscar/0000-0002-1320-8471; Panach Navarrete, Jose
   Ignacio/0000-0002-7043-6227
FU Accenture LTD (Accenture Labs, Tel Aviv, Israel); Spanish State Research
   Agency under the projects "Digital Knowledge Graph - Adaptable Analytics
   API"; ERDF; European Union NextGenerationEU/PRTR
FX This work has been developed with the financial support of the Accenture
   LTD (Accenture Labs, Tel Aviv, Israel) and Spanish State Research Agency
   under the projects "Digital Knowledge Graph - Adaptable Analytics API"
   and MICIN/AEI/10.13039/501100011033 and co-financed with ERDF and the
   European Union NextGenerationEU/PRTR.
CR Agrawal V., 2016, HAISA
   Almeida J.P.A., 2019, ONTOBRAS
   Almeida J.P.A., 2019, LIGHTWEIGHT IMPLEMEN
   Almeida JPA, 2013, COMPUT STAND INTER, V35, P257, DOI 10.1016/j.csi.2012.01.007
   [Anonymous], 2018, 270052011 EN IEC
   [Anonymous], 2011, 270052011 ISOIEC
   Babiceanu RF, 2017, STUD COMPUT INTELL, V694, P167, DOI 10.1007/978-3-319-51100-9_15
   Ben-Asher N., 2015, STIDS, P34
   Benevides AB, 2009, LECT NOTES BUS INF P, V24, P528
   Bergner S, 2017, KEOD, P80
   Bizer C, 2011, SEMANTIC SERVICES, INTEROPERABILITY AND WEB APPLICATIONS: EMERGING CONCEPTS, P205, DOI 10.4018/978-1-60960-593-3.ch008
   Blanco C, 2008, ARES 2008: PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON AVAILABILITY, SECURITY AND RELIABILITY, P813, DOI 10.1109/ARES.2008.33
   Booth H., 2016, VULNERABILITY DESCRI
   Borgo S, 2010, THEORY AND APPLICATIONS OF ONTOLOGY: COMPUTER APPLICATIONS, P279, DOI 10.1007/978-90-481-8847-5_13
   Borst W. N., 1997, CONSTRUCTION ENG ONT
   de Almeida Falbo Ricardo, 2009, International Journal of Business Process Integration and Management, V4, P239, DOI 10.1504/IJBPIM.2009.032281
   deAlmeidaFalbo R., 2014, CEUR WORKSHOP PROC, V1301
   DEGEN W, 2001, P INT C FORM ONT INF
   Duarte BB, 2018, LECT NOTES COMPUT SC, V11157, P349, DOI 10.1007/978-3-030-00847-5_25
   Duarte BB, 2016, FRONT ARTIF INTEL AP, V283, P255, DOI 10.3233/978-1-61499-660-6-255
   Elnagdy SA, 2016, 2016 IEEE 3RD INTERNATIONAL CONFERENCE ON CYBER SECURITY AND CLOUD COMPUTING (CSCLOUD), P301, DOI 10.1109/CSCloud.2016.45
   Fensel Dieter, 2001, ONTOLOGIES
   Fernandez-Lopez M., 1997, METHONTOLOGY ONTOLOG
   Gailly F., 2009, INT WORKSH ONT DRIV
   Gamma Erich, 1995, ELEMENTS REUSABLE OB
   Gasmi H, 2019, 2019 INT C CYBER SEC, P1, DOI [10.1109/CSET.2019.8904905, DOI 10.1109/CSET.2019.8904905]
   Giunchiglia F, 2007, LIGHTWEIGHT ONTOLOGI
   Gomez-Perez A, 2002, IEEE INTELL SYST, V17, P54, DOI 10.1109/5254.988453
   Gomez- Perez A., 2004, E COMMERCE SEMANTIC
   Gregio A, 2014, 2014 IEEE 23RD INTERNATIONAL WETICE CONFERENCE (WETICE), P453, DOI 10.1109/WETICE.2014.72
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   GUARINO N, 1995, TOWARDS VERY LARGE KNOWLEDGE BASES, P25
   Guarino N, 1995, INT J HUM-COMPUT ST, V43, P623, DOI 10.1006/ijhc.1995.1064
   Guarino N, 1998, FR ART INT, V46, P3
   Guarino N., 1994, PHILOS COGN SCI
   Guarino N, 2009, LECT NOTES COMPUT SC, V5600, P52, DOI 10.1007/978-3-642-02463-4_4
   Guizzardi G, 2005, LECT NOTES COMPUT SC, V3713, P691
   Guizzardi G., 2013, DOMAIN ENG, P317
   Guizzardi G, 2006, 7 INT BALTIC C DATAB
   Guizzardi G., 2005, THESIS
   Guizzardi G, 2014, SCI COMPUT PROGRAM, V96, P417, DOI 10.1016/j.scico.2014.02.022
   Guizzardi G, 2014, LECT NOTES COMPUT SC, V8824, P13, DOI 10.1007/978-3-319-12206-9_2
   Guizzardi G, 2007, FR ART INT, V155, P18
   Haav H.M., 2001, P 5 E EUR C ADBIS, V2, P29
   Hadar E, 2019, INT REQUIR ENG CONF, P330, DOI 10.1109/RE.2019.00042
   Herre H, 2010, THEORY AND APPLICATIONS OF ONTOLOGY: COMPUTER APPLICATIONS, P297, DOI 10.1007/978-90-481-8847-5_14
   Iannacone M., 2015, P 10 ANN CYBER INFOR, P1
   Islam C, 2019, LECT NOTES COMPUT SC, V11483, P513, DOI 10.1007/978-3-030-21290-2_32
   ISO Central Secretary, 2012, 270322012 ISOIEC
   ISO/IEC, 2016, ISOIEC 270002016 E, V4
   Jacobsen A, 2020, DATA INTELLIGENCE, V2, P10, DOI 10.1162/dint_r_00024
   Jia Y, 2018, ENGINEERING-PRC, V4, P53, DOI 10.1016/j.eng.2018.01.004
   Jurisica I, 1999, P ASIS ANN, V36, P482
   Kang D, 2010, EXPERT SYST APPL, V37, P1456, DOI 10.1016/j.eswa.2009.06.073
   Keil JM, 2019, SEMANT WEB, V10, P33, DOI 10.3233/SW-180310
   Kiesling E, 2019, LECT NOTES COMPUT SC, V11779, P198, DOI 10.1007/978-3-030-30796-7_13
   Langer L, 2015, 2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST), P475, DOI 10.1109/SEDST.2015.7315255
   Lassila O., 2001, 0102 KNOWL SYST LAB
   Li K., 2020, CSKB CYBER SECURITY, V1268, DOI [10.1007/978-981-15-9129-7_8, DOI 10.1007/978-981-15-9129-7_8]
   Martins Beatriz F., 2020, The Practice of Enterprise Modeling. 13th IFIP Working Conference, PoEM 2020. Proceedings. Lecture Notes in Business Information Processing (LNBIP 400), P323, DOI 10.1007/978-3-030-63479-7_22
   Martins B.F., 2021, P 24 IBEROAMERICAN C, P1
   Martins B.F, 2021, CONSOLIDATION CYBERS
   Mascardi V., 2007, COMP UPPER ONTOLOGIE, V2007, P55
   Masolo C., 2003, WONDERWEB DELIVERABL
   Masolo C., 2002, WONDERWEB LIB FDN ON, V17
   Mizoguchi R., 1998, J JPN SOC ARTIF INTE, V13, P9
   Moller DPF., 2020, CYBERSECURITY ONTOLO, P99, DOI [10.1007/978-3-030-60570-4_7, DOI 10.1007/978-3-030-60570-4_7]
   Mozzaquatro BA, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18093053
   Mundie D.A., 2014, STIDS, P62
   Narayanan S., 2018, ARXIV PREPRINT ARXIV
   Nurse J. R. C., 2011, 2011 Workshop on Socio-Technical Aspects in Security and Trust, P60, DOI 10.1109/STAST.2011.6059257
   Obrst L., 2012, STIDS, P49
   Oltramari A, 2010, LREC 2010 - SEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P3873
   Oltramari A., 2014, STIDS, P54
   Oltramari A., 2018, J INF WARF, V17, P49
   Oltramari A., 2015, STIDS, P26
   Oltramari A, 2015, IEEE MILIT COMMUN C, P318, DOI 10.1109/MILCOM.2015.7357462
   Onwubiko C, 2018, 2018 INT C CYBER SIT, P1
   Ou XM, 2005, USENIX ASSOCIATION PROCEEDINGS OF THE 14TH USENIX SECURITY SYMPOSIUM, P113
   Parmelee MC., 2010, STIDS, V713, P116
   Pecina K., 2011, P INT CARN C SEC TEC, P1, DOI [10.1109/CCST.2011.6095895, DOI 10.1109/CCST.2011.6095895]
   Pipa A.M.C., 2018, THESIS I U LISBOA
   Qin S., 2019, CYBERSPACE DATA INTE, P3
   Sales TP, 2018, LECT NOTES COMPUT SC, V11157, P121, DOI 10.1007/978-3-030-00847-5_11
   Scarpato N, 2019, APPL ARTIF INTELL, V33, P643, DOI 10.1080/08839514.2019.1592344
   Schumacher M., 2003, SECURITY ENG PATTERN, P87
   Serrano L., 2021, ENCUESTA ACERCA DEFI
   Sikos LF, 2019, INTEL SYST REF LIBR, V151, P1, DOI 10.1007/978-3-319-98842-9_1
   Simperl E, 2012, J WEB SEMANT, V16, P1, DOI 10.1016/j.websem.2012.07.001
   Singhal Anoop, 2017, NETWORK SECURITY MET, P53
   Souag A, 2012, LECT NOTES BUS INF P, V112, P61
   Studer R, 1998, DATA KNOWL ENG, V25, P161, DOI 10.1016/S0169-023X(97)00056-6
   Syed R, 2018, CYBERSECURITY VULNER
   Syed R, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2020.103334
   Syed Z., 2016, P WORKSH 30 AAAI C A
   Takahama T., 2010, IEEE C EV COMP, P1, DOI DOI 10.1109/CEC.2010.5586484
   Takahashi T., 2010, P 3 INT C SEC INF NE, P100
   Takahashi T, 2011, J NATL I INF COMMUN, V58
   Takahashi T, 2015, COMPUT J, V58, P2297, DOI 10.1093/comjnl/bxu101
   Tissir Najat, 2021, Journal of Reliable Intelligent Environments, V7, P69, DOI 10.1007/s40860-020-00115-0
   Undercofer J., 2003, WORKSHOP ONTOLOGIES
   Uschold M, 2004, SIGMOD REC, V33, P58
   Uschold M, 1996, KNOWL ENG REV, V11, P93, DOI 10.1017/S0269888900007797
   vanHeijst G, 1997, INT J HUM-COMPUT ST, V46, P183, DOI 10.1006/ijhc.1996.0090
   WAND Y, 1995, INFORM SYST J, V5, P203, DOI 10.1111/j.1365-2575.1995.tb00108.x
   Wang JQ, 2009, PROCEEDINGS OF 2009 CONFERENCE ON SYSTEMS SCIENCE, MANAGEMENT SCIENCE & SYSTEM DYNAMICS, VOL 7, P1
   Wang JZ, 2005, 2005 IEEE/WIC/ACM International Conference on Web Intelligence, Proceedings, P372, DOI 10.1109/WI.2005.28
   Wieringa RJ., 2014, DESIGN SCI METHODOLO, DOI [10.1007/978-3-662-43839-8, DOI 10.1007/978-3-662-43839-8]
   Zuanelli E., 2017, E AGE2017, P1
NR 109
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1619-1366
EI 1619-1374
J9 SOFTW SYST MODEL
JI Softw. Syst. Model.
DI 10.1007/s10270-022-01013-0
EA JUL 2022
PG 28
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2P7GA
UT WOS:000819903400001
DA 2022-08-23
ER

PT J
AU Gao, JY
   Li, X
   Xu, YE
   Sisman, B
   Dong, XL
   Yang, J
AF Gao, Junyang
   Li, Xian
   Xu, Yifan Ethan
   Sisman, Bunyamin
   Dong, Xin Luna
   Yang, Jun
TI Efficient Knowledge Graph Accuracy Evaluation
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB Estimation of the accuracy of a large-scale knowledge graph (KG) often requires humans to annotate samples from the graph. How to obtain statistically meaningful estimates for accuracy evaluation while keeping human annotation costs low is a problem critical to the development cycle of a KG and its practical applications. Surprisingly, this challenging problem has largely been ignored in prior research. To address the problem, this paper proposes an efficient sampling and evaluation framework, which aims to provide quality accuracy evaluation with strong statistical guarantee while minimizing human efforts. Motivated by the properties of the annotation cost function observed in practice, we propose the use of cluster sampling to reduce the overall cost. We further apply weighted and two-stage sampling as well as stratification for better sampling designs. We also extend our framework to enable efficient incremental evaluation on evolving KG, introducing two solutions based on stratified sampling and a weighted variant of reservoir sampling. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of our proposed solution. Compared to baseline approaches, our best solutions can provide up to 60% cost reduction on static KG evaluation and up to 80% cost reduction on evolving KG evaluation, without loss of evaluation quality.
C1 [Gao, Junyang; Yang, Jun] Duke Univ, Durham, NC 27706 USA.
   [Li, Xian; Xu, Yifan Ethan; Sisman, Bunyamin; Dong, Xin Luna] Amazon Com, Seattle, WA USA.
RP Gao, JY (通讯作者)，Duke Univ, Durham, NC 27706 USA.
EM jygao@cs.duke.edu; xianlee@amazon.com; Xuyifa@amazon.com;
   bunyamis@amazon.com; lunadong@amazon.com; junyang@cs.duke.edu
FU NSF [IIS-1408846, IIS-1718398, IIS-1814493]
FX Junyang Gao and Jun Yang were supported by NSF grants IIS-1408846,
   IIS-1718398, and IIS-1814493. Any opinions, findings, and conclusions or
   recommendations expressed in this publication are those of the author(s)
   and do not necessarily reflect the views of the funding agencies.
CR Acosta M., 2016, SEMANTIC WEB, P1
   [Anonymous], 2019, TECHNICAL REPORT
   Bragg J., 2013, 1 AAAI C HUM COMP CR
   Brocheler M., 2012, P C UNC ART INT UAI
   Casella G., 2002, STAT INFERENCE, V2nd ed.
   Christen P., 2007, QUALITY MEASURES DAT, P127, DOI DOI 10.1007/978-3-540-44918-8_6
   Chu X, 2016, SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P2201, DOI 10.1145/2882903.2912574
   DALENIUS T, 1959, J AM STAT ASSOC, V54, P88, DOI 10.2307/2282141
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Efraimidis PS, 2006, INFORM PROCESS LETT, V97, P181, DOI 10.1016/j.ipl.2005.11.003
   Gerber D, 2015, J WEB SEMANT, V35, P85, DOI 10.1016/j.websem.2015.08.001
   Hansen MH, 1943, ANN MATH STAT, V14, P333, DOI 10.1214/aoms/1177731356
   Heath T., 2011, SYNTHESIS LECT SEMAN, DOI [DOI 10.2200/S00334ED1V01Y201102WBE001, 10.2200/s00334ed1v01y201102wbe001]
   Hellerstein J. M., 1997, SIGMOD Record, V26, P171
   Huiying Li, 2015, Database and Expert Systems Applications. 26th International Conference, DEXA 2015. Proceedings: LNCS 9261, P61, DOI 10.1007/978-3-319-22849-5_5
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Liu SY, 2017, LECT NOTES ARTIF INT, V10318, P343, DOI 10.1007/978-3-319-59888-8_29
   Marchant NG, 2017, PROC VLDB ENDOW, V10, P1322, DOI 10.14778/3137628.3137642
   Mitchell T, 2018, COMMUN ACM, V61, P103, DOI 10.1145/3191513
   Ojha P., 2017, P 2017 C EMP METH NA, P1741
   Ortona S, 2018, PROC INT CONF DATA, P1168, DOI 10.1109/ICDE.2018.00108
   Suchanek F., 2007, 16 INT WORLD WID WEB, P697
   VITTER JS, 1985, ACM T MATH SOFTWARE, V11, P37, DOI 10.1145/3147.3165
   Wang JN, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P469, DOI 10.1145/2588555.2610505
NR 24
TC 7
Z9 7
U1 2
U2 6
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD JUL
PY 2019
VL 12
IS 11
BP 1679
EP 1691
DI 10.14778/3342263.3342642
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JO5VK
UT WOS:000497645900033
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Ray, A
   Agarwal, P
   Maurya, CK
   Dasgupta, GB
AF Ray, A.
   Agarwal, P.
   Maurya, C. K.
   Dasgupta, G. B.
TI Creative tagline generation framework for product advertisement
SO IBM JOURNAL OF RESEARCH AND DEVELOPMENT
LA English
DT Article
AB The ubiquitous nature of the Internet and its applicability as an inexpensive advertising media has resulted in Web and mobile platforms to be on target for product advertising. Existing advertising approaches on different social media platforms include sponsored search, e-mail advertising, banner ads, etc., to attract customers. The current systems for advertising on these platforms require the advertisers to manually come up with a catchy tagline, which is time-consuming and a challenging task. To overcome this problem, we propose a novel framework to automatically generate novel and catchy taglines tailored for advertisers' products. The framework consists of a domain-specific knowledge graph, a novel modified long short-term memory (LSTM) (we call it CopyLSTM), and a neural paraphrase model. We also curate and build a database of fashion products with 100K input product features and 300K sentences (three sentences per product data). This dataset is used to populate the knowledge graph and learn the modified LSTM to generate taglines. The proposed framework is evaluated on different metrics for sentence quality and compared with the state-of-art methods for tagline generation. Subjective evaluation was performed to understand the novelty and creativity of the generated tagline.
C1 [Ray, A.; Maurya, C. K.; Dasgupta, G. B.] IBM Res, Bangalore 560045, Karnataka, India.
   [Agarwal, P.] IBM Res, New Delhi 110070, India.
RP Ray, A (通讯作者)，IBM Res, Bangalore 560045, Karnataka, India.
EM anupamar@in.ibm.com; preragar@in.ibm.com; cmaurya1@in.ibm.com;
   gaargidasgupta@in.ibm.com
OI Maurya, Chandresh/0000-0003-3519-600X
CR Abadi M., 2015, TENSORFLOW LARGE SCA
   Androutsopoulos I, 2010, J ARTIF INTELL RES, V38, P135, DOI 10.1613/jair.2985
   Banko N, 2000, 38TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P318
   Brill R., 2018, 7 THINGS YOU SHOULD
   Danescu-Niculescu-Mizil C, 2012, P 50 ANN M ASS COMP, P892
   Heafield K., 2011, P 6 WORKSH STAT MACH, P187
   Kincaid J. P., 1975, 875 U CENTR FLOR
   Kohli Chiranjeev, 2007, Business Horizons, V50, P415, DOI 10.1016/j.bushor.2007.05.002
   Maher M. L, 2010, P 1 DESIRE NETW C CR, P22
   Mallinson J, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P881
   Nallapati R, 2016, CONLL 2016 20 SIGNLL, P280, DOI [10.18653/v1/k16-1028, DOI 10.18653/V1/K16-1028]
   Sennrich R., 2015, NEURAL MACHINE TRANS
   Sennrich R., 2016, WMT, P371, DOI DOI 10.18653/V1/W16-2323
   Shrivastava D., 2017, MACHINE LEARNING APP
   Suggett P., 2018, DOES ADVERTISING AGE
   Sutskever Ilya, 2014, ADV NEURAL INFORM PR, V27, P3104
   Yamane H, 2013, J ADV COMPUT INTELL, V17, P185, DOI 10.20965/jaciii.2013.p0185
   Yamane H, 2015, IEEE INT C SEMANT CO, P358, DOI 10.1109/ICOSC.2015.7050834
   Yamane Hiroaki, 2012, International Journal of Fuzzy Logic and Intelligent systems, V12, P94
NR 19
TC 0
Z9 0
U1 2
U2 6
PU IBM CORP
PI ARMONK
PA 1 NEW ORCHARD ROAD, ARMONK, NY 10504 USA
SN 0018-8646
EI 2151-8556
J9 IBM J RES DEV
JI IBM J. Res. Dev.
PD JAN-FEB
PY 2019
VL 63
IS 1
AR 6
DI 10.1147/JRD.2019.2893900
PG 10
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Computer Science, Software Engineering; Computer Science,
   Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IT3VG
UT WOS:000482784600007
DA 2022-08-23
ER

PT J
AU Zheng, WG
   Yu, JX
   Zou, L
   Cheng, H
AF Zheng, Weiguo
   Yu, Jeffrey Xu
   Zou, Lei
   Cheng, Hong
TI Question Answering Over Knowledge Graphs: Question Understanding Via
   Template Decomposition
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB The gap between unstructured natural language and structured data makes it challenging to build a system that supports using natural language to query large knowledge graphs. Many existing methods construct a structured query for the input question based on a syntactic parser. Once the input question is parsed incorrectly, a false structured query will be generated, which may result in false or incomplete answers. The problem gets worse especially for complex questions. In this paper, we propose a novel systematic method to understand natural language questions by using a large number of binary templates rather than semantic parsers. As sufficient templates are critical in the procedure, we present a low-cost approach that can build a huge number of templates automatically. To reduce the search space, we carefully devise an index to facilitate the online template decomposition. Moreover, we design effective strategies to perform the two-level disambiguations (i.e., entity-level ambiguity and structure-level ambiguity) by considering the query semantics. Extensive experiments over several benchmarks demonstrate that our proposed approach is effective as it significantly outperforms state-of-the-art methods in terms of both precision and recall.
C1 [Zheng, Weiguo; Yu, Jeffrey Xu; Cheng, Hong] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China.
   [Zou, Lei] Peking Univ, Beijing, Peoples R China.
RP Zheng, WG (通讯作者)，Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China.
EM wgzheng@se.cuhk.edu.hk; yu@se.cuhk.edu.hk; zoulei@pku.edu.cn;
   hcheng@se.cuhk.edu.hk
FU Research Grant Council of the Hong Kong Special Administrative Region,
   China [CUHK 14205617, CUHK 14221716]; NSFC [61622201, 61532010]
FX This work was supported by grants from the Research Grant Council of the
   Hong Kong Special Administrative Region, China [Project No.: CUHK
   14205617] and [Project No.: CUHK 14221716]. Lei Zou was supported by
   NSFC under grant 61622201 and 61532010.
CR Abujabal A, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1191, DOI 10.1145/3038912.3052583
   [Anonymous], 2007, P 2007 JOINT C EMP M, DOI DOI 10.1145/2187836.2187900
   Arasu A., 2006, VLDB, P918
   Bast H., 2015, P 24 ACM INT C INF K, P1431, DOI [10.1145/2806416.2806472, DOI 10.1145/2806416.2806472]
   Beaumont R., 2015, WORKING NOTES CLEF 2, P1
   Berant J, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1415, DOI 10.3115/v1/p14-1133
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A, 2014, P 2014 C EMP METH NA, P615, DOI DOI 10.3115/V1/D14-1067
   Chen D., 2014, P 2014 C EMP METH NA, P740, DOI DOI 10.3115/V1/D14-1082
   Clark K, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P643
   Cui W, 2016, P 25 INT JOINT C ART, P4240
   Cui WY, 2017, PROC VLDB ENDOW, V10, P565, DOI 10.14778/3055540.3055549
   Daiber J, 2013, P 9 INT C SEM SYST 1
   De Marneffe M. C., 2006, P LREC, V6, P449
   Delmonte R., 2006, P WORKSH CAN COMP LI, P9, DOI [10.3115/1629808.1629811., DOI 10.3115/1629808.1629811]
   Deng D, 2015, PROC VLDB ENDOW, V9, P360
   DICE LR, 1945, ECOLOGY, V26, P297, DOI 10.2307/1932409
   Diefenbach D., 2017, KNOWL INF SYST, P1
   Dong L, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P260
   Dong Li, 2017, P C EMP METH NAT LAN, P886
   Durrett Greg, 2013, P 51 ANN M ASS COMP, V1, P114
   Fader A, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P1156, DOI 10.1145/2623330.2623677
   Finkel J.R., 2005, P 43 ANN M ASS COMP, P363, DOI DOI 10.3115/1219840.1219885
   Kaufmann E, 2010, J WEB SEMANT, V8, P377, DOI 10.1016/j.websem.2010.06.001
   Kim J, 2012, PROC INT CONF DATA, P822, DOI 10.1109/ICDE.2012.79
   Kwiatkowski Tom, 2013, P 2013 C EMP METH NA
   Li F, 2014, PROC VLDB ENDOW, V8, P73, DOI 10.14778/2735461.2735468
   Mendes PN, 2012, LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P1813
   Mikolov T., 2013, CORR
   Moldovan Dan I., 2002, INFORM EXTRACTION WE, P129, DOI [10.1007/978-3-540-45092-4_6, DOI 10.1007/978-3-540-45092-4_6]
   Nakashole N, 2013, P 51 ANN M ASS COMP, P1488, DOI DOI 10.1016/J.ARTINT.2012.07.001
   Niu LF, 2012, PROCEDIA COMPUT SCI, V9, P1249, DOI 10.1016/j.procs.2012.04.136
   Recasens M., 2013, P HUM LANG TECHN 11, P897
   Reddy S, 2016, T ASS COMPUT LINGUIS, V4, P127
   Ruseti S., 2015, CLEF 2015 C LABS EV, P1
   Tao WB, 2017, PROC VLDB ENDOW, V11, P53, DOI 10.14778/3151113.3151118
   Unger C., 2012, P 21 INT C WORLD WID, P639, DOI DOI 10.1145/2187836.2187923
   Unger Christina, 2015, CLEF 2015 C LABS EV, V1391, P1
   Wang JX, 2012, PROCEEDINGS OF THE 7TH EURO-ASIA CONFERENCE ON ENVIRONMENT AND CSR: TOURISM, MICE, HOSPITALITY MANAGEMENT AND EDUCATION SESSION, PT II, P85
   Xiao C., P 25 INT C DAT ENG, P916
   Xu K, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2326
   Xu K, 2015, LECT NOTES COMPUT SC, V9283, P414, DOI 10.1007/978-3-319-24027-5_43
   Xu Kun, 2014, WORKING NOTES CLEF 2, P1260
   Yahya Mohamed, 2012, EMNLP
   Yao X., 2015, P C N AM CHAPT ASS C, P66
   Yao XC, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P956
   Yih WT, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P1321
   Yin J, 2017, J COMPUT SCI TECH-CH, V32, P805, DOI 10.1007/s11390-017-1761-8
   Yu M., 2015, FRONTIERS COMPUTER S, P1
   Yu MH, 2016, FRONT COMPUT SCI-CHI, V10, P399, DOI 10.1007/s11704-015-5900-5
   Zhang Y, 2017, PROC INT CONF DATA, P759, DOI 10.1109/ICDE.2017.127
   Zheng WG, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1809, DOI 10.1145/2723372.2747648
   Zou L., 2014, SIGMOD C, P47
NR 54
TC 36
Z9 38
U1 2
U2 16
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD JUL
PY 2018
VL 11
IS 11
BP 1373
EP 1386
DI 10.14778/3236187.3236192
PG 14
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HD4ZN
UT WOS:000452537300006
DA 2022-08-23
ER

PT J
AU Lecue, F
   Wu, JW
AF Lecue, Freddy
   Wu, Jiewen
TI Explaining and predicting abnormal expenses at large scale using
   knowledge graph based reasoning
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Semantic web; Reasoning system; Intelligent system; Smart expenses;
   Intelligent operation system; Knowledge graph
AB Global business travel spend topped record-breaking $1.2 Trillion USD in 2015, and will reach. $1.6 Trillion by 2020 according to the Global Business Travel Association, the world's premier business travel and meetings trade organization. Existing expenses systems are designed for reporting expenses, their type and amount over pre-defined views such as time period, service or employee group. However such systems do not aim at systematically detecting abnormal expenses, and more importantly explaining their causes. Therefore deriving any actionable insight for optimizing spending and saving from their analysis is time-consuming, cumbersome and often impossible. Towards this challenge we present AIFS, a system designed for expenses business owner and auditors. Our system is manipulating and combining semantic web and machine learning technologies for (i) identifying, (ii) explaining and (iii) predicting abnormal expenses claim by employees of large organizations. Our prototype of semantics-aware employee expenses analytics and reasoning, experimented with 191,346 unique Accenture employees in 2015, has demonstrated scalability and accuracy for the tasks of explaining and predicting abnormal expenses. (c) 2017 Elsevier B.V. All rights reserved.
C1 [Lecue, Freddy] INRIA, Sophia Antipolis, France.
   [Lecue, Freddy; Wu, Jiewen] Accenture Labs, Dublin, Ireland.
RP Lecue, F (通讯作者)，Accenture Labs, Dublin, Ireland.
EM freddy.lecue@inria.fr; jiewen.wu@accenture.com
RI Wu, Jiewen/A-2284-2013
OI Wu, Jiewen/0000-0002-0034-0334
CR Agrawal R., 1994, P 20 INT C VER LARG, P487
   Baader F, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P364
   Babu S, 2001, SIGMOD REC, V30, P109
   Chambers J. C., 1971, HARV BUS REV
   Enoch M, 2012, TRANS MOBIL SER, P1
   Fayyad U., 1996, KDD-96 Proceedings. Second International Conference on Knowledge Discovery and Data Mining, P82
   Gama J, 2014, ACM COMPUT SURV, V46, DOI 10.1145/2523813
   Gehrke J, 2001, SIGMOD REC, V30, P13
   Haase C, 2008, FRONT ARTIF INTEL AP, V178, P25, DOI 10.3233/978-1-58603-891-5-25
   Han JW, 2012, PROC INT CONF DATA, P1214, DOI 10.1109/ICDE.2012.145
   Huang ZS, 2005, LECT NOTES COMPUT SC, V3729, P398
   Krotzsch M., 2011, P 20 INT C WORLD WID, P645
   Lecue F., 2013, IJCAI, P2662
   Lecue F., 2012, P 26 AAAI C ART INT
   Lecue F, 2015, AAAI CONF ARTIF INTE, P189
   Li L., 2003, P 12 INT C WORLD WID, P331, DOI DOI 10.1145/775152.775199
   LUO C, 2005, P ACM SIGMOD, P873
   McGuinness DL, 1995, INT JOINT CONF ARTIF, P816
   Mutharaju Raghava, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P407, DOI 10.1007/978-3-642-35173-0_30
   Mutharaju R, 2015, LECT NOTES COMPUT SC, V9088, P88, DOI 10.1007/978-3-319-18818-8_6
   Pan JZ, 2016, ARTIF INTELL, V235, P95, DOI 10.1016/j.artint.2015.10.004
   Paolucci M, 2002, LECT NOTES COMPUT SC, V2342, P333
   Papadimitriou S., 2005, VLDB, P697
   Ribeiro MT, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1135, DOI 10.1145/2939672.2939778
   ROBNIKSIKONJA M, 2004, EUR C MACH LEARN, V3201, P359
   Taskar B., 2005, P 22 INT C MACH LEAR, P896, DOI DOI 10.1145/1102351.1102464
   Teijeiro T., 2016, ARXIV160905632
   Thagard P, 1997, SYNTH LIBR, V259, P413
   Zaki MJ, 2001, MACH LEARN, V42, P31, DOI 10.1023/A:1007652502315
NR 29
TC 7
Z9 8
U1 1
U2 15
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1570-8268
J9 J WEB SEMANT
JI J. Web Semant.
PD MAY
PY 2017
VL 44
BP 89
EP 103
DI 10.1016/j.websem.2017.05.003
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FQ6LQ
UT WOS:000418476400006
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Fang, Y
   Zhang, YC
   Huang, C
AF Fang, Yong
   Zhang, Yuchi
   Huang, Cheng
TI CyberEyes: Cybersecurity Entity Recognition Model Based on Graph
   Convolutional Network
SO COMPUTER JOURNAL
LA English
DT Article
DE cybersecurity entity recognition; non-local dependencies; graph
   convolutional network; BiLSTM; CRF; cybersecurity corpus
AB Cybersecurity has gradually become the public focus between common people and countries with the high development of Internet technology in daily life. The cybersecurity knowledge analysis methods have achieved high evolution with the help of knowledge graph technology, especially a lot of threat intelligence information could be extracted with fine granularity. But named entity recognition (NER) is the primary task for constructing security knowledge graph. Traditional NER models are difficult to determine entities that have a complex structure in the field of cybersecurity, and it is difficult to capture non-local and non-sequential dependencies. In this paper, we propose a cybersecurity entity recognition model CyberEyes that uses non-local dependencies extracted by graph convolutional neural networks. The model can capture both local context and graph-level non-local dependencies. In the evaluation experiments, our model reached an F1 score of 90.28% on the cybersecurity corpus under the gold evaluation standard for NER, which performed better than the 86.49% obtained by the classic CNN-BiLSTM-CRF model.
C1 [Fang, Yong; Zhang, Yuchi; Huang, Cheng] Sichuan Univ, Coll Cybersecur, Chengdu, Peoples R China.
RP Huang, C (通讯作者)，Sichuan Univ, Coll Cybersecur, Chengdu, Peoples R China.
EM opcodesec@gmail.com
FU National Natural Science Foundation of China [61902265]; Key Research
   and Development Plan Project of Sichuan Province [2020YFG0047,
   2020YFG0076]; Sichuan University Postdoc Research Foundation
   [2019SCU12068]; Fundamental Research Funds for the Central Universities
FX National Natural Science Foundation of China (61902265);Key Research and
   Development Plan Project of Sichuan Province (Grant Nos. 2020YFG0047 and
   2020YFG0076); Sichuan University Postdoc Research Foundation (Grant
   2019SCU12068); Fundamental Research Funds for the Central Universities.
CR Alex W., PYTORCH
   Auli, 2019, P 2019 C EMP METH NA, DOI DOI 10.18653/V1/D19-1539
   Bahuguna A, 2020, INF SECUR J, V29, P250, DOI 10.1080/19393555.2020.1767239
   Ballesteros M., 2016, P 2016 C N AM CHAPT, P260, DOI [10.18653/v1/N16-1030, DOI 10.18653/V1/N16-1030]
   Bhamare D, 2020, COMPUT SECUR, V89, DOI 10.1016/j.cose.2019.101677
   Deng YL, 2019, PROCEEDINGS OF THE ACM CONFERENCE ON GLOBAL COMPUTING EDUCATION (COMPED '19), P194, DOI 10.1145/3300115.3309531
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Finkel J.R., 2005, P 43 ANN M ASS COMP, P363, DOI DOI 10.3115/1219840.1219885
   Gasmi H, 2018, THIRTEENTH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING ADVANCES (ICSEA 2018), P1
   Graves A, 2005, NEURAL NETWORKS, V18, P602, DOI 10.1016/j.neunet.2005.06.042
   Graves A, 2013, INT CONF ACOUST SPEE, P6645, DOI 10.1109/ICASSP.2013.6638947
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Kim Y, 2016, AAAI CONF ARTIF INTE, P2741
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Lafferty J., 2001, P 18 INT C MACH LEAR
   Li J, 2022, IEEE T KNOWL DATA EN, V34, P50, DOI [10.1109/TKDE.2020.2981314, 10.1109/TKDE.2020.3038670]
   Ma XZ, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1064
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   Mao XN, 2007, PACLIC 21: THE 21ST PACIFIC ASIA CONFERENCE ON LANGUAGE, INFORMATION AND COMPUTATION, PROCEEDINGS, P303
   McCallum A, 2003, P 7 C NAT LANG LEARN, V4, P188, DOI DOI 10.3115/1119176.1119206
   Mikolov T., 2013, ARXIV PREPRINT ARXIV, P1
   Narayanan S, 2018, 2018 4TH IEEE INTERNATIONAL CONFERENCE ON COLLABORATION AND INTERNET COMPUTING (CIC 2018), P354, DOI 10.1109/CIC.2018.00054
   Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
   Paszke A., 2019, ADV NEUR IN, V32, P8024
   Peters M., 2018, IMPROVING LANGUAGE U
   Pingle A, 2019, PROCEEDINGS OF THE 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2019), P879, DOI 10.1145/3341161.3343519
   Quirk C., 15 C EUR CHAPT ASS C, P1171
   Sang E.F.T.K., 2003, P N AM CHAPT ASS COM, P142, DOI [10.3115/1119176.1119195, DOI 10.3115/1119176.1119195]
   Satyapanich T, 2020, AAAI CONF ARTIF INTE, V34, P8749
   Satyapanich T, 2019, IEEE INT CONF BIG DA, P5034, DOI 10.1109/BigData47090.2019.9006444
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Srinivasa-Desikan B, 2011, NATURAL LANGUAGE PRO
   The Python Team, PYTHON
   The Stanford Corenlp Team, STANF CORENLP
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Zhang Y., 2018, P 2018 C EMP METH NA, DOI [10.18653/v1/D18-1244, DOI 10.18653/V1/D18-1244]
   Zhou GD, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P473
NR 37
TC 0
Z9 0
U1 11
U2 14
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0010-4620
EI 1460-2067
J9 COMPUT J
JI Comput. J.
PD AUG
PY 2021
VL 64
IS 8
BP 1215
EP 1225
DI 10.1093/comjnl/bxaa141
PG 11
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Computer Science, Software Engineering; Computer Science,
   Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WD8OD
UT WOS:000705193200005
DA 2022-08-23
ER

PT J
AU Cai, GW
   Xu, L
   Gao, WJ
   Wang, K
   Hong, Y
   Wang, Y
AF Cai, Gangwei
   Xu, Lei
   Gao, Weijun
   Wang, Ka
   Hong, Yan
   Wang, Yan
TI Knowledge archaeology on relations between the Venice Architecture
   Biennale (1980-2018) and the Pritzker Architecture Prize (1979-2019)
SO JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING
LA English
DT Article
DE Venice Architecture Biennale; Pritzker Architecture Prize; curating;
   knowledge archaeology; domain-specific knowledge graph
AB This paper used a philosophy method called knowledge archaeology to identify the relations between the Venice Architecture Biennale and the Pritzker Architecture Prize. Two questions were posed: (1) "Who is speaking" in the biennale and prize, respectively? (2) Do the biennale and curating change architectural thinking? Through knowledge archaeology, the domain-specific knowledge graphs of the Venice Architecture Biennale participants and the Pritzker Architecture Prize laureates are compared from three aspects: "persons," "words," and "Asia." Comparing the number, type, and nationality of exhibitors and laureates, the importance of the biennale and its influence on architectural development are confirmed. Results show that the thinking and curating of the Venice Architecture Biennale have influenced and changed who wins the Pritzker Architecture Prize. Moreover, Asian architects played increasingly important roles in the exhibition and prize process. Starting from the architectural exhibition, we can connect the entire architectural world. In other words, we may be able to "curate the whole world."
C1 [Cai, Gangwei; Hong, Yan] Zhejiang Sci Tech Univ, Sch Civil Engn & Architecture, Hangzhou, Peoples R China.
   [Cai, Gangwei; Xu, Lei; Wang, Ka] Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China.
   [Cai, Gangwei; Gao, Weijun] Univ Kitakyushu, Fac Environm Engn, Fukuoka, Japan.
   [Gao, Weijun] Qingdao Univ Technol, iSMART, Qingdao, Peoples R China.
   [Wang, Yan] Tianjin Chengjian Univ, Sch Architecture, Tianjin, Peoples R China.
RP Xu, L (通讯作者)，Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China.
EM xulei8563@zju.edu.cn
OI WANG, Yan/0000-0002-6937-5033
FU Zhejiang University Excellent Doctoral Dissertation Funding [420022C];
   Key Technologies Research and Development Program [2018YFE0106100];
   National Natural Science Foundation of China [51678386]
FX This research was funded and supported by Zhejiang University Excellent
   Doctoral Dissertation Funding, 420022C; The Key Technologies Research
   and Development Program, grant number 2018YFE0106100; National Natural
   Science Foundation of China, grant number 51878592; National Natural
   Science Foundation of China, grant number 51678386.
CR Arata Isozaki Named, 2019, PRITZK PRIZ LAUR
   Attiwill S., 2018, ARTICHOKE
   Blau E., 2010, LOG, P18
   Branscome E., 2017, HANS HOLLEIN POSTMOD
   Brown A., 2019, ARCHITECTURE EXHIBIT
   Brown A, 2019, ARCHIT THEORY REV, V23, P380, DOI 10.1080/13264826.2019.1694399
   Cai G., GAO 2020A OBSERVATIO
   Cai GW, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17051489
   Cheng L., 2018, ARCHITECTUS AUSTR, V107
   Di Martino E., 2005, HIST VENICE BIENNALE
   Dudley J., 2019, J VISUAL ART PRACTIC, V18, DOI [10.1080/14702029.2018.1549192, DOI 10.1080/14702029.2018.1549192]
   Farrell T., 2019, REVISITING POSTMODER
   Figueiredo R., 2018, THESIS ETH ZURICH
   FOUCAULT M, 1970, SOC SC INFORM, V9, P175, DOI 10.1177/053901847000900108
   Foucault M., 1970, ORDER THINGS ARCHAEO
   Foucault Michel, 1970, ORDER THINGS ARCHEOL
   Giamarelos S., 2019, J ARCHITECTURE, V24, P121, DOI [10.1080/13602365.2019.1569388, DOI 10.1080/13602365.2019.1569388]
   Graham D, 1978, 2 WAY MIRROR POWER
   Greenaway J., 2020, ARCHITECTUS AUSTR, V109
   Hansen M.V., 2019, CURATORIAL CHALLENGE
   Heynen H, 2012, ARCHIT THEORY REV, V17, P331, DOI 10.1080/13264826.2012.727443
   Kalayci P.D., 2019, ONLINE J ART DES, V7
   Kose M., 2019, THESIS MIDDLE E TECH
   Levy A., 2010, ARCHITECTURE DISPLAY
   Lingzhi L., 2017, ARCHIT CULT, P39
   Liu S, 2017, NEW ARCHITECTURE, P18
   Mahdavinejad M, 2019, J ARCHIT URBAN, V43, P71, DOI 10.3846/jau.2019.5209
   Mandarano D.E., 2020, THESIS CARLETON U
   McCann E, 2010, URBAN GEOGR, V31, P1004, DOI 10.2747/0272-3638.31.7.1004
   McNeill D, 2009, GLOBAL ARCHITECT: FIRMS, FAME AND URBAN FORM, P1
   Obrist H.U., 2004, WAYS CURATING FARRAR
   Paine A, 2019, FABRICATIONS, V29, P109, DOI 10.1080/10331867.2019.1539892
   Peltason R., 2017, ARCHITECT PRITZKER P
   Pernice R., 2004, J ASIAN ARCH BUILD E, V3, P357, DOI [10.3130/jaabe.3.357, DOI 10.3130/JAABE.3.357]
   Pesic M., 2013, SAJ SERBIAN ARCHITEC, V5
   Rahmoun A., 2018, THESIS GAZI U
   Stratigakos D., 2016, ARE WOMEN ARCHITECTS
   Stratigakos D, 2016, UNFORGETTING WOMEN A
   Szacka L.-C., 2012, THESIS U COLL LONDON
   Szacka LC, 2011, ARCHIT DESIGN, P98, DOI 10.1002/ad.1299
   Walker L., 2000, VIEW INT FEMALE WOME, P90
   Xuanbing Y, 2018, TIME ARCHITECTS, P13
NR 42
TC 0
Z9 0
U1 7
U2 22
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1346-7581
EI 1347-2852
J9 J ASIAN ARCHIT BUILD
JI J. Asian Archit. Build. Eng.
PD MAR 4
PY 2022
VL 21
IS 2
BP 224
EP 233
DI 10.1080/13467581.2020.1869010
EA MAR 2021
PG 10
WC Architecture; Construction & Building Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Architecture; Construction & Building Technology
GA 0D8UJ
UT WOS:000627178800001
OA gold
DA 2022-08-23
ER

PT J
AU Zhu, YQ
   Zhou, WW
   Xu, Y
   Liu, J
   Tan, YJ
AF Zhu, Yueqin
   Zhou, Wenwen
   Xu, Yang
   Liu, Ji
   Tan, Yongjie
TI Intelligent Learning for Knowledge Graph towards Geological Data
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
ID BIG DATA; FUSION
AB Knowledge graph (KG) as a popular semantic network has been widely used. It provides an effective way to describe semantic entities and their relationships by extending ontology in the entity level. This article focuses on the application of KG in the traditional geological field and proposes a novel method to construct KG. On the basis of natural language processing ( NLP) and data mining (DM) algorithms, we analyze those key technologies for designing a KG towards geological data, including geological knowledge extraction and semantic association. Through this typical geological ontology extracting on a large number of geological documents and open linked data, the semantic interconnection is achieved, KG framework for geological data is designed, application system of KG towards geological data is constructed, and dynamic updating of the geological information is completed accordingly. Specifically, unsupervised intelligent learning method using linked open data is incorporated into the geological document preprocessing, which generates a geological domain vocabulary ultimately. Furthermore, some application cases in the KG system are provided to show the effectiveness and efficiency of our proposed intelligent learning approach for KG.
C1 [Zhu, Yueqin; Tan, Yongjie] China Geol Survey, Ctr Res & Dev, Beijing 100037, Peoples R China.
   [Zhu, Yueqin; Zhou, Wenwen; Xu, Yang; Liu, Ji; Tan, Yongjie] Minist Land & Resources, Key Lab Geol Informat Technol, Beijing 100037, Peoples R China.
   [Zhou, Wenwen; Xu, Yang; Liu, Ji] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China.
   [Zhou, Wenwen; Xu, Yang; Liu, Ji] Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China.
RP Zhu, YQ (通讯作者)，China Geol Survey, Ctr Res & Dev, Beijing 100037, Peoples R China.; Zhu, YQ; Xu, Y (通讯作者)，Minist Land & Resources, Key Lab Geol Informat Technol, Beijing 100037, Peoples R China.; Xu, Y (通讯作者)，Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China.; Xu, Y (通讯作者)，Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China.
EM yueqin_zhu@126.com; b20160304@xs.ustb.edu.cn
FU Special Funds Project for Scientific Research of Public Welfare Industry
   from the Ministry of Land and Resources of China [201511079]; National
   Key Technologies RAMP; D Program of China [2015BAK38B01]
FX This work was partially supported by the Special Funds Project for
   Scientific Research of Public Welfare Industry from the Ministry of Land
   and Resources of China under Grant 201511079 and the National Key
   Technologies R& D Program of China under Grant 2015BAK38B01.
CR Altman A., 2005, P 6 ACM C EL COMM, P1, DOI DOI 10.1145/1064009.1064010
   Ballatore A, 2015, ISPRS INT J GEO-INF, V4, P471, DOI 10.3390/ijgi4020471
   Danciulescu D, 2016, CARPATHIAN J MATH, V32, P49
   Dang Y, 2011, DECIS SUPPORT SYST, V50, P415, DOI 10.1016/j.dss.2010.10.003
   Le-Phuoc D, 2016, J WEB SEMANT, V37-38, P25, DOI 10.1016/j.websem.2016.02.003
   Hook P. A., 2007, P 11 INT C INF VIS I
   Jayaram N, 2015, IEEE T KNOWL DATA EN, V27, P2797, DOI 10.1109/TKDE.2015.2426696
   Kamsu-Foguem B, 2013, COMPUT IND, V64, P998, DOI 10.1016/j.compind.2013.06.013
   [李超岭 Li Chaoling], 2015, [地质通报, Geological Bulletin of China], V34, P1288
   Liu HP, 2017, IEEE T AUTOM SCI ENG, V14, P996, DOI 10.1109/TASE.2016.2549552
   Liu HP, 2017, IEEE T INSTRUM MEAS, V66, P2, DOI 10.1109/TIM.2016.2618978
   Liu HP, 2016, IEEE T INSTRUM MEAS, V65, P656, DOI 10.1109/TIM.2016.2514779
   Liu HP, 2015, IEEE T NEUR NET LEAR, V26, P1816, DOI 10.1109/TNNLS.2014.2357036
   [刘峤 Liu Qiao], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P582
   Liu Z. Y., 2014, STUDIES IN PHILOSOPH, V31, P82
   Luo X, 2016, FUTURE GENER COMP SY, V61, P85, DOI 10.1016/j.future.2015.10.022
   Mengxue Cao, 2015, ICIC Express Letters, V9, P2491
   [曲永强 Qu Yongqiang], 2010, [地学前缘, Earth Science Frontiers], V17, P112
   Runge MG, 2016, J VOLCANOL GEOTH RES, V311, P41, DOI 10.1016/j.jvolgeores.2016.01.007
   Song MM, 2014, APPL MECH MATER, V475-476, P306, DOI 10.4028/www.scientific.net/AMM.475-476.306
   Tang J. M., 2010, J INTELLIGENCE, V29, P5
   Vermeesch P, 2015, CHEM GEOL, V409, P20, DOI 10.1016/j.chemgeo.2015.05.004
   [吴冲龙 Wu Chonglong], 2016, [科学通报, Chinese Science Bulletin], V61, P1797
   [严光生 Yan Guangsheng], 2015, [地质通报, Geological Bulletin of China], V34, P1273
   Zhang L., 2015, ELECT J GEOTECHNICAL, V20, P4423
   Zhang T, 2016, J APPL GEOPHYS, V125, P14, DOI 10.1016/j.jappgeo.2015.12.005
   Zhao Y., 2008, LIB TRIBUNE, V28, P63
   Zhu Y., 2015, P 4 INT C ID INF KNO
NR 28
TC 21
Z9 22
U1 5
U2 35
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PY 2017
VL 2017
AR 5072427
DI 10.1155/2017/5072427
PG 13
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EM2SL
UT WOS:000395165800001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Xia, SL
   Zhong, KY
AF Xia, Shiliang
   Zhong, Kaiyang
TI Knowledge Mapping of Green Technology Visualization with Bibliometric
   Tools
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
ID DEEP EUTECTIC SOLVENTS; FERTILIZER TECHNOLOGY; THERMAL HAZARD; ADOPTION;
   REVOLUTION; MANAGEMENT; REMOVAL; SYSTEM; IMPACT; PHYTOREMEDIATION
AB An increasing number of research literature studies about green technology have been accepted by journals of different disciplines due to the rapid technical progress and innovation in all types of industry. This study uses bibliometric tools of CiteSpace and VOSviewer to analyse the key authors' co-citation network, institution cooperation, the keyword clusters of green technology, and the evolution trend of green technology. We find that since 1960s, the number of research papers with green technology theme has been growing. These papers are mainly involved in fields of economics and business, engineering, and chemistry, in which there exist 13 highest cited papers from 2009 to 2020 based on the Web of Science database. In this study, we find the top 20 journals of green technology with the parameter of literature count and centrality. We find that the cooperation of authors is quite weak with the co-authorship analysis, and we obtain the top 12 institutions in 15 countries dominantly in green technology research through country and institution analysis. We conduct a cluster analysis of keywords related to green technology, and we obtain 10 clusters, with three economical clusters, five engineering clusters, and two chemical clusters. Finally, we summarise green technology with the aid of the timeline view function of CiteSpace system.
C1 [Xia, Shiliang] Huaiyin Normal Univ, Sch Econ & Management, Huaian 223001, Jiangsu, Peoples R China.
   [Zhong, Kaiyang] Southwestern Univ Finance & Econ, Sch Econ Informat, Chengdu 611130, Sichuan, Peoples R China.
RP Zhong, KY (通讯作者)，Southwestern Univ Finance & Econ, Sch Econ Informat, Chengdu 611130, Sichuan, Peoples R China.
EM zhongky0626@163.com
OI zhong, kaiyang/0000-0002-5575-0306
FU Jiangsu Federation of Social Sciences [21SCC-24]
FX ,is work was supported by the financial projects funded by Jiangsu
   Federation of Social Sciences (Grant no. 21SCC-24).
CR Adnan N, 2019, TRENDS FOOD SCI TECH, V86, P439, DOI 10.1016/j.tifs.2019.02.040
   Adnan N, 2018, LAND USE POLICY, V79, P575, DOI 10.1016/j.landusepol.2018.08.033
   Ahmed J, 2011, INT J FOOD PROP, V14, P37, DOI 10.1080/10942910903125284
   Al Khawli F, 2019, MAR DRUGS, V17, DOI 10.3390/md17120689
   Annamalai S, 2018, SCI TOTAL ENVIRON, V624, P1649, DOI 10.1016/j.scitotenv.2017.10.047
   Ariga J, 2019, AGR ECON-BLACKWELL, V50, P63, DOI 10.1111/agec.12533
   Ashraf MA, 2014, J SCI FOOD AGR, V94, P388, DOI 10.1002/jsfa.6371
   Baig N, 2019, CHEM ENG J, V360, P1613, DOI 10.1016/j.cej.2018.10.042
   BASCOM W, 1962, ANN NY ACAD SCI, V96, P581, DOI 10.1111/j.1749-6632.1962.tb50148.x
   BAYRI TY, 1989, ECON DEV CULT CHANGE, V38, P113, DOI 10.1086/451778
   Bishop CV, 2003, T I MET FINISH, V81, P13, DOI 10.1080/00202967.2003.11871477
   Bolla R, 2011, IEEE COMMUN MAG, V49, P80, DOI 10.1109/MCOM.2011.5978419
   Bubalo MC, 2015, J CHEM TECHNOL BIOT, V90, P1631, DOI 10.1002/jctb.4668
   BURKE RV, 1979, ECON DEV CULT CHANGE, V28, P135, DOI 10.1086/451157
   Cao BR, 2017, J CLEAN PROD, V142, P1002, DOI 10.1016/j.jclepro.2016.08.145
   Chan APC, 2017, J MANAGE ENG, V33, DOI 10.1061/(ASCE)ME.1943-5479.0000507
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen H., 2019, PERS INDIV DIFFER, V150, P1
   Chen T, 2011, IEEE WIREL COMMUN, V18, P30, DOI 10.1109/MWC.2011.6056690
   Cohen MC, 2016, MANAGE SCI, V62, P1235, DOI 10.1287/mnsc.2015.2173
   Dai YT, 2013, ANAL CHIM ACTA, V766, P61, DOI 10.1016/j.aca.2012.12.019
   Dao V, 2011, J STRATEGIC INF SYST, V20, P63, DOI 10.1016/j.jsis.2011.01.002
   Darko A, 2018, J CLEAN PROD, V200, P687, DOI 10.1016/j.jclepro.2018.07.318
   Darko A, 2017, BUILD ENVIRON, V125, P206, DOI 10.1016/j.buildenv.2017.08.053
   Darko A, 2017, ENERG BUILDINGS, V144, P320, DOI 10.1016/j.enbuild.2017.03.060
   Das S, 2019, INT J BIOL MACROMOL, V133, P294, DOI 10.1016/j.ijbiomac.2019.04.070
   de Melo MMR, 2014, J SUPERCRIT FLUID, V92, P115, DOI 10.1016/j.supflu.2014.04.007
   deGraft-Johnson M, 2014, AGR ECON-BLACKWELL, V45, P555, DOI 10.1111/agec.12106
   Desmet J, 2014, J MATER CHEM A, V2, P20560, DOI 10.1039/c4ta04659e
   Duxson P, 2007, CEMENT CONCRETE RES, V37, P1590, DOI 10.1016/j.cemconres.2007.08.018
   Emamjomeh MM, 2019, SEP PURIF TECHNOL, V217, P85, DOI 10.1016/j.seppur.2019.02.019
   FEDER G, 1981, ECON DEV CULT CHANGE, V30, P59, DOI 10.1086/452539
   Felipe A, 2014, TRANSPORT RES E-LOG, V71, P111, DOI 10.1016/j.tre.2014.09.003
   Flathman PE, 1998, J SOIL CONTAM, V7, P415
   Frayne C, 2010, J GREEN BUILD, V5, P56, DOI 10.3992/jgb.5.1.56
   Gallagher KS, 2011, J ENVIRON ECON MANAG, V61, P1, DOI 10.1016/j.jeem.2010.05.004
   Grenni P, 2019, MICROCHEM J, V147, P1010, DOI 10.1016/j.microc.2019.04.026
   Guo ZY, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16071282
   Hein JR, 2013, ORE GEOL REV, V51, P1, DOI 10.1016/j.oregeorev.2012.12.001
   Helwani Z, 2009, FUEL PROCESS TECHNOL, V90, P1502, DOI 10.1016/j.fuproc.2009.07.016
   Huang C, 2015, FOOD HYDROCOLLOID, V50, P166, DOI 10.1016/j.foodhyd.2015.04.016
   Jenkin TA, 2011, INFORM ORGAN-UK, V21, P17, DOI 10.1016/j.infoandorg.2010.09.003
   Khanal U, 2018, LAND USE POLICY, V76, P645, DOI 10.1016/j.landusepol.2018.02.045
   Krass D, 2013, PROD OPER MANAG, V22, P1035, DOI 10.1111/poms.12023
   Kumar SPJ, 2017, CHEM CENT J, V11, DOI 10.1186/s13065-017-0238-8
   Li XJ, 2017, INT J HOSP MANAG, V60, P77, DOI 10.1016/j.ijhm.2016.10.006
   Liu B, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15051039
   Mason BP, 2007, CHEM REV, V107, P2300, DOI 10.1021/cr050944c
   Munshi K, 2004, J DEV ECON, V73, P185, DOI 10.1016/j.jdeveco.2003.03.003
   Muradov NZ, 2008, INT J HYDROGEN ENERG, V33, P6804, DOI 10.1016/j.ijhydene.2008.08.054
   Najjar YSH, 2016, INT J SUSTAIN ENERGY, V9, P3, DOI 10.1080/19397038.2014.991775
   Naqi A, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11020537
   Neoh CH, 2016, CHEM ENG J, V283, P582, DOI 10.1016/j.cej.2015.07.060
   Novalin S, 2009, BIOFUEL BIOPROD BIOR, V3, P20, DOI 10.1002/bbb.118
   Otsuka K, 2010, J AFR ECON, V19, pii60, DOI 10.1093/jae/ejp025
   Pieczynska A, 2017, CRIT REV ENV SCI TEC, V47, P1282, DOI 10.1080/10643389.2017.1370990
   Poliakoff M, 2007, NATURE, V450, P810, DOI 10.1038/450810a
   Rajha HN, 2019, LWT-FOOD SCI TECHNOL, V111, P138, DOI 10.1016/j.lwt.2019.05.004
   Salama ES, 2019, WORLD J MICROB BIOT, V35, DOI 10.1007/s11274-019-2648-3
   Santamouris M, 2014, SOL ENERGY, V103, P682, DOI 10.1016/j.solener.2012.07.003
   Schiederig T, 2012, R&D MANAGE, V42, P180, DOI 10.1111/j.1467-9310.2011.00672.x
   Sharma VK, 2017, WATER SCI TECH-W SUP, V17, P107, DOI 10.2166/ws.2016.115
   Sharma VK, 2015, ACCOUNTS CHEM RES, V48, P182, DOI 10.1021/ar5004219
   Si J, 2016, SUSTAIN CITIES SOC, V27, P106, DOI 10.1016/j.scs.2016.06.013
   Snoonian D, 2003, ARCHIT REC, V191, P100
   Song ML, 2018, MANAGE DECIS, V56, P188, DOI 10.1108/MD-04-2017-0375
   Song ML, 2017, RESOUR CONSERV RECY, V122, P261, DOI 10.1016/j.resconrec.2017.03.001
   Su XL, 2019, CHEMOSPHERE, V218, P778, DOI 10.1016/j.chemosphere.2018.11.108
   Tiwari BK, 2015, TRAC-TREND ANAL CHEM, V71, P100, DOI 10.1016/j.trac.2015.04.013
   Torpy FR, 2017, AIR QUAL ATMOS HLTH, V10, P575, DOI 10.1007/s11869-016-0452-x
   Tseng JM, 2012, AICHE J, V58, P3792, DOI 10.1002/aic.13745
   Tseng JM, 2011, IND ENG CHEM RES, V50, P9487, DOI 10.1021/ie200712n
   Vachon S, 2007, INT J PROD RES, V45, P4357, DOI 10.1080/00207540701440303
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Viitanen J, 2014, ENVIRON PLANN A, V46, P803, DOI 10.1068/a46242
   Wade S, 2018, BAYESIAN ANAL, V13, P559, DOI 10.1214/17-BA1073
   Wang W., 2018, SUSTAINABILITY-BASEL, V10, P1
   WERNERFELT B, 1984, STRATEGIC MANAGE J, V5, P171, DOI 10.1002/smj.4250050207
   Xu XP, 2017, INT J PROD ECON, V183, P433, DOI 10.1016/j.ijpe.2016.08.029
   Zhang YJ, 2014, IND ENG CHEM RES, V53, P2114, DOI 10.1021/ie403186h
   Zhong KY, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2021.102728
   Zhong KY, 2021, WIREL COMMUN MOB COM, V2021, DOI 10.1155/2021/5552626
   Ziogou I, 2018, SUSTAIN CITIES SOC, V40, P233, DOI 10.1016/j.scs.2018.04.007
NR 83
TC 1
Z9 1
U1 15
U2 15
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD NOV 27
PY 2021
VL 2021
AR 6298813
DI 10.1155/2021/6298813
PG 14
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA ZE9XN
UT WOS:000759230500001
OA gold
DA 2022-08-23
ER

PT J
AU Wei, W
   Ge, J
   Xu, S
   Li, M
   Zhao, Z
   Li, XX
   Zheng, JC
AF Wei, Wei
   Ge, Jie
   Xu, Sha
   Li, Ming
   Zhao, Zhe
   Li, Xiaoxue
   Zheng, Jingchen
TI Knowledge Maps of Disaster Medicine in China Based on Co-Word Analysis
SO DISASTER MEDICINE AND PUBLIC HEALTH PREPAREDNESS
LA English
DT Article
DE cluster analysis; co-word analysis; disaster medicine; social network
   analysis; strategic diagram analysis
ID SOCIAL NETWORK ANALYSIS; COMMUNITY
AB Objective We analyzed research themes in the field of disaster medicine in China to provide references for researchers to understand the research status and developing trends of this field. Methods Published journal articles were retrieved. A social network analysis was conducted to visualize the relations of high-frequency key words. A cluster analysis was used to classify key words. A strategic diagram analysis was conducted to visualize clusters across the entire research field. Results We retrieved 3,079 articles, from which 1,749 articles and 8,284 key words were identified after screening. High-frequency key words were classified into 6 clusters. "Medical rescue" had the highest degree and betweenness centralities. Cluster 4 was located in Quadrant I of the strategic diagrams. Conclusions "Medical rescue" is the core key word, and it serves a pivotal "bridge" function. "Emergencies" and similar terms are key words with special statuses. "Natural disaster medical rescue" and "fundamental theories of disaster medicine" constitute the primary and secondary core themes, respectively. "On-site emergency treatment techniques" is a marginalized theme. The other themes are emerging themes that offer considerable scope for future development. Generally, the scope and depth of investigations in this field should be improved. (Disaster Med Public Health Preparedness. 2019;13:405-409)
C1 [Wei, Wei; Zheng, Jingchen] Fudan Univ, Sch Publ Hlth, Shanghai, Peoples R China.
   [Wei, Wei; Ge, Jie; Xu, Sha; Zhao, Zhe; Li, Xiaoxue; Zheng, Jingchen] Chinese Peoples Armed Police Forces, Gen Hosp, Inst Modern Hosp Management, Beijing, Peoples R China.
   [Li, Ming] Chinese Peoples Armed Police Forces, Gen Hosp, Dept Med, Beijing, Peoples R China.
   [Ge, Jie] Tsinghua Univ, Inst Hosp Management, Beijing, Peoples R China.
RP Zheng, JC (通讯作者)，Chinese Peoples Armed Police Forces, Gen Hosp, Beijing 100039, Peoples R China.
EM hustmyth@163.com
FU National Key Technology Research and Development Program of the Ministry
   of Science and Technology of China [2015BAK08B00]
FX This research was funded by the National Key Technology Research and
   Development Program of the Ministry of Science and Technology of China
   (Grant No. 2015BAK08B00).
CR Burt RS, 2013, ANNU REV PSYCHOL, V64, P527, DOI 10.1146/annurev-psych-113011-143828
   Casella G, 2014, BAYESIAN ANAL, V9, P613, DOI 10.1214/14-BA869
   Coulter N, 1998, J AM SOC INFORM SCI, V49, P1206, DOI 10.1002/(SICI)1097-4571(1998)49:13<1206::AID-ASI7>3.3.CO;2-6
   Davis LB, 2013, INT J PROD ECON, V141, P561, DOI 10.1016/j.ijpe.2012.09.012
   Fu H, 2015, MALARIA J, V14, DOI 10.1186/s12936-015-0715-2
   Gan CM, 2015, SCIENTOMETRICS, V105, P1167, DOI 10.1007/s11192-015-1723-2
   Ghosh R, 2014, DISCRETE CONT DYN-B, V19, P1355, DOI 10.3934/dcdsb.2014.19.1355
   Gong B, 2018, DISASTER MED PUBLIC, V12, P265, DOI 10.1017/dmp.2017.52
   Hossain L, 2015, DISASTER MED PUBLIC, V9, P496, DOI 10.1017/dmp.2015.58
   Hou S, 2016, DISASTER MED PUBLIC, V10, P1
   Khasseh AA, 2017, INFORM PROCESS MANAG, V53, P705, DOI 10.1016/j.ipm.2017.02.001
   Lennquist S, 2003, INT J DIS MED, V1, P9
   Liu GY, 2012, SCIENTOMETRICS, V91, P203, DOI 10.1007/s11192-011-0586-4
   Lurie SJ, 2009, ACAD MED, V84, P1029, DOI 10.1097/ACM.0b013e3181ad16d3
   Mryglod O, 2016, SCIENTOMETRICS, V106, P1151, DOI 10.1007/s11192-015-1820-2
   Price, 1963, Q REV BIOL, V7, P443
   Sweileh WM, 2017, BMC MED INFORM DECIS, V17, DOI 10.1186/s12911-017-0476-7
   [王乾贝 Wang Qianbei], 2016, [护理学杂志, Journal of Nursing Science], V31, P103
   Yang Y, 2012, SCIENTOMETRICS, V90, P659, DOI 10.1007/s11192-011-0541-4
   Zhang X, 2013, NAT HAZARDS, V65, P2215, DOI 10.1007/s11069-012-0471-3
   Zong QJ, 2013, SCIENTOMETRICS, V94, P781, DOI 10.1007/s11192-012-0799-1
NR 21
TC 6
Z9 6
U1 0
U2 32
PU CAMBRIDGE UNIV PRESS
PI NEW YORK
PA 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA
SN 1935-7893
EI 1938-744X
J9 DISASTER MED PUBLIC
JI Dis. Med. Public Health Prep.
PD JUN
PY 2019
VL 13
IS 3
BP 405
EP 409
DI 10.1017/dmp.2018.63
PG 5
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA IK6BI
UT WOS:000476670100004
PM 30033890
DA 2022-08-23
ER

PT J
AU Chen, BY
   Chen, XL
   Lu, P
   Du, Y
AF Chen, Baiyang
   Chen, Xiaoliang
   Lu, Peng
   Du, Yajun
TI CAREA: Cotraining Attribute and Relation Embeddings for Cross-Lingual
   Entity Alignment in Knowledge Graphs
SO DISCRETE DYNAMICS IN NATURE AND SOCIETY
LA English
DT Article
AB Knowledge graphs (KGs) are one of the most widely used techniques of knowledge organizations and have been extensively used in many application fields related to artificial intelligence, for example, web search and recommendations. Entity alignment provides a useful tool for how to integrate multilingual KGs automatically. However, most of the existing studies evaluated ignore the abundant information of entity attributes except for entity relationships. This paper sets out to investigate cross-lingual entity alignment and proposes an iterative cotraining approach (CAREA) to train a pair of independent models. The two models can extract the attribute and the relation features of multilingual KGs, respectively. In each iteration, the two models alternate to predict a new set of potentially aligned entity pairs. Besides, this method further filters through the dynamic threshold value to enhance the two models' supervision. Experimental results on three real-world datasets demonstrate the effectiveness and superiority of the proposed method. The CAREA model improves the performance with at least an absolute increase of 3.9% across all experiment datasets. The code is available at .
C1 [Chen, Baiyang; Chen, Xiaoliang; Du, Yajun] Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Peoples R China.
   [Lu, Peng] Univ Montreal, Dept Comp Sci & Operat Res, Montreal, PQ H3C 3J7, Canada.
RP Chen, XL (通讯作者)，Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Peoples R China.
EM chenby@stu.xhu.edu.cn; xdxlchen@gmail.com; peng.lu@umontreal.ca;
   duyajun@mail.xhu.edu.cn
OI Chen, Xiao-liang/0000-0002-8201-9631; Chen, Baiyang/0000-0001-6038-0541;
   Lu, Peng/0000-0003-2936-1400
FU Social Science Planning Project of Sichuan Province [SC20TJ020];
   National Natural Science Foundation [61902324, 11426179, 61872298];
   Science and Technology Program of Sichuan Province [2020JDRC0067,
   2016JY0244, 2017JQ0059, 2019GFW131, 2020JY, 2020GFW]; Innovation Fund of
   Postgraduate, Xihua University [ycjj2019021, ycjj2020023
   20170410143123]; Fund Project of Chengdu Science and Technology Bureau
   [2017-RK00-00026-ZF]; Scientific Research Fund of Sichuan Provincial
   Education Committee [15ZB0134, 17ZA0360]; Foundation of Cyberspace
   Security Key Laboratory of Sichuan Higher Education Institutions
   [sjzz2016-73]; Sichuan Youth Science and Technology Innovation Research
   Team [2021X]; Open Fund Project of Xihua University [20170410143123]
FX This work was supported by the Social Science Planning Project of
   Sichuan Province (no. SC20TJ020), the National Natural Science
   Foundation (nos. 61902324, 11426179, and 61872298), the Science and
   Technology Program of Sichuan Province (nos. 2020JDRC0067, 2016JY0244,
   2017JQ0059, 2019GFW131, 2020JY, and 2020GFW), the Innovation Fund of
   Postgraduate, Xihua University (nos. ycjj2019021 and ycjj2020023
   20170410143123), the Fund Project of Chengdu Science and Technology
   Bureau (no. 2017-RK00-00026-ZF), the Scientific Research Fund of Sichuan
   Provincial Education Committee (nos. 15ZB0134 and 17ZA0360), the
   Foundation of Cyberspace Security Key Laboratory of Sichuan Higher
   Education Institutions (no. sjzz2016-73), the Sichuan Youth Science and
   Technology Innovation Research Team (no. 2021X), and the Open Fund
   Project of Xihua University (no. 20170410143123). Xianyong Li is
   gratefully acknowledged for his discussion with the authors.
CR [Anonymous], 2011, P 28 INT C INT C MAC
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Blum A., 1998, Proceedings of the Eleventh Annual Conference on Computational Learning Theory, P92, DOI 10.1145/279943.279962
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cao Y., 2020, P 57 ANN M ASS COMP, P1452
   Chen XM, 2017, CHIN AUTOM CONGR, P2115, DOI 10.1109/CAC.2017.8243121
   Cochez M, 2017, LECT NOTES COMPUT SC, V10587, P190, DOI 10.1007/978-3-319-68288-4_12
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Hu  W., 2011, P 20 INT C WORLD WID, P87
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kingma D., 2015, INT C LEARN REPR
   Kipf T.N., 2017, SEMISUPERVISED CLASS
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   Lacoste-Julien S, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P572
   Lin Y., 2016, P 25 INT JOINT C ART, P2866
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Mao X, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P420, DOI 10.1145/3336191.3371804
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Nigam K., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, P86, DOI 10.1145/354756.354805
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9981, P498, DOI 10.1007/978-3-319-46523-4_30
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Socher R., 2013, P 27 ANN C NEUR INF
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Suchanek FM, 2011, PROC VLDB ENDOW, V5, P157, DOI 10.14778/2078331.2078332
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   Sun Z., 2017, LECT NOTES COMPUTER
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Velickovic Petar, 2018, INT C LEARN REPR ICL
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Wu Y, 2019, JOVE-J VIS EXP, DOI 10.3791/58527
   Yang F., 2017, P 31 ANN C NEUR INF, P2320
   Zhu H., 2017, P 26 INT JOINT C ART, P4258, DOI [10.24963/ijcai.2017/595, DOI 10.24963/IJCAI.2017/595]
   Zhu Q., 2019, P 28 INT JOINT C ART, P1943
NR 35
TC 0
Z9 0
U1 1
U2 11
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1026-0226
EI 1607-887X
J9 DISCRETE DYN NAT SOC
JI Discrete Dyn. Nat. Soc.
PD DEC 8
PY 2020
VL 2020
AR 6831603
DI 10.1155/2020/6831603
PG 11
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA PI5HK
UT WOS:000601121100001
OA gold
DA 2022-08-23
ER

PT J
AU Sun, ZQ
   Zhang, QH
   Hu, W
   Wang, CM
   Chen, MH
   Akrami, F
   Li, CK
AF Sun, Zequn
   Zhang, Qingheng
   Hu, Wei
   Wang, Chengming
   Chen, Muhao
   Akrami, Farahnaz
   Li, Chengkai
TI A Benchmarking Study of Embedding-based Entity Alignment for Knowledge
   Graphs
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
ID SCALE
AB Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the same real-world object. Recent advancement in KG embedding impels the advent of embedding-based entity alignment, which encodes entities in a continuous embedding space and measures entity similarities based on the learned embeddings. In this paper, we conduct a comprehensive experimental study of this emerging field. We survey 23 recent embedding-based entity alignment approaches and categorize them based on their techniques and characteristics. We also propose a new KG sampling algorithm, with which we generate a set of dedicated benchmark datasets with various heterogeneity and distributions for a realistic evaluation. We develop an open-source library including 12 representative embedding-based entity alignment approaches, and extensively evaluate these approaches, to understand their strengths and limitations. Additionally, for several directions that have not been explored in current approaches, we perform exploratory experiments and report our preliminary findings for future studies. The benchmark datasets, open-source library and experimental results are all accessible online and will be duly maintained.
C1 [Sun, Zequn; Zhang, Qingheng; Hu, Wei; Wang, Chengming] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R China.
   [Chen, Muhao] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA.
   [Akrami, Farahnaz; Li, Chengkai] Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX USA.
RP Hu, W (通讯作者)，Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R China.
EM zqsun.nju@gmail.com; qhzhang.nju@gmail.com; whu@nju.edu.cn;
   cmwangi.nju@gmail.com; muhaochen@ucla.edu; farahnaz.akrami@mavs.uta.edu;
   cli@uta.edu
RI Akrami, Farahnaz/ABE-5418-2020
OI Sun, Zequn/0000-0003-4177-9199
FU National Key R&D Program of China [2018YFB1004300]; National Natural
   Science Foundation of China [61872172]; Collaborative Innovation Center
   of Novel Software Technology and Industrialization
FX This work was supported by the National Key R&D Program of China (No.
   2018YFB1004300), the National Natural Science Foundation of China (No.
   61872172), and the Collaborative Innovation Center of Novel Software
   Technology and Industrialization.
CR Akrami Farahnaz, 2020, SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, P1995, DOI 10.1145/3318464.3380599
   Bahmani B, 2010, PROC VLDB ENDOW, V4, P173
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185
   Bojanowski P., 2017, T ASSOC COMPUT LING, V5, P135, DOI [DOI 10.1162/TACL_A_00051, 10.1162/tacl_a_00051]
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bruna J., 2014, ICLR
   Cai L, 2018, NAACL, P1470
   CAO YX, 2019, ACL, P1452
   Chen MH, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3998
   Conneau A., 2018, ICLR
   Defferrard M., 2016, NIPS, P3844
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ebraheem M, 2018, PROC VLDB ENDOW, V11, P1454, DOI 10.14778/3236187.3236198
   El-Roby A, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1839, DOI 10.1145/2723372.2749428
   Elmagarmid AK, 2007, IEEE T KNOWL DATA EN, V19, P1, DOI 10.1109/TKDE.2007.250581
   GALE D, 1962, AM MATH MON, V69, P9, DOI 10.2307/2312726
   Gao Yuqing, BUILDING LARGE SCALE
   Ge WY, 2010, LECT NOTES COMPUT SC, V6089, P257
   Getoor L, 2012, ENTITY RESOLUTION TU
   Gionis A, 1999, PROCEEDINGS OF THE TWENTY-FIFTH INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P518
   Glaser H., 2009, WWW WORKSH LINK DAT WWW WORKSH LINK DAT
   Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
   Guo LB, 2019, PR MACH LEARN RES, V97
   Guo LB, 2019, DATA INTELLIGENCE, V1, P289, DOI 10.1162/dint_a_00016
   Hamilton W.L., 2017, ARXIV170905584
   Han X., 2018, P C EMP METH NAT LAN, P139
   HE FZ, 2019, DASFAA, V1446, P367, DOI DOI 10.1007/978-3-030-18576-3_22
   Heimann M, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P117, DOI 10.1145/3269206.3271788
   Hendrickson B, 1995, SUPERCOMP PROC, P626
   Hu  W., 2011, P 20 INT C WORLD WID, P87
   Isele R, 2012, PROC VLDB ENDOW, V5, P1638, DOI 10.14778/2350229.2350276
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Jimenez-Ruiz E, 2011, LECT NOTES COMPUT SC, V7031, P273, DOI 10.1007/978-3-642-25073-6_18
   Kadlec R., 2017, P 2 WORKSH REPR LEAR, P69, DOI [10.18653/v1/W17-2609, DOI 10.18653/V1/W17-2609]
   Kazemi S. M., 2018, NEURAL INFORM PROCES, P4289
   Kimmig A, 2017, PROC INT CONF DATA, P921, DOI 10.1109/ICDE.2017.140
   Kipf T. N, 2017, 2017 INT C LEARN REP
   Lacoste-Julien S, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P572
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Leskovec J., 2006, PROC 12 ACM SIGKDD I, P631, DOI DOI 10.1145/1150402.1150479
   Li C., 2019, EMNLP IJCNLP, P2723, DOI 10.18653/v1/D19-1274
   Li FR, 2017, PROC VLDB ENDOW, V10, P1370, DOI 10.14778/3137628.3137646
   Li SN, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4180
   Lian DF, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1744, DOI 10.1145/3219819.3220034
   LIN JH, 1991, IEEE T INFORM THEORY, V37, P145, DOI 10.1109/18.61115
   Lin XX, 2019, IEEE DATA MINING, P429, DOI 10.1109/ICDM.2019.00053
   Lin Y., 2018, CORR
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   MCVITIE DG, 1971, COMMUN ACM, V14, P486, DOI 10.1145/362619.362631
   Megdiche I, 2016, LECT NOTES COMPUT SC, V9981, P393, DOI 10.1007/978-3-319-46523-4_24
   Mudgal S, 2018, INT CONF MANAGE DATA, P19, DOI 10.1145/3183713.3196926
   Nickel M, 2017, ADV NEUR IN, V30
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Nie J.-Y., 2019, ICLR
   Ormazabal A, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4990
   Otani N., 2018, P 27 INT C COMP LING, P1508
   Pei SC, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3231
   Pei SC, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3130, DOI 10.1145/3308558.3313646
   Qian K, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1379, DOI 10.1145/3132847.3132949
   Radovanovic M, 2010, J MACH LEARN RES, V11, P2487
   Rebele T, 2016, LECT NOTES COMPUT SC, V9982, P177, DOI 10.1007/978-3-319-46547-0_19
   Rossi A., 2020, CORR
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   SCHONEMA.PH, 1966, PSYCHOMETRIKA, V31, P1, DOI 10.1007/BF02289451
   Shao C, 2016, J COMPUT SCI TECH-CH, V31, P185, DOI 10.1007/s11390-016-1620-z
   Shi BX, 2018, AAAI CONF ARTIF INTE, P1957
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Shi Xiaofei, 2019, EMNLP IJCNLP, P813
   Stumpf MPH, 2005, P NATL ACAD SCI USA, V102, P4221, DOI 10.1073/pnas.0501179102
   Suchanek Fabian M., 2012, PROC VLDB ENDOW, V5, P157
   Sun ZQ, 2020, AAAI CONF ARTIF INTE, V34, P222
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Trisedya BD, 2019, AAAI CONF ARTIF INTE, P297
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Velickovic P., 2018, STAT-US, V1050, P4
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Wu YT, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5278
   Wu Yuting, 2019, EMNLP IJCNLP, P240
   Xu K., 2019, ACL
   Yan Zhuang, 2016, Database Systems for Advanced Applications. 21st International Conference, DASFAA 2016. Proceedings: LNCS 9642, P415, DOI 10.1007/978-3-319-32025-0_26
   Yang B., 2015, ICLR
   Yang Hsiu-Wei, 2019, P 2019 C EMP METH NA, P4430
   Yang Mohan, 2017, IJCAI, P1511
   Ye R, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4135
   Zhang J, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P327, DOI 10.1145/3269206.3271705
   Zhang QH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5429
   Zhou X., 2019, CIKM, P1009
   Zhou ZH, 2019, SCI CHINA INFORM SCI, V62, DOI 10.1007/s11432-018-9801-4
   Zhu H., 2017, IJCAI, P4258, DOI DOI 10.24963/IJCAI.2017/595
   Zhu QN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1943
   Zhuang Y, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1917, DOI 10.1145/3132847.3132912
NR 96
TC 17
Z9 19
U1 9
U2 18
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD JUL
PY 2020
VL 13
IS 11
BP 2326
EP 2340
DI 10.14778/3407790.3407828
PG 15
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NU9OR
UT WOS:000573965600038
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Li, ZF
   Liu, H
   Zhang, ZL
   Liu, TT
   Xiong, NN
AF Li, Zhifei
   Liu, Hai
   Zhang, Zhaoli
   Liu, Tingting
   Xiong, Neal N.
TI Learning Knowledge Graph Embedding With Heterogeneous Relation Attention
   Networks
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article; Early Access
DE Semantics; Task analysis; Aggregates; Graph neural networks; Computer
   architecture; Learning systems; Fuses; Graph heterogeneity; graph neural
   networks (GNNs); knowledge graph (KG) embedding; KGs; link prediction
AB Knowledge graph (KG) embedding aims to study the embedding representation to retain the inherent structure of KGs. Graph neural networks (GNNs), as an effective graph representation technique, have shown impressive performance in learning graph embedding. However, KGs have an intrinsic property of heterogeneity, which contains various types of entities and relations. How to address complex graph data and aggregate multiple types of semantic information simultaneously is a critical issue. In this article, a novel heterogeneous GNNs framework based on attention mechanism is proposed. Specifically, the neighbor features of an entity are first aggregated under each relation-path. Then the importance of different relation-paths is learned through the relation features. Finally, each relation-path-based features with the learned weight values are aggregated to generate the embedding representation. Thus, the proposed method not only aggregates entity features from different semantic aspects but also allocates appropriate weights to them. This method can capture various types of semantic information and selectively aggregate informative features. The experiment results on three real-world KGs demonstrate superior performance when compared with several state-of-the-art methods.
C1 [Li, Zhifei; Liu, Hai; Zhang, Zhaoli] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
   [Liu, Tingting] Hubei Univ, Sch Educ, Wuhan 430062, Peoples R China.
   [Xiong, Neal N.] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
   [Xiong, Neal N.] Northeastern State Univ, Dept Math & Comp Sci, Tahlequah, OK 74464 USA.
RP Liu, H (通讯作者)，Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
EM zhifei1993@gmail.com; hailiu0204@ccnu.edu.cn; zl.zhang@ccnu.edu.cn;
   tingtingliu89619@gmail.com
OI Zhang, Zhaoli/0000-0002-0844-0719
FU National Natural Science Foundation of China [6201101288, 62077020,
   62005092, 61875068, 61505064]; Fundamental Research Funds for the
   Central Universities [2020YBZZ006, CCNU20ZT017, CCNU2020ZN008]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 6201101288, Grant 62077020, Grant
   62005092, Grant 61875068, and Grant 61505064 and in part by the
   Fundamental Research Funds for the Central Universities under Grant
   2020YBZZ006, Grant CCNU20ZT017, and Grant CCNU2020ZN008.
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Boski M, 2017, 2017 10TH INTERNATIONAL WORKSHOP ON MULTIDIMENSIONAL (ND) SYSTEMS (NDS)
   Bruna J., 2014, 2 INT C LEARNING REP
   Chen L, 2019, IEEE T CYBERNETICS, V49, P3307, DOI 10.1109/TCYB.2018.2841504
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Cheng JH, 2018, CHI 2018: EXTENDED ABSTRACTS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3170427.3188467
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ebisu T, 2020, IEEE T KNOWL DATA EN, V32, P941, DOI 10.1109/TKDE.2019.2893920
   Hamilton Will, 2017, NEURIPS
   Hongwei Wang, 2019, ACM Transactions on Information Systems, V37, DOI 10.1145/3312738
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Hu Y., 2020, ARXIV
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Ioffe S., 2015, P INT C MACH LEARN L, VVolume 37, P448, DOI DOI 10.1007/S13398-014-0173-7.2
   Jagvaral B, 2020, EXPERT SYST APPL, V142, DOI 10.1016/j.eswa.2019.112960
   Jegelka Stefanie, 2019, P 7 INT C LEARN REPR
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kingma D. P., 2017, ARXIV
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Li FY, 2020, IEEE T CYBERNETICS, V50, P4508, DOI 10.1109/TCYB.2018.2887340
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Livi, 2019, ARXIV PREPRINT ARXIV
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Nguyen D. Q., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Rosa RL, 2019, IEEE T IND INFORM, V15, P2124, DOI 10.1109/TII.2018.2867174
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Shang C, 2019, AAAI CONF ARTIF INTE, P3060
   Shi C, 2017, IEEE T KNOWL DATA EN, V29, P17, DOI 10.1109/TKDE.2016.2598561
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun Z., 2019, 7 INT C LEARN REPR
   Szegedy C, 2016, PROC CVPR IEEE, P2818, DOI 10.1109/CVPR.2016.308
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vashishth S, 2020, AAAI CONF ARTIF INTE, V34, P3009
   Vaswani A, 2017, ADV NEUR IN, V30
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang HW, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P968, DOI 10.1145/3292500.3330836
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu F, 2019, PR MACH LEARN RES, V97
   Wu ZH, 2021, IEEE T NEUR NET LEAR, V32, P4, DOI 10.1109/TNNLS.2020.2978386
   Xu KYL, 2018, PR MACH LEARN RES, V80
   Yang B., 2014, ABS14126575 CORR
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
NR 48
TC 50
Z9 50
U1 22
U2 31
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
DI 10.1109/TNNLS.2021.3055147
EA FEB 2021
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA XT3GN
UT WOS:000733480500001
PM 33606639
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Abad-Navarro, F
   Bernabe-Diaz, JA
   Garcia-Castro, A
   Fernandez-Breis, JT
AF Abad-Navarro, Francisco
   Antonio Bernabe-Diaz, Jose
   Garcia-Castro, Alexander
   Tomas Fernandez-Breis, Jesualdo
TI Semantic Publication of Agricultural Scientific Literature Using
   Property Graphs
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph; property graph; semantic web; digital publishing;
   literature search
ID REVOLUTION; WEB
AB During the last decades, there have been significant changes in science that have provoked a big increase in the number of articles published every year. This increment implies a new difficulty for scientists, who have to do an extra effort for selecting literature relevant for their activity. In this work, we present a pipeline for the generation of scientific literature knowledge graphs in the agriculture domain. The pipeline combines Semantic Web and natural language processing technologies, which make data understandable by computer agents, empowering the development of final user applications for literature searches. This workflow consists of (1) RDF generation, including metadata and contents; (2) semantic annotation of the content; and (3) property graph population by adding domain knowledge from ontologies, in addition to the previously generated RDF data describing the articles. This pipeline was applied to a set of 127 agriculture articles, generating a knowledge graph implemented in Neo4j, publicly available on Docker. The potential of our model is illustrated through a series of queries and use cases, which not only include queries about authors or references but also deal with article similarity or clustering based on semantic annotation, which is facilitated by the inclusion of domain ontologies in the graph.
C1 [Abad-Navarro, Francisco; Antonio Bernabe-Diaz, Jose; Tomas Fernandez-Breis, Jesualdo] Univ Murcia, Dept Informat & Sistemas, IMIB Arrixaca, E-30100 Murcia, Spain.
   [Abad-Navarro, Francisco; Antonio Bernabe-Diaz, Jose; Garcia-Castro, Alexander] BASF SE, G FDR BI-G200,Carl Bosch Str 38, D-67056 Ludwigshafen, Germany.
RP Fernandez-Breis, JT (通讯作者)，Univ Murcia, Dept Informat & Sistemas, IMIB Arrixaca, E-30100 Murcia, Spain.
EM francisco.abad@um.es; joseantonio.bernabe1@um.es; alexgarciac@gmail.com;
   jfernand@um.es
RI Fernandez Breis, Jesualdo Tomas/F-4269-2010
OI Fernandez Breis, Jesualdo Tomas/0000-0002-7558-2880; Abad-Navarro,
   Francisco/0000-0003-0201-3115; garcia, alexander/0000-0003-1238-2539
FU Ministerio de Economia, Industria y Competitividad, Gobierno de Espana;
   European Regional Development Fund [TIN2017-85949-C2-1-R]; Fundacion
   Seneca, Agencia Regional de Ciencia y Tecnologia [20963/PI/18]
FX "This research was partially funded by the Ministerio de Economia,
   Industria y Competitividad, Gobierno de Espana and the European Regional
   Development Fund through grant number TIN2017-85949-C2-1-R, and by the
   Fundacion Seneca, Agencia Regional de Ciencia y Tecnologia through grant
   number 20963/PI/18.
CR [Anonymous], 2001, SCI AM
   Avril P., 2016, CAHIER TECH INRA, V89, P1
   Bagnacani A., 2014, INT C KNOWL ENG KNOW, P101
   Baker T, 2016, COMM COM INF SC, V672, P311, DOI 10.1007/978-3-319-49157-8_27
   Bolker B., 2020, R PACKAGE VERSION 21
   Bornmann L, 2015, J ASSOC INF SCI TECH, V66, P2215, DOI 10.1002/asi.23329
   Bouhali R, 2015, IFIP ADV INF COMM TE, V458, P177, DOI 10.1007/978-3-319-23868-5_13
   Buttigieg PL, 2013, J BIOMED SEMANT, V4, DOI 10.1186/2041-1480-4-43
   Ciccarese P, 2013, J BIOMED SEMANT, V4, DOI 10.1186/2041-1480-4-37
   Ciccarese Paolo, 2011, J Biomed Semantics, V2 Suppl 2, pS4, DOI 10.1186/2041-1480-2-S2-S4
   Constantin A, 2016, SEMANT WEB, V7, P167, DOI 10.3233/SW-150177
   Dooley DM, 2018, NPJ SCI FOOD, V2, DOI 10.1038/s41538-018-0032-6
   Dumontier M, 2014, J BIOMED SEMANT, V5, DOI 10.1186/2041-1480-5-14
   Erling O., 2009, NETWORKED KNOWLEDGE, P7
   Fellows I., 2018, WORDCLOUD WORD CLOUD
   Frazzetto G, 2004, EMBO REP, V5, P18, DOI 10.1038/sj.embor.7400061
   Galili T, 2015, BIOINFORMATICS, V31, P3718, DOI 10.1093/bioinformatics/btv428
   Gangemi A, 2017, SEMANT WEB, V8, P873, DOI 10.3233/SW-160240
   Garcia A, 2018, PEERJ, V6, DOI 10.7717/peerj.4201
   Garcia Castro L Jael, 2013, J Biomed Semantics, V4 Suppl 1, pS5, DOI 10.1186/2041-1480-4-S1-S5
   Guha RV, 2016, COMMUN ACM, V59, P44, DOI 10.1145/2844544
   Huh S, 2014, SCI ED, V1, P99, DOI 10.6087/kcse.2014.1.99
   Jackson RC, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-3002-3
   Jaradeh MY, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P243, DOI 10.1145/3360901.3364435
   Jonquet C, 2018, COMPUT ELECTRON AGR, V144, P126, DOI 10.1016/j.compag.2017.10.012
   Lebo T., 2013, W3C RECOMMENDATION, V30
   Martini D., 2013, P CIGR C SUST AGR IC
   McGuinness D.L., 2004, OWL WEB ONTOLOGY LAN, V10
   Miller E., 1998, D-Lib Magazine, DOI 10.1002/bult.105
   Miller J.J., 2013, P SO ASS INF SYST C, V2324
   Penev L, 2019, PUBLICATIONS, V7, DOI 10.3390/publications7020038
   Peroni S, 2018, LECT NOTES COMPUT SC, V11137, P119, DOI 10.1007/978-3-030-00668-6_8
   Price Derek J. de Solla, 1986, LITTLE SCI BIG SCI
   R Core Team, 2018, R LANG ENV STAT COMP
   Robinson I., 2013, GRAPH DATABASES
   ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7
   Seaborne A., 2008, SPARQL QUERY LANGUAG
   Senderov V, 2018, J BIOMED SEMANT, V9, DOI 10.1186/s13326-017-0174-5
   Shotton D, 2009, LEARN PUBL, V22, P85, DOI 10.1087/2009202
   Sporny M., 2014, W3C RECOMM, V16, P41
   Vanclay JK, 2011, J INFORMETR, V5, P265, DOI 10.1016/j.joi.2010.12.001
   Vrandecic D., 2012, P 21 INT C WORLD WID, P1063, DOI [DOI 10.1145/2187980.2188242, 10.1145/2187980.2188242]
   Weibel S, 1997, B AM SOC INFORM INF, V24, P9, DOI 10.1002/bult.70
NR 43
TC 3
Z9 3
U1 5
U2 21
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD FEB
PY 2020
VL 10
IS 3
AR 861
DI 10.3390/app10030861
PG 25
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA LC4PI
UT WOS:000525305900132
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Choi, W
   Lee, Y
AF Choi, Wonjun
   Lee, Hyunju
TI Inference of Biomedical Relations Among Chemicals, Genes, Diseases, and
   Symptoms Using Knowledge Representation Learning
SO IEEE ACCESS
LA English
DT Article
DE Knowledge representation learning; biomedical knowledge graph
ID DATABASE; ONTOLOGY
AB Knowledge representation learning represents entities and relations of knowledge graph in a continuous low-dimensional semantic space. Recently, various representation learning models have successfully been developed to infer novel relations in general-purpose knowledge bases such as FreeBase and WordNet. However, few studies have used such models for biomedical data for inferring useful relations among biomedical entities such as genes, chemicals, diseases, and symptoms. This study aimed to compare the potential of representation learning models in extracting biomedical relations by using four different types of representation learning models, viz., TransE, PTransE, TransR, and TransH. For training and evaluating the models, we collected and utilized manually curated data from public databases, including relations among chemicals, genes, diseases, and symptoms. Overall, TransE, the most efficient translation-based monolingual knowledge graph embedding model, displayed the best performance with a higher learning speed for large-scale biomedical data. Using TransE, we inferred new relations among chemicals, genes, diseases, and symptoms, and evaluated the reliability of these inferred relations. Furthermore, TransE outperformed an existing statistical method used in the Comparative Toxicogenomics Database for inferring new chemical-disease relations. Together, the present results show that the representation learning model is useful for inferring new biological data from numerous existing biomedical data.
C1 [Choi, Wonjun; Lee, Hyunju] Gwangju Inst Sci & Technol, Sch Elect Engn & Comp Sci, Gwangju 61005, South Korea.
RP Lee, Y (通讯作者)，Gwangju Inst Sci & Technol, Sch Elect Engn & Comp Sci, Gwangju 61005, South Korea.
EM hyunjulee@gist.ac.kr
RI Choi, Wonjun/AAW-4388-2020
FU Bio-Synergy Research Project of the Ministry of Science and ICT through
   the National Research Foundation of Korea (NRF) [NRF-2016M3A9C4939665];
   NRF of Korea - Korean Government (MSIT) [NRF-2018M3A9A7053266]; Korea
   Health Technology Research and Development Project through Korea Health
   Industry Development Institute (KHIDI) - Ministry of Health&Welfare,
   South Korea [HI18C0460]
FX This work was supported in part by the Bio-Synergy Research Project of
   the Ministry of Science and ICT through the National Research Foundation
   of Korea (NRF) under Grant NRF-2016M3A9C4939665, in part by the NRF of
   Korea Grant Funded by the Korean Government (MSIT) under Grant
   NRF-2018M3A9A7053266, and in part by a grant of the Korea Health
   Technology Research and Development Project through the Korea Health
   Industry Development Institute (KHIDI), funded by the Ministry of
   Health&Welfare, South Korea, under Grant HI18C0460.
CR Ayme S., 2010, ORPHANET J RARE DIS, V5, DOI [10.1186/1750-1172-5-S1-P1, DOI 10.1186/1750-1172-5-S1-P1]
   Baldwin B., LINGPIPE
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Choi W, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw011
   Du P, 2009, BIOINFORMATICS, V25, pI63, DOI 10.1093/bioinformatics/btp193
   Fader A, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P1156, DOI 10.1145/2623330.2623677
   Getoor L., 2007, ADAPTIVE COMPUTATION
   Hamosh A, 2005, NUCLEIC ACIDS RES, V33, pD514
   Kibbe WA, 2015, NUCLEIC ACIDS RES, V43, pD1071, DOI 10.1093/nar/gku1011
   King BL, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0046524
   Kuhn M, 2008, NUCLEIC ACIDS RES, V36, pD684, DOI 10.1093/nar/gkm795
   Li H, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0006410
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Mattingly CJ, 2006, J EXP ZOOL PART A, V305A, P689, DOI 10.1002/jez.a.307
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nelson SJ, 2004, ST HEAL T, V107, P67
   Rappaport N, 2013, DATABASE-OXFORD, DOI 10.1093/database/bat018
   Stark C, 2006, NUCLEIC ACIDS RES, V34, pD535, DOI 10.1093/nar/gkj109
   SWANSON DR, 1986, PERSPECT BIOL MED, V30, P7
   Tung CW, 2015, J CHEMINFORMATICS, V7, DOI 10.1186/s13321-015-0077-3
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao H., 2015, ARXIV150905490
   Xue RC, 2013, NUCLEIC ACIDS RES, V41, pD1089, DOI 10.1093/nar/gks1100
NR 26
TC 2
Z9 3
U1 2
U2 4
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 179373
EP 179384
DI 10.1109/ACCESS.2019.2957812
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA KF8JT
UT WOS:000509483800160
OA gold
DA 2022-08-23
ER

PT J
AU Sun, YT
   Bai, YP
   Yang, WL
   Bu, KM
   Tanveer, SK
   Hai, JB
AF Sun, Yitao
   Bai, Yinping
   Yang, Wenlong
   Bu, Kangmin
   Tanveer, Sikander Khan
   Hai, Jiangbo
TI Global Trends in Natural Biopolymers in the 21st Century: A
   Scientometric Review
SO FRONTIERS IN CHEMISTRY
LA English
DT Review
DE natural biopolymer; quantitative visualization; bibliometric analysis;
   VOS viewer; knowledge mapping; Citespace
ID SILVER NANOPARTICLES; CHITOSAN; POLYSACCHARIDES; POLYMER; FIBROIN;
   GLYCOSAMINOGLYCANS; ANTIBACTERIAL; BIOMATERIALS; PERSPECTIVE; COMPOSITES
AB Since the 21st century, natural biopolymers have played an indispensable role in long-term global development strategies, and their research has shown a positive growth trend. However, these substantive scientific results are not conducive to our quick grasp of hotspots and insight into future directions and to understanding which local changes have occurred and which trend areas deserve more attention. Therefore, this study provides a new data-driven bibliometric analysis strategy and framework for mining the core content of massive bibliographic data, based on mathematical models VOS Viewer and CiteSpace software, aiming to understand the research prospects and opportunities of natural biopolymers. The United States is reported to be the most important contributor to research in this field, with numerous publications and active institutions; polymer science is the most popular subject category, but the further emphasis should be placed on interdisciplinary teamwork; mainstream research in this field is divided into five clusters of knowledge structures; since the explosion in the number of articles in 2018, researchers are mainly engaged in three fields: "medical field," "biochemistry field," and "food science fields." Through an in-depth analysis of natural biopolymer research, this article provides a better understanding of trends emerging in the field over the past 22 years and can also serve as a reference for future research.
C1 [Sun, Yitao; Yang, Wenlong; Bu, Kangmin; Hai, Jiangbo] Northwest A&F Univ, Coll Agron, Xianyan, Peoples R China.
   [Bai, Yinping] Southwest Univ Sci & Technol, Coll Life Sci & Engn, Mianyang, Peoples R China.
   [Tanveer, Sikander Khan] Natl Agr Res Ctr, Islamabad, Pakistan.
RP Hai, JB (通讯作者)，Northwest A&F Univ, Coll Agron, Xianyan, Peoples R China.
EM haijiangbo@126.com
FU Ecological Security and Bioremediation Mechanism of Saline-alkali Soil
   Improvement in the Middle Yellow River [DL2021172002L]
FX Funding This study was supported by the Ecological Security and
   Bioremediation Mechanism of Saline-alkali Soil Improvement in the Middle
   Yellow River (No. DL2021172002L).
CR Abriat C, 2021, ACS APPL MATER INTER, V13, P35356, DOI 10.1021/acsami.1c07455
   Vieira MGA, 2011, EUR POLYM J, V47, P254, DOI 10.1016/j.eurpolymj.2010.12.011
   Alizadeh-Sani M, 2020, TRENDS FOOD SCI TECH, V105, P93, DOI 10.1016/j.tifs.2020.08.014
   Alves A, 2010, CARBOHYD RES, V345, P2194, DOI 10.1016/j.carres.2010.07.039
   An S, 2017, CELLULOSE, V24, P951, DOI 10.1007/s10570-016-1141-5
   Arango MC, 2021, INT J POLYM MATER PO, V70, P1115, DOI 10.1080/00914037.2020.1785454
   Arbenz A, 2015, GREEN CHEM, V17, P2626, DOI [10.1039/c5gc00282f, 10.1039/C5GC00282F]
   Berri M, 2016, J APPL PHYCOL, V28, P2999, DOI 10.1007/s10811-016-0822-7
   Bornmann L, 2015, J ASSOC INF SCI TECH, V66, P2215, DOI 10.1002/asi.23329
   Bouhlouli M, 2021, ASAIO J, V67, P709, DOI 10.1097/MAT.0000000000001356
   Boyack KW, 2010, J AM SOC INF SCI TEC, V61, P2389, DOI 10.1002/asi.21419
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen HY, 2015, SCI TOTAL ENVIRON, V512, P143, DOI 10.1016/j.scitotenv.2015.01.025
   Crini G, 2005, PROG POLYM SCI, V30, P38, DOI 10.1016/j.progpolymsci.2004.11.002
   Donthu N, 2021, J BUS RES, V133, P285, DOI 10.1016/j.jbusres.2021.04.070
   Durand D, 2010, J STRUCT BIOL, V169, P45, DOI 10.1016/j.jsb.2009.08.009
   Durieux V, 2010, RADIOLOGY, V255, P342, DOI 10.1148/radiol.09090626
   Dutta S, 2014, ENERG ENVIRON SCI, V7, P3574, DOI 10.1039/c4ee01075b
   Esmaeilzadeh P, 2018, ACS APPL MATER INTER, V10, P8507, DOI 10.1021/acsami.7b19022
   Farokhi M, 2017, INT MATER REV, V62, P367, DOI 10.1080/09506608.2016.1252551
   Faruk O, 2012, PROG POLYM SCI, V37, P1552, DOI 10.1016/j.progpolymsci.2012.04.003
   Finkenstadt VL, 2005, APPL MICROBIOL BIOT, V67, P735, DOI 10.1007/s00253-005-1931-4
   Gadenne V, 2015, J BIOMED MATER RES A, V103, P1021, DOI 10.1002/jbm.a.35245
   Ghasemi-Mobarakeh L, 2019, CURR OPIN BIOMED ENG, V10, P45, DOI 10.1016/j.cobme.2019.02.004
   Gheorghita R, 2021, POLYMERS-BASEL, V13, DOI 10.3390/polym13162729
   Gomez-Guillen MC, 2011, FOOD HYDROCOLLOID, V25, P1813, DOI 10.1016/j.foodhyd.2011.02.007
   Grochowski DM, 2018, COMPR REV FOOD SCI F, V17, P1395, DOI 10.1111/1541-4337.12389
   Guerra NB, 2021, MAT SCI ENG C-MATER, V126, DOI 10.1016/j.msec.2021.112126
   Guo Q, 2021, FOOD BIOSCI, V44, DOI 10.1016/j.fbio.2021.101367
   Harzing AW, 2016, SCIENTOMETRICS, V106, P787, DOI 10.1007/s11192-015-1798-9
   Hassan SU, 2020, SCIENTOMETRICS, V124, P973, DOI 10.1007/s11192-020-03499-1
   Hernes PJ, 2004, GEOCHIM COSMOCHIM AC, V68, P1293, DOI 10.1016/j.gca.2003.09.015
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Howard D, 2008, J ANAT, V213, P66, DOI 10.1111/j.1469-7580.2008.00878.x
   Huang L, 2020, J CLEAN PROD, V252, DOI 10.1016/j.jclepro.2019.119908
   Huber D, 2017, CARBOHYD POLYM, V157, P814, DOI 10.1016/j.carbpol.2016.10.012
   Jain AS, 2021, INT J MOL SCI, V22, DOI 10.3390/ijms222111993
   Jeong B, 2002, ADV DRUG DELIVER REV, V54, P37, DOI 10.1016/S0169-409X(01)00242-3
   Jia MP, 2021, BIOPOLYMERS, V112, DOI 10.1002/bip.23433
   John M, 2019, J INFECT PUBLIC HEAL, V12, P700, DOI 10.1016/j.jiph.2019.03.020
   Kampf G, 2020, J HOSP INFECT, V104, P246, DOI 10.1016/j.jhin.2020.01.022
   Khan FI, 2017, APPL MICROBIOL BIOT, V101, P3513, DOI 10.1007/s00253-017-8229-1
   Khanbabaee K, 2001, NAT PROD REP, V18, P641, DOI 10.1039/b101061l
   Khansari S, 2012, J APPL PHYS, V111, DOI 10.1063/1.3682757
   Kitsara M, 2015, MICROELECTRON ENG, V144, P46, DOI 10.1016/j.mee.2015.02.034
   Koh LD, 2015, PROG POLYM SCI, V46, P86, DOI 10.1016/j.progpolymsci.2015.02.001
   Koyyada A, 2021, J DRUG DELIV SCI TEC, V63, DOI 10.1016/j.jddst.2021.102431
   Lahaye M, 2007, BIOMACROMOLECULES, V8, P1765, DOI 10.1021/bm061185q
   Lima PAL, 2013, MAT SCI ENG C-MATER, V33, P3389, DOI 10.1016/j.msec.2013.04.026
   Li YN, 2021, CHEMOSPHERE, V263, DOI 10.1016/j.chemosphere.2020.128238
   Ling SJ, 2018, ADV FUNCT MATER, V28, DOI 10.1002/adfm.201705291
   Liu DF, 2002, SEMIN THROMB HEMOST, V28, P67, DOI 10.1055/s-2002-20565
   Lutolf MP, 2005, NAT BIOTECHNOL, V23, P47, DOI 10.1038/nbt1055
   Lutz JF, 2016, NAT REV MATER, V1, DOI 10.1038/natrevmats.2016.24
   Lvov Y, 2016, ADV MATER, V28, P1227, DOI 10.1002/adma.201502341
   Madruga LYC, 2022, ADV HEALTHC MATER, V11, DOI 10.1002/adhm.202101979
   Maghsoudi S, 2020, INT J NANOMED, V15, P4363, DOI 10.2147/IJN.S252237
   Malda J, 2013, ADV MATER, V25, P5011, DOI 10.1002/adma.201302042
   McDevitt JP, 2017, ENVIRON SCI TECHNOL, V51, P6611, DOI 10.1021/acs.est.6b05812
   Mizumoto S, 2013, FEBS J, V280, P2462, DOI 10.1111/febs.12156
   Mohanty AK, 2002, J POLYM ENVIRON, V10, P19, DOI 10.1023/A:1021013921916
   Mondal S, 2020, CHEM SCI, V11, P3547, DOI 10.1039/c9sc04392f
   Mongeon P, 2016, SCIENTOMETRICS, V106, P213, DOI 10.1007/s11192-015-1765-5
   Morisada S, 2011, WATER RES, V45, P4028, DOI 10.1016/j.watres.2011.05.010
   Nair LS, 2007, PROG POLYM SCI, V32, P762, DOI 10.1016/j.progpolymsci.2007.05.017
   Oliveira Patricia E., 2020, Nano-Structures & Nano-Objects, V23, DOI 10.1016/j.nanoso.2020.100525
   Prabaharan M, 2008, J BIOMATER APPL, V23, P5, DOI 10.1177/0885328208091562
   Reddy MSB, 2021, POLYMERS-BASEL, V13, DOI 10.3390/polym13071105
   Ripamonti U, 2004, J CELL MOL MED, V8, P169, DOI 10.1111/j.1582-4934.2004.tb00272.x
   Robic A, 2008, CARBOHYD POLYM, V74, P344, DOI 10.1016/j.carbpol.2008.02.020
   Roy H, 2020, CURR PHARM DESIGN, V26, P1666, DOI 10.2174/1381612826666200203121241
   Roy S, 2022, FOOD CHEM, V375, DOI 10.1016/j.foodchem.2021.131885
   Sadeghi A, 2019, INT J BIOL MACROMOL, V121, P625, DOI 10.1016/j.ijbiomac.2018.10.022
   Samir MASA, 2005, BIOMACROMOLECULES, V6, P612, DOI 10.1021/bm0493685
   Sell SA, 2009, ADV DRUG DELIVER REV, V61, P1007, DOI 10.1016/j.addr.2009.07.012
   Shen S, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0191250
   Shi QM, 2017, CARBOHYD RES, V453-454, P1, DOI 10.1016/j.carres.2017.10.020
   Shiffrin RM, 2004, P NATL ACAD SCI USA, V101, P5183, DOI 10.1073/pnas.0307852100
   Shivananda CS, 2020, J MATER SCI-MATER EL, V31, P41, DOI 10.1007/s10854-019-00786-3
   Simoneit BRT, 2002, APPL GEOCHEM, V17, P129, DOI 10.1016/S0883-2927(01)00061-0
   Sionkowska A, 2013, J MOL LIQ, V178, P5, DOI 10.1016/j.molliq.2012.10.042
   Sun YQ, 2019, TRENDS FOOD SCI TECH, V83, P86, DOI 10.1016/j.tifs.2018.11.015
   Tan H, 2021, J ENVIRON MANAGE, V297, DOI 10.1016/j.jenvman.2021.113382
   Terasaka S, 2006, NEUROSURGERY, V58, P134, DOI 10.1227/01.NEU.0000193515.95039.49
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Van Vlierberghe S, 2011, BIOMACROMOLECULES, V12, P1387, DOI 10.1021/bm200083n
   Verlee A, 2017, CARBOHYD POLYM, V164, P268, DOI 10.1016/j.carbpol.2017.02.001
   Vert M, 2005, BIOMACROMOLECULES, V6, P538, DOI 10.1021/bm0494702
   Vishwanath V, 2016, J BIOMAT SCI-POLYM E, V27, P657, DOI 10.1080/09205063.2016.1148303
   Volova TG, 2018, POLYM TEST, V65, P54, DOI 10.1016/j.polymertesting.2017.10.023
   Wang H, 2012, CHEM SOC REV, V41, P1519, DOI 10.1039/c2cs15311d
   Wang WJ, 2011, ELECTROCHIM ACTA, V56, P7347, DOI 10.1016/j.electacta.2011.06.032
   Wang Y, 2021, NPG ASIA MATER, V13, DOI 10.1038/s41427-020-00267-8
   Winnacker M, 2017, POLYM INT, V66, P497, DOI 10.1002/pi.5261
   Xie P, 2015, SCIENTOMETRICS, V105, P611, DOI 10.1007/s11192-015-1689-0
   Xiong R, 2020, CHEM SOC REV, V49, P983, DOI 10.1039/c8cs01007b
   Yadav A, 2021, FOOD REV INT, DOI 10.1080/87559129.2021.1940198
   You J, 2017, ADV MATER, V29, DOI 10.1002/adma.201606895
   Zeeb B, 2018, FOOD FUNCT, V9, P2261, DOI [10.1039/C7FO01849E, 10.1039/c7fo01849e]
   Zhang HJ, 2008, CARBOHYD POLYM, V71, P428, DOI 10.1016/j.carbpol.2007.06.012
   Zhang L, 2020, MATER CHEM FRONT, V4, P1315, DOI 10.1039/d0qm00026d
   Zhang SY, 2013, ACS APPL MATER INTER, V5, P11971, DOI 10.1021/am403203q
   Zhang W, 2021, CURR STEM CELL RES T, V16, P414, DOI 10.2174/1574888X15666201014145703
   Zhang ZH, 2021, ADV MATER TECHNOL-US, V6, DOI 10.1002/admt.202100296
   Zhao XB, 2017, AUTOMAT CONSTR, V80, P37, DOI 10.1016/j.autcon.2017.04.002
   Zhou GY, 2016, INT J ARTIF ORGANS, V39, P37, DOI 10.5301/ijao.5000468
   Zhou ZT, 2017, ADV MATER, V29, DOI 10.1002/adma.201605471
NR 108
TC 0
Z9 0
U1 6
U2 6
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-2646
J9 FRONT CHEM
JI Front. Chem.
PD JUL 7
PY 2022
VL 10
AR 915648
DI 10.3389/fchem.2022.915648
PG 17
WC Chemistry, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry
GA 3D9ZH
UT WOS:000829652100001
PM 35873047
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU KYRIAKIS, JM
   APP, H
   ZHANG, XF
   BANERJEE, P
   BRAUTIGAN, DL
   RAPP, UR
   AVRUCH, J
AF KYRIAKIS, JM
   APP, H
   ZHANG, XF
   BANERJEE, P
   BRAUTIGAN, DL
   RAPP, UR
   AVRUCH, J
TI RAF-1 ACTIVATES MAP KINASE-KINASE
SO NATURE
LA English
DT Article
ID THREONINE PROTEIN-KINASE; EPIDERMAL GROWTH-FACTOR; T-CELL ACTIVATION;
   TYROSINE PHOSPHORYLATION; SIGNAL-TRANSDUCTION; INSULIN; PATHWAYS;
   CASCADE
AB THE normal cellular homologue of the acutely transforming oncogene v-raf is c-raf-1, which encodes a serine/threonine protein kinase that is activated by many extracellular stimuli1. The physiological substrates of the protein c-Raf-1 are unknown. The mitogen-activated protein (MAP) kinases Erk1 and 2 are also activated by mitogens through phosphorylation of Erk tyrosine and threonine residues catalysed by a protein kinase of relative molecular mass 50,000, MAP kinase-kinase (MAPK-K)2-7. Here we report that MAPK-K as well as Erk1 and 2 are constitutively active in v-raf-transformed cells. MAPK-K partially purified from v-raf-transformed cells or from mitogen-treated cells3 can be deactivated by phosphatase 2A. c-Raf-1 purified after mitogen stimulation can reactivate the phosphatase 2A-inactivated MAPK-K over 30-fold in vitro. c-Raf-1 reactivation of MAPK-K coincides with the selective phosphorylation at serine/threonine residues of a polypeptide with M(r) 50,000 which coelutes precisely on cation-exchange chromatography with the MAPK-K activatable by c-Raf-1. These results indicate that c-Raf-1 is an immediate upstream activator of MAPK-K in vivo. To our knowledge, MAPK-K is the first physiological substrate of the c-raf-1 protooncogene product to be identified.
C1 HARVARD UNIV,SCH MED,DEPT MED,BOSTON,MA 02115.
   NCI,FREDERICK CANC RES & DEV CTR,VIRAL CARCINOGENESIS LAB,FREDERICK,MD 21702.
   BROWN UNIV,DIV BIOL & MED,PROVIDENCE,RI 02912.
RP KYRIAKIS, JM (通讯作者)，MASSACHUSETTS GEN HOSP,MED SERV,DIABET UNIT,149 13TH ST,BOSTON,MA 02129, USA.
CR AHN NG, 1991, J BIOL CHEM, V266, P4220
   AHN NG, 1990, J BIOL CHEM, V265, P11487
   ALLEMAIN GL, 1992, MOL CELL BIOL, V12, P2222
   ANDERSON NG, 1991, BIOCHEM J, V277, P573, DOI 10.1042/bj2770573
   ANDERSON NG, 1990, NATURE, V343, P651, DOI 10.1038/343651a0
   BRUDER JT, 1992, GENE DEV, V6, P545, DOI 10.1101/gad.6.4.545
   BURGERING BMT, 1991, EMBO J, V10, P1103, DOI 10.1002/j.1460-2075.1991.tb08050.x
   DENT P, 1990, NATURE, V348, P302, DOI 10.1038/348302a0
   DOWNWARD J, 1990, NATURE, V346, P719, DOI 10.1038/346719a0
   GOMEZ N, 1991, NATURE, V353, P170, DOI 10.1038/353170a0
   GROVE JR, 1987, SCIENCE, V238, P530
   HSI ED, 1989, J BIOL CHEM, V264, P10836
   JONES SW, 1989, J BIOL CHEM, V264, P7747
   KYRIAKIS JM, 1990, BIOCHIM BIOPHYS ACTA, V1054, P73, DOI 10.1016/0167-4889(90)90207-T
   KYRIAKIS JM, 1990, J BIOL CHEM, V265, P17355
   KYRIAKIS JM, 1991, J BIOL CHEM, V266, P10043
   LEE RM, 1992, J BIOL CHEM, V267, P1088
   LEEVERS SJ, 1992, EMBO J, V11, P569, DOI 10.1002/j.1460-2075.1992.tb05088.x
   MATSUDA S, 1992, EMBO J, V11, P973, DOI 10.1002/j.1460-2075.1992.tb05136.x
   PULVERER BJ, 1991, NATURE, V353, P670, DOI 10.1038/353670a0
   RAPP UR, 1991, ONCOGENE, V6, P495
   RAPP UR, 1983, J VIROL, V45, P914, DOI 10.1128/JVI.45.3.914-924.1983
   SATOH T, 1991, P NATL ACAD SCI USA, V88, P3314, DOI 10.1073/pnas.88.8.3314
   STURGILL TW, 1988, NATURE, V334, P715, DOI 10.1038/334715a0
   TAKEDA M, 1988, J BIOL CHEM, V258, P10455
   TROPPMAIR J, IN PRESS ONCOGENE
NR 26
TC 1182
Z9 1189
U1 3
U2 50
PU MACMILLAN MAGAZINES LTD
PI LONDON
PA PORTERS SOUTH, 4 CRINAN ST, LONDON, ENGLAND N1 9XW
SN 0028-0836
J9 NATURE
JI Nature
PD JUL 30
PY 1992
VL 358
IS 6385
BP 417
EP 421
DI 10.1038/358417a0
PG 5
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA JF853
UT WOS:A1992JF85300057
PM 1322500
DA 2022-08-23
ER

PT J
AU Liu, K
AF Liu Kang
TI A survey on neural relation extraction
SO SCIENCE CHINA-TECHNOLOGICAL SCIENCES
LA English
DT Review
DE knowledge graph; relation extraction; event extraction and information
   extraction
AB Relation extraction is a key task for knowledge graph construction and natural language processing, which aims to extract meaningful relational information between entities from plain texts. With the development of deep learning, many neural relation extraction models were proposed recently. This paper introduces a survey on the task of neural relation extraction, including task description, widely used evaluation datasets, metrics, typical methods, challenges and recent research progresses. We mainly focus on four recent research problems: (1) how to learn the semantic representations from the given sentences for the target relation, (2) how to train a neural relation extraction model based on insufficient labeled instances, (3) how to extract relations across sentences or in a document and (4) how to jointly extract relations and corresponding entities? Finally, we give out our conclusion and future research issues.
C1 [Liu Kang] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China.
   [Liu Kang] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
RP Liu, K (通讯作者)，Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China.; Liu, K (通讯作者)，Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
EM kliu@nlpr.ia.ac.cn
FU National Natural Science Foundation of China [61922085, 61533018];
   Natural Key R&D Program of China [2018YFC0830101]; Key Research Program
   of the Chinese Academy of Sciences [ZDBS-SSW-JSC006]; Beijing Academy of
   Artificial Intelligence [BAAI2019QN0301]; Open Project of Beijing Key
   Laboratory of Mental Disorders [2019JSJB06]; independent research
   project of National Laboratory of Pattern Recognition
FX This work was supported by the National Natural Science Foundation of
   China (Grant Nos. 61922085 and 61533018), the Natural Key R&D Program of
   China (Grant No. 2018YFC0830101), the Key Research Program of the
   Chinese Academy of Sciences (Grant No. ZDBS-SSW-JSC006), Beijing Academy
   of Artificial Intelligence (BAAI2019QN0301), the Open Project of Beijing
   Key Laboratory of Mental Disorders (2019JSJB06), and the independent
   research project of National Laboratory of Pattern Recognition.
CR Akimoto K, 2019, P 2019 C EMP METH NA, P6225
   Alt C, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1388
   [Anonymous], 2004, P 8 C COMP NAT LANG
   [Anonymous], ARXIV160902907
   Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   Banko M, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2670
   Beltagy I., 2019, P NAACL MINN MINN US, V1, P1858, DOI 10.18653/v1/N19-1184
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bunescu R.C., 2005, P C HUM LANG TECHN E, P724, DOI DOI 10.3115/1220575.1220666
   Chan Y. S., 2011, P 49 ANN M ASS COMP, P551
   Chang, 2017, P 2017 C EMP METH NA, P1790, DOI DOI 10.18653/V1/D17-1189
   Chen, 2019, P 2019 C N AM CHAPT, V1, P3216
   Christopoulou F, 2019, P 2019 C EMP METH NA, P4927, DOI 10.18653/v1/D19-1498
   Collins M., 2001, CONVOLUTION KERNELS, P625
   Deng X, 2019, PROCEEDINGS OF THE ASME TURBO EXPO: TURBOMACHINERY TECHNICAL CONFERENCE AND EXPOSITION, 2019, VOL 7B
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   dos Santos CN, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P626
   Eberts M, 2019, CORR
   Feng J, 2018, AAAI CONF ARTIF INTE, P5779
   Fu G., 2017, P2017 C EMP METH NAT, P1730, DOI DOI 10.18653/V1/D17-1182
   Fu TJ, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1409
   Gao T, 2019, CORR
   Gao TY, 2019, AAAI CONF ARTIF INTE, P6407
   Guo ZJ, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P241
   GuoDong Z., 2005, P 43 ANN M ASS COMP, P427, DOI DOI 10.3115/1219840.1219893
   Gupta P., 2016, COLING, P2537
   Han J., 2018, ARXIV PREPRINT ARXIV, P2216, DOI DOI 10.18653/V1/D18-1245
   Han X, 2018, AAAI CONF ARTIF INTE, P4832
   Han Xu, 2018, P 2018 C EMPIRICAL M, P4803
   Harale N, 2019, PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND SYSTEMS MANAGEMENT (IESM 2019), P336
   He ZQ, 2019, ARTIF INTELL, V275, P602, DOI 10.1016/j.artint.2019.07.004
   Hendrickx I, 2009, P WORKSH SEM EV REC, P94, DOI DOI 10.3115/1621969.1621986
   Hu L., 2019, P 2019 C EMP METH NA, P3821, DOI 10.18653/v1/D19-1395
   Huang Y., 2019, P C EMP METH NAT LAN, P389
   Jat S., 2017, P 6 WORKSH AUT KNOWL
   Ji GL, 2017, AAAI CONF ARTIF INTE, P3060
   Jia W, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1399
   Joulin A., 2016, CORR
   Kambhatla N, 2004, PROC ACL, P178
   Lam W, 2010, P INT C COMP LING BE, P1399, DOI DOI 10.1061/41123(383)134
   Li Q, 2019, P 2019 C EMP METH NA, P229
   Li Q, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P402, DOI 10.3115/v1/p14-1038
   Li ZR, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4377
   Lin HT, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1073, DOI 10.1145/3308558.3313573
   Lin YK, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P34, DOI 10.18653/v1/P17-1004
   Lin YK, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2124
   Liu, 2018, P 2018 C EMP METH NA, P2236, DOI DOI 10.18653/V1/D18-1247
   Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687
   Luan Y., 2019, NAACL HLT, P3036, DOI 10.18653/v1/N19-1308
   Manning CD, 2017, EMNLP, P35
   Mikolov T., 2013, PROBLEM SOLVING ALGO, P3111
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Mitchell T, 2018, COMMUN ACM, V61, P103, DOI 10.1145/3191513
   Miwa M, 2014, P 2014 C EMP METH NA, P1858, DOI DOI 10.3115/V1/D14-1200
   Miwa M, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1105
   Nagabandi N, 2017, INTERSOC C THERMAL T, P400
   Peng N., 2017, T ASS COMPUTATIONAL, V5, P101, DOI [10.1162/tacl_a_00049, DOI 10.1162/TACL_A_00049]
   Peters M., 2018, IMPROVING LANGUAGE U
   Petroni Fabio, 2019, P 2019 C EMP METH NA, P2463
   Porner N, 2019, CORR
   Qin PD, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P496
   Qin PD, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P2137
   Quirk C, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P1171
   Radford A., 2018, IMPROVING LANGUAGE U
   Radford A., 2018, LANGUAGE MODELS ARE
   Riedel S, 2010, LECT NOTES ARTIF INT, V6323, P148, DOI 10.1007/978-3-642-15939-8_10
   Sabour S., 2017, ADV NEURAL INFORM PR, P3856
   Sahu SK, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4309
   SHEN Y, 2016, TECHNICAL PAPERS, P252, DOI DOI 10.1109/WAINA.2016.74
   Soares LB, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P2895
   Socher R., 2014, PROC C EMPIRICAL MET, P1532, DOI 10.3115/v1/D14-1162
   Song L., 2018, EMNLP, DOI DOI 10.18653/V1/D18-1246
   Song L., 2019, P 2019 C EMP METH NA, P208, DOI DOI 10.18653/V1/D19-1020
   Sorokin D., 2017, P 2017 C EMP METH NA, P1784
   Speer R, 2012, LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P3679
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   Tan Y, 2018, INTL CONF POWER SYST, P986, DOI 10.1109/POWERCON.2018.8601679
   Vashishth Shikhar, 2018, P 2018 C EMP METH NA, P1257
   Vaswani A., 2017, P ADV NEURAL INFORM, P5998
   Wang G., 2018, P 2018 C EMPIRICAL M, P2246, DOI DOI 10.18653/V1/D18-1248
   Wang HY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1371
   Wang LL, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1298
   Wei Z, 2019, CORR
   Xu Y, 2015, P 2015 C EMP METH NA, P1785, DOI DOI 10.18653/V1/D15-1206
   Yang DD, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4532
   Yao Y, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P764
   Yates A., 2004, P 13 INT C WORLD WID, P100, DOI DOI 10.1145/988672.988687
   Ye Q, 2019, P C EMP METH NAT LAN, P3839
   Ye ZX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P2872
   Ye ZX, 2019, P 2019 C N AM CHAPT, P2810, DOI [10.18653/v1/n19-1288, DOI 10.18653/V1/N19-1288]
   Zelenko D, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P71
   Zeng D., 2014, P COLING, P2335
   Zeng D., 2015, P C EMP METH NAT LAN, P1753, DOI DOI 10.18653/V1/D15-1203
   Zeng W, 2017, P 2017 C EMP METH NA, P1768, DOI DOI 10.18653/V1/D17-1186
   Zeng XR, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P506
   Zeng XR, 2018, AAAI CONF ARTIF INTE, P5658
   Zhang Ningyu, 2019, LONG SHORT PAPERS, P3016, DOI [10.18653/v1/n19-1306, DOI 10.18653/V1/N19-1306]
   Zhang S., 2015, PACLIC
   Zhang XS, 2019, AAAI CONF ARTIF INTE, P7484
   Zhang Y., 2018, P 2018 C EMP METH NA, DOI [10.18653/v1/D18-1244, DOI 10.18653/V1/D18-1244]
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
   Zhao Y., 2019, AS C MACH LEARN, P1156
   Zheng SC, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1227, DOI 10.18653/v1/P17-1113
   Zhou X, 2016, INT SYMP INERT SENSO, P54, DOI 10.1109/ISISS.2016.7435543
   Zhu H, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1331
NR 108
TC 5
Z9 5
U1 27
U2 76
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1674-7321
EI 1869-1900
J9 SCI CHINA TECHNOL SC
JI Sci. China-Technol. Sci.
PD OCT
PY 2020
VL 63
IS 10
SI SI
BP 1971
EP 1989
DI 10.1007/s11431-020-1673-6
EA SEP 2020
PG 19
WC Engineering, Multidisciplinary; Materials Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Materials Science
GA OC9OE
UT WOS:000571751900001
DA 2022-08-23
ER

PT J
AU Kramer, A
   Billaud, JN
   Tugendreich, S
   Shiffman, D
   Jones, M
   Green, J
AF Kramer, Andreas
   Billaud, Jean-Noel
   Tugendreich, Stuart
   Shiffman, Dan
   Jones, Martin
   Green, Jeff
TI The Coronavirus Network Explorer: mining a large-scale knowledge graph
   for effects of SARS-CoV-2 on host cell function
SO BMC BIOINFORMATICS
LA English
DT Article
DE COVID-19; Knowledge graph; Drug repurposing; Network biology
ID COVID-19; GENES
AB Background: Leveraging previously identified viral interactions with human host proteins, we apply a machine learning-based approach to connect SARS-CoV-2 viral proteins to relevant host biological functions, diseases, and pathways in a large-scale knowledge graph derived from the biomedical literature. Our goal is to explore how SARS-CoV-2 could interfere with various host cell functions, and to identify drug targets amongst the host genes that could potentially be modulated against COVID-19 by repurposing existing drugs. The machine learning model employed here involves gene embeddings that leverage causal gene expression signatures curated from literature. In contrast to other network-based approaches for drug repurposing, our approach explicitly takes the direction of effects into account, distinguishing between activation and inhibition.
   Results: We have constructed 70 networks connecting SARS-CoV-2 viral proteins to various biological functions, diseases, and pathways reflecting viral biology, clinical observations, and co-morbidities in the context of COVID-19. Results are presented in the form of interactive network visualizations through a web interface, the Coronavirus Network Explorer (CNE), that allows exploration of underlying experimental evidence. We find that existing drugs targeting genes in those networks are strongly enriched in the set of drugs that are already in clinical trials against COVID-19.
   Conclusions: The approach presented here can identify biologically plausible hypotheses for COVID-19 pathogenesis, explicitly connected to the immunological, virological and pathological observations seen in SARS-CoV-2 infected patients. The discovery of repurposable drugs is driven by prior knowledge of relevant functional endpoints that reflect known viral biology or clinical observations, therefore suggesting potential mechanisms of action. We believe that the CNE offers relevant insights that go beyond more conventional network approaches, and can be a valuable tool for drug repurposing. The CNE is available at https://digitalinsights.qiagen.com/coronavirus-.network-.explorer.
C1 [Kramer, Andreas; Billaud, Jean-Noel; Tugendreich, Stuart; Shiffman, Dan; Jones, Martin; Green, Jeff] QIAGEN, Digital Insights, Redwood City, CA 94063 USA.
RP Kramer, A (通讯作者)，QIAGEN, Digital Insights, Redwood City, CA 94063 USA.
EM andreas.kramer@qiagen.com
OI Kramer, Andreas/0000-0003-1352-7242
CR Ackermann M, 2020, NEW ENGL J MED, V383, P120, DOI 10.1056/NEJMoa2015432
   Becker RC, 2020, J THROMB THROMBOLYS, V50, P54, DOI 10.1007/s11239-020-02134-3
   Brann DH, 2020, SCI ADV, V6, DOI 10.1126/sciadv.abc5801
   Chen QF, 2021, BRIEF FUNCT GENOMICS, V20, P249, DOI 10.1093/bfgp/elab006
   Chen ZY, 2020, NAT REV IMMUNOL, V20, P529, DOI 10.1038/s41577-020-0402-6
   Chevrier S, 2021, CELL REP MED, V2, DOI 10.1016/j.xcrm.2020.100166
   Cho H, 2016, CELL SYST, V3, P540, DOI 10.1016/j.cels.2016.10.017
   Costanzo L, 2020, J VASC SURG-VENOUS L, V8, P711, DOI 10.1016/j.jvsv.2020.05.018
   Daniloski Z, 2021, CELL, V184, P92, DOI 10.1016/j.cell.2020.10.030
   Du JC, 2019, BMC GENOMICS, V20, DOI 10.1186/s12864-018-5370-x
   Giamarellos-Bourboulis EJ, 2020, CELL HOST MICROBE, V27, P992, DOI 10.1016/j.chom.2020.04.009
   Gordon DE, 2020, NATURE, V583, P459, DOI 10.1038/s41586-020-2286-9
   Gysi DM, 2021, P NATL ACAD SCI USA, V118, DOI 10.1073/pnas.2025581118
   Hadjadj J, 2020, SCIENCE, V369, P718, DOI 10.1126/science.abc6027
   Herold T, 2020, J ALLERGY CLIN IMMUN, V146, P128, DOI 10.1016/j.jaci.2020.05.008
   Kochi AN, 2020, J CARDIOVASC ELECTR, V31, P1003, DOI 10.1111/jce.14479
   Kramer A, 2014, BIOINFORMATICS, V30, P523, DOI 10.1093/bioinformatics/btt703
   Kumar N, 2020, ISCIENCE, V23, DOI [10.2139/ssrn.3581857, 10.1016/j.isci.2020.101526]
   Lee JS, 2020, SCI IMMUNOL, V5, DOI 10.1126/sciimmunol.abd1554
   Markovsky I, 2012, COMMUN CONTROL ENG, P1, DOI 10.1007/978-1-4471-2227-2_1
   Nelson W, 2019, FRONT GENET, V10, DOI 10.3389/fgene.2019.00381
   Prasad K, 2020, INT J BIOL MACROMOL, V163, P1, DOI 10.1016/j.ijbiomac.2020.06.228
   RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
   Sadegh S, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-17189-2
   Schulte-Schrepping J, 2020, CELL, V182, P1419, DOI 10.1016/j.cell.2020.08.001
   Tang N, 2020, J THROMB HAEMOST, V18, P844, DOI 10.1111/jth.14768
   Terpos E, 2020, AM J HEMATOL, V95, P834, DOI 10.1002/ajh.25829
   Teuwen LA, 2020, NAT REV IMMUNOL, V20, P389, DOI 10.1038/s41577-020-0343-0
   Weiskopf D, 2020, SCI IMMUNOL, V5, DOI 10.1126/sciimmunol.abd2071
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Zhao YW, 2020, FRONT GENET, V11, DOI 10.3389/fgene.2020.00400
   Zhou YD, 2020, CELL DISCOV, V6, DOI 10.1038/s41421-020-0153-3
NR 32
TC 3
Z9 3
U1 3
U2 8
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD MAY 3
PY 2021
VL 22
IS 1
AR 229
DI 10.1186/s12859-021-04148-x
PG 20
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
GA SI4RL
UT WOS:000654812800002
PM 33941085
OA gold, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Feng, BY
   Gao, J
AF Feng, Baiyang
   Gao, Jing
TI AnthraxKP: a knowledge graph-based, Anthrax Knowledge Portal mined From
   biomedical literature
SO DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
LA English
DT Article
ID ANNOTATION; ONTOLOGY
AB Anthrax is a zoonotic infectious disease caused by Bacillus anthracis (anthrax bacterium) that affects not only domestic and wild animals worldwide but also human health. As the study develops in-depth, a large quantity of related biomedical publications emerge. Acquiring knowledge from the literature is essential for gaining insight into anthrax etiology, diagnosis, treatment and research. In this study, we used a set of text mining tools to identify nearly 14 000 entities of 29 categories, such as genes, diseases, chemicals, species, vaccines and proteins, from nearly 8000 anthrax biomedical literature and extracted 281 categories of association relationships among the entities. We curated Anthrax-related Entities Dictionary and Anthrax Ontology. We formed Anthrax Knowledge Graph (AnthraxKG) containing more than 6000 nodes, 6000 edges and 32 000 properties. An interactive visualized Anthrax Knowledge Portal(AnthraxKP) was also developed based on AnthraxKG by using Web technology. AnthraxKP in this study provides rich and authentic relevant knowledge in many forms, which can help researchers carry out research more efficiently.
C1 [Feng, Baiyang; Gao, Jing] Inner Mongolia Agr Univ, Coll Comp & Informat Engn, Erdos East St 29, Hohhot 010011, Peoples R China.
   [Feng, Baiyang; Gao, Jing] Inner Mongolia Autonomous Reg Key Lab Big Data Re, Zhaowuda Rd 306, Hohhot 010018, Peoples R China.
   [Gao, Jing] Inner Mongolia Autonomous Reg Big Data Ctr, Chilechuan St 1, Hohhot 010091, Peoples R China.
RP Gao, J (通讯作者)，Inner Mongolia Agr Univ, Coll Comp & Informat Engn, Erdos East St 29, Hohhot 010011, Peoples R China.; Gao, J (通讯作者)，Inner Mongolia Autonomous Reg Key Lab Big Data Re, Zhaowuda Rd 306, Hohhot 010018, Peoples R China.; Gao, J (通讯作者)，Inner Mongolia Autonomous Reg Big Data Ctr, Chilechuan St 1, Hohhot 010091, Peoples R China.
EM gaojing@imau.edu.cn
OI Feng, Baiyang/0000-0002-6533-9713
FU Inner Mongolia Autonomous Region Natural Science Foundation
   [2019MS03014]; Inner Mongolia Autonomous Region Science and Technology
   Major Special Projects [2019ZD016, 2020ZD0007, 2021ZD0005]; Inner
   Mongolia Autonomous Region 2021 Graduate Research Innovation Project
FX Inner Mongolia Autonomous Region Natural Science Foundation
   (2019MS03014); Inner Mongolia Autonomous Region Science and Technology
   Major Special Projects (2019ZD016, 2020ZD0007, 2021ZD0005); Inner
   Mongolia Autonomous Region 2021 Graduate Research Innovation Project.
CR Amberger JS, 2019, NUCLEIC ACIDS RES, V47, pD1038, DOI 10.1093/nar/gky1151
   Angeli G, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P344
   Bakal G, 2018, J BIOMED INFORM, V82, P189, DOI 10.1016/j.jbi.2018.05.003
   Baumgartner WA, 2007, BIOINFORMATICS, V23, pI41, DOI 10.1093/bioinformatics/btm229
   Bhasuran B, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0200699
   Bird S, 2009, NATURAL LANGUAGE PRO
   Breit A, 2020, BIOINFORMATICS, V36, P4097, DOI 10.1093/bioinformatics/btaa274
   Centers for Disease Control and Prevention,, 2019, CONS I HIGH ED
   Chen CM, 2021, BIOINFORMATICS, V37, P4597, DOI 10.1093/bioinformatics/btab694
   Chen HJ, 2021, DATA INTELLIGENCE, V3, P205, DOI 10.1162/dint_a_00095
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Cohen AM, 2005, BRIEF BIOINFORM, V6, P57, DOI 10.1093/bib/6.1.57
   Davis AP, 2019, NUCLEIC ACIDS RES, V47, pD948, DOI 10.1093/nar/gky868
   Ehrlinger L., 2016, SEMANTICS POSTERS DE, V48, P2
   Gene Ontology Consortium, 2015, NUCLEIC ACIDS RES, V43, pD1049
   Hendricks Katherine A, 2014, Emerg Infect Dis, V20, DOI 10.3201/eid2002.130687
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Inglesby TV, 2002, JAMA-J AM MED ASSOC, V287, P2236, DOI 10.1001/jama.287.17.2236
   Jonnalagadda Siddhartha, 2010, AMIA Annu Symp Proc, V2010, P351
   Kohler S, 2019, NUCLEIC ACIDS RES, V47, pD1018, DOI 10.1093/nar/gky1105
   Larsen PO, 2010, SCIENTOMETRICS, V84, P575, DOI 10.1007/s11192-010-0202-z
   Levine-Clark M, 2006, REF USER SERV Q, V45, P265
   Lipscomb CE, 2000, B MED LIBR ASSOC, V88, P265
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   Mohamed SK, 2020, BIOINFORMATICS, V36, P603, DOI 10.1093/bioinformatics/btz600
   Muller HM, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2103-8
   Natale DA, 2011, NUCLEIC ACIDS RES, V39, pD539, DOI 10.1093/nar/gkq907
   Neveol A, 2011, J BIOMED INFORM, V44, P310, DOI 10.1016/j.jbi.2010.11.001
   Nicholson DN, 2020, COMPUT STRUCT BIOTEC, V18, P1414, DOI 10.1016/j.csbj.2020.05.017
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Sang ST, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2167-5
   Sayers EW, 2020, NUCLEIC ACIDS RES, V48, pD9, DOI 10.1093/nar/gkz899
   Schriml LM, 2012, NUCLEIC ACIDS RES, V40, pD940, DOI 10.1093/nar/gkr972
   Stark JF, 2013, SCI CULT NINET CENT, P1
   Tate JG, 2019, NUCLEIC ACIDS RES, V47, pD941, DOI 10.1093/nar/gky1015
   Thul PJ, 2017, SCIENCE, V356, DOI 10.1126/science.aal3321
   Wei CH, 2019, NUCLEIC ACIDS RES, V47, pW587, DOI 10.1093/nar/gkz389
   WHO, 2008, ANTHRAX HUMANS ANIMA
   Xing WH, 2018, BIOINFORMATICS, V34, P386, DOI 10.1093/bioinformatics/bty263
   Zhang YJ, 2018, J BIOMED INFORM, V81, P83, DOI 10.1016/j.jbi.2018.03.011
NR 40
TC 0
Z9 0
U1 1
U2 1
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1758-0463
J9 DATABASE-OXFORD
JI Database
PD JUN 2
PY 2022
VL 2022
AR baac037
DI 10.1093/database/baac037
PG 9
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA 1T6TB
UT WOS:000804861800001
PM 35653350
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Chen, LH
   Jiang, SH
   Liu, JP
   Wang, C
   Zhang, S
   Xie, CH
   Liang, JQ
   Xiao, YH
   Song, R
AF Chen, Lihan
   Jiang, Sihang
   Liu, Jingping
   Wang, Chao
   Zhang, Sheng
   Xie, Chenhao
   Liang, Jiaqing
   Xiao, Yanghua
   Song, Rui
TI Rule mining over knowledge graphs via reinforcement learning
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Rule mining; Reinforcement learning; Representation learning
AB Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community. Many solutions have been proposed for the rule mining from large-scale KGs, which however are limited in the inefficiency of rule generation and ineffectiveness of rule evaluation. To solve these problems, in this paper we propose a generation-then-evaluation rule mining approach guided by reinforcement learning. Specifically, a two-phased framework is designed. The first phase aims to train a reinforcement learning agent for rule generation from KGs, and the second is to utilize the value function of the agent to guide the step-by-step rule generation. We conduct extensive experiments on several datasets and the results prove that our rule mining solution achieves state-of-the-art performance in terms of efficiency and effectiveness. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
C1 [Chen, Lihan; Jiang, Sihang; Liu, Jingping; Wang, Chao; Xie, Chenhao; Liang, Jiaqing; Xiao, Yanghua] Fudan Univ, Sch Comp Sci, Shanghai Key Lab Data Sci, Shanghai, Peoples R China.
   [Xiao, Yanghua] Fudan Aishu Cognit Intelligence Joint Res Ctr, Shanghai, Peoples R China.
   [Zhang, Sheng; Song, Rui] North Carolina State Univ, Raleigh, NC USA.
RP Xiao, YH (通讯作者)，Fudan Univ, Sch Comp Sci, Shanghai Key Lab Data Sci, Shanghai, Peoples R China.; Xiao, YH (通讯作者)，Fudan Aishu Cognit Intelligence Joint Res Ctr, Shanghai, Peoples R China.
EM lhc825@gmail.com; tedsihangjiang@gmail.com; jpliu17@fudan.edu.cn;
   17110240038@fudan.edu.cn; szhang37@ncsu.edu; redreamality@gmail.com;
   l.j.q.light@gmail.com; shawyh@fudan.edu.cn; rsong@ncsu.edu
RI XIAO, YANG/GPW-5529-2022
OI Chen, Lihan/0000-0002-7670-291X; Song, Rui/0000-0003-1875-2115
FU National Key Research and Development Project [2020AAA0109302]; Shanghai
   Sci-ence and Technology Innovation Action Plan [19511120400]; Shanghai
   Municipal Science and Technology Major Project [2021SHZDZX0103];
   National Natural Science Foundation of China [62102095]; China
   Postdoctoral Science Foundation [2020M681173, 2021T140124]
FX Acknowledgments We thank anonymous reviewers for their comments and
   sug-gestions. This work was supported by National Key Research and
   Development Project (No. 2020AAA0109302) , Shanghai Sci-ence and
   Technology Innovation Action Plan (No. 19511120400) , Shanghai Municipal
   Science and Technology Major Project (No. 2021SHZDZX0103) , National
   Natural Science Foundation of China (No. 62102095) , and China
   Postdoctoral Science Foundation (No. 2020M681173 and No. 2021T140124) .
CR Andrieu C, 2003, MACH LEARN, V50, P5, DOI 10.1023/A:1020281327116
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Barati M, 2017, KNOWL-BASED SYST, V133, P183, DOI 10.1016/j.knosys.2017.07.009
   Bengio Y., 2009, ICML, DOI [10.1145/1553374, DOI 10.1145/1553374.1553380]
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen Y, 2016, VLDB J, V25, P893, DOI 10.1007/s00778-016-0444-3
   Deng Zhi-Hong, 2019, ICLR
   Fang Z, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P438, DOI 10.1145/3308558.3313517
   Florensa C, 2018, PR MACH LEARN RES, V80
   Galarraga L.A., 2013, P 22 INT C WORLD WID, P413, DOI DOI 10.1145/2488388.2488425
   Galarraga L, 2015, VLDB J, V24, P707, DOI 10.1007/s00778-015-0394-1
   Gauci J., 2018, ARXIV181100260
   Graves A, 2017, PR MACH LEARN RES, V70
   Gu Y., 2020, ARXIV200715779
   Ho V.T, 2018, INT SEMANTIC WEB C
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hou Z., 2021, FINDINGS ASS COMPUTA, P4687
   Jabri A, 2019, ADV NEUR IN, V32
   Jiang HW, 2020, NEURAL NETWORKS, V131, P276, DOI 10.1016/j.neunet.2020.07.036
   Lacroix T, 2018, PR MACH LEARN RES, V80
   Lajus J., 2020, EUR SEM WEB C ESWC, P36, DOI DOI 10.1007/978-3-030-49461-2\_3
   Meilicke C., 2020, ARXIV PREPRINT ARXIV
   Mnih V., 2013, ARXIV 2013 T1312 560
   Neelakantan A, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P156
   Omran PG, 2021, IEEE T KNOWL DATA EN, V33, P1348, DOI 10.1109/TKDE.2019.2941685
   Omran PG, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2149
   Ortona S, 2018, PROC INT CONF DATA, P1168, DOI 10.1109/ICDE.2018.00108
   Qu M., 2021, 2021 10 INT C LEARNI, P1
   Sadeghian A., 2019, P 33 INT C NEUR INF, V32, P15347
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Silver D, 2014, PR MACH LEARN RES, V32
   Silver D, 2017, NATURE, V550, P354, DOI 10.1038/nature24270
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Son LH, 2018, KNOWL-BASED SYST, V154, P68, DOI 10.1016/j.knosys.2018.04.038
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sukhbaatar S., 2018, INT C LEARN REPR
   Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
   Tao S., 2021, KNOWL-BASED SYST
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang Q, 2021, KNOWL-BASED SYST, V233, DOI 10.1016/j.knosys.2021.107526
   Wang Q, 2020, KNOWL-BASED SYST, V197, DOI 10.1016/j.knosys.2020.105910
   Yang B., 2014, ABS14126575 CORR
   Yang F, 2017, ADV NEUR IN, V30
   Yang Y., 2019, INT C LEARNING REPRE
   Zou B., 2021, IEEE T CYBERN
NR 47
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD APR 22
PY 2022
VL 242
AR 108371
DI 10.1016/j.knosys.2022.108371
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V6AP
UT WOS:000788424700007
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Djenouri, Y
   Belhadi, A
   Srivastava, G
   Houssein, EH
   Lin, JCW
AF Djenouri, Youcef
   Belhadi, Asma
   Srivastava, Gautam
   Houssein, Essam H.
   Lin, Jerry Chun-Wei
TI Sensor data fusion for the industrial artificial intelligence of things
SO EXPERT SYSTEMS
LA English
DT Article
DE AIoT; cognitive smart cities; deep learning; multi-agent system
ID INTERNET; BLOCKCHAIN; SECURITY; MODEL
AB The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi-agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor-based data. Multi-agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state-of-the-art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions.
C1 [Djenouri, Youcef] SINTEF Digital, Oslo, Norway.
   [Belhadi, Asma] Kristiania Univ Coll, Oslo, Norway.
   [Srivastava, Gautam] Brandon Univ, Brandon, MB, Canada.
   [Srivastava, Gautam] China Med Univ, Res Ctr Interneural Comp, Taichung, Taiwan.
   [Houssein, Essam H.] Minia Univ, Al Minya, Egypt.
   [Lin, Jerry Chun-Wei] Western Norway Univ Appl Sci, Bergen, Norway.
RP Lin, JCW (通讯作者)，Western Norway Univ Appl Sci, Bergen, Norway.
EM jerrylin@ieee.org
RI Houssein, Essam H/C-8941-2016
OI Houssein, Essam H/0000-0002-8127-7233; Srivastava,
   Gautam/0000-0001-9851-4103; Lin, Jerry Chun-Wei/0000-0001-8768-9709
CR Alqahtani F, 2020, COMPUT COMMUN, V150, P216, DOI 10.1016/j.comcom.2019.11.030
   Alsboui T, 2020, COMPUTING, V102, P1345, DOI 10.1007/s00607-020-00806-9
   Arafeh M, 2021, FUTURE GENER COMP SY, V115, P769, DOI 10.1016/j.future.2020.09.030
   Baza M., 2020, P IEEE 91 VEH TECHN
   BELHADI A, 2021, EXPERT SYST
   Belhadi A, 2020, IEEE T CYBERNETICS
   Belhadi A, 2021, INFORM FUSION, V65, P13, DOI 10.1016/j.inffus.2020.08.003
   Bellini P, 2014, J VISUAL LANG COMPUT, V25, P827, DOI 10.1016/j.jvlc.2014.10.023
   Casado-Vara R, 2018, PROCEDIA COMPUT SCI, V134, P393, DOI 10.1016/j.procs.2018.07.193
   Chen CH, 2018, IEEE NETWORK, V32, P24, DOI 10.1109/MNET.2018.1700146
   Chen CY, 2021, IEEE T IND INFORM, V17, P2336, DOI 10.1109/TII.2020.3001095
   Chen Q, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2020.102370
   Ciatto Giovanni, 2019, Advances in Practical Applications of Survivable Agents and Multi-Agent Systems. The PAAMS Collection. 17th International Conference, PAAMS 2019. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 11523), P29, DOI 10.1007/978-3-030-24209-1_3
   Cicirelli F, 2019, 2019 IEEE 5TH WORLD FORUM ON INTERNET OF THINGS (WF-IOT), P35, DOI 10.1109/WF-IoT.2019.8767246
   Dai YY, 2020, IEEE T VEH TECHNOL, V69, P4312, DOI 10.1109/TVT.2020.2973705
   Dai YY, 2019, IEEE NETWORK, V33, P10, DOI 10.1109/MNET.2019.1800376
   Le-Phuoc D, 2016, J WEB SEMANT, V37-38, P25, DOI 10.1016/j.websem.2016.02.003
   Darwish A, 2020, SWARM EVOL COMPUT, V52, DOI 10.1016/j.swevo.2019.100616
   De Mulder W, 2015, COMPUT SPEECH LANG, V30, P61, DOI 10.1016/j.csl.2014.09.005
   Deng HY, 2021, IEEE T IND INFORM, V17, P8357, DOI 10.1109/TII.2020.3046013
   Djenouri Y, 2021, IEEE T IND INFORM, V17, P2947, DOI 10.1109/TII.2020.3001493
   Djenouri Y, 2017, INFORM SCIENCES, V420, P1, DOI 10.1016/j.ins.2017.08.043
   Dwivedi AD, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19020326
   Fournier-Viger P, 2019, STUD BIG DATA, V51, P1, DOI 10.1007/978-3-030-04921-8_1
   Gan WS, 2017, WIRES DATA MIN KNOWL, V7, DOI 10.1002/widm.1216
   Girshick R., 2015, ADV NEUR IN
   Goodwin JC, 2009, EXPERT SYST, V26, P409, DOI 10.1111/j.1468-0394.2009.00505.x
   Iwendi C, 2020, COMPUT COMMUN, V161, P160, DOI 10.1016/j.comcom.2020.07.032
   Li GL, 2018, IEEE T IND INFORM, V14, P4702, DOI 10.1109/TII.2018.2845844
   Li JN, 2019, IEEE T COMPUT SOC SY, V6, P1395, DOI 10.1109/TCSS.2019.2917335
   Lin H, 2021, IEEE T IND INFORM, V17, P7114, DOI 10.1109/TII.2020.3038780
   Lin JCW, 2021, INFORM FUSION, V76, P122, DOI 10.1016/j.inffus.2021.05.011
   Lin JCW, 2021, APPL SOFT COMPUT, V108, DOI 10.1016/j.asoc.2021.107422
   Lin JCW, 2021, IEEE INTERNET THINGS, V8, P5340, DOI 10.1109/JIOT.2020.3032896
   Liu MT, 2019, IEEE T IND INFORM, V15, P3559, DOI 10.1109/TII.2019.2897805
   Lom M, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102092
   Ma YX, 2018, IEEE ACCESS, V6, P14451, DOI 10.1109/ACCESS.2018.2806483
   Ming F. P., 2021, IEEE CONSUM ELECTR M
   Mishkin D, 2017, COMPUT VIS IMAGE UND, V161, P11, DOI 10.1016/j.cviu.2017.05.007
   Qiu J, 2020, IEEE T COMPUT SOC SY, V7, P225, DOI 10.1109/TCSS.2019.2946181
   Qiu J, 2018, IEEE ACCESS, V6, P74854, DOI 10.1109/ACCESS.2018.2881422
   Rose G, 2021, T I BRIT GEOGR, V46, P59, DOI 10.1111/tran.12400
   Tang, 2020, BLOCKCHAIN ENABLED S
   Tange K, 2020, IEEE COMMUN SURV TUT, V22, P2489, DOI 10.1109/COMST.2020.3011208
   Thams F, 2020, IEEE T POWER SYST, V35, P30, DOI 10.1109/TPWRS.2018.2890769
   Weng JS, 2021, IEEE T DEPEND SECURE, V18, P2438, DOI 10.1109/TDSC.2019.2952332
   Wu H, 2020, IEEE ACCESS, V8, P153826, DOI 10.1109/ACCESS.2020.3018170
   Xu LD, 2014, IEEE T IND INFORM, V10, P2233, DOI 10.1109/TII.2014.2300753
   Yun U., 2021, KNOWL-BASED SYST, V212
   Zhang J, 2021, IEEE INTERNET THINGS, V8, P7789, DOI 10.1109/JIOT.2020.3039359
   Zhang QC, 2017, IEEE T IND INFORM, V13, P1193, DOI 10.1109/TII.2017.2684807
   Zhou R, 2019, INFORM SCIENCES, V491, P251, DOI 10.1016/j.ins.2019.04.003
NR 52
TC 0
Z9 0
U1 6
U2 8
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0266-4720
EI 1468-0394
J9 EXPERT SYST
JI Expert Syst.
PD JUN
PY 2022
VL 39
IS 5
SI SI
AR e12875
DI 10.1111/exsy.12875
EA NOV 2021
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1K9FS
UT WOS:000718420900001
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU AlMousa, M
   Benlamri, R
   Khoury, R
AF AlMousa, Mohannad
   Benlamri, Rachid
   Khoury, Richard
TI A novel word sense disambiguation approach using WordNet knowledge graph
SO COMPUTER SPEECH AND LANGUAGE
LA English
DT Article
DE Semantic word sense disambiguation; Knowledge-based; Knowledge graph;
   WordNet
ID REPRESENTATION
AB Various applications in computational linguistics and artificial intelligence rely on high performing word sense disambiguation techniques to solve challenging tasks such as information retrieval, machine translation, question answering, and document clustering. While text comprehension is intuitive for humans, machines face tremendous challenges in processing and interpreting a human's natural language. This paper presents a novel knowledge-based word sense disambiguation algorithm, namely Sequential Contextual Similarity Matrix Multiplication (SCSMM). The SCSMM algorithm combines semantic similarity, heuristic knowledge, and document context to respectively exploit the merits of local sense-based context between consecutive terms, human knowledge about terms, and a document's main topic in disambiguating terms. Unlike other algorithms, the SCSMM algorithm guarantees the capture of the maximum sentence context while maintaining the terms' order within the sentence. The proposed algorithm outperformed all other algorithms when disambiguating nouns on the combined gold standard datasets, while demonstrating comparable results to current stateof-the-art word sense disambiguation systems when dealing with each dataset separately. Furthermore, the paper discusses the impact of granularity level, ambiguity rate, sentence size, and part of speech distribution on the performance of the proposed algorithm.
C1 [AlMousa, Mohannad] Lakehead Univ, Dept Software Engn, Thunder Bay, ON P7B 5E1, Canada.
   [Benlamri, Rachid] Coll North Atlantic Qatar, Doha, Qatar.
   [Khoury, Richard] Univ Laval, Dept Comp Sci & Software Engn, Quebec City, PQ G1V 0A6, Canada.
RP AlMousa, M (通讯作者)，Lakehead Univ, Dept Software Engn, Thunder Bay, ON P7B 5E1, Canada.
EM malmous@lakeheadu.ca; rachid.benlamri@cna-qatar.edu.qa;
   richard.khoury@ift.ulaval.ca
CR Abualigah LM, 2020, CURR MED IMAGING, V16, P296, DOI 10.2174/1573405614666180903112541
   Agirre E., 2018, ARXIV PREPRINT ARXIV
   Agirre E., 2010, P 5 INT WORKSH SEM E, P75
   Agirre E, 2014, COMPUT LINGUIST, V40, P57, DOI 10.1162/COLI_a_00164
   Agirre Eneko, 2009, P 12 C EUR CHAPT ASS, DOI DOI 10.3115/1609067.1609070
   Aliwy A.H., 2019, J COMPUT SCI-NETH, V15, P1004, DOI [10.3844/jcssp.2019.1004.1011, DOI 10.3844/JCSSP.2019.1004.1011]
   AlMousa M., 2020, ARXIV PREPRINT ARXIV
   Banerjee S., 2003, IJCAI, V3, P805
   Basile Pierpaolo, 2014, 25 INT C COMP LING T, P1591, DOI DOI 10.1024/1012-5302/A000007
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Borah P.P., 2014, INT J RECENT TECHNOL, V3, P35
   Cai YY, 2018, J INTELL INF SYST, V51, P23, DOI 10.1007/s10844-017-0479-y
   Camacho-Collados J, 2016, ARTIF INTELL, V240, P36, DOI 10.1016/j.artint.2016.07.005
   Chan Y.S., 2005, AAAI, V5, P1037
   Chaplot D.S., 2018, ARXIV PREPRINT ARXIV
   Chklovski T., 2003, P RECENT ADV NLP RAN
   Edmonds P., 2001, P 2 INT WORKSH EV WO, P1
   GALE W, 1992, 30TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P249
   Giyanani R., 2013, IOSR J COMPUT ENG, V14, P30, DOI [10.9790/0661-1463033, DOI 10.9790/0661-1463033]
   Heng Ji, 2010, 2010Proceedings of 4th International Universal Communication Symposium (IUCS 2010), P181, DOI 10.1109/IUCS.2010.5666225
   Iacobacci I, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P897
   Jurafsky D., 2018, SPEECH LANGUAGE PROC
   Lacerra C, 2020, AAAI CONF ARTIF INTE, V34, P8123
   Landauer T.K, 2007, HDB LATENT SEMANTIC, P15
   Leacock C, 1998, LANG SPEECH & COMMUN, P265
   LESK Michael, 1986, P 5 ANN INT C SYST D, V5, P24, DOI [10.1145/318723.318728, DOI 10.1145/318723.318728]
   Liao XH, 2017, NEUROSCI BIOBEHAV R, V77, P286, DOI 10.1016/j.neubiorev.2017.03.018
   Liu HF, 2001, J BIOMED INFORM, V34, P249, DOI 10.1006/jbin.2001.1023
   Mihalcea R, 2005, P C HUM LANG TECHN E, P411, DOI DOI 10.3115/1220575.1220627
   Mihalcea Rada, 2004, P SENSEVAL 3 3 INT W, P155
   Miller George A, 1994, HUMAN LANGUAGE TECHN
   Mittal K., 2015, ICTACT J SOFT COMPUT, V5
   Moro A., 2015, P 9 INT WORKSH SEM E, P288, DOI DOI 10.18653/V1/S15-2049
   Navigli R., 2013, P SEMEVAL, V2, P222, DOI DOI 10.1016/S0044-328X(82)80082-2
   Navigli R, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1683
   Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355
   Pal A.R., 2015, ARXIV PREPRINT ARXIV
   Palmer M., 2007, Natural Language Engineering, P137, DOI 10.1017/S135132490500402X
   Pantel P., 2002, P ACM SIGKDD C KNOWL, P613
   Pasini T, 2020, ARTIF INTELL, V279, DOI 10.1016/j.artint.2019.103215
   Patterson K, 2007, NAT REV NEUROSCI, V8, P976, DOI 10.1038/nrn2277
   Pedersen T., 2005, 200525 UMSI
   Pradhan S., 2007, P 4 INT WORKSH SEM E, P87
   Raganato A, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P99
   Sarmah J., 2016, IJEM, V6, P37
   Scarlini B, 2020, AAAI CONF ARTIF INTE, V34, P8758
   Sinha R, 2007, ICSC 2007: INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING, PROCEEDINGS, P363, DOI 10.1109/ICSC.2007.87
   Snyder B., 2004, SENSEVAL 3, P41
   SPARCKJONES K, 1972, J DOC, V28, P11, DOI 10.1108/eb026526
   Taghipour K., 2015, P 19 C COMP NAT LANG, P338
   Tong HH, 2006, IEEE DATA MINING, P613
   van den Heuvel MP, 2013, TRENDS COGN SCI, V17, P683, DOI 10.1016/j.tics.2013.09.012
   Vial L., 2019, ARXIV PREPRINT ARXIV
   WU ZB, 1994, 32ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P133
   Yu S. Y., 2019, J MACH LEARN RES
   Zhong Z., 2010, P ACL 2010 SYST DEM, V10, P78
NR 56
TC 0
Z9 0
U1 0
U2 0
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 0885-2308
EI 1095-8363
J9 COMPUT SPEECH LANG
JI Comput. Speech Lang.
PD JUL
PY 2022
VL 74
AR 101337
DI 10.1016/j.csl.2021.101337
PG 23
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Q1QM
UT WOS:000820203600002
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Basawaraj
   Starzyk, JA
   Horzyk, A
AF Basawaraj
   Starzyk, Janusz A.
   Horzyk, Adrian
TI Episodic Memory in Minicolumn Associative Knowledge Graphs
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article
DE Neurons; Training; Semantics; Organizations; Biological neural networks;
   Synapses; Knowledge engineering; Associative episodic memory; distance
   measure; knowledge representation; minicolumn structure; recall quality
ID NETWORKS
AB A generalization of active neural associative knowledge graphs (ANAKGs) to their minicolumn form is presented in this paper. Each minicolumn represents a single symbol, and the activation of an individual neuron in a minicolumn depends on the context of the activation of the presynaptic neuron. The implemented memory model combines the ANAKG associative spiking neuron idea with the idea of the hierarchical temporal memory. This new associative memory organization preserves all properties of ANAKG memories, such as storage of knowledge based on the association of spatiotemporal input sequences, self-organization, quick learning, and recall of the sequential memories, while increasing the recall quality and the memory capacity. The recall quality advantage of the new approach over ANAKG increases with the length of the recalled episodes and the number of neurons used in each minicolumn. We introduced a new distance measure to compare the recalled sequences and defined a recall quality to determine the memory capacity. Performed tests confirmed our claims. Additional tests were performed to illustrate the computational complexity and the efficiency of the developed approach.
C1 [Basawaraj; Starzyk, Janusz A.] Ohio Univ, Sch Elect Engn & Comp Sci, Athens, OH 45701 USA.
   [Starzyk, Janusz A.] Univ Informat Technol & Management Rzeszow, Dept Appl Informat Syst, PL-35225 Rzeszow, Poland.
   [Horzyk, Adrian] AGH Univ Sci & Technol, Dept Automat Biocybernet & Biomed Engn, PL-30059 Krakow, Poland.
RP Horzyk, A (通讯作者)，AGH Univ Sci & Technol, Dept Automat Biocybernet & Biomed Engn, PL-30059 Krakow, Poland.
EM horzyk@agh.edu.pl
RI , Basawaraj/AAX-5275-2021; Horzyk, Adrian/C-6661-2013
OI , Basawaraj/0000-0003-2584-9652; Horzyk, Adrian/0000-0001-9001-4198
FU National Science Centre of Poland [DEC2016/21/B/ST7/02220, AGH
   11.11.120.612]
FX This work was supported by the National Science Centre of Poland under
   Grant DEC2016/21/B/ST7/02220 and Contract AGH 11.11.120.612.
CR Binder JR, 2011, TRENDS COGN SCI, V15, P527, DOI 10.1016/j.tics.2011.10.001
   Buttcher S, 2010, INFORM RETRIEVAL IMP
   Cui YW, 2016, NEURAL COMPUT, V28, P2474, DOI 10.1162/NECO_a_00893
   Graves A, 2014, ARXIVABS14105401
   Hawkins J., 2011, CORTICAL LEARNING AL, P1
   Hawkins J, 2016, FRONT NEURAL CIRCUIT, V10, DOI 10.3389/fncir.2016.00023
   Hawkins J, 2009, PHILOS T R SOC B, V364, P1203, DOI 10.1098/rstb.2008.0322
   Haykin S., 1994, NEURAL NETWORKS COMP, V1st ed.
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Horzyk A., 2013, P 8 INT C KNOWL INF, P1
   Horzyk A., 2017, P KEOD 2017, P67, DOI DOI 10.5220/0006504100670079
   Horzyk A., 2016, P IEEE S COMP INT AT, P1, DOI DOI 10.1109/SSCI.2016.7850029
   Horzyk A, 2018, 2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), P2068, DOI 10.1109/SSCI.2018.8628828
   Horzyk A, 2017, IEEE T NEUR NET LEAR, V28, P3084, DOI 10.1109/TNNLS.2017.2728203
   Horzyk A, 2014, NEUROCOMPUTING, V144, P238, DOI 10.1016/j.neucom.2014.04.046
   Iyyer M., 2014, P 2014 C EMP METH NA, P633, DOI DOI 10.3115/V1/D14-1070
   Izhikevich EM, 2003, IEEE T NEURAL NETWOR, V14, P1569, DOI 10.1109/TNN.2003.820440
   Izhikevich EM, 2006, NEURAL COMPUT, V18, P245, DOI 10.1162/089976606775093882
   Jaeger Herbert, 2003, ADV NEURAL INFORM PR, P609
   KRAMER MA, 1992, COMPUT CHEM ENG, V16, P313, DOI 10.1016/0098-1354(92)80051-A
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Levenshtein V.I., 1966, SOV PHYS DOKL, V10, P707
   Maass W, 1997, NEURAL NETWORKS, V10, P1659, DOI 10.1016/S0893-6080(97)00011-7
   Mikolov T, 2011, INT CONF ACOUST SPEE, P5528
   Plaku E., 2007, ROBOTICS SCI SYSTEMS, P326
   Postle N, 2013, FRONT HUM NEUROSCI, V7, DOI 10.3389/fnhum.2013.00011
   Radev D. R., 2002, P LANG RES EV C, P1
   Ray SK, 2010, PATTERN RECOGN LETT, V31, P1935, DOI 10.1016/j.patrec.2010.06.012
   Schmidhuber J, 2015, NEURAL NETWORKS, V61, P85, DOI 10.1016/j.neunet.2014.09.003
   Sporns O, 2005, PLOS COMPUT BIOL, V1, P245, DOI 10.1371/journal.pcbi.0010042
   Squire LR, 2004, NEUROBIOL LEARN MEM, V82, P171, DOI 10.1016/j.nlm.2004.06.005
   Tellex S., 2003, P 26 ANN INT ACM SIG, P41, DOI DOI 10.1145/860435.860445
   Weston J., 2014, ARXIV14103916
NR 33
TC 4
Z9 4
U1 2
U2 13
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
PD NOV
PY 2019
VL 30
IS 11
BP 3505
EP 3516
DI 10.1109/TNNLS.2019.2927106
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA JK2UN
UT WOS:000494702100024
PM 31395554
DA 2022-08-23
ER

PT J
AU Shin, S
   Jin, X
   Jung, J
   Lee, KH
AF Shin, Sangjin
   Jin, Xiongnan
   Jung, Jooik
   Lee, Kyong-Ho
TI Predicate constraints based question answering over knowledge graph
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Question answering; Predicate constraints; Lexicon; Subgraph matching
ID DISAMBIGUATION; ENTITY
AB Generally, QA systems suffer from the structural difference where a question is composed of unstructured data, while its answer is made up of structured data in a Knowledge Graph (KG). To bridge this gap, most approaches use lexicons to cover data that are represented differently. However, the existing lexicons merely deal with representations for entity and relation mentions rather than consulting the comprehensive meaning of the question. To resolve this, we design a novel predicate constraints lexicon which restricts subject and object types for a predicate. It facilitates a comprehensive validation of a subject, predicate and object simultaneously. In this paper, we propose Predicate Constraints based Question Answering (PCQA). Our method prunes inappropriate entity/relation matchings to reduce search space, thus leading to an improvement of accuracy. Unlike the existing QA systems, we do not use any templates but generates query graphs to cover diverse types of questions. In query graph generation, we put more focus on matching relations rather than linking entities. This is well-suited to the use of predicate constraints. Our experimental results prove the validity of our approach and demonstrate a reasonable performance compared to other methods which target WebQuestions and Free917 benchmarks.
C1 [Shin, Sangjin; Jin, Xiongnan; Jung, Jooik; Lee, Kyong-Ho] Yonsei Univ, Dept Comp Sci, 50 Yonsei Ro, Seoul 03722, South Korea.
RP Lee, KH (通讯作者)，Yonsei Univ, Dept Comp Sci, 50 Yonsei Ro, Seoul 03722, South Korea.
EM khlee89@yonsei.ac.kr
OI Shin, Sangjin/0000-0003-3687-7587; Jin, Xiongnan/0000-0001-5080-1883
FU National Research Foundation of Korea (NRF) - Korean government [MSIP]
   [NRF-2016R1A2B4015873]
FX This work was supported by National Research Foundation of Korea (NRF)
   grant, which are funded by the Korean government. [MSIP(no.
   NRF-2016R1A2B4015873)].
CR Abujabal A, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1191, DOI 10.1145/3038912.3052583
   Bao JW, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P967
   Bast H., 2015, P 24 ACM INT C INF K, P1431, DOI [10.1145/2806416.2806472, DOI 10.1145/2806416.2806472]
   Berant J, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1415, DOI 10.3115/v1/p14-1133
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   BREIMAN L, 2001, MACH LEARN, V0045
   Cai Qingqing, 2013, ACL
   Capannini G, 2016, INFORM PROCESS MANAG, V52, P1161, DOI 10.1016/j.ipm.2016.05.004
   Cornolti M., 2014, ERD 14, P25, DOI [10.1145/ 2633211.2634348, DOI 10.1145/2633211.2634348]
   Das R, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2, P358, DOI 10.18653/v1/P17-2057
   Fader A, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P1156, DOI 10.1145/2623330.2623677
   Fader Anthony, 2013, ACL
   Feng Y., 2016, P COLING 2016 26 INT, P2397
   Finkel J.R., 2005, P 43 ANN M ASS COMP, P363, DOI DOI 10.3115/1219840.1219885
   Franco-Salvador M, 2016, INFORM PROCESS MANAG, V52, P550, DOI 10.1016/j.ipm.2015.12.004
   Franco-Salvador M, 2015, KNOWL-BASED SYST, V86, P46, DOI 10.1016/j.knosys.2015.05.020
   Hao YC, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P221, DOI 10.18653/v1/P17-1021
   Hazrina S, 2017, INFORM PROCESS MANAG, V53, P52, DOI 10.1016/j.ipm.2016.06.006
   Hearst M. A., 1992, P 14 INT C COMP LING, V2, P539, DOI DOI 10.3115/992133.992154
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Iyyer M., 2014, P 2014 C EMP METH NA, P633, DOI DOI 10.3115/V1/D14-1070
   Iyyer M, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1821, DOI 10.18653/v1/P17-1167
   Ji GL, 2017, AAAI CONF ARTIF INTE, P3060
   Jurafsky, 2009, P JOINT C 47 ANN M A, P1003
   Kaur S, 2017, INT J MACHINE LEARNI, V1, P33
   Lukovnikov D, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1211, DOI 10.1145/3038912.3052675
   Reddy S, 2016, T ASSOC COMPUT LING, V4, P127, DOI DOI 10.1162/TACL_A_00088
   Ru CS, 2018, INFORM PROCESS MANAG, V54, P593, DOI 10.1016/j.ipm.2018.04.002
   Savenkov D, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P235, DOI 10.1145/2911451.2911536
   Spitkovsky VI, 2012, LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P3168
   ULLMANN JR, 1976, J ACM, V23, P31, DOI 10.1145/321921.321925
   Unger C., 2012, P 21 INT C WORLD WID, P639, DOI DOI 10.1145/2187836.2187923
   Wen D., 2008, NLPKE18, P1, DOI DOI 10.1109/NLPKE.2008.4906769
   Winkler W. E, 1990, P SEC SURV RES WASH, P433
   Xu K, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2326
   Yahya Mohamed, 2012, EMNLP
   Yang Y, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P504
   Yih WT, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P1321
   Yin W, 2016, ARXIV160603391
   Zhao G, 2016, INFORM PROCESS MANAG, V52, P1247, DOI 10.1016/j.ipm.2016.06.002
   Zheng SC, 2017, NEUROCOMPUTING, V257, P59, DOI 10.1016/j.neucom.2016.12.075
   Zou L, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P313, DOI 10.1145/2588555.2610525
NR 42
TC 20
Z9 20
U1 2
U2 56
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD MAY
PY 2019
VL 56
IS 3
BP 445
EP 462
DI 10.1016/j.ipm.2018.12.003
PG 18
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA HR4OT
UT WOS:000463126500005
DA 2022-08-23
ER

PT J
AU Cui, ZH
   Chen, HX
   Cui, LZ
   Liu, SJ
   Liu, XY
   Xu, GD
   Yin, HZ
AF Cui, Zhihong
   Chen, Hongxu
   Cui, Lizhen
   Liu, Shijun
   Liu, Xueyan
   Xu, Guandong
   Yin, Hongzhi
TI Reinforced KGs reasoning for explainable sequential recommendation
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Reinforcement learning; Sequential recommendation; Path reasoning;
   Knowledge graphs
ID KNOWLEDGE
AB We explore the semantic-rich structured information derived from the knowledge graphs (KGs) associated with the user-item interactions and aim to reason out the motivations behind each successful purchase behavior. Existing works on KGs-based explainable recommendations focus purely on path reasoning based on current user-item interactions, which generally result in the incapability of conjecturing users' subsequence preferences. Considering this, we attempt to model the KGs-based explainable recommendation in sequential settings. Specifically, we propose a novel architecture called Reinforced Sequential Learning with Gated Recurrent Unit (RSL-GRU), which is composed of a Reinforced Path Reasoning Network (RPRN) component and a GRU component. RSL-GRU takes users' sequential behaviors and their associated KGs in chronological order as input and outputs potential top-N items for each user with appropriate reasoning paths from a global perspective. Our RPRN features a remarkable path reasoning capacity, which is regulated by a user-conditioned derivatively action pruning strategy, a soft reward strategy based on an improved multi-hop scoring function, and a policy-guided sequential path reasoning algorithm. Experimental results on four of Amazon's large-scale datasets show that our method achieves excellent results compared with several state-of-the-art alternatives.
C1 [Cui, Zhihong; Cui, Lizhen; Liu, Shijun] Shandong Univ, Sch Software, Jinan, Peoples R China.
   [Chen, Hongxu; Xu, Guandong] Univ Technol Sydney, Ultimo, Australia.
   [Liu, Xueyan] Jilin Univ, Sch Comp Sci & Technol, Changchun 130012, Peoples R China.
   [Yin, Hongzhi] Univ Queensland, Brisbane, Qld, Australia.
RP Liu, SJ (通讯作者)，Shandong Univ, Sch Software, Jinan, Peoples R China.; Chen, HX (通讯作者)，Univ Technol Sydney, Ultimo, Australia.
EM czh@mail.sdu.edu.cn; hongxu.chen@uts.edu.au; clz@sdu.edu.cn;
   lsj@sdu.edu.cn; xueyanl17@mails.jlu.edu.cn; guandong.xu@uts.edu.au;
   h.yin1@uq.edu.cn
OI Yin, Hongzhi/0000-0003-1395-261X
FU National Natural Science Foundation of China [61872222]; Natural Science
   Foundation of Shandong Province [ZR2020LZH011]; Young Scholars Program
   of Shandong University; ARC Discovery Project [DP200101374, LP170100891,
   DP190101985]
FX The authors would like to acknowledge the support provided by the
   National Natural Science Foundation of China under Grant 61872222, the
   Natural Science Foundation of Shandong Province (ZR2020LZH011), the
   Young Scholars Program of Shandong University, and the ARC Discovery
   Project (Grant No. DP200101374, LP170100891, and DP190101985).
CR Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   Alkawsi GA, 2020, IEEE ACCESS, V8, P42794, DOI 10.1109/ACCESS.2020.2977060
   Bianchi F, 2020, STUD SEMANTIC WEB, V47, P49, DOI 10.3233/SSW200011
   Bo Zhao, 2020, Web Information Systems and Applications. 17th International Conference, WISA 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12432), P96, DOI 10.1007/978-3-030-60029-7_9
   Cui Q, 2020, IEEE T KNOWL DATA EN, V32, P317, DOI 10.1109/TKDE.2018.2881260
   Dai FF, 2019, LECT NOTES COMPUT SC, V11537, P580, DOI 10.1007/978-3-030-22741-8_41
   Gong JB, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P79, DOI [10.1145/3397271.3401057, 10.1145/11221.27]
   He X, 2020, RECSYS 2020: 14TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, P210, DOI 10.1145/3383313.3412233
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Huang XW, 2019, PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19), P548, DOI 10.1145/3343031.3350893
   Kolahkaj M, 2020, ELECTRON COMMER R A, V42, DOI 10.1016/j.elerap.2020.100978
   Liang W., 2017, NEUROCOMPUTING
   Liu Y., 2019, ARXIV190900672
   Liu Z, 2020, INT J COMPUT INT SYS, V13, P24, DOI 10.2991/ijcis.d.200114.001
   Ma C, 2020, AAAI CONF ARTIF INTE, V34, P5045
   Palumbo E, 2018, LECT NOTES COMPUT SC, V11155, P117, DOI 10.1007/978-3-319-98192-5_22
   Qu, 2020, ARXIV200413579
   Saito T, 2020, INT J DIST EDUC, V18, P36, DOI 10.4018/IJDET.2020010103
   Shi DQ, 2020, KNOWL-BASED SYST, V195, DOI 10.1016/j.knosys.2020.105618
   Songtao Wu, 2019, arXiv
   Suzuki T, 2019, IEEE INT CONF BIG DA, P4638, DOI 10.1109/BigData47090.2019.9005590
   Tang JX, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P565, DOI 10.1145/3159652.3159656
   Vollgraf, 2017, CEUR WS ORG, V1922, P45
   Wang BC, 2020, INFORMATION, V11, DOI 10.3390/info11080388
   Wang LQ, 2017, IEEE IJCNN, P619, DOI 10.1109/IJCNN.2017.7965910
   Wang L, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2447, DOI 10.1145/3219819.3219961
   Wang PF, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P209, DOI 10.1145/3397271.3401134
   Wang T, 2020, IEEE ACCESS, V8, P134817, DOI 10.1109/ACCESS.2020.3011279
   Wang X, 2013, AUST J MECH ENG, V11, P21, DOI 10.7158/M12-058.2013.11.1
   WANG X, 2019, KDD 19 P 25 ACM SIGK, V9, P950
   WANG X, 2019, 33 AAAI C ARTIFICIAL, V8, P5329
   Xian YK, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P285, DOI 10.1145/3331184.3331203
   Xiong Q., 2020, ARXIV200804185
   Xu CY, 2020, 2020 6TH INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING AND COMMUNICATIONS (BIGCOM 2020), P1, DOI 10.1109/BigCom51056.2020.00007
   Yang C, 2017, ACM T INFORM SYST, V35, DOI 10.1145/3041658
   Yu X., 2013, RECSYS, P347
   Yuan WH, 2020, INFORM SCIENCES, V510, P122, DOI 10.1016/j.ins.2019.09.007
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhao H, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P635, DOI 10.1145/3097983.3098063
   Zhao KZ, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P239, DOI 10.1145/3397271.3401171
   Zhao ZY, 2019, IEICE T INF SYST, VE102D, P737, DOI 10.1587/transinf.2018DAP0003
   Zhe Wang, 2019, 2019 IEEE 5th International Conference on Computer and Communications (ICCC), P1338, DOI 10.1109/ICCC47050.2019.9064099
   Zhu HP, 2018, KNOWL-BASED SYST, V143, P102, DOI 10.1016/j.knosys.2017.12.011
   Zhu QN, 2020, AAAI CONF ARTIF INTE, V34, P6999
   Zhu QN, 2019, AAAI CONF ARTIF INTE, P5973
   Zhu R, 2019, INFORMATION, V10, DOI 10.3390/info10040127
NR 46
TC 0
Z9 0
U1 7
U2 16
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
PD MAR
PY 2022
VL 25
IS 2
SI SI
BP 631
EP 654
DI 10.1007/s11280-021-00902-6
EA JUN 2021
PG 24
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZK2AM
UT WOS:000662116400001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Qin, Y
   Shen, GW
   Zhao, WB
   Chen, YP
   Yu, M
   Jin, X
AF Qin, Ya
   Shen, Guo-wei
   Zhao, Wen-bo
   Chen, Yan-ping
   Yu, Miao
   Jin, Xin
TI A network security entity recognition method based on feature template
   and CNN-BiLSTM-CRF
SO FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
LA English
DT Article
DE Network security entity; Security knowledge graph (SKG); Entity
   recognition; Feature template; Neural network
AB By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template (FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.
C1 [Qin, Ya; Shen, Guo-wei; Zhao, Wen-bo; Chen, Yan-ping] Guizhou Univ, Coll Comp Sci & Technol, Guiyang 550025, Guizhou, Peoples R China.
   [Qin, Ya; Shen, Guo-wei; Zhao, Wen-bo; Chen, Yan-ping] Guizhou Prov Key Lab Publ Big Data, Guiyang 550025, Guizhou, Peoples R China.
   [Yu, Miao] Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China.
   [Jin, Xin] Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China.
RP Shen, GW (通讯作者)，Guizhou Univ, Coll Comp Sci & Technol, Guiyang 550025, Guizhou, Peoples R China.; Shen, GW (通讯作者)，Guizhou Prov Key Lab Publ Big Data, Guiyang 550025, Guizhou, Peoples R China.
EM qyamail@163.com; gwshen@gzu.edu.cn
FU National Natural Science Foundation of China [61802081]; Guizhou
   Provincial Natural Science Foundation, China [20161052]; Guizhou
   Provincial Public Big Data Key Laboratory Open Project, China
   [2017BDKFJJ024]; Guizhou University Doctoral Fund, China [201526]; Major
   Scientific and Technological Special Project of Guizhou Province, China
   [20183001]
FX Project supported by the National Natural Science Foundation of China
   (No. 61802081), the Guizhou Provincial Natural Science Foundation, China
   (No. 20161052), the Guizhou Provincial Public Big Data Key Laboratory
   Open Project, China (No. 2017BDKFJJ024), the Guizhou University Doctoral
   Fund, China (No. 201526), and the Major Scientific and Technological
   Special Project of Guizhou Province, China (No. 20183001)
CR Bergstra J, 2012, J MACH LEARN RES, V13, P281
   chiu J.P.C., 2015, NAMED ENTITY RECOGNI
   Collobert R., 2008, P 25 ICML, P160
   Collobert R, 2011, J MACH LEARN RES, V12, P2493
   Dong C, 2016, LECT NOTES COMPUT SC, V10102, P239, DOI 10.1007/978-3-319-50496-4_20
   [冯艳红 Feng Yanhong], 2018, [计算机科学, Computer Science], V45, P261
   Finkel J. R., 2009, P HUM LANG TECHN 200, P326
   Gers FA, 2000, NEURAL COMPUT, V12, P2451, DOI 10.1162/089976600300015015
   Goller C, 1996, IEEE IJCNN, P347, DOI 10.1109/ICNN.1996.548916
   Guimaraes V, 2015, PROC 5 NAMED ENTITY, DOI DOI 10.18653/V1/W15-3904
   HAMMERTON J, 2003, P 7 C NAT LANG LEARN, V4, P172, DOI DOI 10.3115/1119176.1119202
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Huang Z., 2015, BIDIRECTIONAL LSTM C
   Joshi A, 2013, IEEE INT C SEMANT CO, P252, DOI 10.1109/ICSC.2013.50
   Koeling R., 2000, P CONLL 2000 LLL 200, P139, DOI [10.3115/1117601.1117634, DOI 10.3115/1117601.1117634]
   Lafferty J. D., 2001, P INT C MACH LEARN, P282, DOI DOI 10.1038/NPROT.2006.61
   Lample G., 2016, P NAACL HLT, P260
   Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791
   Li JH, 2016, CSEE J POWER ENERGY, V2, P11, DOI 10.17775/CSEEJPES.2016.00004
   [刘峤 Liu Qiao], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P582
   Luo G., 2015, P 2015 C EMP METH NA, P879
   Ma X., 2016, END TO END SEQUENCE
   Mikolov T., 2013, NEURIPS
   Mikolov T., P 1 INT C LEARNING R
   Passos A, 2014, P 18 C COMP NAT LANG, DOI [10.3115/v1/w14-1609, DOI 10.3115/V1/W14-1609]
   Peng N., 2015, P 2015 C EMP METH NA, P548, DOI DOI 10.18653/V1/D15-1064
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   [邱泉清 Qiu Quanqing], 2013, [计算机科学, Computer Science], V40, P196
   RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626
   Tang BZ, 2014, BIOMED RES INT, V2014, DOI 10.1155/2014/240403
   Pham V, 2014, INT CONF FRONT HAND, P285, DOI 10.1109/ICFHR.2014.55
   Yu Hong-kui, 2006, Journal of China Institute of Communications, V27, P87
   [张晓艳 Zhang Xiaoyan], 2005, [计算机科学, Computer Science], V32, P44
   杨云峰, 1999, [西安公路交通大学学报, Journal of Xian Highway University], P67
NR 34
TC 19
Z9 24
U1 7
U2 50
PU ZHEJIANG UNIV
PI HANGZHOU
PA EDITORIAL BOARD, 20 YUGU RD, HANGZHOU, 310027, PEOPLES R CHINA
SN 2095-9184
EI 2095-9230
J9 FRONT INFORM TECH EL
JI Front. Inform. Technol. Elect. Eng.
PD JUN
PY 2019
VL 20
IS 6
BP 872
EP 884
DI 10.1631/FITEE.1800520
PG 13
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA IJ0BT
UT WOS:000475565900011
DA 2022-08-23
ER

PT J
AU Nelson, CA
   Bove, R
   Butte, AJ
   Baranzini, SE
AF Nelson, Charlotte A.
   Bove, Riley
   Butte, Atul J.
   Baranzini, Sergio E.
TI Embedding electronic health records onto a knowledge network recognizes
   prodromal features of multiple sclerosis and predicts diagnosis
SO JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
LA English
DT Article
DE knowledge graph; electronic health records; multiple sclerosis;
   preventative medicine
ID T-CELL SUBSETS; CARE; AUTOIMMUNE
AB Objective Early identification of chronic diseases is a pillar of precision medicine as it can lead to improved outcomes, reduction of disease burden, and lower healthcare costs. Predictions of a patient's health trajectory have been improved through the application of machine learning approaches to electronic health records (EHRs). However, these methods have traditionally relied on "black box" algorithms that can process large amounts of data but are unable to incorporate domain knowledge, thus limiting their predictive and explanatory power. Here, we present a method for incorporating domain knowledge into clinical classifications by embedding individual patient data into a biomedical knowledge graph. Materials and Methods A modified version of the Page rank algorithm was implemented to embed millions of deidentified EHRs into a biomedical knowledge graph (SPOKE). This resulted in high-dimensional, knowledge-guided patient health signatures (ie, SPOKEsigs) that were subsequently used as features in a random forest environment to classify patients at risk of developing a chronic disease. Results Our model predicted disease status of 5752 subjects 3 years before being diagnosed with multiple sclerosis (MS) (AUC = 0.83). SPOKEsigs outperformed predictions using EHRs alone, and the biological drivers of the classifiers provided insight into the underpinnings of prodromal MS. Conclusion Using data from EHR as input, SPOKEsigs describe patients at both the clinical and biological levels. We provide a clinical use case for detecting MS up to 5 years prior to their documented diagnosis in the clinic and illustrate the biological features that distinguish the prodromal MS state.
C1 [Nelson, Charlotte A.; Baranzini, Sergio E.] Univ Calif San Francisco, Integrated Program Quantitat Biol, San Francisco, CA 94143 USA.
   [Nelson, Charlotte A.; Butte, Atul J.; Baranzini, Sergio E.] Univ Calif San Francisco, Bakar Computat Hlth Sci Inst, San Francisco, CA 94143 USA.
   [Bove, Riley; Baranzini, Sergio E.] Univ Calif San Francisco, Dept Neurol, UCSF Weill Inst Neurosci, 675 Nelson Rising Lane, San Francisco, CA 94143 USA.
   [Butte, Atul J.] Univ Calif San Francisco, Dept Pediat, San Francisco, CA 94143 USA.
RP Baranzini, SE (通讯作者)，Univ Calif San Francisco, Dept Neurol, UCSF Weill Inst Neurosci, 675 Nelson Rising Lane, San Francisco, CA 94143 USA.
EM sergio.baranzini@ucsf.edu
OI Baranzini, Sergio/0000-0003-0067-194X; Nelson,
   Charlotte/0000-0002-3687-1102
FU US National Science Foundation [NSF_1937160]; Bakar Family Foundation;
   Bakar Computational Health Sciences Institute
FX This work was supported in part by a grant from the US National Science
   Foundation (Convergence Accelerator NSF_1937160). Partial support also
   comes from the Bakar Family Foundation and the Bakar Computational
   Health Sciences Institute. SEB holds the Heidrich Family and Friends
   endowed chair in Neurology at UCSF. SEB holds the Professorship in
   Neurology I at UCSF.
CR Auffray C, 2010, GENOME MED, V2, DOI 10.1186/gm178
   Bar-Or A, 2008, SEMIN NEUROL, V28, P29, DOI 10.1055/s-2007-1019124
   Barabasi AL, 2011, NAT REV GENET, V12, P56, DOI 10.1038/nrg2918
   Bomprezzi R, 2015, THER ADV NEUROL DISO, V8, P20, DOI 10.1177/1756285614564152
   Bush WS, 2011, GENES IMMUN, V12, P335, DOI 10.1038/gene.2011.3
   Chernov AV, 2020, J BIOL CHEM, V295, P10807, DOI 10.1074/jbc.RA120.013696
   Couronne R, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2264-5
   Crane IJ, 2005, CRIT REV IMMUNOL, V25, P75, DOI 10.1615/CritRevImmunol.v25.i2.10
   Denny JC, 2010, BIOINFORMATICS, V26, P1205, DOI 10.1093/bioinformatics/btq126
   Disanto G, 2018, ANN NEUROL, V83, P1162, DOI 10.1002/ana.25247
   Eagar T.N., 2019, CLIN IMMUNOL, V5th ed., P235
   Goh KI, 2007, P NATL ACAD SCI USA, V104, P8685, DOI 10.1073/pnas.0701361104
   Gustafsson M, 2014, GENOME MED, V6, DOI 10.1186/s13073-014-0082-6
   Haveliwala T.H., 2002, P 11 INT C WORLD WID, P517, DOI DOI 10.1145/511446.511513
   Himmelstein DS, 2015, PLOS COMPUT BIOL, V11, DOI 10.1371/journal.pcbi.1004259
   Hirahara K, 2016, INT IMMUNOL, V28, P163, DOI 10.1093/intimm/dxw006
   Hirtz D, 2007, NEUROLOGY, V68, P326, DOI 10.1212/01.wnl.0000252807.38124.a3
   Jensen PB, 2012, NAT REV GENET, V13, P395, DOI 10.1038/nrg3208
   Kappos L, 2016, NEUROLOGY, V87, P978, DOI 10.1212/WNL.0000000000003078
   Kho AN, 2011, SCI TRANSL MED, V3, DOI 10.1126/scitranslmed.3001807
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Mikolov T., 2013, ARXIV PREPRINT ARXIV, P1
   Nelson CA, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-11069-0
   Nicholson DN, 2020, COMPUT STRUCT BIOTEC, V18, P1414, DOI 10.1016/j.csbj.2020.05.017
   O'Connor PJ, 2011, ANN FAM MED, V9, P12, DOI 10.1370/afm.1196
   Okuda DT, 2009, NEUROLOGY, V72, P800, DOI 10.1212/01.wnl.0000335764.14513.1a
   Okuda DT, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0090509
   Raphael I, 2015, CYTOKINE, V74, P5, DOI 10.1016/j.cyto.2014.09.011
   Ritchie MD, 2010, AM J HUM GENET, V86, P560, DOI 10.1016/j.ajhg.2010.03.003
   Shickel B, 2018, IEEE J BIOMED HEALTH, V22, P1589, DOI 10.1109/JBHI.2017.2767063
   Stang PE, 2010, ANN INTERN MED, V153, P600, DOI 10.7326/0003-4819-153-9-201011020-00010
   Tate AR, 2014, J AM MED INFORM ASSN, V21, P292, DOI 10.1136/amiajnl-2013-001847
   Tremlett HL, 2002, QJM-INT J MED, V95, P753, DOI 10.1093/qjmed/95.11.753
   Wallin MT, 2019, NEUROLOGY, V92, pE1029, DOI 10.1212/WNL.0000000000007035
   Wang Lili, 2015, F1000Res, V4, P485, DOI 10.12688/f1000research.6836.1
   Wijnands JMA, 2019, MULT SCLER J, V25, P1092, DOI 10.1177/1352458518783662
   Wijnands JMA, 2017, MULT SCLER J, V23, P1506, DOI 10.1177/1352458516681198
   Wijnands JMA, 2017, LANCET NEUROL, V16, P445, DOI 10.1016/S1474-4422(17)30076-5
   Woolhandler S, 2017, ANN INTERN MED, V167, P527, DOI 10.7326/L17-0326
   Xia ZQ, 2017, JAMA NEUROL, V74, P291, DOI 10.1001/jamaneurol.2016.5056
   Ye CY, 2018, J MED INTERNET RES, V20, DOI 10.2196/jmir.9268
   Zhou XZ, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms5212
   Zouk H, 2019, AM J HUM GENET, V105, P588, DOI 10.1016/j.ajhg.2019.07.018
NR 43
TC 3
Z9 3
U1 2
U2 3
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1067-5027
EI 1527-974X
J9 J AM MED INFORM ASSN
JI J. Am. Med. Inf. Assoc.
PD JAN 29
PY 2022
VL 29
IS 3
BP 424
EP 434
DI 10.1093/jamia/ocab270
EA DEC 2021
PG 11
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Health Care Sciences & Services;
   Information Science & Library Science; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Health Care Sciences & Services; Information Science &
   Library Science; Medical Informatics
GA ZI2JA
UT WOS:000761451900003
PM 34915552
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Wu, Y
   Mu, TT
   Goulermas, JY
AF Wu, Yu
   Mu, Tingting
   Goulermas, John Y.
TI Translating on pairwise entity space for knowledge graph embedding
SO NEUROCOMPUTING
LA English
DT Article
DE Statistical relational learning; Link prediction; Knowledge graphs;
   Hidden representation; Embedding space
AB In addition to feature-based representations that characterize objects with feature vectors, relation-based representations constitute another type of data representation strategies. They typically store patterns as a knowledge graph (KG), consisting of nodes (objects) and edges (relationships between objects). Given that most KGs are noisy and far from being complete, KG analysis and completion is required to establish the likely truth of new facts and correct unlikely ones based on the existing data within the KG. An effective way for tackling this, is through translation techniques which encode entities and links with hidden representations in embedding spaces. In this paper, we aim at improving the state-of-the-art translation techniques by taking into account the multiple facets of the different patterns and behaviors of each relation type. To the best of our knowledge, this is the first latent representation model which considers relational representations to be dependent on the entities they relate. The multi-modality of the relation type over different entities is effectively formulated as a projection matrix over the space spanned by the entity vectors. We develop an economic computation of the projection matrix by directly providing an analytic formulation other than relying on a more consuming iterative optimization procedure. Two large benchmark knowledge bases are used to evaluate the performance with respect to the link prediction task. A new test data partition scheme is proposed to offer better understanding of the behavior of a link prediction model. Experimental results show that the performance of the proposed algorithm is consistently among the top under different evaluation schemes. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Wu, Yu] Univ Liverpool, Dept Elect Engn & Elect, Brownlow Hill, Liverpool L69 3GJ, Merseyside, England.
   [Mu, Tingting] Univ Manchester, Sch Comp Sci, Kilburn Bldg, Manchester M13 9PL, Lancs, England.
   [Goulermas, John Y.] Univ Liverpool, Dept Comp Sci, Ashton Bldg, Liverpool L69 3BX, Merseyside, England.
RP Wu, Y (通讯作者)，Univ Liverpool, Dept Elect Engn & Elect, Brownlow Hill, Liverpool L69 3GJ, Merseyside, England.
EM yu.wu@liverpool.ac.uk; tingtingmu@me.com; j.y.goulermas@liverpool.ac.uk
RI Mu, Tingting/AAV-4795-2020
OI Mu, Tingting/0000-0001-6315-3432
FU University of Liverpool; China Scholarships Council
FX This research was supported by a PhD studentship, jointly funded from
   the University of Liverpool and the China Scholarships Council.
CR [Anonymous], P C ART INT
   Betteridge J., 2009, P AAAI SPRING S LEAR
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   DAVIS R, 1993, AI MAG, V14, P17
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Fagin R., 2004, REASONING KNOWLEDGE
   Fahad A, 2014, IEEE T EMERG TOP COM, V2, P267, DOI 10.1109/TETC.2014.2330519
   Fan M, 2014, P 28 PAC AS C LANG I, V28, P328, DOI DOI 10.3115/V1/P14-1048.URL
   Ferrucci D, 2010, AI MAG, V31, P59, DOI 10.1609/aimag.v31i3.2303
   Franz T, 2009, LECT NOTES COMPUT SC, V5823, P213, DOI 10.1007/978-3-642-04930-9_14
   Friedman N, 1999, IJCAI-99: PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 & 2, P1300
   Garcia-Duran A., 2015, P 2015 C EMP METH NA, P286, DOI DOI 10.18653/V1/D15-1034
   Glorot X., 2010, J MACH LEARN RES, V9, P249
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Kemp C., 2006, AAAI, V3, P5
   Kok S., 2007, P 24 INT C MACH LEAR, P433, DOI DOI 10.1145/1273496.1273551
   Kulkarni SR, 1998, IEEE T INFORM THEORY, V44, P2178, DOI 10.1109/18.720536
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lin Y., 2015, P AAAI
   Lin Yankai, 2015, EMNLP
   Luo CZ, 2016, IEEE T MULTIMEDIA, V18, P40, DOI 10.1109/TMM.2015.2495248
   Mikolov T., 2013, ADV NEURAL INFORM PR
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Miller Kurt, 2009, ADV NEURAL INFORM PR, P1276
   Ng V, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P104
   Nickel M., 2015, ARXIV1503007592015
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M., 2016, P IEEE
   Pearl Judea, 1985, GRAPHOIDS GRAPH BASE
   Prakash R.S.S., 2007, P EMNLP CONLL 2007, V1005
   Qian R., UNDERSTANDING YOUR W
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Singh A. P., 2008, P KDD
   Singhal A., INTRO KNOWLEDGE GRAP
   Singla P, 2006, IEEE DATA MINING, P572
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Sowa JF, 2008, FOUND ARTIF INTELL, P213, DOI 10.1016/S1574-6526(07)03005-2
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sutskever I., 2009, NIPS
   Wang M, 2016, IEEE T KNOWL DATA EN, V28, P1864, DOI 10.1109/TKDE.2016.2535367
   Wang M, 2015, IEEE T KNOWL DATA EN, V27, P2564, DOI 10.1109/TKDE.2015.2415497
   Wang M, 2015, IEEE T CYBERNETICS, V45, P1561, DOI 10.1109/TCYB.2014.2356136
   Wang M, 2012, IEEE T IMAGE PROCESS, V21, P4649, DOI 10.1109/TIP.2012.2207397
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weikum G, 2010, PODS 2010: PROCEEDINGS OF THE TWENTY-NINTH ACM SIGMOD-SIGACT-SIGART SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS, P65, DOI 10.1145/1807085.1807097
   West R., 2014, KNOWLEDGE BASE COMPL
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xu Z., 2006, CORR
NR 49
TC 5
Z9 5
U1 0
U2 44
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD OCT 18
PY 2017
VL 260
BP 411
EP 419
DI 10.1016/j.neucom.2017.04.045
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FA6EU
UT WOS:000405536900042
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Cheng, JR
   Zheng, JC
   Yu, XM
AF Cheng, Jieren
   Zheng, Jiachen
   Yu, Xiaomei
TI An ensemble framework for interpretable malicious code detection
SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
LA English
DT Article; Early Access
DE feature extraction; knowledge graph; machine learning; malicious code;
   malware detection
ID ANDROID MALWARE DETECTION; NETWORKS
AB Malicious code is an ever-growing security threats to computer systems and networks, while malware detection provides effective defense against malicious codes. In this paper, a brief overview is presented on currently prevalent methods to detect malicious codes, including signature-based methods, behavioral-based detection and machine learning (ML) based ones. More specifically, the potentially effective malicious features are summarized and the novel methods using ML are deeply discussed. Furthermore, an ensemble interpretable framework is explored for automatic and efficient malicious code detection. Based on the knowledge graph of malware, the novel framework inclines to achieve robust malware detection even confronted with unseen malicious codes. Finally, both advantages and disadvantages are discussed and experimental results are outlined to verify the effectiveness of the novel methods.
C1 [Cheng, Jieren] Hainan Univ, Sch Comp Sci & Cyberspace Secur, Haikou, Hainan, Peoples R China.
   [Zheng, Jiachen] East China Normal Univ, Sch Data Sci & Engn, Shanghai, Peoples R China.
   [Yu, Xiaomei] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China.
RP Yu, XM (通讯作者)，Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China.
EM yxm0708@126.com
FU National Nature Science Foundation of China [61672329]; Shandong
   Provincial Project of Graduate Education Quality Improvement
   [SDYJG19171]; Industry-University Cooperation and Education Project of
   Ministry of Education [201901025009, 201901140022]
FX This study is partly funded by the National Nature Science Foundation of
   China (No. 61672329), Shandong Provincial Project of Graduate Education
   Quality Improvement (No. SDYJG19171), Industry-University Cooperation
   and Education Project of Ministry of Education (No. 201901025009 and
   201901140022).
CR Chen CM, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0223994
   Christodorescu M, 2007, P 6 JOINT M EUR SOFT, P5, DOI DOI 10.1145/1287624.1287628
   Christodorescu Mihai, 2003, P 12 C USENIX SEC S
   Cui BJ, 2015, PERVASIVE MOB COMPUT, V24, P101, DOI 10.1016/j.pmcj.2015.06.006
   Damodaran A, 2017, J COMPUT VIROL HACKI, V13, P1, DOI 10.1007/s11416-015-0261-z
   Egele M, 2012, ACM COMPUT SURV, V44, DOI 10.1145/2089125.2089126
   Elhadi Ammar Ahmed E., 2012, American Journal of Applied Sciences, V9, P283, DOI 10.3844/ajassp.2012.283.288
   Eskandari M, 2013, J COMPUT VIROL HACKI, V9, P77, DOI 10.1007/s11416-013-0181-8
   Eskandari M, 2012, J VISUAL LANG COMPUT, V23, P154, DOI 10.1016/j.jvlc.2012.02.002
   Firdausi Ivan, 2010, P 2 INT C ADV COMP C
   Haoran Guo, 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2010), P411, DOI 10.1109/ICICISYS.2010.5658586
   Heo J, 2019, NEURIPS 2019 ICCV WO
   Hu BF, 2019, J AMB INTEL HUM COMP, V10, P175, DOI 10.1007/s12652-017-0630-1
   [化盈盈 Hua Yingying], 2020, [信息安全学报, Journal of Cyber Security], V5, P1
   Idika N, 2007, SURVEY MALWARE DETEC, P1
   Jacob G, 2008, J COMPUT VIROL HACKI, V4, P251, DOI 10.1007/s11416-008-0086-0
   Jiang N, 2020, INFORM SCIENCES, V530, P167, DOI 10.1016/j.ins.2020.03.080
   Jiang N, 2020, IEEE INTERNET THINGS, V7, P2901, DOI 10.1109/JIOT.2020.2963927
   Jiang N, 2020, INFORM SCIENCES, V512, P1, DOI 10.1016/j.ins.2019.09.068
   KALPA, 2011, INTR MALW
   Khaderbad MA, 2012, NANOTECHNOLOGY, V23, DOI 10.1088/0957-4484/23/2/025501
   KOLBITSCH C., 2009, P 18 C USENIX SEC S, P351, DOI DOI 10.1093/MP/SSQ045
   Liu R, 2018, INFORM SCIENCES, V450, P200, DOI 10.1016/j.ins.2018.03.031
   Martin A, 2019, INFORM FUSION, V52, P128, DOI 10.1016/j.inffus.2018.12.006
   MOSER A., 2007, 23 ANN COMP SEC APPL
   Moskovitch R, 2008, IEEE INTELLIGENCE SE
   Nguyen G, 2018, DATA KNOWL ENG, V115, P129, DOI 10.1016/j.datak.2018.03.002
   Palumbo P, 2017, COMPUT SECUR, V70, P689, DOI 10.1016/j.cose.2017.07.013
   Park Y, 2010, PROCEEDINGS OF THE 2010 INTERNATIONAL CONFERENCE ON COMPILERS, ARCHITECTURES AND SYNTHESIS FOR EMBEDDED SYSTEMS (CASES '10), P21, DOI 10.1145/1878921.1878925
   Park Y, 2013, COMPUT SECUR, V39, P419, DOI 10.1016/j.cose.2013.09.006
   Pektas A, 2017, J INF SECUR APPL, V37, P91, DOI 10.1016/j.jisa.2017.10.005
   Pfoh J, 2013, NETWORK SYSTEM SECUR
   Qiao Y, 2013, IEEE INT CONF TRUST, P265, DOI 10.1109/TrustCom.2013.36
   Santos Igor, 2013, P INT C CISIS ICEUTE
   Saxe J, 2015, 2015 10TH INTERNATIONAL CONFERENCE ON MALICIOUS AND UNWANTED SOFTWARE (MALWARE), P11, DOI 10.1109/MALWARE.2015.7413680
   SCHULTZ MG, 2001, P IEEE S SEC PRIV OA
   Souri A, 2018, HUM-CENTRIC COMPUT I, V8, DOI 10.1186/s13673-018-0125-x
   Stokes Jack W., 2012, Computer Security - ESORICS 2012. Proceedings 17th European Symposium on Research in Computer Security, P788, DOI 10.1007/978-3-642-33167-1_45
   Tian Ronghua, 2010, P 5 INT C MAL UNW SO
   Ulrich B, 2009, P 16 ANN NETW DISTR
   Wang YL, 2020, INFORM SCIENCES, V530, P85, DOI 10.1016/j.ins.2020.03.098
   Wang Y, 2015, ADV DIFFER EQU-NY, DOI 10.1186/s13662-015-0358-1
   Wong W, 2006, J COMPUTER VIROLOGY, V2, P201
   Xiao XC, 2017, SECUR COMMUN NETW, DOI 10.1155/2017/5258010
   Xu Z, 2017, DES AUT TEST EUR C E
   Xu Z, 2017, 2017 DES AUT TEST EU
   Yan J, 2018, SECUR COMMUN NETW, V2018, DOI DOI 10.1155/2018/7247095
   Ye Y., 2011, P 17 ACM SIGKDD INT
   Ye YF, 2017, ACM COMPUT SURV, V50, DOI 10.1145/3073559
   Ye Yanfang, 2007, P ACM INT C KNOWL DI
   Yu XM, 2020, SOFT COMPUT, V24, P5831, DOI 10.1007/s00500-019-04367-8
   Yu XM, 2016, INT J AD HOC UBIQ CO, V23, P137, DOI 10.1504/IJAHUC.2016.10000377
   Yu Z, 2020, ASS ADV ARTIFICIAL I
   Yuan WH, 2020, INFORM SCIENCES, V510, P122, DOI 10.1016/j.ins.2019.09.007
   Zhang L, 2019, COMPUT SECUR, V80, P120, DOI 10.1016/j.cose.2018.10.001
   Zheng XW, 2017, INT J WEB GRID SERV, V13, P75, DOI 10.1504/IJWGS.2017.10002834
   Zheng XW, 2017, J INF SCI ENG, V33, P37
   Zheng XW, 2016, J INTERNET TECHNOL, V17, P633, DOI 10.6138/JIT.2016.17.4.20160517
   Zheng XW, 2019, SOFT COMPUT, V23, P1453, DOI 10.1007/s00500-018-3152-7
   Zhu HJ, 2018, NEURAL COMPUT APPL, V30, P3353, DOI 10.1007/s00521-017-2914-y
NR 60
TC 6
Z9 6
U1 13
U2 44
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0884-8173
EI 1098-111X
J9 INT J INTELL SYST
JI Int. J. Intell. Syst.
DI 10.1002/int.22310
EA OCT 2020
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NZ7QJ
UT WOS:000577298200001
DA 2022-08-23
ER

PT J
AU Masmoudi, M
   Karray, MH
   Ben Lamine, SB
   Zghal, HB
   Archimede, B
AF Masmoudi, Maroua
   Karray, Mohamed Hedi
   Ben Lamine, Sana Ben Abdallah
   Zghal, Hajer Baazaoui
   Archimede, Bernard
TI MEMOn: Modular Environmental Monitoring Ontology to link heterogeneous
   Earth observed data
SO ENVIRONMENTAL MODELLING & SOFTWARE
LA English
DT Article
DE Modular ontology; Environmental monitoring; Earth observation semantic
   interoperability data integration; Knowledge graph
ID SEMANTIC WEB; KNOWLEDGE; INTEGRATION; NETWORK; DESIGN
AB Earth observation (EO) systems play a significant role in environmental monitoring and the prediction of natural disasters. These systems generate a massive amount of heterogeneous data stored in different formats. The exploitation of this data is still limited while, in most cases, data are not linked, and sources are not interoperable. Hence, data cannot be exploited as an interoperable global knowledge graph to have more in-depth analyzes of environmental phenomena. Ontology, as a knowledge representation formalism, is a promising solution for the semantic interoperability between this data. In this work, we present a modular ontology for environmental monitoring developed based on an original agile methodology. The so-called MEMOn (Modular Environmental Monitoring Ontology) aims to support semantic interoperability, data integration, and linking of heterogeneous data collected through a variety of observation techniques and systems. We also present real use case studies to show the usefulness of the proposed ontology.
C1 [Masmoudi, Maroua; Karray, Mohamed Hedi; Archimede, Bernard] Univ Toulouse, INP ENIT, Toulouse, France.
   [Masmoudi, Maroua; Ben Lamine, Sana Ben Abdallah; Zghal, Hajer Baazaoui] Univ Manouba, ENSI, Manouba, Tunisia.
RP Masmoudi, M (通讯作者)，Univ Toulouse, INP ENIT, Toulouse, France.
EM maroua.masmoudi@enit.fr
RI KARRAY, Mohamed Hedi/AAS-8313-2020
OI KARRAY, Mohamed Hedi/0000-0002-9652-5164
FU "PHC Utique" program of the French Ministry of Foreign Affairs; Tunisian
   Ministry of higher education and scientific research [17G1122/CODE CF
   37T03]
FX This research was financially supported by the "PHC Utique" program of
   the French Ministry of Foreign Affairs, managed by Campus France, and
   the Tunisian Ministry of higher education and scientific research,
   managed by the CMCU (project number 17G1122/CODE CF 37T03 NJ).
CR [Anonymous], 2017, COMM COR ONT DAT INT
   [Anonymous], 2018, SEMANT WEB
   [Anonymous], 2016, SEMANTICS
   [Anonymous], BENCHMARKING THEORY
   [Anonymous], SMARTER ENVIRONMENTA
   [Anonymous], ONTOLINGUISTICS ONTO
   [Anonymous], WORKING PAPER
   [Anonymous], 11 IEEE INT C SERV O
   [Anonymous], QUDT QUANTITIES UNIT
   [Anonymous], 2013, AGILE METHODOLOGY DE
   [Anonymous], WORKING PAPER
   [Anonymous], 2015, INT S ENV SOFTW SYST
   [Anonymous], 2019, ENTERPRISE INTEROPER
   [Anonymous], GEOGR INF SERV CAGIS
   [Anonymous], ONTOLOGICAL ENG
   [Anonymous], TECHNICAL REPORT OGC
   [Anonymous], P SPIE
   [Anonymous], THESIS
   [Anonymous], TECH REP
   [Anonymous], WONDER WEB LIB FOUND
   [Anonymous], 2012, EXTENDED SEMANTIC WE
   Arp R, 2015, BUILDING ONTOLOGIES WITH BASIC FORMAL ONTOLOGY, P1, DOI 10.7551/mitpress/9780262527811.001.0001
   Buttigieg PL, 2016, J BIOMED SEMANT, V7, DOI 10.1186/s13326-016-0097-6
   Compton M, 2012, J WEB SEMANT, V17, P25, DOI 10.1016/j.websem.2012.05.003
   Corominas L, 2018, ENVIRON MODELL SOFTW, V106, P89, DOI 10.1016/j.envsoft.2017.11.023
   Curry E, 2013, ADV ENG INFORM, V27, P206, DOI 10.1016/j.aei.2012.10.003
   Devaraju A, 2015, INT J GEOGR INF SCI, V29, P1, DOI 10.1080/13658816.2014.933480
   Ding YH, 2007, LECT NOTES COMPUT SC, V4592, P131
   Falquet G, 2011, ADV INFORM KNOWL PRO, P1, DOI 10.1007/978-0-85729-724-2
   Gangemi A, 2005, LECT NOTES COMPUT SC, V3729, P262
   Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081
   Henson CA, 2009, PROCEEDINGS OF THE 2009 INTERNATIONAL SYMPOSIUM ON COLLABORATIVE TECHNOLOGIES AND SYSTEMS, P44, DOI 10.1109/CTS.2009.5067461
   Karray MH, 2012, APPL ONTOL, V7, P269, DOI 10.3233/AO-2012-0112
   Khan ZC, 2016, LECT NOTES COMPUT SC, V10024, P400, DOI 10.1007/978-3-319-49004-5_26
   Lenzerini M., 2002, P 21 ACM SIGMOD SIGA, V21, P233, DOI DOI 10.1145/543613.543644
   Llaves A, 2014, INT J GEOGR INF SCI, V28, P1085, DOI 10.1080/13658816.2014.882513
   Lv X, 2016, ADV ENG INFORM, V30, P737, DOI 10.1016/j.aei.2016.08.004
   Ma XG, 2014, ENVIRON MODELL SOFTW, V61, P191, DOI 10.1016/j.envsoft.2014.08.002
   Madin J, 2007, ECOL INFORM, V2, P279, DOI 10.1016/j.ecoinf.2007.05.004
   Noy N.F., 2001, ONTOLOGY DEV
   Noy NF, 2004, SIGMOD REC, V33, P65
   Obrst L., 2007, SEMANTIC WEB REVOLUT, P139, DOI DOI 10.1007/978-0-387-48438-9_8
   Oliva-Felipe L, 2017, ENVIRON MODELL SOFTW, V89, P106, DOI 10.1016/j.envsoft.2016.11.009
   Qiu LY, 2017, ENVIRON MODELL SOFTW, V91, P111, DOI 10.1016/j.envsoft.2017.01.025
   Raskin RG, 2005, COMPUT GEOSCI-UK, V31, P1119, DOI 10.1016/j.cageo.2004.12.004
   Sauve S, 2016, ENVIRON DEV, V17, P48, DOI 10.1016/j.envdev.2015.09.002
   Shu YF, 2016, ENVIRON MODELL SOFTW, V79, P10, DOI 10.1016/j.envsoft.2016.01.004
   Smith B, 2010, APPL ONTOL, V5, P139, DOI 10.3233/AO-2010-0079
   Stasch C, 2014, ENVIRON MODELL SOFTW, V51, P149, DOI 10.1016/j.envsoft.2013.09.006
   Suarez-Figueroa M., 2008, P 8 INT C TERM KNOWL
   Warmerdam F., 2008, OPEN SOURCE APPROACH, P87, DOI DOI 10.1007/978-3-540-74831-1_5
   Zhang FS, 2016, KYBERNETES, V45, P798, DOI 10.1108/K-10-2014-0205
   Zimmermann A, 2008, LECT NOTES COMPUT SC, V5341, P43, DOI 10.1007/978-3-540-88737-9_5
NR 53
TC 5
Z9 5
U1 6
U2 8
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1364-8152
EI 1873-6726
J9 ENVIRON MODELL SOFTW
JI Environ. Modell. Softw.
PD FEB
PY 2020
VL 124
AR 104581
DI 10.1016/j.envsoft.2019.104581
PG 21
WC Computer Science, Interdisciplinary Applications; Engineering,
   Environmental; Environmental Sciences; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Environmental Sciences & Ecology; Water
   Resources
GA KH7TU
UT WOS:000510853300012
DA 2022-08-23
ER

PT J
AU Vemu, S
   Denaro, K
   Sato, BK
   Williams, AE
AF Vemu, Sheela
   Denaro, Kameryn
   Sato, Brian K.
   Williams, Adrienne E.
TI Moving the Needle: Evidence of an Effective Study Strategy Intervention
   in a Community College Biology Course
SO CBE-LIFE SCIENCES EDUCATION
LA English
DT Article
ID RETRIEVAL PRACTICE; DISTRIBUTED PRACTICE; KNOWLEDGE MAPS; STUDENTS;
   METACOGNITION; PERFORMANCE; REGRESSION; RETENTION; WRAPPERS; ERROR
AB Many science, technology, engineering, and math (STEM) community college students do not complete their degree, and these students are more likely to be women or in historically excluded racial or ethnic groups. In introductory courses, low grades can trigger this exodus. Implementation of high-impact study strategies could lead to increased academic performance and retention. The examination of study strategies rarely occurs at the community college level, even though community colleges educate approximately half of all STEM students in the United States who earn a bachelor's degree. To fill this research gap, we studied students in two biology courses at a Hispanic-serving community college. Students were asked their most commonly used study strategies at the start and end of the semester. They were given a presentation on study skills toward the beginning of the semester and asked to self-assess their study strategies for each exam. We observed a significantly higher course grade for students who reported spacing their studying and creating drawings when controlling for demographic factors, and usage of these strategies increased by the end of the semester. We conclude that high-impact study strategies can be taught to students in community college biology courses and result in higher course performance.
C1 [Vemu, Sheela] Waubonsee Community Coll, Liberal Arts & Sci Div, Sugar Grove, IL 60554 USA.
   [Denaro, Kameryn; Sato, Brian K.] Univ Calif Irvine, Div Teaching Excellence & Innovat, Irvine, CA 92697 USA.
   [Sato, Brian K.] Univ Calif Irvine, Dept Mol Biol & Biochem, Irvine, CA 92697 USA.
   [Williams, Adrienne E.] Univ Calif Irvine, Dept Dev & Cell Biol, Irvine, CA 92697 USA.
RP Vemu, S (通讯作者)，Waubonsee Community Coll, Liberal Arts & Sci Div, Sugar Grove, IL 60554 USA.; Williams, AE (通讯作者)，Univ Calif Irvine, Dept Dev & Cell Biol, Irvine, CA 92697 USA.
EM svemu@waubonsee.edu; adriw@uci.edu
RI Denaro, Kameryn/AAD-7801-2019
OI Denaro, Kameryn/0000-0001-9175-1640
CR Ainsworth S, 2011, SCIENCE, V333, P1096, DOI 10.1126/science.1204153
   ALESANDRINI KL, 1981, J EDUC PSYCHOL, V73, P358, DOI 10.1037/0022-0663.73.3.358
   [Anonymous], 2018, CHART COURS SUCC AM
   Austin PC, 2018, J STAT COMPUT SIM, V88, P3151, DOI 10.1080/00949655.2018.1504945
   Bahr PR, 2019, COMMUNITY COLL REV, V47, P178, DOI 10.1177/0091552119840705
   Bailey T. R, 2005, WHAT WE KNOW COMMUNI
   Bates D, 2015, J STAT SOFTW, V67, P1, DOI 10.18637/jss.v067.i01
   Belfield C. R., 2012, PREDICTING SUCCESS C
   Bell ML, 2020, TRIALS, V21, DOI 10.1186/s13063-020-4114-9
   BLOOM KC, 1981, J EDUC RES, V74, P245, DOI 10.1080/00220671.1981.10885317
   Blunt JR, 2014, J EDUC PSYCHOL, V106, P849, DOI 10.1037/a0035934
   Bobek E, 2016, COGN RES, V1, DOI 10.1186/s41235-016-0031-6
   Bragg D. D., 2001, PEABODY J EDUC, V76, P93, DOI DOI 10.1207/S15327930PJE7601_06
   California Community Colleges, 2021, KEY FACTS
   Campbell T., 2019, PROFILE UNDERGRADUAT
   Carpenter SK, 2012, CURR DIR PSYCHOL SCI, V21, P279, DOI 10.1177/0963721412452728
   Cepeda NJ, 2006, PSYCHOL BULL, V132, P354, DOI 10.1037/0033-2909.132.3.354
   Chen X., 2013, 2014001 NCES US DEP
   Clarke B. R, 2008, LINEAR MODELS THEORY, V634
   Clarke P, 2007, SOCIOL METHOD RES, V35, P311, DOI 10.1177/0049124106292362
   Clovis MA, 2021, J COLL STUD RETENT-R, V23, P322, DOI 10.1177/1521025119826245
   Community College FAQs, 2021, COMMUNITY COLL RES C
   Diggle P., 2002, ANAL LONGITUDINAL DA, VSecond Edition
   Dunlosky J, 2013, PSYCHOL SCI PUBL INT, V14, P4, DOI 10.1177/1529100612453266
   Edens K. M., 2003, SCH SCI MATH ICS, V103, P135, DOI [DOI 10.1111/j.1949-8594.2003.tb18230.x, 10.1111/j.1949-8594.2003.tb18230.x, DOI 10.1111/J.1949-8594.2003.TB18230.X]
   Fahrmeier L, 2001, MULTIVARIATE STAT MO, P77
   Fiorella L, 2018, EDUC PSYCHOL REV, V30, P1115, DOI 10.1007/s10648-018-9444-8
   Fiorella L, 2016, EDUC PSYCHOL REV, V28, P717, DOI 10.1007/s10648-015-9348-9
   Freeman S., 2020, J COLL SCI TEACH, V49, P7
   Gurlitt J, 2008, J COMPUT ASSIST LEAR, V24, P407, DOI 10.1111/j.1365-2729.2008.00277.x
   Hartwig MK, 2012, PSYCHON B REV, V19, P126, DOI 10.3758/s13423-011-0181-y
   Hodges L. C., 2020, J COLL SCI TEACH, V50, P69
   Hopkins RF, 2016, EDUC PSYCHOL REV, V28, P853, DOI 10.1007/s10648-015-9349-8
   Johnson CW, 2016, EDUC RESEARCHER, V45, P312, DOI 10.3102/0013189X16656939
   Karpicke JD, 2011, SCIENCE, V331, P772, DOI 10.1126/science.1199327
   Karpicke JD, 2009, MEMORY, V17, P471, DOI 10.1080/09658210802647009
   Kustritz MVR, 2017, J VET MED EDUC, V44, P338, DOI 10.3138/jvme.0216-042R
   Labov JB, 2012, CBE-LIFE SCI EDUC, V11, P121, DOI 10.1187/cbe.12-03-0031
   LAIRD NM, 1982, BIOMETRICS, V38, P963, DOI 10.2307/2529876
   Latimier A, 2021, EDUC PSYCHOL REV, V33, P959, DOI 10.1007/s10648-020-09572-8
   Long QX, 2020, NAT MED, V26, P845, DOI 10.1038/s41591-020-0897-1
   Lovett M., 2013, USING REFLECTION MET, P18
   Lyle KB, 2020, EDUC PSYCHOL REV, V32, P277, DOI 10.1007/s10648-019-09489-x
   Ma J., 2019, TRENDS COLL PRICING
   Maas C.J., 2005, METHODOLOGY, V1, P86, DOI [10.1027/1614-2241.1.3.86, 10.1027/1614-2241.1.3.85, DOI 10.1027/1614-2241.1.3.86]
   McCabe J, 2011, MEM COGNITION, V39, P462, DOI 10.3758/s13421-010-0035-2
   McNemar Q, 1947, PSYCHOMETRIKA, V12, P153, DOI 10.1007/BF02295996
   Miyatsu T, 2018, PERSPECT PSYCHOL SCI, V13, P390, DOI 10.1177/1745691617710510
   Morehead K, 2016, MEMORY, V24, P257, DOI 10.1080/09658211.2014.1001992
   National Science Board, 2007, NAT ACT PLAN ADDR CR
   Nesbit JC, 2006, REV EDUC RES, V76, P413, DOI 10.3102/00346543076003413
   Novak J., 1984, LEARNING LEARN
   NOVAK JD, 1990, J RES SCI TEACH, V27, P937, DOI 10.1002/tea.3660271003
   O'Donnell AM, 2002, EDUC PSYCHOL REV, V14, P71, DOI 10.1023/A:1013132527007
   Park T, 2018, J HIGH EDUC-UK, V89, P318, DOI 10.1080/00221546.2017.1390970
   Persky AM, 2016, CURR PHARM TEACH LEA, V8, P141, DOI 10.1016/j.cptl.2015.12.010
   R Core Team, 2021, R LANG ENV STAT COMP
   Rawson K, 2013, EDUC PSYCHOL REV, V25, P523, DOI 10.1007/s10648-013-9240-4
   Ray S., 2016, THESIS U TEXAS PASO
   Reed S., 2021, UC DAVIS WHEELHOUSE, V6, P1
   Rodriguez F, 2021, ACT LEARN HIGH EDUC, V22, P77, DOI 10.1177/1469787418774185
   Rodriguez F, 2019, PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE (LAK'19), P456, DOI 10.1145/3303772.3303841
   Rodriguez F, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0200767
   Roediger HL, 2011, J EXP PSYCHOL-APPL, V17, P382, DOI 10.1037/a0026252
   Roediger HL, 2011, TRENDS COGN SCI, V15, P20, DOI 10.1016/j.tics.2010.09.003
   Schmidgall SP, 2019, LEARN INSTR, V60, P138, DOI 10.1016/j.learninstruc.2018.01.006
   Schroeder NL, 2018, EDUC PSYCHOL REV, V30, P431, DOI 10.1007/s10648-017-9403-9
   Schuler MS, 2019, J NURS EDUC, V58, P417, DOI 10.3928/01484834-20190614-06
   Schwamborn A, 2010, J EDUC PSYCHOL, V102, P872, DOI 10.1037/a0019640
   Sobel HS, 2011, APPL COGNITIVE PSYCH, V25, P763, DOI 10.1002/acp.1747
   Soicher RN, 2017, PSYCHOL LEARN TEACH-, V16, P64, DOI 10.1177/1475725716661872
   Tanner KD, 2012, CBE-LIFE SCI EDUC, V11, P113, DOI 10.1187/cbe.12-03-0033
   Theobald E, 2018, CBE-LIFE SCI EDUC, V17, DOI 10.1187/cbe.17-12-0280
   Theobald R, 2014, CBE-LIFE SCI EDUC, V13, P41, DOI 10.1187/cbe-13-07-0136
   U.S. Department of Education, 2019, WHIT HOUS IN ED EXC
   U.S. Department of Education, 2018, NAT CTR ED STAT HIGH
   Van Meter P, 2005, EDUC PSYCHOL REV, V17, P285, DOI 10.1007/s10648-005-8136-3
   Venables W.N., 2002, MODERN APPL STAT S, V4th
   Williams AE, 2021, J APPL RES HIGH EDUC, V13, P446, DOI 10.1108/JARHE-10-2019-0272
   Wiseheart M, 2019, CAMBRIDGE HANDBOOK OF COGNITION AND EDUCATION, P550
   Wong RM, 2021, EDUC PSYCHOL-UK, V41, P206, DOI 10.1080/01443410.2020.1761299
   Wood E, 1998, J EDUC PSYCHOL, V90, P698, DOI 10.1037/0022-0663.90.4.698
NR 82
TC 0
Z9 0
U1 0
U2 0
PU AMER SOC CELL BIOLOGY
PI BETHESDA
PA 8120 WOODMONT AVE, STE 750, BETHESDA, MD 20814-2755 USA
SN 1931-7913
J9 CBE-LIFE SCI EDUC
JI CBE-Life Sci. Educ.
PD JUN 1
PY 2022
VL 21
IS 2
AR ar24
DI 10.1187/cbe.21-08-0216
PG 10
WC Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research
GA 2S0HV
UT WOS:000821483900022
PM 35544204
OA gold
DA 2022-08-23
ER

PT J
AU Wu, T
   Ma, HY
   Wang, C
   Qiao, SJ
   Zhang, L
   Yu, S
AF Wu, Tao
   Ma, Hongyu
   Wang, Chao
   Qiao, Shaojie
   Zhang, Liang
   Yu, Shui
TI Heterogeneous representation learning and matching for few-shot relation
   prediction
SO PATTERN RECOGNITION
LA English
DT Article
DE Knowledge graphs; Few-shot learning; Relation prediction; Representation
   learning; Convolutional network
AB The recent explosive development of knowledge graphs (KGs) in artificial intelligence tasks coupled with incomplete or partial information has triggered considerable research interest in relation prediction. However, many challenges still remain unsolved: (i) the previous relation prediction methods require a significant amount of training instances (i.e., head-tail entity pairs) for every relation, which is infeasible in practical scenarios; and (ii) the representation learning of entities and relations always assumes that all local neighbors and their features contribute equally to the embedding, not sufficiently considering the heterogeneity of the information; and (iii) the state-of-the-art methods usually require a lot of training time, resulting in a high cost in real-world applications. To overcome these challenges, we propose a heterogeneous representation learning and matching approach, Multi-metric Feature Extraction Network (MFEN for short), for few-shot relation prediction in KGs. Our method focuses on knowledge graphs to sufficiently explore the topological structure and node content in graphs. Rather than taking the average of the embeddings of all relational neighbors, a heterogeneity-aware representation learning method is proposed to generate high-expressive embeddings, which capture the heterogenous roles of the relational neighbors of given entity and all of their features via a convolutional encoder. To learn the expressive representations efficiently, a single-layer CNN architecture with multi-scale filters is devised. In addition, multiple heuristic metrics are combined to efficiently improve the accuracy of similarity calculation. The proposed MFEN model is evaluated on two representative benchmark datasets NELL and Wiki. Extensive experiments have demonstrated that our method gets more than 5% accuracy improvement and three times speedup to state-of-the-art models. Code is available on https://github.com/summer-funny/MFEN. (C) 2022 Elsevier Ltd. All rights reserved.
C1 [Wu, Tao; Ma, Hongyu; Zhang, Liang] Chongqing Univ Posts & Telecommun, Sch Cybersecur & Informat Law, Chongqing, Peoples R China.
   [Wang, Chao] Chongqing Normal Univ, Sch Comp & Informat Sci, Chongqing, Peoples R China.
   [Qiao, Shaojie] Chengdu Univ Informat Technol, Sch Software Engn, Chengdu, Peoples R China.
   [Yu, Shui] Univ Technol Sydney, Sch Comp Sci, Ultimo, NSW, Australia.
RP Wang, C (通讯作者)，Chongqing Normal Univ, Sch Comp & Informat Sci, Chongqing, Peoples R China.; Qiao, SJ (通讯作者)，Chengdu Univ Informat Technol, Sch Software Engn, Chengdu, Peoples R China.
EM chaosimpler@gmail.com; sjqiao@cuit.edu.cn
OI Qiao, Shaojie/0000-0002-4703-780X
FU Natural Science Foundation of Chongqing [cstc2020jcyj-msxmX0804];
   National Natural Science Foundation of China [62106030, 61802039];
   Sichuan Science and Technology Program [2021JDJQ0021, 22ZDYF2680];
   Chengdu Technology Innovation and Research and Development Project
   [2021-YF05-00491-SN]; Chengdu Major Science and Technology Innovation
   Project [2021-YF08-00156-GX]
FX This work was partially supported by the Natural Science Foundation of
   Chongqing under Grant no. cstc2020jcyj-msxmX0804 , National Natural
   Science Foundation of China under Grant nos. 62106030 and 61802039 ;
   Sichuan Science and Technology Program under Grant nos. 2021JDJQ0021 ,
   22ZDYF2680 ; Chengdu Technology Innovation and Research and Development
   Project under Grant no. 2021-YF05-00491-SN; Chengdu Major Science and
   Technology In-novation Project under Grant no. 2021-YF08-00156-GX.
CR Baek J., 2020, ADV NEURAL INF PROCE, V33, P546
   Barentsen G, 2013, MON NOT R ASTRON SOC, V429, P1981, DOI 10.1093/mnras/sts462
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Chen M., 2019, INT JOINT C NAT LANG, P4208
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Finn C, 2017, PR MACH LEARN RES, V70
   Guo LB, 2019, PR MACH LEARN RES, V97
   Guo Q., IEEE T KNOWL DATA EN
   Jamal MA, 2019, PROC CVPR IEEE, P11711, DOI 10.1109/CVPR.2019.01199
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kim G, 2021, PATTERN RECOGN, V120, DOI 10.1016/j.patcog.2021.108118
   Kim Y., 2014, PROC C EMPIRICAL MET, P1746, DOI [10.3115/v1/d14-1181, 10.3115/v1/D14-1181]
   Koch G., 2015, P 32 INT C MACH LEAR, P1
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lv X., 2019, P 2019 C EMPIRICAL M, P3376
   Mahdisoltani F., 2015, CIDR 2015
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Niu GL, 2021, SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P213, DOI 10.1145/3404835.3462925
   Ravi S., 2017, P 5 INT C LEARN REPR
   Santoro A, 2016, ARXIV
   Sheng J., 2020, EMNLP PUNTA CANA DOM, P1681
   Snell J, 2017, ADV NEUR IN, V30
   Sun Z., 2019, 7 INT C LEARN REPR
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vinyals O, 2016, ADV NEURAL INFORM PR, V29, P1
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang MH, 2021, SYMMETRY-BASEL, V13, DOI 10.3390/sym13030485
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wojtas M, 2020, AD VANCES NEURAL INF, V33, P5105
   Xiong W., 2018, C EMP METH NAT LANG, P1980
   Xu H, 2021, PATTERN RECOGN, V116, DOI 10.1016/j.patcog.2021.107951
   Yang B., 2015, P INT C LEARN REPR
   Zhang CX, 2020, AAAI CONF ARTIF INTE, V34, P3041
   Zhang Ningyu, 2019, LONG SHORT PAPERS, P3016, DOI [10.18653/v1/n19-1306, DOI 10.18653/V1/N19-1306]
   Zhang QJ, 2022, NEUROCOMPUTING, V470, P109, DOI 10.1016/j.neucom.2021.10.088
   Zhang Y, 2017, LONG PAPERS, V1, P253
   Zheng WF, 2021, PATTERN RECOGN, V120, DOI 10.1016/j.patcog.2021.108153
NR 41
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0031-3203
EI 1873-5142
J9 PATTERN RECOGN
JI Pattern Recognit.
PD NOV
PY 2022
VL 131
AR 108830
DI 10.1016/j.patcog.2022.108830
PG 14
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 3J6TN
UT WOS:000833527000002
DA 2022-08-23
ER

PT J
AU Uyar, A
   Aliyu, FM
AF Uyar, Ahmet
   Aliyu, Farouk Musa
TI Evaluating search features of Google Knowledge Graph and Bing Satori
   Entity types, list searches and query interfaces
SO ONLINE INFORMATION REVIEW
LA English
DT Article
DE Google Knowledge Graph and Bing Satori; Natural language queries; Search
   engine evaluation; Semantic search engines
ID WEB SEARCH
AB Purpose - The purpose of this paper is to better understand three main aspects of semantic web search engines of Google Knowledge Graph and Bing Satori. The authors investigated: coverage of entity types, the extent of their support for list search services and the capabilities of their natural language query interfaces.
   Design/methodology/approach - The authors manually submitted selected queries to these two semantic web search engines and evaluated the returned results. To test the coverage of entity types, the authors selected the entity types from Freebase database. To test the capabilities of natural language query interfaces, the authors used a manually developed query data set about US geography.
   Findings - The results indicate that both semantic search engines cover only the very common entity types. In addition, the list search service is provided for a small percentage of entity types. Moreover, both search engines support queries with very limited complexity and with limited set of recognised terms.
   Research limitations/implications - Both companies are continually working to improve their semantic web search engines. Therefore, the findings show their capabilities at the time of conducting this research.
   Practical implications - The results show that in the near future the authors can expect both semantic search engines to expand their entity databases and improve their natural language interfaces.
   Originality/value - As far as the authors know, this is the first study evaluating any aspect of newly developing semantic web search engines. It shows the current capabilities and limitations of these semantic web search engines. It provides directions to researchers by pointing out the main problems for semantic web search engines.
C1 [Uyar, Ahmet; Aliyu, Farouk Musa] Meliksah Univ, Dept Comp Engn, Kayseri, Turkey.
RP Uyar, A (通讯作者)，Meliksah Univ, Dept Comp Engn, Kayseri, Turkey.
CR Angles R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1322432.1322433
   Barroso LA, 2003, IEEE MICRO, V23, P22, DOI 10.1109/MM.2003.1196112
   Bernstein A., 2005, 15 WORKSH INFORM TEC, P45
   Bharat K, 1998, COMPUT NETWORKS ISDN, V30, P379, DOI 10.1016/S0169-7552(98)00127-5
   Dalvi N, 2009, PODS'09: PROCEEDINGS OF THE TWENTY-EIGHTH ACM SIGMOD-SIGACT-SIGART SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS, P1, DOI 10.1145/1559795.1559797
   Damljanovic D, 2010, LECT NOTES COMPUT SC, V6088, P106, DOI 10.1007/978-3-642-13486-9_8
   EFRATI A, 2012, WALL STREET J
   Elbedweihy K., 2012, P 2 INT WORKSH EV SE, P25
   Fluit C., 2006, VISUALIZING SEMANTIC, P45, DOI DOI 10.1007/1-84628-290-X_3
   Hannak A., 2013, P 22 INT C WORLD WID, P527, DOI DOI 10.1145/2488388.2488435
   Hirsch C., 2009, WORKSH VIS INT SOC S, V443
   Hochstotter N, 2009, INFORM SCIENCES, V179, P1796, DOI 10.1016/j.ins.2009.01.028
   Holzinger Andreas, 2014, Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. State-of-the-Art and Future Challenges: LNCS 8401, P241, DOI 10.1007/978-3-662-43968-5_14
   Kaufmann E., 2007, THESIS U ZURICH ZURI
   Kaufmann E, 2006, LECT NOTES COMPUT SC, V4273, P980
   Kaufmann E, 2010, J WEB SEMANT, V8, P377, DOI 10.1016/j.websem.2010.06.001
   Koumenides CL, 2014, J ASSOC INF SCI TECH, V65, P1091, DOI 10.1002/asi.23018
   Lawrence S, 1999, NATURE, V400, P107, DOI 10.1038/21987
   Lewandowski D, 2006, J INF SCI, V32, P131, DOI 10.1177/0165551506062326
   Lewandowski D., 2012, WEB SEARCH ENGINE RE
   Pogue David, 2012, NY TIMES
   Qian R., 2013, BING SEARCH BLOG MAR
   Qingqing Gan, 2008, Proceedings of the First International Workshop on Location and the Web 2008 (LOCWEB 08), P49
   Singhal A., 2012, INTRO KNOWLEDGE GRAP
   Thompson CW, 2005, IEEE INTERNET COMPUT, V9, P75, DOI 10.1109/MIC.2005.135
   Tominski C, 2009, COMPUT GRAPH-UK, V33, P660, DOI 10.1016/j.cag.2009.06.002
   TORRES SD, 2010, P 33 INT ACM SIGIR C, P847
   Uyar A, 2009, J INF SCI, V35, P469, DOI 10.1177/0165551509103598
   Vaughan L, 2004, INFORM PROCESS MANAG, V40, P693, DOI 10.1016/S0306-4573(03)00063-3
   von Landesberger T, 2011, COMPUT GRAPH FORUM, V30, P1719, DOI 10.1111/j.1467-8659.2011.01898.x
   Wilkinson D, 2013, LIBR INFORM SCI RES, V35, P318, DOI 10.1016/j.lisr.2013.04.006
   Zelle JM, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P1050
NR 32
TC 31
Z9 34
U1 2
U2 66
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1468-4527
EI 1468-4535
J9 ONLINE INFORM REV
JI Online Inf. Rev.
PY 2015
VL 39
IS 2
BP 197
EP 213
DI 10.1108/OIR-10-2014-0257
PG 17
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA CI3MR
UT WOS:000354652400005
DA 2022-08-23
ER

PT J
AU Gu, DX
   Li, TT
   Wang, XY
   Yang, XJ
   Yu, ZR
AF Gu, Dongxiao
   Li, Tongtong
   Wang, Xiaoyu
   Yang, Xuejie
   Yu, Zhangrui
TI Visualizing the intellectual structure and evolution of electronic
   health and telemedicine research
SO INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
LA English
DT Article
DE Electronic health; Healthcare informatics; Knowledge structure;
   Bibliometrics
ID SCIENCE; TRENDS; COCITATION; NETWORKS; RECORDS; CARE; TECHNOLOGY;
   ADOPTION; SUPPORT; CONTEXT
AB Background: In recent years, the development and application of emerging information technologies, such as artificial intelligence, cloud computing, Internet of Things, and wearable devices, has expanded the content of electronic health (e-health). Electronic health has become a research focus, but few studies have explored its knowledge structure from a global perspective.
   Methods: To detect the evolution track, knowledge base and research hotspots of e-health, we conducted a series of bibliometric analyses on the retrieved 3,085 papers from the Web of Science core database in 1992-2017. We used several bibliometric tools, such as HistCite, CiteSpace, NetDraw, and NEViewer, to describe the evolution process, time-and-space knowledge map, and hotspots in e-health.
   Results: The research results are as follows. (a) The number of publications has been obviously increasing after 2005 and according to the trend line it is expected to continue increase exponentially in the future. (b) Countries/regions conducing e-health research have close cooperative relationship, among which European countries have the closest cooperation. (c) Electronic health records, mobile health and health information technology are research hotspots in electronic health. Moreover, scholars also pay attention to the hot issues such as privacy, security, and quality improvement.
   Conclusions: Electronic health is a large and growing field with quite a number of research articles in medical journals. This study provides a comprehensive knowledge structure of electronic health for scholars in the healthcare informatics field, which can help them quickly grasp research hotspots and choose future research projects.
C1 [Gu, Dongxiao; Li, Tongtong; Yang, Xuejie] Hefei Univ Technol, Sch Management, 193 Tunxi Rd, Hefei 230009, Anhui, Peoples R China.
   [Wang, Xiaoyu] Anhui Univ Tradit Chinese Med, Affiliated Hosp 1, 117 Meishan Rd, Hefei 230031, Anhui, Peoples R China.
   [Yu, Zhangrui] China Univ Min & Technol, Sch Management, 1 Daxue Rd, Xuzhou 221116, Jiangsu, Peoples R China.
   [Gu, Dongxiao] Minist Educ, Key Lab Proc Optimizat & Intelligent Decis Making, Hefei 230009, Anhui, Peoples R China.
RP Wang, XY (通讯作者)，Anhui Univ Tradit Chinese Med, Affiliated Hosp 1, 117 Meishan Rd, Hefei 230031, Anhui, Peoples R China.
EM gudongxiao@hfut.edu.cn; 2017170577@mail.hfut.edu.cn;
   mikehfut0551@163.com; xuejie_y@mail.hfut.edu.cn; 13003051371@163.com
OI Gu, Dongxiao/0000-0003-3557-009X
FU National Natural Science Foundation (NSFC) of China [71771075, 71771077,
   71601061, 71501054]
FX The data set collection and analysis of this research were partially
   supported by the National Natural Science Foundation (NSFC) of China
   under Grant Nos. 71771075, 71771077, 71601061 and 71501054.
CR Adler-Milstein J, 2017, J AM MED INFORM ASSN, V24, P1142, DOI 10.1093/jamia/ocx080
   ANDO Y, 1992, COMPUT METH PROG BIO, V37, P319, DOI 10.1016/0169-2607(92)90045-9
   Armfield NR, 2014, INT J MED INFORM, V83, P715, DOI 10.1016/j.ijmedinf.2014.07.001
   Bates DW, 2003, J AM MED INFORM ASSN, V10, P523, DOI 10.1197/jamia.M1370
   Beratarrechea A, 2014, TELEMED E-HEALTH, V20, P75, DOI 10.1089/tmj.2012.0328
   Blumenthal D, 2010, NEW ENGL J MED, V363, P501, DOI 10.1056/NEJMp1006114
   Bortolotto IM, 2017, REG ENVIRON CHANGE, V17, P1329, DOI 10.1007/s10113-016-1088-y
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Choi J, 2011, INFORM MANAGE-AMSTER, V48, P371, DOI 10.1016/j.im.2011.09.004
   de Veer AJE, 2015, BMC HEALTH SERV RES, V15, DOI 10.1186/s12913-015-0765-8
   DesRoches CM, 2008, NEW ENGL J MED, V359, P50, DOI 10.1056/NEJMsa0802005
   Devaraj S, 2007, J OPER MANAG, V25, P1199, DOI 10.1016/j.jom.2007.01.002
   Dinev T, 2016, ANN INFORM SYST, V19, P19, DOI 10.1007/978-3-319-23294-2_2
   Ding Y, 2001, INFORM PROCESS MANAG, V37, P817, DOI 10.1016/S0306-4573(00)00051-0
   Domenichiello M, 2015, PROC ECON FINANC, V23, P1110, DOI 10.1016/S2212-5671(15)00364-0
   Dongxiao Gu, 2018, Smart Health. International Conference, ICSH 2018. Proceedings: Lecture Notes in Computer Science (LNCS 10983), P263, DOI 10.1007/978-3-030-03649-2_26
   Dornan L, 2019, BIOMED RES INT, V2019, DOI 10.1155/2019/7341841
   Ebadi A, 2015, J INFORMETR, V9, P809, DOI 10.1016/j.joi.2015.08.002
   Emami Zahra, 2018, Med J Islam Repub Iran, V32, P84, DOI 10.14196/mjiri.32.84
   Eysenbach G, 2001, J MED INTERNET RES, V3, DOI 10.2196/jmir.3.2.e20
   Fatehi F, 2012, J TELEMED TELECARE, V18, P460, DOI 10.1258/jtt.2012.GTH108
   Garfield E., 2006, INFORM WISSENSCHAFT, V57, P391
   Gu DX, 2017, ARTIF INTELL MED, V77, P31, DOI 10.1016/j.artmed.2017.02.003
   Gu DX, 2017, INT J MED INFORM, V98, P22, DOI 10.1016/j.ijmedinf.2016.11.006
   Hayrinen K, 2008, INT J MED INFORM, V77, P291, DOI 10.1016/j.ijmedinf.2007.09.001
   Herrera M, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0010355
   Hood WW, 2001, SCIENTOMETRICS, V52, P291, DOI 10.1023/A:1017919924342
   Hu XJ, 2018, J INFORMETR, V12, P842, DOI 10.1016/j.joi.2018.07.004
   Huenteler J, 2016, RES POLICY, V45, P1195, DOI 10.1016/j.respol.2016.03.014
   Jiang LC, 2015, INT J MED INFORM, V84, P24, DOI 10.1016/j.ijmedinf.2014.09.003
   Lavner Y, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON THE SCIENCE OF ELECTRICAL ENGINEERING (ICSEE)
   Liu Q., 2012, J INF RESOUR MANAG, V2, P50, DOI [10.13365/j.jirm.2012.01.012, DOI 10.13365/J.JIRM.2012.01.012]
   Liu XM, 2005, INFORM PROCESS MANAG, V41, P1462, DOI 10.1016/j.ipm.2005.03.012
   Meier CA, 2013, ANNU REV BIOMED ENG, V15, P359, DOI 10.1146/annurev-bioeng-071812-152350
   Middleton B, 2013, J AM MED INFORM ASSN, V20, pE2, DOI 10.1136/amiajnl-2012-001458
   Mustafee N, 2013, PROCEDIA COMPUT SCI, V19, P586, DOI 10.1016/j.procs.2013.06.078
   Mustaqeem A, 2017, INT J MED INFORM, V108, P134, DOI 10.1016/j.ijmedinf.2017.10.008
   Nijeweme-d'Hollosy WO, 2018, INT J MED INFORM, V110, P31, DOI 10.1016/j.ijmedinf.2017.11.010
   Ricciardi L, 2013, HEALTH AFFAIR, V32, P376, DOI 10.1377/hlthaff.2012.1216
   Rothnie KJ, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0151357
   Roy N, 2011, INT CONF PERVAS COMP, P63, DOI 10.1109/PERCOM.2011.5767596
   Short CE, 2014, JMIR RES PROTOC, V3, DOI 10.2196/resprot.2776
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Stone EE, 2015, IEEE J BIOMED HEALTH, V19, P290, DOI 10.1109/JBHI.2014.2312180
   Sunyaev A, 2015, J AM MED INFORM ASSN, V22, pE28, DOI 10.1136/amiajnl-2013-002605
   Sweileh WM, 2017, BMC MED INFORM DECIS, V17, DOI 10.1186/s12911-017-0476-7
   Synnestvedt Marie B, 2005, AMIA Annu Symp Proc, P724
   Wang XG, 2014, SCIENTOMETRICS, V101, P1253, DOI 10.1007/s11192-014-1347-y
   Wang XG, 2007, I C WIREL COMM NETW, P3717
   White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-W
   Wu CC, 2014, INT J HYDROGEN ENERG, V39, P19262, DOI 10.1016/j.ijhydene.2014.05.006
   Yang R., 2015, INFORM RES
   Yang YT, 2015, COMPUT METH PROG BIO, V122, P471, DOI 10.1016/j.cmpb.2015.09.008
   Yarbrough AK, 2007, MED CARE RES REV, V64, P650, DOI 10.1177/1077558707305942
NR 54
TC 24
Z9 24
U1 11
U2 126
PU ELSEVIER IRELAND LTD
PI CLARE
PA ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000,
   IRELAND
SN 1386-5056
EI 1872-8243
J9 INT J MED INFORM
JI Int. J. Med. Inform.
PD OCT
PY 2019
VL 130
AR 103947
DI 10.1016/j.ijmedinf.2019.08.007
PG 11
WC Computer Science, Information Systems; Health Care Sciences & Services;
   Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Health Care Sciences & Services; Medical Informatics
GA IV7ZX
UT WOS:000484486600016
PM 31450080
DA 2022-08-23
ER

PT J
AU Huang, L
   Zhou, M
AF Huang, Li
   Zhou, Mi
TI Trends in global research on sanitation: a 30-year perspective from 1990
   to 2019
SO ENVIRONMENTAL REVIEWS
LA English
DT Review
DE bibliometric analysis; research trends; sanitation; WaSH; climate
   change; sustainability; knowledge mapping
ID HELICOBACTER-PYLORI INFECTION; CHILDHOOD DIARRHEA; DRINKING-WATER; RURAL
   WATER; HYGIENE; HEALTH; QUALITY; INFRASTRUCTURE; SUSTAINABILITY;
   EPIDEMIOLOGY
AB In the past three decades, the field of sanitation has attracted much academic attention, and a large volume of relevant research results have emerged. To explore the characteristics and trends of global sanitation research literature, this paper presents a bibliometric analysis of 9559 articles published between 1990-2019 and extracted from the Web of Science Core Collection database. With the rapid increase in the number of related publications, both the breadth and depth of sanitation studies have increased significantly in the 21st century. In terms of multiple criteria, the comprehensive research strength of developed countries is greater than that of developing countries. The field is highly interdisciplinary, meaning that collaborations between research institutions are increasing. Keyword clustering analysis shows that the main research topics in the domain of sanitation are: (i) drinking water, (ii) sustainability, (iii) biofilm, (iv) epidemiology, and (v) WaSH (water, sanitation and hygiene). Meanwhile, keyword burst analysis showed that the new research hotspots and frontiers mainly concentrated on: (i) sustainable development of sanitation services adapting to climate change, (ii) main determinants affecting child malnutrition, (iii) research based on global and multi-national data, and (iv) evaluations on various aspects of performance. This paper provides a better understanding of sanitation research trends that have emerged over the past 30 years and can serve as a reference for future research.
C1 [Huang, Li; Zhou, Mi] Shenyang Agr Univ, Coll Econ & Management, Shenyang 110866, Liaoning, Peoples R China.
RP Zhou, M (通讯作者)，Shenyang Agr Univ, Coll Econ & Management, Shenyang 110866, Liaoning, Peoples R China.
EM zhoumi2011@syau.edu.cn
CR Abdelrahman AA, 2011, ENVIRON MONIT ASSESS, V175, P37, DOI 10.1007/s10661-010-1491-7
   Abu-Elyazeed R, 1999, J INFECT DIS, V179, P382, DOI 10.1086/314593
   Abubakar IR, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9040547
   Adhikari D, 2017, FRONT PUBLIC HEALTH, V5, DOI 10.3389/fpubh.2017.00350
   Agoramoorthy G, 2009, J ECON POLICY REFORM, V12, P333, DOI 10.1080/17487870903314625
   Al-Hmoud RB, 2005, WATER INT, V30, P350, DOI 10.1080/02508060508691875
   Al-Khatib IA, 2015, J ENVIRON HEALTH, V77, P50
   Iribarnegaray MA, 2012, SUSTAINABILITY-BASEL, V4, P2922, DOI 10.3390/su4112922
   Alexander CC, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16111963
   Alhassan S, 2017, INT J ENV RES PUB HE, V14, DOI 10.3390/ijerph14070749
   Ali M, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0183100
   Andrade EL, 2019, GLOB HEALTH PROMOT, V26, P69, DOI 10.1177/1757975917695072
   Anwar WA, 2003, INT J HYG ENVIR HEAL, V206, P339, DOI 10.1078/1438-4639-00230
   Appiah-Brempong E, 2018, BMC PUBLIC HEALTH, V18, DOI 10.1186/s12889-018-5491-9
   Baig SA, 2012, RURAL REMOTE HEALTH, V12
   Bartram J, 2010, PERSPECT PUBLIC HEAL, V130, P215, DOI 10.1177/1757913910379193
   Behailu BM, 2016, SAGE OPEN, V6, DOI 10.1177/2158244016682292
   Biran A, 2012, TROP MED INT HEALTH, V17, P1133, DOI 10.1111/j.1365-3156.2012.03045.x
   Boukhari S, 2018, WATER SCI TECH-W SUP, V18, P1282, DOI 10.2166/ws.2017.194
   Brentlinger PE, 1999, JAMA-J AM MED ASSOC, V281, P184, DOI 10.1001/jama.281.2.184
   Brown MG, 2000, ANN TROP MED PARASIT, V94, P497, DOI 10.1080/00034983.2000.11813568
   Budge S, 2019, J WATER HEALTH, V17, P655, DOI 10.2166/wh.2019.224
   Cha S, 2017, GLOBAL HEALTH ACTION, V10, DOI 10.1080/16549716.2017.1327170
   Chattopadhyay A, 2019, BMC WOMENS HEALTH, V19, DOI 10.1186/s12905-019-0787-1
   Chen C., 2017, TEXT MINING VISUALIZ, V2
   Chen CM, 2008, DATA KNOWL ENG, V67, P234, DOI 10.1016/j.datak.2008.05.004
   [陈悦 Chen Yue], 2008, [科学学研究, Studies in Science of Science], V26, P449
   Choi SYP, 2003, J HEALTH POPUL NUTR, V21, P264
   Cisse G, 2019, ACTA TROP, V194, P181, DOI 10.1016/j.actatropica.2019.03.012
   Clark R, 2004, J WATER HEALTH, V2, P157, DOI 10.2166/wh.2004.0014
   Cotton A, 2013, WATER ENVIRON J, V27, P1, DOI 10.1111/j.1747-6593.2012.00317.x
   Dearden KA, 2017, BMJ OPEN, V7, DOI 10.1136/bmjopen-2016-013201
   Dey NC, 2019, INT J HYG ENVIR HEAL, V222, P1098, DOI 10.1016/j.ijheh.2019.08.006
   Dorice K, 2010, AFR J MICROBIOL RES, V4, P568
   Ducrot R, 2017, NAT RESOUR FORUM, V41, P131, DOI 10.1111/1477-8947.12128
   Dzwairo B, 2006, PHYS CHEM EARTH, V31, P779, DOI 10.1016/j.pce.2006.08.031
   Environmental Systems Research Institute (ESRI), 2016, ARCGIS DESKT 10 4 1
   Enwonwu CO, 1999, AM J TROP MED HYG, V60, P223, DOI 10.4269/ajtmh.1999.60.223
   Ercumen A, 2018, ENVIRON SCI TECHNOL, V52, P12089, DOI 10.1021/acs.est.8b02988
   Ezbakhe F, 2018, WATER RESOUR MANAG, V32, P5169, DOI 10.1007/s11269-018-2152-9
   Fancony C, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16030466
   Forget G, 2000, ENVIRON TOXICOL, V15, P264, DOI 10.1002/1522-7278(2000)15:4<264::AID-TOX2>3.0.CO;2-Q
   Fry LM, 2008, ENVIRON SCI TECHNOL, V42, P4298, DOI 10.1021/es7025856
   Galiani S, 2005, J POLIT ECON, V113, P83, DOI 10.1086/426041
   Garcia PA, 2006, ENVIRON URBAN, V18, P129, DOI 10.1177/0956247806063969
   Garn JV, 2016, EPIDEMIOLOGY, V27, P752, DOI 10.1097/EDE.0000000000000522
   Gil M, 2020, SAFETY SCI, V128, DOI 10.1016/j.ssci.2020.104717
   Goh KL, 2011, HELICOBACTER, V16, P1, DOI 10.1111/j.1523-5378.2011.00874.x
   Grojec A., 2017, PROGR DRINKING WATER
   Guerrini A, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8121250
   Hacker ME, 2017, J WATER SANIT HYG DE, V7, P407, DOI 10.2166/washdev.2017.188
   Hall NL, 2019, J WATER SANIT HYG DE, V9, P429, DOI 10.2166/washdev.2019.154
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Hoque BA, 2006, INT J HYG ENVIR HEAL, V209, P451, DOI 10.1016/j.ijheh.2006.04.008
   Hsan K, 2019, J WATER SANIT HYG DE, V9, P794, DOI 10.2166/washdev.2019.038
   Humphrey JH, 2019, LANCET GLOB HEALTH, V7, pE132, DOI 10.1016/S2214-109X(18)30374-7
   Imada KS, 2016, REV SAUDE PUBL, V50, DOI [10.1590/S1518-8787.2016050006505, 10.1590/s1518-8787.2016050006505]
   Johannessen A, 2017, ECOL SOC, V22, DOI 10.5751/ES-08870-220101
   Johri M, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0209054
   Jones SA, 2009, DESALINATION, V248, P500, DOI 10.1016/j.desal.2008.05.094
   Joseph K, 2006, HABITAT INT, V30, P863, DOI 10.1016/j.habitatint.2005.09.009
   Kaminsky JA, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0188905
   Kim H, 2018, WATER-SUI, V10, DOI 10.3390/w10060682
   Kirshen P, 2018, URBAN CLIM, V24, P247, DOI 10.1016/j.uclim.2018.03.005
   Kwami CS, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16203793
   Lauer JM, 2018, AM J TROP MED HYG, V99, P1606, DOI 10.4269/ajtmh.18-0143
   Li YX, 2017, SCIENTOMETRICS, V112, P1025, DOI 10.1007/s11192-017-2421-z
   Mallow M, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0198235
   Mara D, 2007, WATER POLICY, V9, P305, DOI 10.2166/wp.2007.009
   McKee M, 2006, PUBLIC HEALTH, V120, P364, DOI 10.1016/j.puhe.2005.05.013
   MERTENS TE, 1992, INT J EPIDEMIOL, V21, P1157, DOI 10.1093/ije/21.6.1157
   Ministry of Foreign Affairs (MFA), 2015, REP CHIN IMPL MILL D
   MOE CL, 1991, B WORLD HEALTH ORGAN, V69, P305
   Naik KS, 2012, WATER SCI TECHNOL, V66, P644, DOI 10.2166/wst.2012.144
   Nakagiri A, 2017, J WATER SANIT HYG DE, V7, P92, DOI 10.2166/washdev.2017.085
   Ngasala TM, 2019, SCI TOTAL ENVIRON, V686, P1238, DOI 10.1016/j.scitotenv.2019.05.202
   Le NT, 2018, J ENVIRON SCI MANAG, V21, P39
   Null C, 2018, LANCET GLOB HEALTH, V6, pE316, DOI 10.1016/S2214-109X(18)30005-6
   Nurgalieva ZZ, 2002, AM J TROP MED HYG, V67, P201, DOI 10.4269/ajtmh.2002.67.201
   Oderda G, 1999, CAN J GASTROENTEROL, V13, P595, DOI 10.1155/1999/760675
   Oluyege JO, 2009, WATER SCI TECHNOL, V59, P1929, DOI 10.2166/wst.2009.219
   Oswald .WE, 2017, B WORLD HEALTH ORGAN, V95, P250, DOI [10.2471/BLT.16.177758, 10.2471/blt.16.177758]
   Goncalves MCP, 2019, PROCESS BIOCHEM, V76, P95, DOI 10.1016/j.procbio.2018.09.016
   Pickering AJ, 2019, LANCET GLOB HEALTH, V7, pE1139, DOI 10.1016/S2214-109X(19)30268-2
   Sansom K, 2006, PUBLIC ADMIN DEVELOP, V26, P207, DOI 10.1002/pad.419
   Sastry N, 1996, DEMOGRAPHY, V33, P211, DOI 10.2307/2061873
   Saxton J, 2016, MATERN CHILD NUTR, V12, P869, DOI 10.1111/mcn.12323
   SCHMITT F, 1992, WATER SCI TECHNOL, V25, P83, DOI 10.2166/wst.1992.0182
   Speer J, 2016, URBAN GEOGR, V37, P1049, DOI 10.1080/02723638.2016.1142150
   Stephensen CB, 1999, J NUTR, V129, p534S, DOI 10.1093/jn/129.2.534S
   Sun YQ, 2019, TRENDS FOOD SCI TECH, V83, P86, DOI 10.1016/j.tifs.2018.11.015
   TULLOCH J, 1993, MED J AUSTRALIA, V159, P46, DOI 10.5694/j.1326-5377.1993.tb137705.x
   Ulukanligil M, 2003, BMC PUBLIC HEALTH, V3, DOI 10.1186/1471-2458-3-29
   UNICEF & WHO, 2015, PROGR SAN DRINK WAT
   Vasquez WF, 2018, J ENVIRON MANAGE, V218, P477, DOI 10.1016/j.jenvman.2018.04.085
   VELLA V, 1995, J TROP PEDIATRICS, V41, P89, DOI 10.1093/tropej/41.2.89
   Wamuchiru E, 2018, J ENVIRON PLANN MAN, V61, P2166, DOI 10.1080/09640568.2017.1389699
   Wayland J, 2018, J WATER SANIT HYG DE, V8, P44, DOI 10.2166/washdev.2017.095
   Whitley L, 2019, INT J HYG ENVIR HEAL, V222, P1133, DOI 10.1016/j.ijheh.2019.08.001
   WHO, 2019, SAN
   WHO and UNICEF, 2019, JOINT MON PROGR 2019
   Wong SL, 2020, MAR POLLUT BULL, V158, DOI 10.1016/j.marpolbul.2020.111432
   Xiao FJ, 2017, FRONT CHEM, V5, DOI 10.3389/fchem.2017.00067
   Yu DJ, 2017, RENEW SUST ENERG REV, V74, P1314, DOI 10.1016/j.rser.2016.11.144
   Yu H, 2016, APPL ENERG, V184, P960, DOI 10.1016/j.apenergy.2016.07.129
   Zhong W., 2008, J INTELL-BASEL, V27, P118
NR 106
TC 1
Z9 1
U1 3
U2 16
PU CANADIAN SCIENCE PUBLISHING
PI OTTAWA
PA 65 AURIGA DR, SUITE 203, OTTAWA, ON K2E 7W6, CANADA
SN 1208-6053
EI 1181-8700
J9 ENVIRON REV
JI Environ. Rev.
PD MAR
PY 2021
VL 29
IS 1
BP 75
EP 86
DI 10.1139/er-2020-0030
PG 12
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA QT7GL
UT WOS:000626756900005
OA Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Huang, LJ
   Xia, ZQ
   Cao, Y
AF Huang, Linjia
   Xia, Ziqian
   Cao, Yang
TI A Bibliometric Analysis of Global Fine Roots Research in Forest
   Ecosystems during 1992-2020
SO FORESTS
LA English
DT Article
DE citations; core journals; knowledge mapping; network analysis; Lotka's
   Law; VOS viewer
ID GROUND CARBON ALLOCATION; NUTRIENT DYNAMICS; TURNOVER; DECOMPOSITION;
   CLIMATE; RESPIRATION; PATTERNS; BIOMASS; SEQUESTRATION; ECOLOGY
AB (1) Background: Fine roots (<= 2 mm in diameter) play a critical role in forest ecosystem ecological processes and has been widely identified as a major research topic. This study aimed to synthesize the global literature based on the Web of Science Core Collection scientific database from 1992 to 2020 and summarize the research trends and prospects on research of fine roots in forest ecosystems. A quantitative bibliometric analysis was presented with information related to authors, countries, institutions, journals, top cited publications, research hotspots, trends, and prospects. (2) Results: The results showed that the amount of publications has increased exponentially. USA, China, and Germany were the most productive countries. Chinese Academy of Science was the most productive institution on fine roots research and also has a key position in both domestic and international cooperation networks. Leuschner C and Hertel D were the most productive authors. Six core journals were confirmed from 471 journals based on Bradford's law. The distribution of the frequency of authors and the number of their publications were fitted with Lotka's Law. Author collaboration network was mainly limited in the same countries/territories and institutions. Keywords analysis indicates that the hotspots are biomass, decomposition, and respiration of fine roots, especially under climate change. (3) Conclusion: Our results provide a better understanding of global characteristics and trends of fine roots that have emerged in this field, which could offer reference for future research.
C1 [Huang, Linjia; Cao, Yang] Chinese Acad Sci & Minist Water Resources, Inst Soil & Water Conservat, Yangling 712100, Xianyang, Peoples R China.
   [Huang, Linjia] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Xia, Ziqian] Nanchang Univ, Sch Qianhu, Nanchang 330031, Peoples R China.
RP Cao, Y (通讯作者)，Chinese Acad Sci & Minist Water Resources, Inst Soil & Water Conservat, Yangling 712100, Xianyang, Peoples R China.
EM huanglinjia20@mails.ucas.ac.cn; Ziqian.research@gmail.com;
   yang.cao@nwsuaf.edu.cn
RI Cao, Yang/A-8210-2010
OI Cao, Yang/0000-0001-7831-8068
CR Aleixandre-Benavent R, 2017, J CLEAN PROD, V147, P406, DOI 10.1016/j.jclepro.2017.01.112
   Andreo-Martinez P, 2020, ENVIRON TOXICOL PHAR, V77, DOI 10.1016/j.etap.2020.103374
   Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007
   Biresselioglu ME, 2020, SCI TOTAL ENVIRON, V729, DOI 10.1016/j.scitotenv.2020.139027
   Cao Y, 2020, SCI TOTAL ENVIRON, V737, DOI 10.1016/j.scitotenv.2020.140299
   Clemmensen KE, 2013, SCIENCE, V339, P1615, DOI 10.1126/science.1231923
   Cudlin P, 2007, PLANT BIOSYST, V141, P406, DOI 10.1080/11263500701626028
   Ding YY, 2019, FOREST ECOL MANAG, V431, P17, DOI 10.1016/j.foreco.2018.02.039
   Eissenstat DM, 2000, NEW PHYTOL, V147, P33, DOI 10.1046/j.1469-8137.2000.00686.x
   Eissenstat DM, 1997, ADV ECOL RES, V27, P1, DOI 10.1016/S0065-2504(08)60005-7
   Eldhuset TD, 2017, PLANT SOIL, V418, P357, DOI 10.1007/s11104-017-3302-1
   Finer L, 2011, FOREST ECOL MANAG, V261, P265, DOI 10.1016/j.foreco.2010.10.016
   Freschet GT, 2013, J ECOL, V101, P943, DOI 10.1111/1365-2745.12092
   Germon A, 2020, FOREST ECOL MANAG, V466, DOI 10.1016/j.foreco.2020.118135
   Gill RA, 2000, NEW PHYTOL, V147, P13, DOI 10.1046/j.1469-8137.2000.00681.x
   Gower ST, 1996, ECOLOGY, V77, P1750, DOI 10.2307/2265780
   Guo DL, 2008, NEW PHYTOL, V177, P443, DOI 10.1111/j.1469-8137.2007.02242.x
   Guo DL, 2004, OECOLOGIA, V140, P450, DOI 10.1007/s00442-004-1596-1
   Hanson PJ, 2000, BIOGEOCHEMISTRY, V48, P115, DOI 10.1023/A:1006244819642
   HENDRICK RL, 1992, ECOLOGY, V73, P1094, DOI 10.2307/1940183
   Hendricks JJ, 2006, J ECOL, V94, P40, DOI 10.1111/j.1365-2745.2005.01067.x
   HENDRICKS JJ, 1993, TRENDS ECOL EVOL, V8, P174, DOI 10.1016/0169-5347(93)90143-D
   Hong T, 2019, PEERJ, V7, DOI 10.7717/peerj.7103
   Huang L, 2020, J CLEAN PROD, V252, DOI 10.1016/j.jclepro.2019.119908
   Iversen CM, 2017, NEW PHYTOL, V215, P15, DOI 10.1111/nph.14486
   Jackson RB, 1997, P NATL ACAD SCI USA, V94, P7362, DOI 10.1073/pnas.94.14.7362
   Jackson RB, 1996, OECOLOGIA, V108, P389, DOI 10.1007/BF00333714
   Johnson MG, 2001, ENVIRON EXP BOT, V45, P263, DOI 10.1016/S0098-8472(01)00077-6
   Kulak M, 2019, SCI HORTIC-AMSTERDAM, V246, P418, DOI 10.1016/j.scienta.2018.11.031
   Liu YN, 2020, J SOIL SEDIMENT, V20, P1513, DOI 10.1007/s11368-019-02519-9
   Ma Q, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17030830
   Majdi H, 2005, PLANT SOIL, V276, P1, DOI 10.1007/s11104-005-3104-8
   Matamala R, 2003, SCIENCE, V302, P1385, DOI 10.1126/science.1089543
   McCormack ML, 2015, NEW PHYTOL, V207, P505, DOI 10.1111/nph.13363
   McCormack ML, 2012, NEW PHYTOL, V195, P823, DOI 10.1111/j.1469-8137.2012.04198.x
   Mourao PR, 2020, J CLEAN PROD, V256, DOI 10.1016/j.jclepro.2020.120413
   NADELHOFFER KJ, 1992, ECOLOGY, V73, P1139, DOI 10.2307/1940664
   Norby RJ, 2000, NEW PHYTOL, V147, P3, DOI 10.1046/j.1469-8137.2000.00676.x
   Ostonen I, 2007, PLANT BIOSYST, V141, P426, DOI 10.1080/11263500701626069
   Palviainen M, 2004, PLANT SOIL, V263, P53, DOI 10.1023/B:PLSO.0000047718.34805.fb
   Pregitzer KS, 2000, NEW PHYTOL, V147, P105, DOI 10.1046/j.1469-8137.2000.00689.x
   Pregitzer KS, 2002, ECOL MONOGR, V72, P293, DOI 10.1890/0012-9615(2002)072[0293:FRAONN]2.0.CO;2
   Pregitzer KS, 1998, TREE PHYSIOL, V18, P665
   Reich PB, 2014, J ECOL, V102, P275, DOI 10.1111/1365-2745.12211
   Ryan MG, 1996, TREE PHYSIOL, V16, P333
   Silver WL, 2001, OECOLOGIA, V129, P407, DOI 10.1007/s004420100740
   Steele SJ, 1997, TREE PHYSIOL, V17, P577
   Su YA, 2020, SCI TOTAL ENVIRON, V733, DOI 10.1016/j.scitotenv.2020.138984
   Torres RT, 2020, SCI TOTAL ENVIRON, V717, DOI 10.1016/j.scitotenv.2019.135001
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vargas SA, 2019, J CLEAN PROD, V218, P850, DOI 10.1016/j.jclepro.2019.02.015
   VOGT KA, 1986, ADV ECOL RES, V15, P303, DOI 10.1016/S0065-2504(08)60122-1
   Vogt KA, 1996, PLANT SOIL, V187, P159, DOI 10.1007/BF00017088
   Wan RH, 2019, PEERJ, V7, DOI 10.7717/peerj.7116
   Weemstra M, 2016, NEW PHYTOL, V211, P1159, DOI 10.1111/nph.14003
   Xia JY, 2020, GLOBAL CHANGE BIOL, V26, P6040, DOI 10.1111/gcb.15317
   Yuan ZY, 2010, CRIT REV PLANT SCI, V29, P204, DOI 10.1080/07352689.2010.483579
   Zhang Y, 2020, CATENA, V194, DOI 10.1016/j.catena.2020.104798
   Zhang Y, 2020, J HAZARD MATER, V400, DOI 10.1016/j.jhazmat.2020.123110
   Zhang Y, 2017, J CLEAN PROD, V149, P70, DOI 10.1016/j.jclepro.2017.02.067
NR 60
TC 1
Z9 1
U1 17
U2 21
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1999-4907
J9 FORESTS
JI Forests
PD JAN
PY 2022
VL 13
IS 1
AR 93
DI 10.3390/f13010093
PG 16
WC Forestry
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Forestry
GA YN4XR
UT WOS:000747264000001
OA gold
DA 2022-08-23
ER

PT J
AU Ma, HQ
   Chen, HY
   Hou, JL
   Ge, JY
   Lv, W
AF Ma Hongqiang
   Chen Hongyu
   Hou Jinliang
   Ge Jieya
   Lv Wei
TI CiteSpace-based visualization analysis on mappings of alkali-activated
   cementitious materials in the field of engineering research
SO CASE STUDIES IN CONSTRUCTION MATERIALS
LA English
DT Article
DE CiteSpace visual analysis; Knowledge atlas; Alkali-activated materials;
   Solid wastes; Engineering field
ID FLY-ASH; COAL GANGUE; COMPOSITE; MORTARS; GEOPOLYMERS; DURABILITY; PASTE
AB Alkali-activated cementitious materials (AACM) became the most promising alternative materials of Portland cement due to their merits including early strength, high environmental adaptability and low environmental impact. The scientific knowledge mapping was drawn with CiteSpace Visual Analysis Software and the method combining qualitative and quantitative analysis, the co-citation network based on the core collection of Web of Science is generated, and frontiers and hotspots of studies in AACM are explored to provide theoretical guidance for subsequent researches. The research results showed that the publications with respect to the field of AACM rocketed recently and had become an increasingly interdisciplinary research subject. Relevant researches were most conducted in China, Australia and USA, however, European countries have a high degree of research content centrality and are closely related to other countries. The most studied materials were the fly ash, slag and metakaolin based on the Visual Analysis, and researchers focused on the modification of AACM and studies in new solid wastes in recently, and the studies targeting at practical engineering applications. National or international agencies are to intensify cooperation and improve practical engineering applications of different AACM to improve scientific research significance. The follow-up researches are prospected in this paper. The relations between the research interest, subject, hotspot, author, journal and contributing institutions are highlighted, promoting comprehensive understandings on the field of AACM. (C) 2020 The Author(s). Published by Elsevier Ltd.
C1 [Ma Hongqiang; Chen Hongyu; Hou Jinliang; Ge Jieya; Lv Wei] China Univ Min & Technol Beijing, Sch Mech & Civil Engn, Beijing 100083, Peoples R China.
   [Ma Hongqiang] Univ Canterbury, Dept Civil & Nat Resources Engn, Christchurch 8041, New Zealand.
RP Ma, HQ (通讯作者)，China Univ Min & Technol Beijing, Sch Mech & Civil Engn, Beijing 100083, Peoples R China.
EM mahongqiang1992@163.com
FU National Natural Science Foundation of China (NSFC) [51578539]; Beijing
   Natural Science Foundation [8164061]; Open Research Project of State Key
   Laboratory of Coal Resources and Safe Mining, China University of Mining
   and Technology [SKLCRSM16KFD07]; China Scholarship Council
   [CSC201906430005]
FX We would like to thank the editor and the anonymous reviewers for their
   comments, which have greatly improved our paper. This research was
   sponsored by the National Natural Science Foundation of China (NSFC,
   NO.51578539), Beijing Natural Science Foundation funded projects
   (NO.8164061), and the Open Research Project of State Key Laboratory of
   Coal Resources and Safe Mining, China University of Mining and
   Technology, (SKLCRSM16KFD07). The authors gratefully acknowledge
   financial support from China Scholarship Council (CSC201906430005).
CR Al-Mashhadani MM, 2018, CONSTR BUILD MATER, V167, P505, DOI 10.1016/j.conbuildmat.2018.02.061
   Bayat A, 2018, CONSTR BUILD MATER, V167, P775, DOI 10.1016/j.conbuildmat.2018.02.105
   Chi M, 2012, CONSTR BUILD MATER, V35, P240, DOI 10.1016/j.conbuildmat.2012.04.005
   Chindaprasirt P, 2019, INT J GREENH GAS CON, V85, P30, DOI 10.1016/j.ijggc.2019.03.011
   Coppola L, 2019, CEMENT CONCRETE COMP, V104, DOI 10.1016/j.cemconcomp.2019.103341
   Coppola L, 2018, CONSTR BUILD MATER, V173, P111, DOI 10.1016/j.conbuildmat.2018.04.034
   Duxson P, 2007, J MATER SCI, V42, P2917, DOI 10.1007/s10853-006-0637-z
   Habert G, 2011, J CLEAN PROD, V19, P1229, DOI 10.1016/j.jclepro.2011.03.012
   Karozou A, 2019, CONSTR BUILD MATER, V212, P85, DOI 10.1016/j.conbuildmat.2019.03.244
   Keulen A, 2018, CONSTR BUILD MATER, V162, P27, DOI 10.1016/j.conbuildmat.2017.11.136
   Koshy N, 2019, CONSTR BUILD MATER, V206, P287, DOI 10.1016/j.conbuildmat.2019.02.076
   Kramar S, 2016, CONSTR BUILD MATER, V124, P937, DOI 10.1016/j.conbuildmat.2016.08.018
   Kumar S, 2010, J MATER SCI, V45, P607, DOI 10.1007/s10853-009-3934-5
   Lee NK, 2014, CEMENT CONCRETE COMP, V53, P239, DOI 10.1016/j.cemconcomp.2014.07.007
   Li XJ, 2017, INT J HOSP MANAG, V60, P77, DOI 10.1016/j.ijhm.2016.10.006
   Li ZG, 2018, CONSTR BUILD MATER, V163, P668, DOI 10.1016/j.conbuildmat.2017.12.127
   Ma C, 2019, J CLEAN PROD, V220, P188, DOI 10.1016/j.jclepro.2019.02.159
   [马宏强 Ma Hongqiang], 2018, [材料研究学报, Chinese Journal of Materials Research], V32, P898
   Ma J, 2009, ANATOLIA, V20, P62, DOI 10.1080/13032917.2009.10518895
   McLellan BC, 2011, J CLEAN PROD, V19, P1080, DOI 10.1016/j.jclepro.2011.02.010
   Papa E, 2018, J CLEAN PROD, V171, P76, DOI 10.1016/j.jclepro.2017.09.270
   Rangan BV., 2014, INDIAN CONCRETE J, V88, P41
   Rashad AM, 2013, CONSTR BUILD MATER, V41, P751, DOI 10.1016/j.conbuildmat.2012.12.030
   Rickard WDA, 2013, APPL CLAY SCI, V73, P71, DOI 10.1016/j.clay.2012.10.006
   Salahuddin MBM, 2015, CERAM INT, V41, P4273, DOI 10.1016/j.ceramint.2014.11.119
   Shi C.J., 2008, ALKALI ACTIVATED CEM
   Shi CJ, 2011, CEMENT CONCRETE RES, V41, P750, DOI 10.1016/j.cemconres.2011.03.016
   Sturm P, 2016, CONSTR BUILD MATER, V124, P961, DOI 10.1016/j.conbuildmat.2016.08.017
   Thokchom S., 2010, J ENG SCI TECHNOL RE, V3, P31
   Wang R. J., 2016, J INTELLIGENCE, V35, P74
   Yi C, 2018, CONSTR BUILD MATER, V187, P318, DOI 10.1016/j.conbuildmat.2018.07.220
   Yi C, 2018, CONSTR BUILD MATER, V167, P649, DOI 10.1016/j.conbuildmat.2018.02.071
   Zhang J, 2017, CONSTR BUILD MATER, V152, P598, DOI 10.1016/j.conbuildmat.2017.07.027
   Zhu J, 2018, CELL PHYSIOL BIOCHEM, V51, P113, DOI 10.1159/000495168
NR 34
TC 5
Z9 5
U1 5
U2 15
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-5095
J9 CASE STUD CONSTR MAT
JI Case Stud. Constr. Mater.
PD JUN
PY 2020
VL 12
AR e00350
DI 10.1016/j.cscm.2020.e00350
PG 15
WC Construction & Building Technology; Engineering, Civil; Materials
   Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering; Materials Science
GA OZ1EU
UT WOS:000594678800043
OA gold
DA 2022-08-23
ER

PT J
AU Zhao, L
   Tang, ZY
   Zou, X
AF Zhao, Li
   Tang, Zhi-ying
   Zou, Xin
TI Mapping the Knowledge Domain of Smart-City Research: A Bibliometric and
   Scientometric Analysis
SO SUSTAINABILITY
LA English
DT Article
DE Smart city; Scientometrics; Visualization; VOSviewer; CiteSpace
ID BIG DATA ANALYTICS; SUSTAINABLE CITIES; 2 DECADES; INTERNET; THINGS;
   TRENDS; CHALLENGES; MANAGEMENT; ARCHITECTURE; INTEGRATION
AB As urbanization continues to accelerate, the number of cities and their growing populations have created problems, such as the congestion and noise related to transportation, the pollution from industry, and the difficulty of disposing of garbage. An emerging urban strategy is to make use of digital technologies and big data to help improve the quality of life of urban residents. In the past decade, more and more researchers have studied smart cities, and the number of literature in this field grows rapidly, making it "big data". With the aim of better understanding the contexts of smart-city research, including the distribution of topics, knowledge bases, and the research frontiers in the field, this paper is based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) in the Web of Science (WoS) Core Collection, and the method used is that of comprehensive scientometric analysis and knowledge mapping in terms of diversity, time slicing, and dynamics, using VOSviewer and CiteSpace to study the literature in the field. The main research topics can be divided into three areas-"the concepts and elements of the smart city", "the smart city and the Internet of Things", and "the smart city of the future"-through document co-citation analysis. There are four key directions-"research objectives and development-strategy research", "technical-support research", "data-processing and applied research", and "management and applied research"-analyzed using keywords co-occurrence. Finally, the research frontiers are urban-development, sustainable cities, cloud computing, artificial intelligence, integration, undertaken through keyword co-occurrence analysis.
C1 [Zhao, Li; Tang, Zhi-ying] Chengdu Univ Technol, Coll Management Sci, Chengdu 610059, Peoples R China.
   [Zou, Xin] Monash Univ, Inst Transport Studies, Clayton, Vic 3800, Australia.
RP Zou, X (通讯作者)，Monash Univ, Inst Transport Studies, Clayton, Vic 3800, Australia.
EM lizhao@stu.cdut.edu.cn; tangzhiying@cdut.cn; Xin.Zou@monash.edu
CR Abbasi A, 2016, J NEUROINFLAMM, V13, DOI 10.1186/s12974-016-0758-5
   Ahmad K, 2018, ELECTRON LIBR, V36, P696, DOI 10.1108/EL-02-2017-0036
   Ahvenniemi H, 2017, CITIES, V60, P234, DOI 10.1016/j.cities.2016.09.009
   Akter S, 2016, INT J PROD ECON, V182, P113, DOI 10.1016/j.ijpe.2016.08.018
   Al Nuaimi E, 2015, J INTERNET SERV APPL, V6, DOI 10.1186/s13174-015-0041-5
   Al-Fuqaha A, 2015, IEEE COMMUN SURV TUT, V17, P2347, DOI 10.1109/COMST.2015.2444095
   Alawadhi Suha, 2012, Electronic Government. Proceedings of the 11th IFIP WG 8.5 International Conference, EGOV 2012, P40, DOI 10.1007/978-3-642-33489-4_4
   Albino V, 2015, J URBAN TECHNOL, V22, P3, DOI 10.1080/10630732.2014.942092
   Angelidou M, 2014, CITIES, V41, pS3, DOI 10.1016/j.cities.2014.06.007
   Anthopoulos Leonidas G., 2016, Information Polity, V21, P99, DOI 10.3233/IP-150371
   Anthopoulos L, 2017, CITIES, V63, P128, DOI 10.1016/j.cities.2016.10.005
   Bailon-Moreno R, 2006, J AM SOC INF SCI TEC, V57, P949, DOI 10.1002/asi.20362
   Bakici T, 2013, J KNOWL ECON, V4, P135, DOI 10.1007/s13132-012-0084-9
   Balakrishna C., 2012, 2012 6 INT C NEXT GE
   Batty M, 2012, EUR PHYS J-SPEC TOP, V214, P481, DOI 10.1140/epjst/e2012-01703-3
   Ben Letaifa S, 2015, J BUS RES, V68, P1414, DOI 10.1016/j.jbusres.2015.01.024
   Benevolo C., 2016, EMPOWERING ORG
   Bibri SE, 2018, SUSTAIN CITIES SOC, V38, P230, DOI 10.1016/j.scs.2017.12.034
   Bibri SE, 2017, SUSTAIN CITIES SOC, V31, P183, DOI 10.1016/j.scs.2017.02.016
   Boes K, 2016, INT J TOUR CITIES, V2, P108, DOI 10.1108/IJTC-12-2015-0032
   Borgman CL, 2002, ANNU REV INFORM SCI, V36, P3
   Botta A, 2016, FUTURE GENER COMP SY, V56, P684, DOI 10.1016/j.future.2015.09.021
   Buhalis D., 2015, INF COMM TECHN TOUR
   Buhalis D., 2014, INF COMM TECHN TOUR
   Caragliu A, 2011, J URBAN TECHNOL, V18, P65, DOI 10.1080/10630732.2011.601117
   Cardone G, 2014, IEEE COMMUN MAG, V52, P78, DOI 10.1109/MCOM.2014.6917406
   Castelnovo W, 2016, SOC SCI COMPUT REV, V34, P724, DOI 10.1177/0894439315611103
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen M, 2013, MULTIMED TOOLS APPL, V67, P167, DOI 10.1007/s11042-012-1013-4
   Cheng B, 2015, 2015 IEEE INT C BIG
   Chourabi H., 2012, 2012 45th Hawaii International Conference on System Sciences (HICSS), P2289, DOI 10.1109/HICSS.2012.615
   Clohessy T., 2014, 2014 IEEE ACM 7 INT
   Cobo MJ, 2011, J AM SOC INF SCI TEC, V62, P1382, DOI 10.1002/asi.21525
   Cocchia A, 2014, PROGR IS, P13, DOI 10.1007/978-3-319-06160-3_2
   David M, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11164422
   De Lange M., 2013, URBAN LAND USE
   Deakin M, 2011, INTELL BUILD INT, V3, P140, DOI 10.1080/17508975.2011.586671
   Di Nardo A, 2013, WATER RESOUR MANAG, V27, P4493, DOI 10.1007/s11269-013-0421-1
   Dillow Clay, 2011, POPULAR SCI
   Eremia M, 2017, PROCEDIA ENGINEER, V181, P12, DOI 10.1016/j.proeng.2017.02.357
   Eslamian SA, 2016, SUSTAIN CITIES SOC, V27, P419, DOI 10.1016/j.scs.2016.08.003
   Giang N. K., 2015, 2015 5 INT C INT THI
   Giffinger R., 2007, SMART CITIES RANKING
   Giuffre T, 2012, PROCD SOC BEHV, V53, P16, DOI 10.1016/j.sbspro.2012.09.856
   Gohar M, 2018, SUSTAIN CITIES SOC, V41, P114, DOI 10.1016/j.scs.2018.05.008
   Gong S, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11143889
   Gonzalez MC, 2008, NATURE, V453, P779, DOI 10.1038/nature06958
   Goodspeed R, 2015, CAMB J REG ECON SOC, V8, P79, DOI 10.1093/cjres/rsu013
   Gouveia C, 2008, GEOJOURNAL, V72, P185, DOI 10.1007/s10708-008-9183-3
   Gubbi J, 2013, FUTURE GENER COMP SY, V29, P1645, DOI 10.1016/j.future.2013.01.010
   Hashem IAT, 2016, INT J INFORM MANAGE, V36, P748, DOI 10.1016/j.ijinfomgt.2016.05.002
   Hollands R, 2008, CITY, V12, P303, DOI [10.1080/1360481080247926, 10.1080/13604810802479126, DOI 10.1080/13604810802479126]
   Hollands RG, 2015, CAMB J REG ECON SOC, V8, P61, DOI 10.1093/cjres/rsu011
   Hou DD, 2019, PEERJ, V7, DOI 10.7717/peerj.6859
   Hu XP, 2014, IEEE COMMUN MAG, V52, P78, DOI 10.1109/MCOM.2014.6829948
   Ingwersen P, 2018, SCIENTOMETRICS, V117, P1205, DOI 10.1007/s11192-018-2901-9
   Iqbal J, 2018, SUSTAIN CITIES SOC, V43, P443, DOI 10.1016/j.scs.2018.09.020
   Iqbal W, 2019, SCIENTOMETRICS, V119, P1121, DOI 10.1007/s11192-019-03086-z
   Jacso P, 2005, CURR SCI INDIA, V89, P1537
   Jalali R., 2015, 2015 18 INT C INT NE
   Jegadeesan S, 2019, SUSTAIN CITIES SOC, V49, DOI 10.1016/j.scs.2019.101522
   Jeschke S, 2017, SP SER WIRELESS TECH, P3, DOI 10.1007/978-3-319-42559-7_1
   Jiang LH, 2019, SUSTAIN CITIES SOC, V48, DOI 10.1016/j.scs.2019.101497
   Jin J, 2014, IEEE INTERNET THINGS, V1, P112, DOI 10.1109/JIOT.2013.2296516
   Kauf S, 2019, TRANSP RES PROC, V39, P143, DOI 10.1016/j.trpro.2019.06.016
   Kitchin R., 2013, DIALOGUES HUM GEOGR, V3, P262, DOI DOI 10.1177/2043820613513388
   Kitchin R, 2014, BIG DATA SOC, V1, DOI 10.1177/2053951714528481
   Kitchin R, 2015, CAMB J REG ECON SOC, V8, P131, DOI 10.1093/cjres/rsu027
   Kitchin R, 2014, GEOJOURNAL, V79, P1, DOI 10.1007/s10708-013-9516-8
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   Komninos N., 2008, INTELLIGENT CITIES G
   Korczak J, 2019, TRANSP RES PROC, V39, P201, DOI 10.1016/j.trpro.2019.06.022
   Kumar M, 2016, INT J PROD RES, V54, P7181, DOI 10.1080/00207543.2016.1198057
   Kumar TMV, 2017, ADV 21ST CENT HUMAN, P3, DOI 10.1007/978-981-10-1610-3_1
   Kummitha RKR, 2017, CITIES, V67, P43, DOI 10.1016/j.cities.2017.04.010
   Leal B, 2010, INTERNET OF THINGS-BOOK, P3, DOI 10.1007/978-1-4419-1674-7_1
   Lee JH, 2014, TECHNOL FORECAST SOC, V89, P80, DOI 10.1016/j.techfore.2013.08.033
   Lee J, 2014, GOV INFORM Q, V31, pS93, DOI 10.1016/j.giq.2014.01.010
   Lee KA, 2015, 16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5, P2996
   Leonidas A., 2019, SMART CITIES SMART S
   LI CM, 2019, SUSTAINABILITY-BASEL, V11, DOI DOI 10.3390/SU11164346
   Li MN, 2019, SCIENTOMETRICS, V120, P683, DOI 10.1007/s11192-019-03134-8
   Lombardi P, 2012, INNOVATION-ABINGDON, V25, P137, DOI 10.1080/13511610.2012.660325
   Luque-Ayala A, 2015, URBAN STUD, V52, P2105, DOI 10.1177/0042098015577319
   Mahizhnan A, 1999, CITIES, V16, P13, DOI 10.1016/S0264-2751(98)00050-X
   Management and Budget Office, 2018, FEDERAL CLOUD COMPUT
   March H, 2016, EUR URBAN REG STUD, V23, P816, DOI 10.1177/0969776414554488
   Massana J, 2017, SUSTAIN CITIES SOC, V28, P108, DOI 10.1016/j.scs.2016.09.001
   Mattern, 2016, PLACES J
   Mattoni B, 2015, SUSTAIN CITIES SOC, V15, P105, DOI 10.1016/j.scs.2014.12.002
   Meijer A, 2016, INT REV ADM SCI, V82, P392, DOI 10.1177/0020852314564308
   Mijumbi R, 2016, IEEE COMMUN SURV TUT, V18, P236, DOI 10.1109/COMST.2015.2477041
   Mitton N, 2012, EURASIP J WIREL COMM, DOI 10.1186/1687-1499-2012-247
   Mongeon P, 2016, SCIENTOMETRICS, V106, P213, DOI 10.1007/s11192-015-1765-5
   Monzon A., 2015, 2015 INT C SMART CIT
   Mora L., 2018, SCI REGIONALI ITALIA, V17, P15, DOI [https://doi.org/10.14650/88815, DOI 10.14650/88815]
   Mora L, 2017, J URBAN TECHNOL, V24, P3, DOI 10.1080/10630732.2017.1285123
   Moral-Munoz JA, 2019, INTEGR CANCER THER, V18, DOI 10.1177/1534735419846401
   Moustaka V, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3239566
   Nam T., 2011, 5 INT C THOER PRACT
   Naphade M, 2011, COMPUTER, V44, P32, DOI 10.1109/MC.2011.187
   National Development and Reform Commission, 2014, GUID PROM HLTH DEV S
   National Development and Reform Commission, 2016, NOT ORG NEW SMART CI
   National Development and Reform Commission, 2016, NOT 3 BATCH NAT NEW
   Neirotti P, 2014, CITIES, V38, P25, DOI 10.1016/j.cities.2013.12.010
   Oberg C, 2016, PROD PLAN CONTROL, V27, P529, DOI 10.1080/09537287.2016.1147095
   Pan XL, 2018, J INFORMETR, V12, P481, DOI 10.1016/j.joi.2018.03.005
   PARK S, 2019, SUSTAINABILITY-BASEL, V11, DOI DOI 10.3390/su11164479
   Persson O., 2009, CELEBRATING SCHOLARL, P9
   Petrolo R., 2014, 2014 ACM INT WORKSH
   Petrolo R, 2017, T EMERG TELECOMMUN T, V28, DOI 10.1002/ett.2931
   Piro G, 2014, J SYST SOFTWARE, V88, P169, DOI 10.1016/j.jss.2013.10.029
   Pollio A, 2016, URBAN GEOGR, V37, P514, DOI 10.1080/02723638.2015.1118991
   PRITCHARD A, 1969, J DOC, V25, P348
   Qiu T, 2018, IEEE COMMUN SURV TUT, V20, P2011, DOI 10.1109/COMST.2018.2803740
   Rathore MM, 2016, COMPUT NETW, V101, P63, DOI 10.1016/j.comnet.2015.12.023
   Roman R, 2013, COMPUT NETW, V57, P2266, DOI 10.1016/j.comnet.2012.12.018
   Schaffers H, 2011, LECT NOTES COMPUT SC, V6656, P431, DOI 10.1007/978-3-642-20898-0_31
   Scuotto V, 2016, BUS PROCESS MANAG J, V22, P357, DOI 10.1108/BPMJ-05-2015-0074
   Sethi P, 2017, J ELECTR COMPUT ENG, V2017, DOI 10.1155/2017/9324035
   Shapiro JM, 2006, REV ECON STAT, V88, P324, DOI 10.1162/rest.88.2.324
   Shelton T, 2015, CAMB J REG ECON SOC, V8, P13, DOI 10.1093/cjres/rsu026
   Sheng J, 2017, INT J PROD ECON, V191, P97, DOI 10.1016/j.ijpe.2017.06.006
   Shi WS, 2016, IEEE INTERNET THINGS, V3, P637, DOI 10.1109/JIOT.2016.2579198
   Silva BN, 2018, SUSTAIN CITIES SOC, V38, P697, DOI 10.1016/j.scs.2018.01.053
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Soderstrom O, 2014, CITY, V18, P307, DOI DOI 10.1080/13604813.2014.906716
   Speel PH, 1999, KNOWLEDGE MAPPING IN
   Stopar K, 2019, SCIENTOMETRICS, V118, P479, DOI 10.1007/s11192-018-2990-5
   Stritih U, 2018, SUSTAIN CITIES SOC, V41, P286, DOI 10.1016/j.scs.2018.04.036
   Su LX, 2015, SCIENTOMETRICS, V105, P449, DOI 10.1007/s11192-015-1697-0
   Tachizawa EM, 2015, SUPPLY CHAIN MANAG, V20, P237, DOI 10.1108/SCM-03-2014-0108
   Taewoo N., 2011, P 12 ANN INT DIG GOV, P282, DOI [10.1145/2037556.2037602, DOI 10.1145/2037556.2037602]
   Thelwall M, 2008, J INF SCI, V34, P605, DOI 10.1177/0165551507087238
   Toppeta D., 2010, INNOVATION KNOWLEDGE, V5, P1
   Torres-Sospedra J, 2015, INT J GEOGR INF SCI, V29, P1955, DOI 10.1080/13658816.2015.1049541
   Tranos E, 2012, INNOVATION-ABINGDON, V25, P175, DOI 10.1080/13511610.2012.660327
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vanolo A, 2016, FUTURES, V82, P26, DOI 10.1016/j.futures.2016.05.010
   Vanolo A, 2014, URBAN STUD, V51, P883, DOI 10.1177/0042098013494427
   Vazquez-Canteli JR, 2019, SUSTAIN CITIES SOC, V45, P243, DOI 10.1016/j.scs.2018.11.021
   Villegas-Ch W, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11102857
   Walravens N, 2015, TELEMAT INFORM, V32, P282, DOI 10.1016/j.tele.2014.09.004
   Washburn D, 2010, HELPING CIOS UNDERST, V12, P2014
   Whitmore A, 2015, INFORM SYST FRONT, V17, P261, DOI 10.1007/s10796-014-9489-2
   Winters JV, 2011, J REGIONAL SCI, V51, P253, DOI 10.1111/j.1467-9787.2010.00693.x
   Xiao MJ, 2015, IEEE INFOCOM SER
   Xiong H., 2015, 2015 IEEE INT C PERV
   Yang DW, 2019, QUANT IMAG MED SURG, V9, P465, DOI 10.21037/qims.2019.02.10
   YANG YY, 2019, SUSTAINABILITY-BASEL, V11, DOI DOI 10.3390/SU11133713
   Yu Z, 2016, SUSTAIN CITIES SOC, V25, P33, DOI 10.1016/j.scs.2015.12.001
   Zanella A, 2014, IEEE INTERNET THINGS, V1, P22, DOI 10.1109/JIOT.2014.2306328
   Zhang D., 2014, 2014 ACM INT JOINT C
   Zhang DQ, 2014, IEEE COMMUN MAG, V52, P42, DOI 10.1109/MCOM.2014.6871668
   Zhang K, 2017, IEEE COMMUN MAG, V55, P122, DOI 10.1109/MCOM.2017.1600267CM
   ZHANG Q, 2013, INT J DISTRIB SENS N, V2013, P12
   Zygiaris S, 2013, J KNOWL ECON, V4, P217, DOI 10.1007/s13132-012-0089-4
NR 157
TC 22
Z9 22
U1 18
U2 84
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD DEC
PY 2019
VL 11
IS 23
AR 6648
DI 10.3390/su11236648
PG 28
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA KD9MV
UT WOS:000508186400120
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Beckmann, S
   Kunzler-Heule, P
   Biotti, B
   Spirig, R
AF Beckmann, Sonja
   Kunzler-Heule, Patrizia
   Biotti, Beatrice
   Spirig, Rebecca
TI Mastering Together the Highs and Lows: Patients' and Caregivers'
   Perceptions of Self-Management in the Course of Liver Transplantation
SO PROGRESS IN TRANSPLANTATION
LA English
DT Article
DE liver transplantation; qualitative research; focus groups; self-care;
   caregivers; emotions
ID INTENSIVE-CARE-UNIT; QUALITY-OF-LIFE; CHRONIC ILLNESS; INFORMAL
   CAREGIVERS; FAMILY-MEMBERS; FOLLOW-UP; STRESS; KIDNEY; NONADHERENCE;
   EXPERIENCES
AB Context: Self-management in chronic illness involves complex medical, role-related, and emotional tasks. Still, while self-management support of patients and their families has become a cornerstone of chronic illness care, information is limited as to how liver transplant (LTx) patients and close caregivers perceive self-management before and after transplantation. Objective: To explore self-management tasks in view of medical, role-related, and emotional tasks in LTx candidates and recipients and their respective close caregivers. Design and Participants: For this qualitative study, focus group interviews were conducted and analyzed using knowledge mapping according to the 3 above-noted self-management categories. German-speaking adults who were wait-listed for or had received LTx and who were being treated in University Hospital Zurich or who were close caregivers to such patients were eligible for participation. As patients' data were closely related to those of the caregivers, the 2 groups' data sets were merged during the final development of themes. Results: Thirty participants comprised 7 focus groups. The main theme was "The current state of health determines the daily rhythm.'' The essence of how patients and caregivers described their self-management tasks is compiled in "Mastering together the highs and lows'' which comprises 3 core themes: mastering medical management, managing roles together, and managing the highs and lows of emotion. Conclusion: Patients and close caregivers prioritized self-management tasks as follows: first medical, then role-related, and finally emotional management. Over the course of LTx, health-care professionals should acknowledge this ranking while providing individualized support to both patients and caregivers.
C1 [Beckmann, Sonja; Biotti, Beatrice] Univ Zurich Hosp, Dept Abdomen & Metab, Raemistr 100, CH-8091 Zurich, Switzerland.
   [Beckmann, Sonja; Spirig, Rebecca] Univ Basel, Inst Nursing Sci, Basel, Switzerland.
   [Kunzler-Heule, Patrizia] Cantonal Hosp St Gallen, Dept Gastroenterol & Hepatol, St Gallen, Switzerland.
   [Spirig, Rebecca] Univ Zurich Hosp, Dept Nursing & MTTB, Zurich, Switzerland.
RP Beckmann, S (通讯作者)，Univ Zurich Hosp, Dept Abdomen & Metab, Raemistr 100, CH-8091 Zurich, Switzerland.
EM sonja.beckmann@usz.ch
CR Baker M Susan, 2003, Prog Transplant, V13, P47
   BIDDLE BJ, 1986, ANNU REV SOCIOL, V12, P67, DOI 10.1146/annurev.so.12.080186.000435
   Bjork IT, 2008, NURS INQ, V15, P289, DOI 10.1111/j.1440-1800.2008.00418.x
   Bodenheimer T, 2002, JAMA-J AM MED ASSOC, V288, P2469, DOI 10.1001/jama.288.19.2469
   Corbin J.M., 1988, UNENDING WORK CARE M
   Davidson JE, 2007, CRIT CARE MED, V35, P605, DOI 10.1097/01.CCM.0000254067.14607.EB
   Day A, 2013, CRIT CARE, V17, DOI 10.1186/cc12736
   De Geest S, 2014, TRANSPL INT, V27, P657, DOI 10.1111/tri.12312
   Dominguez-Cabello E, 2012, TRANSPL P, V44, P2096, DOI 10.1016/j.transproceed.2012.07.083
   Drent G, 2009, NETH J MED, V67, P161
   EDEN C, 1988, EUR J OPER RES, V36, P1, DOI 10.1016/0377-2217(88)90002-1
   Effing T, 2007, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD002990.pub2
   Epping-Jordan JE, 2004, QUAL SAF HEALTH CARE, V13, P299, DOI 10.1136/qshc.2004.010744
   Forsberg A, 2000, J ADV NURS, V32, P327, DOI 10.1046/j.1365-2648.2000.01480.x
   Foster G, 2007, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD005108.pub2
   Goetzinger AM, 2012, CLIN TRANSPLANT, V26, P97, DOI 10.1111/j.1399-0012.2011.01431.x
   Goetzmann L, 2008, TRANSPLANTATION, V86, P662, DOI 10.1097/TP.0b013e3181817dd7
   Goetzmann L, 2006, SWISS MED WKLY, V136, P281
   Kamath PS, 2001, HEPATOLOGY, V33, P464, DOI 10.1053/jhep.2001.22172
   Kang XF, 2011, J CARDIOVASC NURS, V26, P386, DOI 10.1097/JCN.0b013e3182076a69
   Lorig KR, 2003, ANN BEHAV MED, V26, P1, DOI 10.1207/S15324796ABM2601_01
   McCorkle R, 2011, CA-CANCER J CLIN, V61, P50, DOI 10.3322/caac.20093
   Miyazaki ET, 2010, LIVER TRANSPLANT, V16, P1164, DOI 10.1002/lt.22130
   Onwuegbuzie A. J., 2009, INT J QUAL METH, V8, P1, DOI DOI 10.1177/160940690900800301
   Paterson BL, 2001, J NURS SCHOLARSHIP, V33, P21, DOI 10.1111/j.1547-5069.2001.00021.x
   Pelz C., 2004, FORUM QUAL SOC RES, V5, P35, DOI [10.17169/fqs-5.2.601, DOI 10.17169/FQS-5.2.601]
   Pochard F, 2005, J CRIT CARE, V20, P90, DOI 10.1016/j.jcrc.2004.11.004
   Rigby H, 2009, INT J STROKE, V4, P285, DOI 10.1111/j.1747-4949.2009.00289.x
   Rodrigue JR, 2011, CLIN TRANSPLANT, V25, P239, DOI 10.1111/j.1399-0012.2010.01224.x
   Schafer-Keller P, 2009, PATIENT EDUC COUNS, V74, P110, DOI 10.1016/j.pec.2008.03.027
   Schmid-Mohler G, 2014, PROG TRANSPLANT, V24, P8, DOI 10.7182/pit2014728
   Schmid-Mohler G, 2013, PFLEGE, V26, P191, DOI 10.1024/1012-5302/a000291
   Schulman-Green D, 2012, J NURS SCHOLARSHIP, V44, P136, DOI 10.1111/j.1547-5069.2012.01444.x
   Steinsbekk A, 2012, BMC HEALTH SERV RES, V12, DOI 10.1186/1472-6963-12-213
   Wagner EH, 2001, HEALTH AFFAIR, V20, P64, DOI 10.1377/hlthaff.20.6.64
   Weng LC, 2011, J ADV NURS, V67, P1749, DOI 10.1111/j.1365-2648.2011.05625.x
   Wilkinson A, 2009, INT J NURS STUD, V46, P1143, DOI 10.1016/j.ijnurstu.2008.12.011
   Wulff-Burchfield EM, 2013, BONE MARROW TRANSPL, V48, P469, DOI 10.1038/bmt.2012.123
NR 38
TC 11
Z9 12
U1 0
U2 9
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1526-9248
EI 2164-6708
J9 PROG TRANSPLANT
JI Prog. Transplant.
PD SEP
PY 2016
VL 26
IS 3
BP 215
EP 223
DI 10.1177/1526924816654769
PG 9
WC Surgery; Transplantation
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Surgery; Transplantation
GA DV5FF
UT WOS:000382951400003
PM 27297233
DA 2022-08-23
ER

PT J
AU Lukashina, N
   Kartysheva, E
   Spjuth, O
   Virko, E
   Shpilman, A
AF Lukashina, Nina
   Kartysheva, Elena
   Spjuth, Ola
   Virko, Elizaveta
   Shpilman, Aleksei
TI SimVec: predicting polypharmacy side effects for new drugs
SO JOURNAL OF CHEMINFORMATICS
LA English
DT Article
DE Polypharmacy; Knowledge graph
AB Polypharmacy refers to the administration of multiple drugs on a daily basis. It has demonstrated effectiveness in treating many complex diseases , but it has a higher risk of adverse drug reactions. Hence, the prediction of polypharmacy side effects is an essential step in drug testing, especially for new drugs. This paper shows that the current knowledge graph (KG) based state-of-the-art approach to polypharmacy side effect prediction does not work well for new drugs, as they have a low number of known connections in the KG. We propose a new method , SimVec, that solves this problem by enhancing the KG structure with a structure-aware node initialization and weighted drug similarity edges. We also devise a new 3-step learning process, which iteratively updates node embeddings related to side effects edges, similarity edges, and drugs with limited knowledge. Our model significantly outperforms existing KG-based models. Additionally, we examine the problem of negative relations generation and show that the cache-based approach works best for polypharmacy tasks.
C1 [Lukashina, Nina; Kartysheva, Elena; Shpilman, Aleksei] JetBrains Res, AI Labs, St Petersburg, Russia.
   [Lukashina, Nina; Kartysheva, Elena; Shpilman, Aleksei] HSE Univ, Ctr Data Anal & Machine Learning, St Petersburg, Russia.
   [Spjuth, Ola] Uppsala Univ, Dept Pharmaceut Biosci, Uppsala, Sweden.
   [Spjuth, Ola] Uppsala Univ, Sci Life Lab, Uppsala, Sweden.
   [Virko, Elizaveta] JetBrains, Data Analyt Team, St Petersburg, Russia.
RP Lukashina, N (通讯作者)，JetBrains Res, AI Labs, St Petersburg, Russia.; Lukashina, N (通讯作者)，HSE Univ, Ctr Data Anal & Machine Learning, St Petersburg, Russia.
EM nina.lukashina@jetbrains.com
FU Swedish Research Council [202003731, 2020-01865]
FX OS acknowledges funding from the Swedish Research Council (Grants
   202003731 and 2020-01865).
CR [Anonymous], RDKIT DOCUMENTATION
   Chen X, 2016, PLOS COMPUT BIOL, V12, DOI 10.1371/journal.pcbi.1004975
   Glorot X., 2010, JMLR WRKSHOP C P, P249
   Hajimoradlou A, 2020, GRAPH REPRESENTATION
   Hamilton W.L., 2017, ARXIV170905584
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   Huang H, 2014, SCI REP-UK, V4, DOI 10.1038/srep07160
   Huang L, 2014, BIOINFORMATICS, V30, P228, DOI 10.1093/bioinformatics/btu278
   James P., 1992, Linguistic Instruments in Knowledge Engineering. Proceedings of the 1991 Workshop, P97
   Kantor ED, 2015, JAMA-J AM MED ASSOC, V314, P1818, DOI 10.1001/jama.2015.13766
   Kim S, 2021, NUCLEIC ACIDS RES, V49, pD1388, DOI 10.1093/nar/gkaa971
   Malone B., 2018, INT C DATA INTEGRATI, P144
   Nagano Y, 2019, PR MACH LEARN RES, V97
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Novacek Vit, 2020, AMIA Jt Summits Transl Sci Proc, V2020, P449
   Rogers D, 2010, J CHEM INF MODEL, V50, P742, DOI 10.1021/ci100050t
   Shi JY, 2017, BMC BIOINFORMATICS, V18, DOI 10.1186/s12859-017-1818-2
   Sun Y, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms9481
   Tatonetti NP, 2012, SCI TRANSL MED, V4, DOI 10.1126/scitranslmed.3003377
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   WEININGER D, 1988, J CHEM INF COMP SCI, V28, P31, DOI 10.1021/ci00057a005
   Zhang YQ, 2019, PROC INT CONF DATA, P614, DOI 10.1109/ICDE.2019.00061
   Zitnik M, 2018, BIOINFORMATICS, V34, P457, DOI 10.1093/bioinformatics/bty294
NR 25
TC 0
Z9 0
U1 0
U2 0
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1758-2946
J9 J CHEMINFORMATICS
JI J. Cheminformatics
PD JUL 26
PY 2022
VL 14
IS 1
AR 49
DI 10.1186/s13321-022-00632-5
PG 12
WC Chemistry, Multidisciplinary; Computer Science, Information Systems;
   Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Computer Science
GA 3F5NV
UT WOS:000830716800001
PM 35883105
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Wang, ZH
   Zhang, BK
   Gao, D
AF Wang, Zhenhua
   Zhang, Beike
   Gao, Dong
TI A novel knowledge graph development for industry design: A case study on
   indirect coal liquefaction process
SO COMPUTERS IN INDUSTRY
LA English
DT Article
DE Hazard and operability analysis; Industrial safety knowledge graph;
   Knowledge standardization; Information extraction; Data science;
   Engineering design
ID HAZOP ANALYSIS; SYSTEM; ATTENTION; HAZARD; CONSTRUCTION; WEB
AB Hazard and operability analysis (HAZOP) is a remarkable representative in industrial safety engineering. However, a great storehouse of industrial safety knowledge (ISK) in HAZOP reports has not been thoroughly exploited. In order to reuse and unlock the value of ISK and optimize HAZOP, we have developed a novel knowledge graph for industrial safety (ISKG) with HAZOP as the carrier through bridging data science and engineering design. Specifically, firstly, considering that the knowledge contained in HAZOP reports of different processes in industry is not the same, we creatively developed a general ISK standardization framework, it provides a practical scheme for integrating HAZOP reports from various processes and uniformly representing the ISK with diverse expressions. Secondly, we conceive a novel and reliable information extraction model based on deep learning combined with data science, it can effectively mine ISK from HAZOP reports, which alleviates the obstacle of ISK extraction caused by the particularity of HAZOP text. Finally, we build ISK triples and store them in the Neo4j graph database. We take indirect coal liquefaction process as a case study to develop ISKG, and its oriented applications can optimize HAZOP and mine the potential of ISK, which is of great significance to improve the security of the system and enhance prevention awareness for people. ISKG containing the ISK standardization framework and the information extraction model sets an example of the interaction between data science and engineering design, which can enlighten other researchers and extend the perspectives of industrial safety. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Wang, Zhenhua; Zhang, Beike; Gao, Dong] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China.
RP Gao, D (通讯作者)，Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China.
EM gaodong@mail.buct.edu.cn
OI Wang, Zhenhua/0000-0002-0369-2765
FU National Natural Science Foundation (NNSF) of China [61703026, 61873022]
FX This work was supported by the National Natural Science Foundation
   (NNSF) of China under Grants 61703026 and 61873022.
CR [Anonymous], 2014, PRINCIPLES SEMANTIC
   Basu S, 2017, PLANT HAZARD ANALYSIS AND SAFETY INSTRUMENTATION SYSTEMS, P385, DOI 10.1016/B978-0-12-803763-8.00006-6
   Baybutt P, 2015, J LOSS PREVENT PROC, V33, P52, DOI 10.1016/j.jlp.2014.11.010
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bragatto P, 2007, J LOSS PREVENT PROC, V20, P69, DOI 10.1016/j.jlp.2006.10.005
   Soares MAC, 2020, J KING SAUD UNIV-COM, V32, P635, DOI 10.1016/j.jksuci.2018.08.005
   Cameron I, 2017, PROCESS SAF ENVIRON, V110, P53, DOI 10.1016/j.psep.2017.01.025
   Chen HN, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100959
   Chiarello F., 2019, P DES SOC INT C ENG, V1, P2487
   Chiarello F., 2019, P DES SOC INT C ENG, V1, P2765, DOI DOI 10.1017/DSI.2019.283
   Chiarello F, 2021, COMPUT IND, V129, DOI 10.1016/j.compind.2021.103447
   Chiarello F, 2017, INT CONF ENG DES, P101
   Coli E., 2020, AUTOMATIC BUILDING H, V2005
   Cox S, 2020, JMIR MED INF, V8, DOI 10.2196/17964
   Daramola O., 2013, MANAGING REQUIREMENT, DOI 10.1007/978-3-642-34419-0_6
   Devlin J., 2018, ARXIV 04805
   Doddahonnaiah D, 2021, GENES-BASEL, V12, DOI 10.3390/genes12060898
   Dunjo J, 2010, J HAZARD MATER, V173, P19, DOI 10.1016/j.jhazmat.2009.08.076
   EPSC, 2000, HAZOP GUIDE BEST PRA
   Fang Y, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10175922
   Fantoni G, 2021, COMPUT IND, V124, DOI 10.1016/j.compind.2020.103357
   Fatfouta N, 2021, COMPUT IND, V125, DOI 10.1016/j.compind.2020.103344
   Feng W., 2008, CHEM IND ENG PROG, V12
   Feng XY, 2021, PROCESS SAF ENVIRON, V155, P41, DOI 10.1016/j.psep.2021.09.001
   Galalizadeh Saman, 2020, International Journal of Risk Assessment and Management, V23, P207, DOI 10.1504/IJRAM.2020.114358
   Guo L, 2022, ROBOT CIM-INT MANUF, V73, DOI 10.1016/j.rcim.2021.102222
   Hao XJ, 2021, REMOTE SENS-BASEL, V13, DOI 10.3390/rs13132511
   He R, 2020, EXPERT SYST APPL, V150, DOI 10.1016/j.eswa.2020.113244
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hu JQ, 2015, ENG APPL ARTIF INTEL, V45, P119, DOI 10.1016/j.engappai.2015.06.010
   Huang ZH, 2015, COMPUT INTEL NEUROSC, V2015, DOI 10.1155/2015/685404
   Huang ZY, 2020, COMPUT IND, V117, DOI 10.1016/j.compind.2020.103189
   Huet A, 2021, COMPUT IND, V125, DOI 10.1016/j.compind.2020.103377
   IEC International Electrotechnical Commission, 2001, 61882 IEC
   Janosovsky J, 2017, PROCESS SAF ENVIRON, V107, P12, DOI 10.1016/j.psep.2017.01.018
   Jeerawongsuntorn C, 2011, J LOSS PREVENT PROC, V24, P412, DOI 10.1016/j.jlp.2011.02.005
   Joubert F, 2021, J CONSTR ENG M, V147, DOI 10.1061/(ASCE)CO.1943-7862.0001942
   Kang JX, 2016, SAFETY SCI, V88, P26, DOI 10.1016/j.ssci.2016.04.018
   Kim L, 2021, COMPUT IND, V132, DOI 10.1016/j.compind.2021.103527
   Kiran DR, 2017, TOTAL QUALITY MANAGEMENT: KEY CONCEPTS AND CASE STUDIES, P373, DOI 10.1016/B978-0-12-811035-5.00026-X
   Lample G., 2016, NEURAL ARCHITECTURES
   Lee JC, 2021, FOODS, V10, DOI 10.3390/foods10092169
   Li J., 2020, INT J PLANT ENG MANA, V25, P129
   Li J, 2022, IEEE T KNOWL DATA EN, V34, P50, DOI [10.1109/TKDE.2020.2981314, 10.1109/TKDE.2020.3038670]
   Li XY, 2020, J BIOMED INFORM, V107, DOI 10.1016/j.jbi.2020.103422
   Li XY, 2021, COMPUT IND, V129, DOI 10.1016/j.compind.2021.103449
   Lim CH, 2021, RENEW SUST ENERG REV, V135, DOI 10.1016/j.rser.2020.110223
   Lindemann B, 2021, COMPUT IND, V131, DOI 10.1016/j.compind.2021.103498
   Liu JT, 2021, RELIAB ENG SYST SAFE, V207, DOI 10.1016/j.ress.2020.107352
   Lu Ning, 2007, Tsinghua Science and Technology, V12, P30, DOI 10.1016/S1007-0214(07)70005-6
   Luo L, 2018, BIOINFORMATICS, V34, P1381, DOI 10.1093/bioinformatics/btx761
   Marhavilas PK, 2020, SAFETY SCI, V124, DOI 10.1016/j.ssci.2019.104590
   Mawkhiew HB, 2021, GENET RESOUR CROP EV, V68, P2727, DOI 10.1007/s10722-021-01214-4
   Miller J.J., 2013, P SO ASS INF SYST C, V2324
   Nizzoli L, 2020, DECIS SUPPORT SYST, V136, DOI 10.1016/j.dss.2020.113346
   Nolan P., 2004, EUR MANAG J, V22, P150, DOI [10.1016/j.emj.2004.01.014, DOI 10.1016/J.EMJ.2004.01.014, DOI 10.1016/j.emj.2004.01.014]
   Pasman HJ, 2016, CHEM ENGINEER TRANS, V48, P829, DOI 10.3303/CET1648139
   Patle DS, 2021, COMPUT CHEM ENG, V147, DOI 10.1016/j.compchemeng.2021.107248
   Peng FC, 2006, INFORM PROCESS MANAG, V42, P963, DOI 10.1016/j.ipm.2005.09.002
   Phan T, 2021, NEURAL COMPUT APPL, V33, P14887, DOI 10.1007/s00521-021-06126-z
   Ramzan N, 2007, PROCESS SAF PROG, V26, P35, DOI 10.1002/prs.10161
   Rodriguez M, 2012, COMPUT CHEM ENG, V45, P102, DOI 10.1016/j.compchemeng.2012.06.007
   Sacenti JAP, 2022, J INTELL INF SYST, V58, P43, DOI 10.1007/s10844-021-00650-z
   Sarazin A, 2021, EXPERT SYST APPL, V186, DOI 10.1016/j.eswa.2021.115767
   Sidor G., 2015, INTRO GOOGLE MOM THE
   Single JI, 2020, J LOSS PREVENT PROC, V68, DOI 10.1016/j.jlp.2020.104321
   Single JI, 2019, J LOSS PREVENT PROC, V62, DOI 10.1016/j.jlp.2019.103952
   Souza F., 2019, PORTUGUESE NAMED ENT
   Strubell E., 2017, P C EMPIRICAL METHOD, P2660
   Sutton C, 2012, FOUND TRENDS MACH LE, V4, P267, DOI 10.1561/2200000013
   Suzuki T, 2021, J LOSS PREVENT PROC, V71, DOI 10.1016/j.jlp.2021.104479
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Vechgama W, 2021, PROG NUCL ENERG, V138, DOI 10.1016/j.pnucene.2021.103838
   Venkatasubramanian V, 2000, COMPUT CHEM ENG, V24, P2291, DOI 10.1016/S0098-1354(00)00573-1
   Wang ZH, 2021, PROCESSES, V9, DOI 10.3390/pr9071178
   Wenming Huang, 2020, 2020 8th International Conference on Digital Home (ICDH), P51, DOI 10.1109/ICDH51081.2020.00017
   Wu J, 2018, IFAC PAPERSONLINE, V51, P211, DOI 10.1016/j.ifacol.2018.06.379
   Xiong J, 2021, COMPUT ELECTR ENG, V92, DOI 10.1016/j.compeleceng.2021.107173
   Xu B, 2017, LECT NOTES ARTIF INT, V10351, P428, DOI 10.1007/978-3-319-60045-1_44
   Yahya M, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11115110
   Yu F., 2016, INT C LEARN REPR
   Zhang Q, 2021, INFORM SCIENCES, V553, P305, DOI 10.1016/j.ins.2020.10.023
   Zhang XM, 2018, COMPUT IND, V99, P140, DOI 10.1016/j.compind.2018.03.032
   Zhang XT, 2013, NEUROCOMPUTING, V116, P382, DOI 10.1016/j.neucom.2011.12.057
   Zheng WB, 2021, INFORM FUSION, V75, P168, DOI 10.1016/j.inffus.2021.05.015
   Zhou B, 2021, ROBOT CIM-INT MANUF, V71, DOI 10.1016/j.rcim.2021.102160
   Zhou XG, 2020, J COMPUT SCI TECH-CH, V35, P368, DOI 10.1007/s11390-020-9966-7
   Zhou X, 2021, NEURAL COMPUT APPL, V33, P16005, DOI 10.1007/s00521-021-06221-1
NR 88
TC 0
Z9 0
U1 10
U2 10
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-3615
EI 1872-6194
J9 COMPUT IND
JI Comput. Ind.
PD AUG
PY 2022
VL 139
AR 103647
DI 10.1016/j.compind.2022.103647
PG 14
WC Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1X5MT
UT WOS:000807498400003
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Wang, HF
   Wang, ZJ
   Hu, SH
   Xu, XF
   Chen, SP
   Tu, ZY
AF Wang, Haifang
   Wang, Zhongjie
   Hu, Sihang
   Xu, Xiaofei
   Chen, Shiping
   Tu, Zhiying
TI DUSKG: A fine-grained knowledge graph for effective personalized service
   recommendation
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Service recommendation; Knowledge graph; Text mining; Service value
   features; Graph-based recommendation; User preference; User reviews
ID WEB SERVICES; TRUST
AB The last decade has witnessed the rapid development of service-oriented computing, resulting in a tremendous growth of services which further calls for novel approaches for efficient and effective service recommendations. Various types of data can be used for service recommendations, such as user feedback on services, service profile, user profile, etc. In traditional recommendation algorithms, such data is usually handled separately and in the form of matrix. Since there are underlying logical relations between the data, we conjecture that deep convergence of all the data and full considerations of such logical relations would help boost accuracy of service recommendations. By a comprehensive survey on various existing data convergence methods, we found that knowledge graph had been a typical approach to describe the different relations between data. In this paper, we propose an elegant, natural and compact data representation model incorporating such kinds of logical relations, namely Domain-oriented User and Service Interaction Knowledge Graph (DUSKG). Three types of entities (User, Service and Service Value Feature(VF)), five types of fine-grained relations (FOCUSON, BELONGTO, USIMILAR, SSIMILAR and FSIMILAR), and weight vectors that quantitatively delineate these relations are elaborately defined. An approach for constructing DUSKG from service data is presented. Extracted from DUSKG, each user's value preference is then represented in terms of VFs. Five personalized service recommendation methods are presented in terms of such value preferences and various relations in DUSKG. To verify the proposed methods, a set of experiments are conducted on Yelp dataset. Experimental results show that our approaches achieve better recommendation performance compared to the state-of-the-art methods, and among the five ones, HR-DUSKG (Hybrid Recommendation Depending on DUSKG) exhibits the best performance with quite good achievements but least calculation time. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Wang, Haifang; Wang, Zhongjie; Hu, Sihang; Xu, Xiaofei; Tu, Zhiying] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China.
   [Chen, Shiping] CSIRO, DATA61, Sydney, NSW 2122, Australia.
RP Wang, HF (通讯作者)，Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China.
EM wanghaifang@hit.edu.cn; rainy@hit.edu.cn; 17S103163@stu.hit.edu.cn;
   xiaofei@hit.edu.cn; shiping.chen@data61.csiro.au; tzy_hit@hit.edu.cn
RI Chen, Shiping/Q-8611-2019
OI Chen, Shiping/0000-0002-4603-0024
FU National Key Research and Development Program of China [2017YFB1400604];
   National Nature Science Foundation of China [61772155, 61832014,
   61832004, 61472106]; China Scholarship Council [201706120304]
FX Research in this paper is partially supported by the National Key
   Research and Development Program of China (No 2017YFB1400604), the
   National Nature Science Foundation of China (61772155, 61832014,
   61832004, 61472106) and the China Scholarship Council (File No.
   201706120304).
CR Adomavicius G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P769, DOI 10.1007/978-0-387-85820-3_24
   [Anonymous], 2015, P 1 INT WORKSH MACH
   Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124
   Blake MB, 2007, 2007 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES, PROCEEDINGS, P575
   Breese J. S., 1998, Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference (1998), P43
   Buqing Cao, 2013, 2013 IEEE 20th International Conference on Web Services (ICWS), P99, DOI 10.1109/ICWS.2013.23
   Cao J, 2013, INT J WEB SERV RES, V10, P53, DOI 10.4018/jwsr.2013010103
   Catherine R, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P325, DOI 10.1145/2959100.2959131
   Chi X., 2017, SERVICE ORIENTED COM, P142, DOI [10.1007/978%E2%80%903%E2%80%90319%E2%80%9069035%E2%80%903_10, DOI 10.1007/978%E2%80%903%E2%80%90319%E2%80%9069035%E2%80%903_10]
   Deng SG, 2014, EXPERT SYST APPL, V41, P8075, DOI 10.1016/j.eswa.2014.07.012
   DONG X, 2004, P 30 INT C VER LARG, V30, P372, DOI DOI 10.1016/B978-012088469-8.50035-8
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Hu MQ, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P755
   Jain A, 2015, LECT NOTES COMPUT SC, V9435, P188, DOI 10.1007/978-3-662-48616-0_12
   Jiang WJ, 2014, FUTURE GENER COMP SY, V31, P48, DOI 10.1016/j.future.2012.06.010
   Jiang ZS, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P288, DOI 10.1145/3159652.3159715
   Kalai A, 2018, FUTURE GENER COMP SY, V80, P355, DOI 10.1016/j.future.2017.05.036
   Liang TT, 2016, LECT NOTES COMPUT SC, V9936, P371, DOI 10.1007/978-3-319-46295-0_23
   Liu B., 2005, P 14 INT C WORLD WID, P342, DOI DOI 10.1145/1060745.1060797
   Liu HY, 2013, ELECTRON COMMER R A, V12, P14, DOI 10.1016/j.elerap.2012.05.002
   Liu JX, 2016, IEEE T SERV COMPUT, V9, P686, DOI 10.1109/TSC.2015.2433251
   Liu L., 2013, SEMANTIC CONTENT BAS, V7, P1
   Liu SH, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P861, DOI 10.1145/3077136.3080663
   Ma J, 2012, PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE 2012, VOL 3, PTS A AND B, P1315
   Martin D., 2005, Semantic Web Services and Web Process Composition. First International Workshop, SWSSWPC 2004. Revised Selected Papers (Lecture Notes in Computer Science Vol.3387), P26
   Meng SM, 2014, IEEE T PARALL DISTR, V25, P3221, DOI 10.1109/TPDS.2013.2297117
   Minkov E., J ASS INF SCI TECHNO, V68, P1911
   Robinson E.E., 2013, GRAPH DATABASES, P111
   Rodriguez-Mier P, 2016, IEEE T SERV COMPUT, V9, P537, DOI 10.1109/TSC.2015.2402679
   Roman D, 2015, J WEB SEMANT, V31, P39, DOI 10.1016/j.websem.2014.11.006
   Rose S., 2010, TEXT MINING APPL THE, V1, P1, DOI DOI 10.1002/9780470689646.CH1
   Song D., 2017, IEEE T INTELL TRANSP, P1
   Su Xiaoyuan, 2009, ADV ARTIFICIAL INTEL, DOI DOI 10.1155/2009/421425
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Tian G, 2017, INFORM SYST FRONT, V19, P75, DOI 10.1007/s10796-015-9590-1
   Wan Y, 2016, LECT NOTES ARTIF INT, V9651, P436, DOI 10.1007/978-3-319-31753-3_35
   Wang HF, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017), P277, DOI 10.1109/ICWS.2017.43
   Xi Chen, 2010, 2010 IEEE International Conference on Web Services (ICWS), P9, DOI 10.1109/ICWS.2010.27
   Xie FF, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (IEEE ICWS 2018), P115, DOI 10.1109/ICWS.2018.00022
   Xiong RB, 2018, EXPERT SYST APPL, V110, P191, DOI 10.1016/j.eswa.2018.05.039
   Xu SY, 2016, INT J SIMUL MODEL, V15, P83, DOI 10.2507/IJSIMM15(1)7.326
   Yao LN, 2015, IEEE T SERV COMPUT, V8, P453, DOI 10.1109/TSC.2014.2355842
   Yao WL, 2015, WORLD WIDE WEB, V18, P1351, DOI 10.1007/s11280-014-0307-z
   Yin YY, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17092059
   Yu Q, 2013, 2013 3RD INTERNATIONAL CONFERENCE ON APPLIED SOCIAL SCIENCE (ICASS 2013), VOL 2, P334
   Yu X, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P283, DOI 10.1145/2556195.2556259
   Zhang N, 2020, IEEE T SERV COMPUT, V13, P488, DOI 10.1109/TSC.2017.2693147
   Zhang S, 2017, PLOS ONE, V12, P1
   Zheng ZB, 2011, IEEE T SERV COMPUT, V4, P140, DOI 10.1109/TSC.2010.52
NR 49
TC 16
Z9 16
U1 4
U2 38
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD NOV
PY 2019
VL 100
BP 600
EP 617
DI 10.1016/j.future.2019.05.045
PG 18
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JX6FD
UT WOS:000503827500045
DA 2022-08-23
ER

PT J
AU Chen, YY
   Dai, YQ
   Li, L
   Ma, CQ
   Liu, XG
AF Chen, Yuyilan
   Dai, Yuqian
   Li, Li
   Ma, Chenqu
   Liu, Xiaogang
TI A Graph-Based Representation Method for Fashion Color
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE fashion color; knowledge graph; spatial relation; color abstraction;
   computer-aided analysis
AB Fashion color research takes the color information of fashion apparel as the major focus for further studies, such as style categorization or trend prediction. However, the colors in apparel are treated as isolated elements from each other, disregarding the fact that not only the attributes of each color itself but also the collocation relationship of the colors in apparel are important color factors. To provide a more comprehensive abstraction of the information from the fashion colors as well as emulating the human cognition of fashion colors, in this paper, we are the first to propose a knowledge graph-based representation method that captures not only the individual colors but also abstracts the spatial relation of all the colors that appear in a single piece of fashion apparel. This method provides the fundamental definition of the abstraction of the relation of colors, a detailed method to construct the color graph, as well as the practical matrix-based management and the visualization of the constructed graphs. The case studies for color data extraction and extended usage demonstrate the effectiveness of our method with comprehensive color data representation and effective information extraction.
C1 [Chen, Yuyilan; Dai, Yuqian; Li, Li; Ma, Chenqu; Liu, Xiaogang] Donghua Univ, Coll Fash & Design, Shanghai 200051, Peoples R China.
RP Ma, CQ; Liu, XG (通讯作者)，Donghua Univ, Coll Fash & Design, Shanghai 200051, Peoples R China.
EM yyl.chen@outlook.com; daiyuqian@mail.dhu.edu.cn;
   lili@uinformresearch.com; maryma@dhu.edu.cn; liuxg@dhu.edu.cn
FU National Art and Science Planning Office [19ZD23]
FX This research is partially funded by National Art and Science Planning
   Office, grant number 19ZD23 with the little of "Research of Design
   Aesthetics".
CR [Anonymous], 2013, FASHION MAGAZINE
   [Anonymous], 2013, THE CNN
   [Anonymous], NEWS
   Chen H., 2012, DESCRIBING CLOTHING
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Cheng CI, 2008, PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, P1592, DOI 10.1109/ICMLC.2008.4620660
   Choi TM, 2012, IEEE T SYST MAN CY C, V42, P1003, DOI 10.1109/TSMCC.2011.2176725
   Choi Yoo-Joo, 2009, [JOURNAL OF BROADCAST ENGINEERING, 방송공학회 논문지], V14, P397
   Cui ZY, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P307, DOI 10.1145/3308558.3313444
   FARFETCH, US
   Fensel D., 2020, KNOWLEDGE GRAPHS, P1, DOI DOI 10.1007/978-3-030-37439-6
   Han A, 2022, CLOTH TEXT RES J, V40, P308, DOI 10.1177/0887302X21995948
   Jing PG, 2022, IEEE T MULTIMEDIA, V24, P1277, DOI 10.1109/TMM.2021.3062736
   Kang TJ, 1999, TEXT RES J, V69, P77
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   [刘峤 Liu Qiao], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P582
   O'Donovan P, 2011, ACM T GRAPHIC, V30, DOI 10.1145/1964921.1964958
   SOHU, US
   Wang X., 2011, P ACM INT C MULTIMED
   Yu Y, 2012, EXPERT SYST APPL, V39, P4383, DOI 10.1016/j.eswa.2011.09.153
   Zhang H., 2020, ARXIV
   Zhao L, 2021, CLOTH TEXT RES J, DOI 10.1177/0887302X211004299
   Zhiming W.U., 2009, J TEXT RES, V101, P360
NR 23
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD JUL
PY 2022
VL 12
IS 13
AR 6742
DI 10.3390/app12136742
PG 9
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 2V8RR
UT WOS:000824106600001
OA gold
DA 2022-08-23
ER

PT J
AU Reinanda, R
   Meij, E
   de Rijke, M
AF Reinanda, Ridho
   Meij, Edgar
   de Rijke, Maarten
TI Knowledge Graphs: An Information Retrieval Perspective
SO FOUNDATIONS AND TRENDS IN INFORMATION RETRIEVAL
LA English
DT Article
ID TEXT
AB In this survey, we provide an overview of the literature on knowledge graphs (KGs) in the context of information retrieval (IR). Modern IR systems can benefit from information available in KGs in multiple ways, independent of whether the KGs are publicly available or proprietary ones. We provide an overview of the components required when building IR, systems that leverage KGs and use a task-oriented organization of the material that we discuss. As an understanding of the intersection of IR and KGs is beneficial to many researchers and practitioners, we consider prior work from two complementary angles: leveraging KGs for information retrieval and enriching KGs using TR techniques. We start by discussing how KGs can be employed to support IR tasks, including document and entity retrieval. We then proceed by describing how IR, and language technology in general can be utilized for the construction and completion of KGs. This includes tasks such as entity recognition, typing, and relation extraction. We discuss common issues that appear across the tasks that we consider and identify future directions for addressing them. We also provide pointers to datasets and other resources that should be useful for both newcomers and experienced researchers in the area.
C1 [Reinanda, Ridho; Meij, Edgar] Bloomberg LP, London, England.
   [de Rijke, Maarten] Univ Amsterdam, Amsterdam, Netherlands.
   [de Rijke, Maarten] Ahold Delhaize, Zaandam, Netherlands.
RP Reinanda, R (通讯作者)，Bloomberg LP, London, England.
EM rreinanda@bloomberg.net; emeij@bloomberg.net; m.derijke@uva.nl
OI de Rijke, Maarten/0000-0002-1086-0202
FU Innovation Center for Artificial Intelligence (ICAI)
FX This research was supported by the Innovation Center for Artificial
   Intelligence (ICAI).
CR Abhishek A., 2017, EACL 17
   Agichtein E., 2000, DL 00
   Alfonseca E, 2012, ACL 12
   Alhelbawy A., 2014, ACL 14
   [Anonymous], 2008, WIKAI 08
   [Anonymous], 2002, TOPIC DETECTION TRAC
   Asahara M., 2003, HLT NAACL 03
   Bach N, 2007, SURVEY RELATION EXTR
   Ballesteros M., 2016, P 2016 C N AM CHAPT, P260, DOI [10.18653/v1/N16-1030, DOI 10.18653/V1/N16-1030]
   Balog K., 2013, SIGIR 13
   Balog K., 2006, SIGIR 06
   Balog K., 2009, TREC
   Balog K., 2018, IR SERIES
   Balog K., 2013, P 10 C OP RES AR INF, P121
   Balog K, 2012, FOUND TRENDS INF RET, V6, P127, DOI 10.1561/1500000024
   Balog K, 2011, ACM T INFORM SYST, V29, DOI 10.1145/2037661.2037667
   Balog K, 2009, INFORM PROCESS MANAG, V45, P1, DOI 10.1016/j.ipm.2008.06.003
   Banko M, 2007, IJCAI 07
   Bao J, 2014, ACL 14
   Bast H, 2015, SIGIR 15
   Bast H, 2016, FOUND TRENDS INF RET, V10, P120, DOI 10.1561/1500000032
   Berant J., 2013, EMNLP 13
   Bi B, 2015, WSDM 15
   Bikel DM, 1999, MACH LEARN, V34, P211, DOI 10.1023/A:1007558221122
   Bing L., 2013, WSDM 13
   Blanco R, 2015, WSDM 15
   Blanco R, 2013, ISWC 13
   Bolc L., 1980, NATURAL LANGUAGE QUE, P217
   Bonnefoy L., 2013, SIGIR 13
   Bordes A, 2013, NIPS 13
   Bordes A., 2011, AAAI 11
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Bordino I, 2013, CIKM 13
   Bordino I, 2013, WSDM 13
   Borisov A, 2016, WWW 16
   Borthwick A., 1999, THESIS
   Bota H, 2016, CHIIR 16
   Brambilla M., 2018, WWW 18
   Brambilla M, 2017, WWW 17
   Brin S., 1998, WEBDB 98
   Bron M, 2010, CIKM 10
   Bunescu R. C., 2005, HTL EMNLP 05
   Cai R, 2015, ACL 13
   Cano I., 2014, TREC 2014
   Carmel D, 2014, SIGIR 14
   Charton E., 2014, ACL 14
   Chen M, 2016, ABS161103954 CORR
   Cheng CC, 2017, MACROMOL BIOSCI, V17, DOI 10.1002/mabi.201600370
   Chuklin Aleksandr, 2015, CLICK MODELS WEB SEA
   Collins M., 1999, WWW 13
   Cornolti M, 2016, WWW 16
   Cornolti M, 2013, WWW 13
   Corro L. D, 2015, EMNLP 15
   Craswell N, 2005, TREC 2005 C NOT
   Croft B., 2009, SEARCH ENGINES INFOR
   Cucchiarelli A, 2001, COMPUT LINGUIST, V27, P123, DOI 10.1162/089120101300346822
   Cucerzan S., 2007, EMNLP 07
   Culotta A, 2004, ACL 04
   Daiber J., 2013, SEMANTIC SYSTEMS 13
   Dalton J., 2013, AKBC 13
   DALTON J, 2014, SIGIR, P365, DOI DOI 10.1145/2600428.2609628
   Dalton J., 2011, CIKM 11
   Dalvi B, 2016, WSDM 16
   Dasgupta S. S, 2018, EMNLP 18
   de Vries A. P, 2008, FOCUSED ACCESS XML D
   Devlin J, 2018, NAACL 18
   Dietz L., 2013, TREC 2013
   Dietz L, 2018, SIGIR 18
   Dietz L., 2017, WSDM 17
   Doddington G, 2004, LREC 04
   Dong L., 2015, IJCAI 15
   Dong X. L, 2015, VLDP 15
   Dong X. L, 2014, VLDB 14
   Dong XX, 2014, BMC PUBLIC HEALTH, V14, DOI 10.1186/1471-2458-14-503
   Dutta S., 2015, EMNLP 15
   Efron M., 2014, SIGIR 14
   Ellis J., 2014, TAC 14
   Ensan F., 2017, WSDM 17
   Etzioni O., 2004, WWW 04
   Etzioni O, 2011, IJCAI 11
   Fader A, 2011, EMNLP 11
   Fang L., 2011, VLDB 11
   Fathalla S, 2017, TPDL 17
   Ferragina Paolo, 2010, CIKM 10
   Fetahu B, 2015, CIKM 15
   Finkel J. R., 2005, ACL 05
   Fissaha Adafre S., 2007, ICDM 07 WORKSH DAT M
   Flekova L, 2014, WWW 14
   Foley J, 2016, CIKM 16
   Franco-Salvador M, 2014, EACL 14
   Frank J. R, 2014, TREC 2014
   Frank J, 2012, RESPONSES TO MODERNITY: ESSAYS IN THE POLITICS OF CULTURE, P120
   Gabrilovich E., 2016, SIGIR 16
   Gabrilovich E, 2013, TECH REP
   Gabrilovich Evgeniy, 2007, IJCAI 07
   Galarraga L. A., 2013, AKBC 13
   Ganea O.-E, 2015, WWW 16
   Ganjisaffar Y, 2011, SIGIR 11
   Gao J., 2014, EMNLP 14
   Gao J., 2019, VLDB 19
   Garcia-Duran A, 2018, EMNLP 18
   Gardner M., 2013, EMNLP 13
   Gardner M., 2015, P 2015 C EMP METH NA, P1488, DOI DOI 10.18653/V1/D15-1173
   Gerritse Emma J., 2020, Advances in Information Retrieval, 42nd European Conference on IR Research, ECIR 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12035), P97, DOI 10.1007/978-3-030-45439-5_7
   Gillick D., 2014, ABS14121820 ABS
   Girju R, 2007, SEMEVAL 07
   Globerson A, 2016, ACL 16
   Graus D, 2016, WSDM 16
   Graus D., 2014, ECIR 14
   Graus D, 2018, J ASSOC INF SCI TECH, V69, P773, DOI 10.1002/asi.24004
   GREEN BF, 1963, COMPUT THOUGHT, P207
   Grishman R., 1996, COLING
   Guglierame P, 2006, BMC MICROBIOL, V6, DOI 10.1186/1471-2180-6-66
   Guo Y, 2013, EMNLP
   GuoDong Z, 2005, ACL 05
   Hachey B, 2014, ACL 14
   Hakkani-Tur D, 2014, INTERSPEECH 2014
   Hasibi F, 2016, ICTIR 16
   Hasibi F, 2015, ICTIR 15
   Hegde A, 2015, INT J GEOMECH, V15, DOI 10.1061/(ASCE)GM.1943-5622.0000433
   Heindorf S, 2016, CIKM 16
   Heindorf S, 2015, SIGIR 15
   Hoffart J, 2016, WWW 16 COMPANION
   Hoffart J., 2011, EMNLP 11
   Hoffart J., 2014, WWWW 14
   Hoffmann R, 2011, ACL HLT
   Hovy D, 2014, ACL
   Jatowt A., 2017, NCTIR
   Jenatton R., 2012, NIPS 12
   Ji H., 2011, P TAC KBP 2011 WORKS
   Jin Y, 2014, WSDM 14
   Joshi Mandar, 2014, EMNLP, P1104
   Kambhatla N., 2004, ACL 04
   Kang C., 2011, WWW 11
   Kenter T, 2015, CIKM 15
   Kim J, 2009, ECIR 09
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Kolitsas N, 2018, CONLL 18
   Kong F, 2019, WWW 19
   Kotnis B, 2015, EMNLP 15
   Kotz D, 2014, BMC PUBLIC HEALTH, V14, DOI 10.1186/1471-2458-14-1163
   Kripke S. A., 1972, NAMING NECESSITY
   Krishna Ranjay, 2016, ABS160207332 CORR
   Kulkarni S, 2009, KDD 09
   Kuzey E, 2014, CIKM 14
   Lagun D, 2014, SIGIR 14
   Lao N, 2010, MACH LEARN, V81, P53, DOI 10.1007/s10994-010-5205-8
   Lazic N., 2015, T ASSOC COMPUT LING, V3, P503, DOI DOI 10.1162/tacl_a_00154
   Leblay J., 2018, WWW 18
   Lee J, 2015, KDD 15
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li F. X., 2017, KNOWLEDGE VERIFICATI
   Li L, 2015, BMC PLANT BIOL, V15, DOI 10.1186/s12870-015-0531-4
   Li X, 2015, ECIR 15
   Li X., 2014, SLT 14
   Lin TK, 2012, INT J STRUCT STAB DY, V12, DOI 10.1142/S0219455412500290
   Lin Y, 2015, AAAI 15
   Ling T, 2012, INT J INTEGR CARE, V12
   Liu Q. L., 2016, SIGIR 16
   Liu X., 2013, TREC 2013
   Liu XT, 2015, INFORM RETRIEVAL J, V18, P473, DOI 10.1007/s10791-015-9267-x
   Liu Z, 2018, ACL 18
   Luo G, 2015, EMNLP 15
   Luo Y. Q., 2015, EMNLP 15
   Lv YH, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P655, DOI 10.1145/2766462.2767696
   Ma H., 2015, WWW 15 COMPANION
   Ma W, 2019, WWW 19
   McCallum A., 2003, CONLL 03
   Mei QC, 2015, J MECH MED BIOL, V15, DOI 10.1142/S0219519415500141
   Meij E., 2013, SIGIR 13
   Meij E, 2014, WSDM 14
   Meij E, 2012, WSDM 12
   Meij E., 2009, ISWC 09
   Meij E, 2011, J WEB SEMANT, V9, P418, DOI 10.1016/j.websem.2011.04.001
   Melo G. de, 2016, SIGWEB NEWSLETTER
   Metzler D., 2007, SIGIR 07
   Metzler D., 2005, SIGIR 05
   Mihalcea R., 2007, CIKM 07
   Mikolov T, 2013, EMNLP 04
   Miliaraki I., 2015, WWW 15
   Milne D., 2008, CIKM 08
   Minard A.-L, 2016, LREC 16
   Mintz M., 2009, ACL 09
   Mitra B, 2018, FOUND TRENDS INF RET, V13, P1, DOI 10.1561/1500000061
   Mohapatra H, 2013, EMNLP 13
   Nakashole N, 2013, ACL 13
   Navalpakkam V, 2013, WWW 13
   Nickel M, 2012, WWW 12
   Nickel M, 2011, ICML 11
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Nikolaev AR, 2016, J VISION, V16, DOI 10.1167/16.14.11
   Odijk D, 2015, SIGIR 15
   Ojha P., 2017, EMNLP 17
   Onal KD, 2018, INFORM RETRIEVAL J, V21, P111, DOI 10.1007/s10791-017-9321-y
   Oosterhuis H., 2018, SIGIR 18
   Pasca M., 2006, EACL
   Passos A, 2014, CONLL 14
   Petkova D, 2008, INT J ARTIF INTELL T, V17, P5, DOI 10.1142/S0218213008003741
   Potthast M, 2008, ECIR 08
   Pound J., 2012, CIKM 12
   Pound J, 2010, WWW 10
   Prokofyev R, 2014, WWW 14
   Pujara J., 2017, EMNLP 15
   Ran C., 2018, WWW 18
   Ratinov L, 2011, ACL HLT 11
   Raven J, 2015, BMC PREGNANCY CHILDB, V15, DOI 10.1186/s12884-015-0545-9
   Raviv H., 2016, SIGIR 16
   Reinanda R., 2015, SIGIR 15
   Reinanda R, 2016, CIKM 16
   Ren X, 2016, EMNLP 16
   REN X, 2016, KDD 16
   Ribeiro L, 2016, BMC MED EDUC, V16, DOI 10.1186/s12909-016-0527-2
   Riedel S., 2013, HLT NAACL 13
   Riedel S, 2010, ECML PKDD 10
   Riloff E., 1999, AAAI 99
   Roth C, 2011, BMC CANCER, V11, DOI 10.1186/1471-2407-11-4
   Sanderson M., 2015, FOUND TRENDS INF RET, V4, P247
   Santini T, 2013, BMC MICROBIOL, V13, DOI 10.1186/1471-2180-13-27
   Sarawagi S., 2004, NIPS 04
   Sarmento L., 2007, CIKM 07
   Sarrafzadeh B, 2014, IIIX 14
   Schuhmacher M., 2015, CIKM 15
   Schutze H., 2008, INTRO INFORM RETRIEV, V39
   Sekine S, 2004, LREC
   Sekine S., 1998, MUC 7
   Sekine S., 2009, NAMED ENTITIES RECOG
   Seufert S., 2016, CIKM 16
   Sevgili O, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019:): STUDENT RESEARCH WORKSHOP, P315
   Shen J., 2018, SIGIR 18
   Shimaoka S, 2016, AKBC 16
   Shimaoka S., 2017, EACL 17
   Shirakawa M, 2013, CIKM 13
   Shokouhi M, 2015, SIGIR 15
   Sil A., 2013, CIKM 13
   Singh J, 2016, CIKM 16
   Singh S., 2012, UMCS2012015
   Socher R, 2013, NIPS 13
   Socher R, 2012, CONLL 12
   Strubell E, 2017, ACL 17
   Suchanek F. M, 2007, WWW 07
   Sun H, 2015, WWW 15
   Sun QL, 2014, PROCESSING OF 2014 INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INFORMATION INTEGRATION FOR INTELLIGENT SYSTEMS (MFI)
   Surdeanu M, 2011, P TAC KBP 2011 WORKS
   Surdeanu M., 2012, CONLL 12
   Sutskever I., 2009, NIPS
   Tan C. H., 2014, WSDM 14
   Tang Jian, 2015, WWW 15
   Tjong Kim Sang E. F., 2003, CONLL 03
   Tong H., 2006, KDD 06
   Tonon A., 2012, SIGIR 12
   Toutanova K., 2015, 3 WORKSH CONT VECT S
   Toutanova Kristina, 2015, ACL
   Usbeck R, 2015, WWW 15
   Van Gysel C., 2017, ICTIR 17
   Van Gysel C., 2016, CIKM 16
   Van Gysel C, 2017, SIGIR 2017 WORKSH NE
   Van Gysel C, 2016, WWW 16
   Van Gysel C, 2018, ACM T INFORM SYST, V36, DOI 10.1145/3196826
   Vannella D., 2014, ACL 14
   Vardarinos A, 2017, BMC OPHTHALMOL, V17, DOI 10.1186/s12886-017-0451-1
   Voorhees E. M., 2005, SIGIR Forum, V39, P11
   Voskarides N, 2017, ECIR 17
   Voskarides N, 2015, ACL 15
   Wang J, 2015, SIGIR 15
   Wang Jingang, 2013, TREC
   Wang R. C., 2007, ICDM 07
   Wang T.-X, 2014, ACL 14
   Wang X., 2018, AAAI 18
   Wang X, 2015, SIGMOD 15
   Wang Z, 2013, ACL 13
   Wang Z., 2014, AAAI 14
   Wang Z, 2012, WWW 12
   Woods W. A., 1977, LINGUIST STRUCT PROC, V5, P521
   Wu F., 2010, ACL 10
   Wu Q., 2016, CORR
   Wu Z, 2016, AAAI 16
   Xie X, 2018, SIGIR 18
   Xiong C., 2017, SIGIR 17
   Xiong C., 2015, CIKM 15
   Xiong C, 2017, WWW 17
   Xiong C., 2015, ICTIR 15
   Xu P., 2018, EMNLP 18
   Yaghoobzadeh Y., 2015, EMNLP 15
   Yahya M, 2016, WSDM 16
   Yang B, 2015, ICLR 15
   Yao L, 2010, EMNLP 10
   Yao X., 2014, ACL 14
   Yih W.-t., 2015, ACL 15
   Ying R, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P974, DOI 10.1145/3219819.3219890
   Yogatama D, 2015, ACL 15
   Yosef M. A, 2012, COLING 12
   Yu X., 2014, WSDM 14
   Zelenko D, 2002, EMNLP 02
   Zeng D., 2015, EMNLP 15
   Zhang C, 2017, INT J PROD RES, V55, P7187, DOI 10.1080/00207543.2017.1351643
   Zhang Feng, 2016, Journal of Traffic and Transportation Engineering, V16, P16
   Zhang L, 2016, CIKM 16
   Zhang W., 2019, WWW 19
   Zhang Y., 2017, ISWC 17
   Zhao S., 2005, ACL 05
   Zhao S., 2014, EMNLP 14
   Zhiltsov N, 2015, SIGIR 15
   Zhiltsov N., 2013, CIKM 13
   Zhong H, 2015, EMNLP 15
   Zhou M., 2013, CIKM 13
   Zhu GG, 2018, EXPERT SYST APPL, V101, P8, DOI 10.1016/j.eswa.2018.02.011
   Zhu Y, 2015, ARXIV150705670
NR 307
TC 6
Z9 6
U1 5
U2 18
PU NOW PUBLISHERS INC
PI HANOVER
PA PO BOX 1024, HANOVER, MA 02339 USA
SN 1554-0669
EI 1554-0677
J9 FOUND TRENDS INF RET
JI Found. Trends Inf. Retr.
PY 2020
VL 14
IS 4
BP 289
EP 444
DI 10.1561/1500000063
PG 156
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OH1QT
UT WOS:000582346800001
DA 2022-08-23
ER

PT J
AU Yang, K
   Hu, YK
   Qi, HY
AF Yang, Keng
   Hu, Yekang
   Qi, Hanying
TI Digital Health Literacy: Bibliometric Analysis
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE digital health literacy; eHealth; digital divide; bibliometrics;
   VOSviewer; CiteSpace
AB Background: Digital health is growing at a rapid pace, and digital health literacy has attracted increasing attention from the academic community.
   Objective: The purposes of this study are to conduct a systematic bibliometric analysis on the field of digital health literacy and to understand the research context and trends in this field.
   Methods: Methods: A total of 1955 scientific publications were collected from the Web of Science core collection. Institutional co-operation, journal co-citation, theme bursting, keyword co-occurrence, author co-operation, author co-citation, literature co-citation, and references in the field of digital health literacy were analyzed using the VOSviewer and CiteSpace knowledge mapping tools.
   Results: The results demonstrate that the United States has the highest number of publications and citations in this field. The University of California System was first in terms of institutional contributions. The Journal of Medical Internet Research led in the number of publications, citations, and co-citations. Research areas of highly cited articles in the field of digital health literacy mainly include the definition and scale of health literacy, health literacy and health outcomes, health literacy and the digital divide, and the influencing factors of health literacy.
   Conclusions: We summarized research progress in the field of digital health literacy and reveal the context, trends, and trending topics of digital health literacy research through statistical analysis and network visualization We found that digital health literacy has a significant potential to improve health outcomes, bridge the digital divide, and reduce health inequalities. Our work can serve as a fundamental reference and directional guide for future research in this field.
C1 [Yang, Keng] Tsinghua Univ, Inst Econ, Beijing, Peoples R China.
   [Yang, Keng] Tsinghua Univ, One Belt One Rd Strategy Inst, Beijing, Peoples R China.
   [Hu, Yekang] China Natl Hlth Dev Res Ctr, Beijing, Peoples R China.
   [Qi, Hanying] Zhejiang Univ Finance & Econ, New Type Key Think Tank Zhejiang Prov Res Inst Reg, Hangzhou, Peoples R China.
   [Qi, Hanying] Zhejiang Univ Finance & Econ, China Inst Regulat Res, Hangzhou, Peoples R China.
   [Hu, Yekang] China Natl Hlth Dev Res Ctr, 9 Chegongzhuang St, Beijing 100044, Peoples R China.
RP Hu, YK (通讯作者)，China Natl Hlth Dev Res Ctr, 9 Chegongzhuang St, Beijing 100044, Peoples R China.
EM freyak5277@hotmail.com
CR Braun V., 2006, QUAL RES PSYCHOL, V3, P77, DOI [https://doi.org/10.1191/1478088706qp063oa, 10.1191/1478088706qp063oa]
   Chew LD, 2004, FAM MED, V36, P588
   Cline RJW, 2001, HEALTH EDUC RES, V16, P671, DOI 10.1093/her/16.6.671
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Diviani N, 2015, J MED INTERNET RES, V17, DOI 10.2196/jmir.4018
   Greenhalgh T, 2010, BMJ-BRIT MED J, V341, DOI 10.1136/bmj.c5814
   Kim H, 2017, PATIENT EDUC COUNS, V100, P1073, DOI 10.1016/j.pec.2017.01.015
   Kontos Emily, 2014, J Med Internet Res, V16, pe172, DOI 10.2196/jmir.3117
   Li F, 2015, J MED INTERNET RES, V17, DOI 10.2196/jmir.3326
   Mitsutake S, 2016, J MED INTERNET RES, V18, DOI 10.2196/jmir.5413
   Nutbeam D, 2000, HEALTH PROMOT INT, V15, P259, DOI 10.1093/heapro/15.3.259
   Ouyang W, 2021, J MED INTERNET RES, V23, DOI 10.2196/21974
   Sorensen K, 2012, BMC PUBLIC HEALTH, V12, DOI 10.1186/1471-2458-12-80
   Tubaishat A, 2016, NURS EDUC TODAY, V42, P47, DOI 10.1016/j.nedt.2016.04.003
   van der Vaart R, 2011, J MED INTERNET RES, V13, DOI 10.2196/jmir.1840
   Xie B, 2011, J MED INTERNET RES, V13, DOI 10.2196/jmir.1880
NR 16
TC 0
Z9 0
U1 9
U2 9
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD JUL 5
PY 2022
VL 24
IS 7
AR e35816
DI 10.2196/35816
PG 26
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA 3B8QR
UT WOS:000828200900012
PM 35793141
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Li, SM
   Guo, YW
   Hou, XY
   Liu, JH
   Fan, WL
   Ju, ST
   Matos, PAW
   Rokohl, AC
   Heindl, LM
AF Li, Senmao
   Guo, Yongwei
   Hou, Xiaoyi
   Liu, Jinhua
   Fan, Wanlin
   Ju, Sitong
   Matos, Philomena A. Wawer
   Rokohl, Alexander C.
   Heindl, Ludwig M.
TI Mapping research trends of uveal melanoma: a bibliometric analysis
SO INTERNATIONAL OPHTHALMOLOGY
LA English
DT Article
DE Uveal melanoma; Bibliometric analysis; Knowledge domain; Visualization;
   VOSviewer
ID RUTHENIUM BRACHYTHERAPY; MUTATIONS; SURVIVAL; BAP1
AB Background To explore the research trends for uveal melanoma with bibliometric methods using Web of Science Core Collection (WoSCC) and PubMed (PM). Methods To find UM-related studies, "uveal melanoma" was used as search term in the WoSCC and PM for the period time from 2000 to 2020. Bibliographic coupling analysis was used to investigate the journals with the highest number of UM-related publications. VOSviewer (VV) was used for mapping the knowledge domain and visualizing the co-occurrence of terms, authors, organizations, countries, co-citation literature, and keywords. The knowledge map based on WoSCC and PM was compared. Results In the WoSCC 3,748 articles were found, while in PM the search resulted in 3,403 articles. The number of original articles has steadily grown in general in the past two decades. The top ten authors were contributing to 23% (n = 856) of all publications, while the top 10 institutions published 41% (n = 1524) of all articles. The top 3 journals with the highest number of publications for UM-related research included Investigative ophthalmology & visual science, Ophthalmology, and British Journal of Ophthalmology. Co-occurrence analysis based on author keywords showed 6 clusters. The most frequent keywords included are metastasis, prognosis, and brachytherapy. The latest research hotspots focused on BAP1, immunotherapy and GNAQ. Conclusions Genetics and immunology are the latest research frontiers in uveal melanoma. There is a clear need for interdisciplinary, molecular and clinical research approaches to improve the fatal prognosis of uveal melanoma patients.
C1 [Li, Senmao; Hou, Xiaoyi; Liu, Jinhua; Fan, Wanlin; Ju, Sitong; Matos, Philomena A. Wawer; Rokohl, Alexander C.; Heindl, Ludwig M.] Univ Cologne, Univ Hosp Cologne, Fac Med, Dept Ophthalmol, Kerpener Str 62, D-50937 Cologne, Germany.
   [Guo, Yongwei] Zhejiang Univ, Affiliated Hosp 2, Eye Ctr, Sch Med, Hangzhou, Zhejiang, Peoples R China.
   [Matos, Philomena A. Wawer; Rokohl, Alexander C.; Heindl, Ludwig M.] Ctr Integrated Oncol CIO, Cologne, Germany.
RP Heindl, LM (通讯作者)，Univ Cologne, Univ Hosp Cologne, Fac Med, Dept Ophthalmol, Kerpener Str 62, D-50937 Cologne, Germany.
EM ludwig.heindl@uk-koeln.de
RI Guo, Yongwei/AAB-5700-2020
OI Guo, Yongwei/0000-0001-9195-0770; Fan, Wanlin/0000-0001-7143-6707;
   Rokohl, Alexander Christopher/0000-0002-0224-3597; Heindl, Ludwig
   Maximilian/0000-0002-4413-6132
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
CR Bergman L, 2005, OPHTHALMOLOGY, V112, P834, DOI 10.1016/j.ophtha.2004.11.038
   Chang AE, 1998, CANCER, V83, P1664, DOI 10.1002/(SICI)1097-0142(19981015)83:8<1664::AID-CNCR23>3.0.CO;2-G
   Cohen Y, 2003, INVEST OPHTH VIS SCI, V44, P2876, DOI 10.1167/iovs.02-1329
   Damato B, 2000, BRIT J OPHTHALMOL, V84, P343, DOI 10.1136/bjo.84.4.343
   Ellegaard O, 2015, SCIENTOMETRICS, V105, P1809, DOI 10.1007/s11192-015-1645-z
   Harbour JW, 2010, SCIENCE, V330, P1410, DOI 10.1126/science.1194472
   Komatsubara KM, 2017, CURR ONCOL REP, V19, DOI 10.1007/s11912-017-0606-5
   Koopmans AE, 2014, MODERN PATHOL, V27, P1321, DOI 10.1038/modpathol.2014.43
   Kottschade LA, 2016, MELANOMA RES, V26, P300, DOI 10.1097/CMR.0000000000000242
   Kujala E, 2003, INVEST OPHTH VIS SCI, V44, P4651, DOI 10.1167/iovs.03-0538
   LOMMATZSCH PK, 1986, BRIT J OPHTHALMOL, V70, P844, DOI 10.1136/bjo.70.11.844
   Maio M, 2013, ANN ONCOL, V24, P2911, DOI 10.1093/annonc/mdt376
   Pelster M, 2019, J CLIN ONCOL, V37, DOI 10.1200/JCO.2019.37.15_suppl.9522
   Prescher G, 1996, LANCET, V347, P1222
   Rimoldi D, 2003, CANCER RES, V63, P5712
   Robertson DA, 2003, AM J OPHTHALMOL, V136, P161, DOI 10.1016/S0002-9394(03)00265-4
   Rouberol F, 2004, AM J OPHTHALMOL, V137, P893, DOI 10.1016/j.ajo.2003.12.032
   Seregard S, 1997, ACTA OPHTHALMOL SCAN, V75, P11
   Singh AD, 2004, BRIT J OPHTHALMOL, V88, P962, DOI 10.1136/bjo.2003.029058
   Singh AD, 2003, OPHTHALMOLOGY, V110, P962, DOI 10.1016/S0161-6420(03)00077-0
   Singh Arun D, 2005, Ophthalmol Clin North Am, V18, P75, DOI 10.1016/j.ohc.2004.07.002
   SUMMANEN P, 1993, OPHTHALMIC SURG LAS, V24, P82
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Van Raamsdonk CD, 2010, NEW ENGL J MED, V363, P2191, DOI 10.1056/NEJMoa1000584
   Van Raamsdonk CD, 2009, NATURE, V457, P599, DOI 10.1038/nature07586
NR 25
TC 0
Z9 0
U1 3
U2 9
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0165-5701
EI 1573-2630
J9 INT OPHTHALMOL
JI Int. Ophthalmol.
PD APR
PY 2022
VL 42
IS 4
BP 1121
EP 1131
DI 10.1007/s10792-021-02098-0
EA NOV 2021
PG 11
WC Ophthalmology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Ophthalmology
GA 0L4XW
UT WOS:000714864500001
PM 34739630
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Yin, MC
   Xu, CQ
   Ma, JM
   Ye, J
   Mo, W
AF Yin, Mengchen
   Xu, Chongqing
   Ma, Junming
   Ye, Jie
   Mo, Wen
TI A Bibliometric Analysis and Visualization of Current Research Trends in
   the Treatment of Cervical Spondylotic Myelopathy
SO GLOBAL SPINE JOURNAL
LA English
DT Review
DE cervical spondylotic myelopathy; bibliometric analysis; visualization;
   research trends
ID AOSPINE NORTH-AMERICA; NATURAL-HISTORY; ANTERIOR; SURGERY; EPIDEMIOLOGY;
   PRODUCTIVITY; EFFICACY; OUTCOMES; FUSION
AB Study Design: Bibliometric analysis.
   Objective: Cervical spondylotic myelopathy (CSM) has become the most common cause of spinal cord dysfunction. Many topics of CSM still remain controversial. This study aimed to illustrate the overall knowledge structure and development trends of CSM.
   Methods: Research data sets were acquired from the Web of Science database and the time span was defined as "2000 to 2019." VOS viewer and Citespace software was used to analyze the data and generate visualization knowledge maps. Annual trends of publications, distribution, H-index status, co-authorship status, and research hotspots were analyzed.
   Results: A total of 2367 publications met the requirement. The largest number of articles was from the United States, followed by Japan, China, Canada, and India. The highest H-index was found for articles from the United States. The highest number of articles was published inSpine. The cooperation between the countries, institutes, and authors were relatively weak. Cervical sagittal alignment, predictive factor, diffusion tensor imaging, and the natural history of CSM may become a frontier in this research field.
   Conclusion: The number of publications showed an upward trend with a stable rise. Most of the publications are limited to a few countries and institutions with relatively weak interaction. The United States, Canada, Japan, China, and India have made significant contributions to the field of CSM. The United States is the country with the highest productivity, not only in quality but also in quantity. Cervical sagittal alignment, predictive factor, diffusion tensor imaging, and the natural history of CSM are the research hotspots in the recent years.
C1 [Yin, Mengchen; Xu, Chongqing; Ma, Junming; Ye, Jie; Mo, Wen] Shanghai Univ Tradit Chinese Med, LongHua Hosp, Shanghai, Peoples R China.
RP Mo, W (通讯作者)，Shanghai Univ Tradit Chinese Med, Dept Orthopaed, LongHua Hosp, Shanghai, Peoples R China.
EM mw2218@126.com
OI , Mengchen Yin/0000-0001-7404-1495
CR Allen L, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0005910
   Asher AL, 2019, NEUROSURGERY, V84, P919, DOI 10.1093/neuros/nyy144
   Boyce R, 2015, TROP MED INT HEALTH, V20, P1507, DOI 10.1111/tmi.12590
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Dong FL, 2018, EUR SPINE J, V27, P1839, DOI 10.1007/s00586-018-5579-z
   Ergul S, 2010, INT NURS REV, V57, P49, DOI 10.1111/j.1466-7657.2009.00770.x
   Fajardo-Ortiz D, 2014, J TRANSL MED, V12, DOI 10.1186/s12967-014-0227-9
   Fan GX, 2017, SPINE, V42, P1717, DOI 10.1097/BRS.0000000000001393
   Fehlings MG, 2017, SPINE J, V17, P102, DOI 10.1016/j.spinee.2016.08.019
   Fehlings MG, 2015, NEUROSURGERY, V77, pS1, DOI 10.1227/NEU.0000000000000953
   Fehlings MG, 2013, SPINE, V38, P2247, DOI 10.1097/BRS.0000000000000047
   Fehlings MG, 2013, SPINE, V38, pS1, DOI 10.1097/BRS.0b013e3182a7e9e0
   Fehlings MG, 2013, J BONE JOINT SURG AM, V95A, P1651, DOI 10.2106/JBJS.L.00589
   Holly LT, 2009, J NEUROSURG-SPINE, V11, P112, DOI 10.3171/2009.1.SPINE08718
   Kalsi-Ryan S, 2013, NEUROSCIENTIST, V19, P409, DOI 10.1177/1073858412467377
   Karadimas SK, 2015, EUR SPINE J, V24, pS132, DOI 10.1007/s00586-014-3264-4
   Karadimas SK, 2013, SPINE, V38, pS21, DOI 10.1097/BRS.0b013e3182a7f2c3
   Karpova A, 2013, SPINE, V38, P392, DOI 10.1097/BRS.0b013e3182715bc3
   Kato S, 2017, J BONE JOINT SURG AM, V99, P1013, DOI 10.2106/JBJS.16.00882
   Knott PT, 2010, SPINE J, V10, P994, DOI 10.1016/j.spinee.2010.08.031
   Lin GX, 2020, EUR SPINE J, V29, P153, DOI 10.1007/s00586-019-06171-2
   Liu YH, 2016, BMJ OPEN, V6, DOI 10.1136/bmjopen-2016-011630
   Mei XL, 2016, INT J SURG, V28, P8, DOI 10.1016/j.ijsu.2016.02.029
   Nouri A, 2015, SPINE, V40, pE675, DOI 10.1097/BRS.0000000000000913
   Nukala M, 2019, EUR J RADIOL OPEN, V6, P16, DOI 10.1016/j.ejro.2018.08.006
   Paris G, 1998, NATURE, V396, P210, DOI 10.1038/24249
   Rhee J, 2017, GLOB SPINE J, V7, p35S, DOI 10.1177/2192568217703083
   Sun JS, 2012, MAR POLLUT BULL, V64, P13, DOI 10.1016/j.marpolbul.2011.10.034
   Tang Jessica A, 2015, Neurosurgery, V76 Suppl 1, pS14, DOI 10.1227/01.neu.0000462074.66077.2b
   Tetreault L, 2015, NEUROSURGERY, V77, pS51, DOI 10.1227/NEU.0000000000000951
   Tetreault LA, 2017, GLOB SPINE J, V7, p42S, DOI 10.1177/2192568217700397
   Tetreault LA, 2017, GLOB SPINE J, V7, p28S, DOI 10.1177/2192568217700396
   Tetreault LA, 2015, EUR SPINE J, V24, pS236, DOI 10.1007/s00586-013-2658-z
   Tetreault LA, 2013, J BONE JOINT SURG AM, V95A, P1659, DOI 10.2106/JBJS.L.01323
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Wei MY, 2016, EUR SPINE J, V25, P976, DOI 10.1007/s00586-016-4442-3
   Youssef JA, 2019, SPINE J, V19, P1714, DOI 10.1016/j.spinee.2019.04.019
   Zhang TS, 2015, SPRINGERPLUS, V4, DOI 10.1186/s40064-015-1542-1
   Zheng WP, 2018, WORLD NEUROSURG, V118, pE849, DOI 10.1016/j.wneu.2018.07.067
NR 39
TC 8
Z9 8
U1 3
U2 8
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 2192-5682
EI 2192-5690
J9 GLOB SPINE J
JI Glob. Spine J.
PD JUL
PY 2021
VL 11
IS 6
BP 988
EP 998
AR 2192568220948832
DI 10.1177/2192568220948832
EA SEP 2020
PG 11
WC Clinical Neurology; Orthopedics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology; Orthopedics
GA TH1JP
UT WOS:000566115500001
PM 32869687
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Walters, RS
   Kenzie, ES
   Metzger, AE
   Baltutis, WJ
   Chakrabarti, KB
   Hirsch, SL
   Laursen, BK
AF Walters, Riveraine S.
   Kenzie, Erin S.
   Metzger, Alexander E.
   Baltutis, William Jesse
   Chakrabarti, Kakali B.
   Hirsch, Shana Lee
   Laursen, Bethany K.
TI A systems thinking approach for eliciting mental models from visual
   boundary objects in hydropolitical contexts: a case study from the
   Pilcomayo River Basin
SO ECOLOGY AND SOCIETY
LA English
DT Article
DE boundary objects; hydropolitics; mental models; social-ecological
   systems; systems thinking
ID RIO-PILCOMAYO; WATER; SIMULATION; DYNAMICS
AB Transboundary collaborations related to international freshwater are critical for ensuring equitable, efficient, and sustainable shared access to our planet's most fundamental resources. Visual artifacts, such as knowledge maps, functioning as boundary objects, are used in hydropolitical contexts to convey understandings and facilitate discussion across scales about challenges and opportunities from multiple perspectives. Such focal points for discussion are valuable in creating shared, socially negotiated priorities and integrating diverse and often disparate cultural perspectives that naturally exist in the context of international transboundary water resources. Visual boundary objects can also represent the collective mental models of the actor countries and transboundary institutions and encompass their perspectives on the complex hydro-social cycles within specific "problem-shed" regions of the shared resources. To investigate and synthesize these multiple concepts, we developed a novel method of eliciting mental models from visual boundary objects in social-ecological contexts by combining content analysis with theoretical frameworks for boundary objects and systems thinking Using this method, we analyzed visual boundary objects represented in publicly available documents formally related to decision making in the Pilcomayo River Basin in South America. The Pilcomayo River Basin is a unique case for investigating decision making in international collaboration among represented states, given the unique social and biophysical challenges that have plagued the region for over a century. Using our framework, we were able to develop insight into the collective mental models of stakeholders, organizations, and decision-making institutions, related to priorities, vulnerabilities, and adaptation strategies among the various socioeconomic, cultural, political, and biophysical drivers for different regions and scales of the basin.
C1 [Walters, Riveraine S.] Univ Idaho, Water Resources Program, Moscow, ID 83843 USA.
   [Kenzie, Erin S.] Portland State Univ, Syst Sci Program, Portland, OR 97207 USA.
   [Metzger, Alexander E.] Univ Massachusetts, Boston Sch Environm, Amherst, MA 01003 USA.
   [Baltutis, William Jesse] Univ Victoria, Water Innovat & Global Governance WIGG Lab, Victoria, BC, Canada.
   [Chakrabarti, Kakali B.] Austin Peay State Univ, Dept Commun, Clarksville, TN USA.
   [Hirsch, Shana Lee] Univ Washington, Dept Human Ctr Design & Engn, Seattle, WA 98195 USA.
   [Laursen, Bethany K.] Michigan State Univ, Dept Community Sustainabil, E Lansing, MI 48824 USA.
   [Laursen, Bethany K.] Michigan State Univ, Dept Philosophy, E Lansing, MI 48824 USA.
   [Laursen, Bethany K.] Laursen Evaluat & Design LLC, Boston, MA USA.
RP Walters, RS (通讯作者)，Univ Idaho, Water Resources Program, Moscow, ID 83843 USA.
RI Laursen, Bethany/ABI-7656-2020
OI Walters, Riveraine/0000-0002-7017-2997
FU National Socio-Environmental Synthesis Center (SESYNC); National Science
   Foundation [DBI-1052875]
FX This work was supported by the National Socio-Environmental Synthesis
   Center (SESYNC) under funding received from the National Science
   Foundation DBI-1052875. We would like to acknowledge the valuable
   counsel of our external experts on the project, Barbara Cosens of the
   University of Idaho College of Law and Derek Cabrera at Cornell
   University and his colleagues at the U.S. Department of Agriculture's
   Think Water program.
CR Abel N., 1998, Rangeland Journal, V20, P77, DOI 10.1071/RJ9980077
   ACKOFF RL, 1979, J OPER RES SOC, V30, P93, DOI 10.1057/jors.1979.22
   Arias-Hernandez R, 2012, INTERDISCIPL SCI REV, V37, P4, DOI 10.1179/0308018812Z.0000000001
   Blatter J., 2001, REFLECTIONS WATER NE, P3, DOI [10.7551/mitpress/5844.003.0006, DOI 10.7551/MITPRESS/5844.003.0006]
   Cabrera D, 2008, EVAL PROGRAM PLANN, V31, P299, DOI 10.1016/j.evalprogplan.2007.12.001
   Cabrera D, 2015, SYST RES BEHAV SCI, V32, P534, DOI 10.1002/sres.2351
   Cabrera L, 2015, SYSTEMS THINKING MAD
   CARLEY K, 1992, SOC FORCES, V70, P601, DOI 10.2307/2579746
   CARLEY K, 1993, SOCIOL METHODOL, V23, P75, DOI 10.2307/271007
   Dawes SS, 2009, PUBLIC ADMIN REV, V69, P392, DOI 10.1111/j.1540-6210.2009.01987_2.x
   Dinar S., 2007, INT WATER TREATIES N, DOI [10.4324/9780203934456, DOI 10.4324/9780203934456]
   Eppler Martin J, 2007, Journal of Knowledge Management, V11, P112, DOI 10.1108/13673270710762756
   Faure G. O., 1993, CULTURE NEGOTIATION, P1
   Government of Argentina Government of Brazil and Government of Paraguay, 1995, AGR EST TRIN COMM DE
   Hudson-Edwards KA, 2001, J GEOCHEM EXPLOR, V72, P229, DOI 10.1016/S0375-6742(01)00164-9
   Jensen S, 2016, HEALTH INFORM J, V22, P248, DOI 10.1177/1460458214551846
   Jones NA, 2014, ECOL SOC, V19, DOI 10.5751/ES-06248-190113
   Jones NA, 2011, ECOL SOC, V16
   Laborde LD, 2008, WATER RESOUR DEV MAN, P269
   Lorenz E., 2001, J MANAGEMENT GOVERNA, V5, P307, DOI DOI 10.1023/A:1014098928477
   Miller JR, 2004, SCI TOTAL ENVIRON, V320, P189, DOI 10.1016/j.scitotenv.2003.08.011
   Nandalal KDW, 2003, WATER RESOUR RES, V39, DOI 10.1029/2003WR002172
   RITTEL HWJ, 1973, POLICY SCI, V4, P155, DOI 10.1007/BF01405730
   Rothman Jay, 1995, CULTURAL SURVIVAL Q, V19, P19
   Smolders AJP, 2002, RIVER RES APPL, V18, P415, DOI 10.1002/rra.690
   STAR SL, 1989, SOC STUD SCI, V19, P387, DOI 10.1177/030631289019003001
   Strosnider WHJ, 2014, ENVIRON EARTH SCI, V71, P3223, DOI 10.1007/s12665-013-2734-z
   Turton A., 2002, HYDROPOLITICS DEV WO
   United Nations Environment Programme (UNEP), 2007, HYDR VULN RES INT WA
   Westervelt J. D., 2012, ECOLOGIST DEV SPATIA
   Winz I, 2009, WATER RESOUR MANAG, V23, P1301, DOI 10.1007/s11269-008-9328-7
NR 31
TC 3
Z9 3
U1 0
U2 5
PU RESILIENCE ALLIANCE
PI WOLFVILLE
PA ACADIA UNIV, BIOLOGY DEPT, WOLFVILLE, NS B0P 1X0, CANADA
SN 1708-3087
J9 ECOL SOC
JI Ecol. Soc.
PD JUL
PY 2019
VL 24
IS 2
AR 9
DI 10.5751/ES-10586-240209
PG 21
WC Ecology; Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA IT2VT
UT WOS:000482712400001
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Zhao, L
   Zhang, HA
   Li, XP
   Yang, S
   Song, YF
AF Zhao, Lei
   Zhang, Haonan
   Li, Xiangpeng
   Yang, Sen
   Song, Yuanfeng
TI You should know more: Learning external knowledge for visual dialog
SO NEUROCOMPUTING
LA English
DT Article
DE External knowledge; Graph convolutional network; Structured knowledge
   graph; Attention mechanism; Visual dialog
AB Visual dialog is a task that two agents complete a multi-round conversation based on an image, a caption, and dialog histories. Despite the recent progress, existing methods still undergo degradation on the condition of complex scenarios. Handling these scenarios depends on logical reasoning that requires commonsense priors. In this paper, we propose a novel visual dialog pipeline named Structured Knowledge-Aware Network (SKANet), consisting of an Image Knowledge-Aware Module and a Caption Knowledge-Aware Module. Specifically, the Image and Caption Knowledge-Aware Modules construct commonsense knowledge graphs from ConceptNet. We apply SKANet to two sub-tasks: the conventional visual dialog and a goal-oriented visual dialog named 'image guessing'. For the conventional visual dialog, the SKANet is combined with an additional Multi-Modality Fusion Module, which is designed to explore the visual content and the textual context about the dialog history. For the goal-oriented visual dialog, we directly apply the Image and Caption Knowledge-Aware Modules to two agents, respectively. Experimental results on VisDial v0.9 and VisDial v1.0 datasets show that our proposed method effectively outperforms comparative methods on both sub-tasks.(c) 2022 Elsevier B.V. All rights reserved.
C1 [Zhao, Lei; Zhang, Haonan; Li, Xiangpeng; Yang, Sen; Song, Yuanfeng] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Peoples R China.
   [Zhao, Lei] Sichuan Artificial Intelligence Res Inst, Yibin, Peoples R China.
RP Song, YF (通讯作者)，Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Peoples R China.
EM songyf@uestc.edu.cn
CR Agarwal A, 2019, AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, P1042
   Agrawal A, 2017, INT J COMPUT VISION, V123, P4, DOI 10.1007/s11263-016-0966-6
   Anderson P, 2018, PROC CVPR IEEE, P6077, DOI 10.1109/CVPR.2018.00636
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bengio Y, 2009, FOUND TRENDS MACH LE, V2, P1, DOI 10.1561/2200000006
   Bhakthavatsalam S., ARXIV PREPRINT ARXIV
   Chang YW, 2019, IEEE INT CON MULTI, P520, DOI 10.1109/ICME.2019.00096
   Dan Guo, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P10052, DOI 10.1109/CVPR42600.2020.01007
   Das A, 2017, PROC CVPR IEEE, P1080, DOI 10.1109/CVPR.2017.121
   Das A, 2017, LIGNOCELLULOSIC BIOMASS PRODUCTION AND INDUSTRIAL APPLICATIONS, P1, DOI 10.1109/ICCV.2017.321
   de Vries H, 2017, PROC CVPR IEEE, P4466, DOI 10.1109/CVPR.2017.475
   Dong J., IEEE T PATTERN ANAL
   Nguyen DK, 2018, PROC CVPR IEEE, P6087, DOI 10.1109/CVPR.2018.00637
   Gao LL, 2020, IEEE T PATTERN ANAL, V42, P1112, DOI 10.1109/TPAMI.2019.2894139
   Girshick R., 2015, ADV NEUR IN
   Guo DL, 2019, PROC CVPR IEEE, P10426, DOI 10.1109/CVPR.2019.01068
   Jain U, 2018, PROC CVPR IEEE, P5754, DOI 10.1109/CVPR.2018.00603
   Jiang XZ, 2020, AAAI CONF ARTIF INTE, V34, P11125
   Junbin Xiao, 2020, Computer Vision - ECCV 2020. 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12351), P447, DOI 10.1007/978-3-030-58539-6_27
   Kang Gi-Cheon, 2019, P C EMP METH NAT LAN
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Kottur S, 2018, LECT NOTES COMPUT SC, V11219, P160, DOI 10.1007/978-3-030-01267-0_10
   Krishna R, 2017, INT J COMPUT VISION, V123, P32, DOI 10.1007/s11263-016-0981-7
   Li XP, 2019, PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19), P1166, DOI 10.1145/3343031.3350971
   Li XP, 2019, AAAI CONF ARTIF INTE, P8658
   Li Y., 2021, PROC 29 ACM INT C MU
   Lin Bill Yuchen, 2019, P 2019 C EMP METH NA, P2829, DOI DOI 10.18653/V1/D19-1282
   Liu XL, 2020, ACM T INTEL SYST TEC, V11, DOI 10.1145/3375787
   Lu JS, 2016, ADV NEUR IN, V29
   Ma L, 2016, AAAI CONF ARTIF INTE, P3567
   Massiceti D, 2018, PROC CVPR IEEE, P6097, DOI 10.1109/CVPR.2018.00638
   Niu YL, 2019, PROC CVPR IEEE, P6672, DOI 10.1109/CVPR.2019.00684
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Qiao Y., 2020, ICME, P1
   Saito K, 2017, IEEE INT CON MULTI, P829, DOI 10.1109/ICME.2017.8019436
   Sap M, 2019, AAAI CONF ARTIF INTE, P3027
   Schwartz I, 2019, PROC CVPR IEEE, P2039, DOI 10.1109/CVPR.2019.00214
   Shah S, 2019, AAAI CONF ARTIF INTE, P8876
   Shekhar Ravi, 2018, COLING, P1218
   Song JK, 2019, IEEE T NEUR NET LEAR, V30, P3047, DOI 10.1109/TNNLS.2018.2851077
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Velickovi Petar, 2018, ICLR
   Wang Q, 2019, IEEE INT CON MULTI, P1564, DOI 10.1109/ICME.2019.00270
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Xun Yang, 2020, MM '20: Proceedings of the 28th ACM International Conference on Multimedia, P1939, DOI 10.1145/3394171.3413610
   Yang JF, 2019, NEUROCOMPUTING, V328, P56, DOI 10.1016/j.neucom.2018.03.078
   Yang X, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P1339, DOI 10.1145/3397271.3401151
   Yang X, 2021, SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1, DOI 10.1145/3404835.3462823
   Yu Z, 2019, PROC CVPR IEEE, P6274, DOI 10.1109/CVPR.2019.00644
   Yu Z, 2017, IEEE I CONF COMP VIS, P1839, DOI 10.1109/ICCV.2017.202
   Zhao L, 2021, PATTERN RECOGN, V114, DOI 10.1016/j.patcog.2021.107823
   Zheng ZL, 2019, PROC CVPR IEEE, P3662, DOI 10.1109/CVPR.2019.00683
   Zhuang BH, 2018, PROC CVPR IEEE, P4252, DOI 10.1109/CVPR.2018.00447
NR 56
TC 0
Z9 0
U1 3
U2 3
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JUN 1
PY 2022
VL 488
BP 54
EP 65
DI 10.1016/j.neucom.2021.10.121
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0M9LL
UT WOS:000782470900005
DA 2022-08-23
ER

PT J
AU Hu, S
   Weng, JC
   Liang, Q
   Zhou, W
   Wang, PZ
AF Hu, Song
   Weng, Jiancheng
   Liang, Quan
   Zhou, Wei
   Wang, Peizhao
TI Individual Travel Knowledge Graph-Based Public Transport Commuter
   Identification: A Mixed Data Learning Approach
SO JOURNAL OF ADVANCED TRANSPORTATION
LA English
DT Article
ID RELIABILITY; NETWORK; FUSION
AB Commuters are the stable travel group for the public transportation (PT) service system. Accurately identifying the PT commuters is conducive to promoting PT service quality and development of urban sustainable transportation. This paper extracts individual PT travel chain information and constructs individual travel knowledge graphs of PT passengers based on the association matching algorithm and the theory of multilayer planning. A mixed dataset is formed by associating individual travel chains with travel survey data. Seven travel characteristic indicators regarding travel performance and spatiotemporal travel characteristics are extracted. The identification model of PT commuters is developed based on a three-layer backpropagation neural network (BPNN). The optimal model structure of neuron node number, transfer function, and learning rate are discussed quantitatively according to the minimization of model errors. The evaluation indexes of overall accuracy and kappa coefficient of the constructed model are 94.5% and 87.9% separately. The results indicate that the model identification accuracy is acceptable, and the proposed characteristic indicators and systematic modelling procedure are effective. Then, the model performance is compared with the other five machine learning models further. The results confirm that the proposed model has a better identification accuracy and viability, and the model performance will improve with the increase of the sample size.
C1 [Hu, Song; Weng, Jiancheng] Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing, Peoples R China.
   [Liang, Quan] Minist Transport China, Transport Management Inst, Dept Rd Teaching & Res, Beijing, Peoples R China.
   [Zhou, Wei] Minist Transport Peoples Republ China, Beijing, Peoples R China.
   [Wang, Peizhao] UCL, Ctr Adv Spatial Anal, London, England.
RP Liang, Q (通讯作者)，Minist Transport China, Transport Management Inst, Dept Rd Teaching & Res, Beijing, Peoples R China.
EM lquan0730@163.com
FU National Natural Science Foundation of China [52072011]; Major Program
   of the National Natural Science Foundation of China [U1811463]
FX THis research was supported by the National Natural Science Foundation
   of China under Grant no. 52072011 and the Major Program of the National
   Natural Science Foundation of China under Grant no. U1811463. +e authors
   would like to show great appreciation for the support.
CR Adams EJ, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0107039
   Allahviranloo M, 2013, TRANSPORT RES B-METH, V58, P16, DOI 10.1016/j.trb.2013.09.008
   Barabino B, 2020, PUBLIC TRANSPORT, V12, P27, DOI 10.1007/s12469-019-00225-w
   Barabino B, 2017, QUAL RELIAB ENG INT, V33, P969, DOI 10.1002/qre.2073
   Bernardin VL, 2015, TRANSPORT RES REC, P69, DOI 10.3141/2494-08
   Bosehans G, 2020, TRANSPORTATION, V47, P243, DOI 10.1007/s11116-018-9874-7
   Boubezoul A, 2012, PATTERN RECOGN, V45, P3676, DOI 10.1016/j.patcog.2012.04.015
   Chan KY, 2012, IEEE T INTELL TRANSP, V13, P644, DOI 10.1109/TITS.2011.2174051
   Chen J, 2020, INT J DISTRIB SENS N, V16, DOI 10.1177/1550147720968469
   Chianeh FN, 2017, ENVIRON PROG SUSTAIN, V36, P1778, DOI 10.1002/ep.12650
   Cui Y, 2018, TRANSPORT RES REC, V2672, P68, DOI 10.1177/0361198118772723
   Du H.Q., 2003, J NORTHEAST FOR UNIV, V32, P51
   Guo J., 2020, BEIJING TRANSPORT DE
   Guo LJ, 2009, J LOSS PREVENT PROC, V22, P469, DOI 10.1016/j.jlp.2009.03.003
   [郭茂祖 Guo Maozu], 2019, [哈尔滨工业大学学报, Journal of Harbin Institute of Technology], V51, P1
   Hu S, 2021, INT J SUSTAIN TRANSP, V15, P879, DOI 10.1080/15568318.2020.1827318
   Jun L., 2016, J CHONGQING JIANZHU, V35, P109
   Ke X, 2019, IEEE T INTELL TRANSP, V20, P2157, DOI 10.1109/TITS.2018.2864612
   Kieu LM, 2015, IEEE T INTELL TRANSP, V16, P1537, DOI 10.1109/TITS.2014.2368998
   Kusakabe T, 2014, TRANSPORT RES C-EMER, V46, P179, DOI 10.1016/j.trc.2014.05.012
   Lana I, 2018, TRANSPORT RES C-EMER, V90, P18, DOI 10.1016/j.trc.2018.02.021
   Kieu LM, 2015, TRANSPORT RES C-EMER, V58, P193, DOI 10.1016/j.trc.2015.03.033
   Li JC, 2018, PHYSICA A, V495, P1, DOI 10.1016/j.physa.2017.12.018
   Li LJ, 2014, APPL MECH MATER, V494-495, P1647, DOI 10.4028/www.scientific.net/AMM.494-495.1647
   Liang Q, 2018, J ADV TRANSPORT, DOI 10.1155/2018/3859830
   Ma XL, 2017, J TRANSP GEOGR, V58, P135, DOI 10.1016/j.jtrangeo.2016.12.001
   Ma XL, 2013, TRANSPORT RES C-EMER, V36, P1, DOI 10.1016/j.trc.2013.07.010
   Manley E, 2018, TRANSPORTATION, V45, P703, DOI 10.1007/s11116-016-9747-x
   Muntean CI, 2015, ACM T INTEL SYST TEC, V7, DOI 10.1145/2766459
   Osoba S.B, 2015, INDONESIAN J GEOGRAP, V47, P40, DOI [10.22146/ijg.6744, DOI 10.22146/IJG.6744]
   Qi G., 2016, P EEE INT C PROGR IN, P17
   Rafiq R, 2021, TRANSPORT RES A-POL, V152, P1, DOI 10.1016/j.tra.2021.07.011
   Rastogi R, 2009, J TRANSP ENG-ASCE, V135, P506, DOI 10.1061/(ASCE)0733-947X(2009)135:8(506)
   Ruimin L.I., 2007, J SW JIAO TONG U, V7, P1
   Sameen MI, 2017, APPL SCI-BASEL, V7, DOI 10.3390/app7060476
   Soleymani AR, 2019, CHEM ENG COMMUN, V206, P1297, DOI 10.1080/00986445.2018.1557152
   Sun GB, 2017, J TRANSP HEALTH, V4, P191, DOI 10.1016/j.jth.2017.01.010
   Sun SC, 2018, J ADV TRANSPORT, DOI 10.1155/2018/9693272
   [孙世超 Sun Shichao], 2015, [交通运输系统工程与信息, Journal of Transporation Systems Engineering & Information Technology], V15, P216
   Wang WH, 2011, TRANSPORT RES C-EMER, V19, P1202, DOI 10.1016/j.trc.2011.02.002
   Weiwei Liu, 2008, Journal of Service Science and Management, V1, P266
   [翁剑成 Weng Jiancheng], 2017, [交通运输系统工程与信息, Journal of Transporation Systems Engineering & Information Technology], V17, P67
   [翁小雄 Weng Xiaoxiong], 2019, [重庆交通大学学报. 自然科学版, Journal of Chongqing Jiaotong University. Natural Science], V38, P8
   Wu QL, 2021, INSECT SCI, V28, P649, DOI 10.1111/1744-7917.12852
   Yao Y, 2019, J CLEAN PROD, V238, DOI 10.1016/j.jclepro.2019.117892
   Zhang DF, 2013, PROCD SOC BEHV, V96, P1547, DOI 10.1016/j.sbspro.2013.08.176
   [张义兵 ZHANG Yibing], 2007, [计算机仿真, Computer simulation], V24, P223
   Zhang ZH, 2021, COGN COMPUT, DOI 10.1007/s12559-021-09871-4
   [邹庆茹 Zou Qingru], 2018, [北京交通大学学报. 自然科学版, Journal of Beijing Jiaotong University], V42, P44
NR 49
TC 0
Z9 0
U1 9
U2 9
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 0197-6729
EI 2042-3195
J9 J ADV TRANSPORT
JI J. Adv. Transp.
PD FEB 24
PY 2022
VL 2022
AR 2012579
DI 10.1155/2022/2012579
PG 16
WC Engineering, Civil; Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Transportation
GA 0A0IB
UT WOS:000773644400001
OA gold
DA 2022-08-23
ER

PT J
AU Ge, CC
   Liu, XZ
   Chen, L
   Gao, YJ
   Zheng, BH
AF Ge, Congcong
   Liu, Xiaoze
   Chen, Lu
   Gao, Yunjun
   Zheng, Baihua
TI LargeEA: Aligning Entities for Large-scale Knowledge Graphs
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article; Proceedings Paper
CT 48th International Conference on Very Large Data Bases (VLDB)
CY 2022
CL Sydney, AUSTRALIA
ID ALIGNMENT
AB Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). Current EA approaches suffer from scalability issues, limiting their usage in real-world EA scenarios. To tackle this challenge, we propose LargeEA to align entities between large-scale KGs. LargeEA consists of two channels, i.e., structure channel and name channel. For the structure channel, we present METIS-CPS, a memory-saving mini-batch generation strategy, to partition large KGs into smaller mini-batches. LargeEA, designed as a general tool, can adopt any existing EA approach to learn entities' structural features within each mini-batch independently. For the name channel, we first introduce NFF, a name feature fusion method, to capture rich name features of entities without involving any complex training process; we then exploit a name-based data augmentation to generate seed alignment without any human intervention. Such design fits common real-world scenarios much better, as seed alignment is not always available. Finally, LargeEA derives the EA results by fusing the structural features and name features of entities. Since no widely-acknowledged benchmark is available for large-scale EA evaluation, we also develop a large-scale EA benchmark called DBP1M extracted from real-world KGs. Extensive experiments confirm the superiority of LargeEA against state-of-the-art competitors.
C1 [Ge, Congcong; Liu, Xiaoze; Chen, Lu; Gao, Yunjun] Zhejiang Univ, Coll Comp Sci, Hangzhou, Peoples R China.
   [Zheng, Baihua] Singapore Management Univ, Sch Comp & Informat Syst, Singapore, Singapore.
RP Ge, CC (通讯作者)，Zhejiang Univ, Coll Comp Sci, Hangzhou, Peoples R China.
EM gcc@zju.edu.cn; xiaoze@zju.edu.cn; luchen@zju.edu.cn; gaoyj@zju.edu.cn;
   bhzheng@smu.edu.sg
OI Liu, Xiaoze/0000-0002-9726-3397; Zheng, Baihua/0000-0001-9792-9171
FU NSFC [62025206, 61972338, 62102351]
FX This work was supported by the NSFC under Grant No. 62025206, 61972338,
   and 62102351, and also supported by AZFT. Yunjun Gao is the
   corresponding author of the work.
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   BRISLIN RW, 1970, J CROSS CULT PSYCHOL, V1, P185, DOI 10.1177/135910457000100301
   Cao YX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1452
   Chen MH, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3998
   Chen MH, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1511
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Fey Matthias, 2020, P INT C LEARN REPR
   Jimenez-Ruiz E, 2011, LECT NOTES COMPUT SC, V7031, P273, DOI 10.1007/978-3-642-25073-6_18
   Johnson J, 2021, IEEE T BIG DATA, V7, P535, DOI 10.1109/TBDATA.2019.2921572
   Joulin A., 2017, P C EUR CHAPT ASS CO, V2, P427, DOI [DOI 10.18653/V1/E17-2068, 10.18653/v1/e17-2068]
   Karypis G, 1998, SIAM J SCI COMPUT, V20, P359, DOI 10.1137/S1064827595287997
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Lample G., 2018, INT C LEARN REPR ICL, P1
   Lee BC, 2010, COMMUN ACM, V53, P99, DOI 10.1145/1785414.1785441
   Li Chengjiang, 2019, P 2019 C EMPIRICAL M, P2723
   Li YL, 2020, PROC VLDB ENDOW, V14, P50, DOI 10.14778/3421424.3421431
   Lin XX, 2019, IEEE DATA MINING, P429, DOI 10.1109/ICDM.2019.00053
   Liu FLLD, 2020, OPT EXPRESS, V28, P28969, DOI 10.1364/OE.400876
   Liu Zhiyuan, 2020, P 2020 C EMP METH NA, P6355
   Mahdisoltani F., 2015, CIDR 2015
   Mao X, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P1095, DOI 10.1145/3340531.3412001
   Mao X, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P420, DOI 10.1145/3336191.3371804
   Mudgal S, 2018, INT CONF MANAGE DATA, P19, DOI 10.1145/3183713.3196926
   Nie H, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3658
   Pei SC, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3231
   Pei SC, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3130, DOI 10.1145/3308558.3313646
   Suchanek FM, 2011, PROC VLDB ENDOW, V5, P157, DOI 10.14778/2078331.2078332
   Sun ZQ, 2020, AAAI CONF ARTIF INTE, V34, P222
   Sun ZQ, 2020, PROC VLDB ENDOW, V13, P2326, DOI 10.14778/3407790.3407828
   Sun ZQ, 2019, LECT NOTES COMPUT SC, V11778, P612, DOI 10.1007/978-3-030-30793-6_35
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Sun Zequn, 2020, EMNLP, P5704
   Tang XB, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3174
   Trisedya BD, 2019, AAAI CONF ARTIF INTE, P297
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang Zhichun, 2020, P 2020 C EMP METH NA, P1672, DOI DOI 10.18653/V1/2020.EMNLP-MAIN.130
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Wu YT, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5278
   Wu Yuting, 2019, EMNLP IJCNLP, P240
   Wu Yuting, 2020, ACL, P6477
   Xiong CY, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1271, DOI 10.1145/3038912.3052558
   Xu HT, 2019, ADV NEUR IN, V32
   Xu K, 2020, AAAI CONF ARTIF INTE, V34, P9354
   Xu K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3156
   Yang Hsiu-Wei, 2019, P 2019 C EMP METH NA, P4430
   Yang K, 2020, AAAI CONF ARTIF INTE, V34, P3025
   Zeng WX, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P811, DOI 10.1145/3397271.3401161
   Zeng WX, 2020, PROC INT CONF DATA, P1870, DOI 10.1109/ICDE48307.2020.00191
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhang QH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5429
   Zhao X, 2022, IEEE T KNOWL DATA EN, V34, P2610, DOI 10.1109/TKDE.2020.3018741
   Zhu H, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4258
   Zhu QN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1943
   Zhuang Y, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1917, DOI 10.1145/3132847.3132912
NR 57
TC 0
Z9 0
U1 5
U2 5
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD OCT
PY 2021
VL 15
IS 2
BP 237
EP 245
DI 10.14778/3489496.3489504
PG 9
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA ZB7QK
UT WOS:000757032100009
OA Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Martinez-Rodriguez, JL
   Lopez-Arevalo, I
   Rios-Alvarado, AB
AF Martinez-Rodriguez, Jose L.
   Lopez-Arevalo, Ivan
   Rios-Alvarado, Ana B.
TI OpenIE-based approach for Knowledge Graph construction from text
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge Graph; Semantic Web representation; Fact extraction; Relation
   Extraction; RDF events
ID RELATION EXTRACTION; SEMANTIC WEB; POPULATION; AGREEMENT; SYSTEMS
AB Transforming unstructured text into a formal representation is an important goal of the Semantic Web in order to facilitate the integration and retrieval of information. The construction of Knowledge Graphs (KGs) pursues such an idea, where named entities (real world things) and their relations are extracted from text. In recent years, many approaches for the construction of KGs have been proposed by exploiting Discourse Analysis, Semantic Frames, or Machine Learning algorithms with existing Semantic Web data. Although such approaches are useful for processing taxonomies and connecting beliefs, they provide several linguistic descriptions, which lead to semantic data heterogeneity and thus, complicating data consumption. Moreover, Open Information Extraction (OpenIE) approaches have been slightly explored for the construction of KGs, which provide binary relations representing atomic units of information that could simplify the querying and representation of data. In this paper, we propose an approach to generate KGs using binary relations produced by an OpenIE approach. For such purpose, we present strategies for favoring the extraction and linking of named entities with KG individuals, and additionally, their association with grammatical units that lead to producing more coherent facts. We also provide decisions for selecting the extracted information elements for creating potentially useful RDF triples for the KG. Our results demonstrate that the integration of information extraction units with grammatical structures provides a better understanding of proposition-based representations provided by OpenIE for supporting the construction of KGs. (C) 2018 Elsevier Ltd. All rights reserved.
C1 [Martinez-Rodriguez, Jose L.; Lopez-Arevalo, Ivan] Cinvestav Tamaulipas, Victoria, Mexico.
   [Rios-Alvarado, Ana B.] Univ Autonoma Tamaulipas, Fac Ingn & Ciencias, Victoria, Mexico.
RP Martinez-Rodriguez, JL (通讯作者)，Cinvestav Tamaulipas, Victoria, Mexico.
EM lmartinez@tamps.cinvestav.mx; ilopez@tamps.cinvestav.mx;
   arios@uat.edu.mx
RI Lopez-Arevalo, Ivan/AAH-3462-2021
OI Lopez-Arevalo, Ivan/0000-0002-7464-8438; Martinez-Rodriguez, Jose
   L./0000-0002-1276-6957; Rios-Alvarado, Ana B./0000-0002-6481-4316
CR Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   Augenstein Isabelle, 2012, The Semantic Web: Research and Applications. Proceedings 9th Extended Semantic Web Conference (ESWC 2012), P210, DOI 10.1007/978-3-642-30284-8_21
   Augenstein I, 2016, SEMANT WEB, V7, P335, DOI 10.3233/SW-150180
   Bach N., 2007, LIT REV LANGUAGE STA
   Banko M, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2670
   Bartscherer F, 2016, SEMANTIC WEB, P1
   Berners-Lee T., 2010, FUTURE RDF
   Corcoglioniti F, 2016, IEEE T KNOWL DATA EN, V28, P3261, DOI 10.1109/TKDE.2016.2602206
   Curran J. R., 2007, ANN M ASS COMP LING
   Daconta M. C., 2003, SEMANTIC WEB GUIDE F
   Dietterich TG, 2000, LECT NOTES COMPUT SC, V1857, P1, DOI 10.1007/3-540-45014-9_1
   Dodds L., 2011, LINKED DATA PATTERNS
   Dutta A, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P267, DOI 10.1145/2736277.2741139
   Dutta A, 2014, FRONT ARTIF INTEL AP, V264, P111, DOI 10.3233/978-1-61499-421-3-111
   Ehrlinger L., 2016, 12 INT C SEM SYST
   Elliott S. W., 2017, OXFORD HDB SKILLS TR
   Emani CK, 2016, LECT NOTES COMPUT SC, V9630, P139, DOI 10.1007/978-3-662-49521-6_6
   Erling O., 2012, IEEE DATA ENG B, V35, P3
   Exner P., 2012, WEB LINK ENT WORKSH
   Fang Liu, 2013, Information Retrieval Technology. 9th Asia Information Retrieval Societies Conference, AIRS 2013. Proceedings: LNCS 8281, P320, DOI 10.1007/978-3-642-45068-6_28
   FILLMORE CJ, 1976, ANN NY ACAD SCI, V280, P20, DOI 10.1111/j.1749-6632.1976.tb25467.x
   Foote A, 2018, J COMPUT INFORM SYST, V58, P89, DOI 10.1080/08874417.2016.1198941
   Fossati M., 2017, SEMANTIC WEB J
   Freitas A., 2012, WORKSH WEB LINK ENT
   Gangemi Aldo, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P351
   Gangemi A, 2017, SEMANT WEB, V8, P873, DOI 10.3233/SW-160240
   Giuglea A.-M., 2004, WORKSH ONT KNOWL DIS
   Giuglea AM, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P929
   Grosz B. J., 1983, 21st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, P44
   He DC, 2017, NEURAL COMPUT, V29, P1964, DOI 10.1162/NECO_a_00970
   Hearst MA, 1992, P 14 C COMP LING, P539, DOI DOI 10.3115/992133.992154
   Heath T., 2009, EVOLVING WEB GLOBAL
   Hernandez D., 2015, ISWC 2015, P32, DOI DOI 10.1145/2566486.2567973
   Hoffart J., 2011, P EMNLP, DOI DOI 10.3115/V1/D11-1072
   Hogan A., 2013, LINKED DATA SEMANTIC
   Kamp H, 2011, HANDBOOK OF PHILOSOPHICAL LOGIC, VOL 15, SECOND EDITION, P125, DOI 10.1007/978-94-007-0485-5_3
   LANDIS JR, 1977, BIOMETRICS, V33, P159, DOI 10.2307/2529310
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   llievski F., 2016, INT C LANG RES EV
   Luciano DC., 2013, CLAUSIE CLAUSE BASED, P355, DOI DOI 10.1145/2488388.2488420
   Mandisoltani F., 2014, BIENN C INN DAT SYST
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   Mausam J. Christensen, 2011, P 6 INT C KNOWL CAPT, P113
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Mitchell T, 2015, AAAI CONF ARTIF INTE, P2302
   Moro A., 2014, TACL, V2, P231, DOI DOI 10.1162/TACL_A_00179
   Nakashole N, 2013, SIGMOD REC, V42, P29
   Nebhi K., 2013, INT C NLP DBPEDIA, P74
   Presutti V, 2016, SEMANT WEB, V7, P351, DOI 10.3233/SW-160221
   Punyakanok V, 2008, COMPUT LINGUIST, V34, P257, DOI 10.1162/coli.2008.34.2.257
   Randolph Justus, 2005, JOENSUU LEARNING INS
   Rizzo G., 2012, P DEM 13 C EUR CHAPT, P73
   Roth M., 2014, EMNLP, P407
   Rouces J, 2015, LECT NOTES COMPUT SC, V9088, P505, DOI 10.1007/978-3-319-18818-8_31
   Salloum S. A., 2017, ADV SCI TECHNOLOGY E
   Schutz A, 2005, LECT NOTES COMPUT SC, V3729, P593
   Snow R, 2004, ADV NEURAL INFORM PR, V17, P1297
   Tanwar M, 2015, 2015 4TH INTERNATIONAL CONFERENCE ON RELIABILITY, INFOCOM TECHNOLOGIES AND OPTIMIZATION (ICRITO) (TRENDS AND FUTURE DIRECTIONS), DOI 10.1109/ICRITO.2015.7359270
   Usbeck R, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1133, DOI 10.1145/2736277.2741626
   Usbeck R, 2014, LECT NOTES COMPUT SC, V8796, P457, DOI 10.1007/978-3-319-11964-9_29
   VANDENBERGE JH, 1979, J NEUROL NEUROSUR PS, V42, P1163, DOI 10.1136/jnnp.42.12.1163
   Velasco D. G., 2008, NOUN PHRASE FUNCTION, P195
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Zouaq A, 2017, INFORM SYST, V71, P228, DOI 10.1016/j.is.2017.08.008
   Zuo Z., 2014, P 25 INT C COMP LING, P2075
NR 65
TC 28
Z9 28
U1 4
U2 130
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD DEC 15
PY 2018
VL 113
BP 339
EP 355
DI 10.1016/j.eswa.2018.07.017
PG 17
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA GV7FY
UT WOS:000446288600023
DA 2022-08-23
ER

PT S
AU Reidsma, D
AF Reidsma, D
BE Priss, U
   Corbett, D
   Angelova, G
TI Automatic generation and evaluation of sentence graphs out of word
   graphs
SO CONCEPTUAL STRUCTURES: INTEGRATION AND INTERFACES, PROCEEDINGS
SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
LA English
DT Article; Proceedings Paper
CT 10th International Conference on Conceptual Structures (ICCS 2002)
CY JUL, 2002
CL BOROVETS, BULGARIA
SP European Commiss, Cent Lab, Bulgarian Acad Sci
AB This paper reports on the development of a system that automatically constructs representations of the meaning of sentences using rules of grammar and a dictionary of word meanings. The meanings of words and sentences are expressed using an extension of knowledge graphs, a semantic network formalism. Furthermore the system contains an algorithm that calculates an evaluation measure for alternative sentence graphs of one sentence, making it possible to disambiguate the meaning of sentences on semantical grounds.
C1 Univ Twente, Dept Comp Sci, Parlevink Grp, Enschede, Netherlands.
RP Reidsma, D (通讯作者)，Univ Twente, Dept Comp Sci, Parlevink Grp, Enschede, Netherlands.
OI Reidsma, Dennis/0000-0002-7503-573X
CR GAMUT LTF, LOGIC LANGUAGE MEANI, V2
   HOEDE C, 1526 U TWENT
   HOEDE C, 2000, 1527 U TWENT
   REIDSMA D, 2001, THESIS U TWENTE
   VANDENBERG H, 1993, THESIS U TWENTE
   WILLEMS M, 1993, THESIS U TWENTE
NR 6
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-43901-3
J9 LECT NOTES ARTIF INT
PY 2002
VL 2393
BP 151
EP 165
PG 15
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BW13V
UT WOS:000180979400012
DA 2022-08-23
ER

PT J
AU Mubarak, AA
   Cao, H
   Hezam, IM
   Hao, F
AF Mubarak, Ahmed A.
   Cao, Han
   Hezam, Ibrahim M.
   Hao, Fei
TI Modeling students' performance using graph convolutional networks
SO COMPLEX & INTELLIGENT SYSTEMS
LA English
DT Article
DE Course MOOC; Graph convolutional network; Heterogeneous knowledge graph;
   Semi-classification; Prediction
ID OPEN ONLINE COURSES; LEARNING ANALYTICS; ENGAGEMENT
AB Many models were recently proposed to classify students, relying on a large amount of pre-labeled data to verify their classification effectiveness. However, those models lack to accurately classify students into various behavioral patterns, employing nominal class labels, rather than ordinal ones. Meanwhile, such models cannot analyze high-dimensional learning behaviors among learners according to students' interaction with course videos. Since online learning data are huge, the main challenges associated with data are insufficient labeling and classification using nominal class labels. In this study, we proposed a model based on Graph Convolutional Network, as a semi-supervised classification task to classify students' engagement in various behavioral patterns. First, we proposed a label function to label datasets instead of manual labeling, in which input and output data are labeled for classification to provide a learning foundation for future data processing. Accordingly, we hypothesized four behavioral patterns, namely ("High-engagement", "Normal-engagement", "At-risk", and "Potential-At-risk") based on students' engagement with course videos and their performance on the assessments/quizzes conducted after. Then, we built a heterogeneous knowledge graph representing learners, course videos as entities, and capturing semantic relationships among students according to shared knowledge concepts in videos. Our model intrinsically works for heterogeneous knowledge graphs as a semi-supervised node classification task. It was evaluated on a real-world dataset across multiple settings to achieve a better predictive classification model. Experiment results showed that the proposed model can predict with an accuracy of 84% and an f1-score of 78% compared to baseline approaches.
C1 [Mubarak, Ahmed A.; Cao, Han] Shaanxi Normal Univ, Sch Comp & Sci, Xian 710119, Peoples R China.
   [Mubarak, Ahmed A.] Ibb Univ, Dept Informat Technol, Ibb, Yemen.
   [Hezam, Ibrahim M.] King Saud Univ, Coll Sci, Stat & Operat Res Dept, Riyadh, Saudi Arabia.
   [Hao, Fei] Univ Exeter, Dept Comp Sci, Exeter Ex4 4QF, Devon, England.
RP Hezam, IM (通讯作者)，King Saud Univ, Coll Sci, Stat & Operat Res Dept, Riyadh, Saudi Arabia.
EM ahmedmubarak@ibbuniv.edu.ye; caohan@snnu.edu.cn;
   ialmishnanah@ksu.edu.sa; feehao@gmail.com
RI Hezam, Ibrahim M./N-3759-2019; Hezam, Ibrahim M./K-5312-2015
OI Hezam, Ibrahim M./0000-0003-2747-6722; 
FU King Saud University, Riyadh, Saudi Arabia [RSP-2021/389]
FX This work was supported by the Research Supporting Project Number
   (RSP-2021/389), King Saud University, Riyadh, Saudi Arabia.
CR Angrave L, 2020, SIGCSE 2020: PROCEEDINGS OF THE 51ST ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, P1193, DOI 10.1145/3328778.3366953
   Aouifi H El, 2020, 4 INT C INT COMP DAT
   Bruna J., 2014, 2 INT C LEARNING REP
   Gonzalez JAC, 2020, KNOWL MANAG E-LEARN, V12, P129
   Chi MTH, 2014, EDUC PSYCHOL-US, V49, P219, DOI 10.1080/00461520.2014.965823
   Chien SY, 2020, COMPUT EDUC, V146, DOI 10.1016/j.compedu.2019.103751
   Defferrard M., 2016, 30 INT C NEURAL INFO, P3844
   Ferguson R, 2012, INT J TECHNOL ENHANC, V4, P304, DOI 10.1504/IJTEL.2012.051816
   Grattarola D., 2020, GRAPH NEURAL NETWORK
   Gray CC, 2019, COMPUT EDUC, V131, P22, DOI 10.1016/j.compedu.2018.12.006
   Hmedna B, 2020, CLUSTER COMPUT, V23, P1303, DOI 10.1007/s10586-019-02992-4
   Hu Q., 2019, P 12 INT C ED DAT MI
   Jayaprakash S.M., 2014, J LEARN ANAL, V1, P6, DOI DOI 10.18608/JLA.2014.11.3
   Jordan K, 2015, INT REV RES OPEN DIS, V16, DOI 10.19173/irrodl.v16i3.2112
   Jung Y, 2018, COMPUT EDUC, V122, P9, DOI 10.1016/j.compedu.2018.02.013
   Kamath A, 2016, IEEE WINT CONF APPL
   Keyvanpour MR, 2021, MULTIMED TOOLS APPL, V80, P13879, DOI 10.1007/s11042-020-10418-2
   Kim D, 2019, BRIT J EDUC TECHNOL, V50, P3420, DOI 10.1111/bjet.12746
   Kim D, 2018, COMPUT EDUC, V127, P233, DOI 10.1016/j.compedu.2018.08.023
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Kostopoulos G, 2020, DEEP DENSE NEURAL NE, P291
   Lan A. S., 2017, INT C ED DAT MIN WUH, P64
   Li HT, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P2589, DOI 10.1145/3340531.3412733
   Li QM, 2018, AAAI CONF ARTIF INTE, P3538
   Liu MZ, 2019, TECHTRENDS, V63, P621, DOI 10.1007/s11528-019-00409-y
   Moreno-Marcos PM, 2019, IEEE T LEARN TECHNOL, V12, P384, DOI 10.1109/TLT.2018.2856808
   Min Tu, 2020, Journal of Physics: Conference Series, V1629, DOI 10.1088/1742-6596/1629/1/012067
   Moubayed A, 2020, AM J DISTANCE EDUC, V34, P137, DOI 10.1080/08923647.2020.1696140
   Mubarak AA, 2021, COMPUT APPL ENG EDUC, V29, P710, DOI 10.1002/cae.22328
   Mubarak AA, 2020, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2020.1727529
   Mubarak AA, 2021, EDUC INF TECHNOL, V26, P371, DOI 10.1007/s10639-020-10273-6
   Oriogun P. K., 2003, Australian Journal of Educational Technology, V19, P371
   Pu HT, 2021, NEURAL COMPUT APPL, V33, P637, DOI 10.1007/s00521-020-05045-9
   Qiu L, 2019, SOFT COMPUT, V23, P10287, DOI 10.1007/s00500-018-3581-3
   Ratner Alexander, 2016, Adv Neural Inf Process Syst, V29, P3567
   Re C., 2016, HILDA 16 SIGMOD, DOI [10.1145/2939502.2939515, DOI 10.1145/2939502.2939515]
   Rybakova K, 2019, TECHNOL PEDAGOG EDUC, V28, P575, DOI 10.1080/1475939X.2019.1683065
   Sawang S, 2017, J LEARN DES, V10, P11
   Schlechty PC, 2011, ENGAGING STUDENTS NE
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Shi C, 2019, IEEE T KNOWL DATA EN, V31, P357, DOI 10.1109/TKDE.2018.2833443
   Stanford University, 2017, CAROL LEARN DAT DOC
   Trowler V., 2010, HIGHER ED ACAD, V11, P1, DOI DOI 10.1037/0022-0663.85.4.571.-15
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Verikas A, 2011, PATTERN RECOGN, V44, P330, DOI 10.1016/j.patcog.2010.08.011
   Waheed H, 2020, COMPUT HUM BEHAV, V104, DOI 10.1016/j.chb.2019.106189
   Wu XD, 2008, KNOWL INF SYST, V14, P1, DOI 10.1007/s10115-007-0114-2
   Yang S, 2020, DEEP KNOWLEDGE TRACI, P17
   Zha ZJ, 2009, J VIS COMMUN IMAGE R, V20, P97, DOI 10.1016/j.jvcir.2008.11.009
   Zhang H, 2017, WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P331, DOI 10.1145/3041021.3054166
   Zhu X, 2005, EUR SP AGENCY SPECIA, V2, P1
NR 51
TC 0
Z9 0
U1 6
U2 6
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 2199-4536
EI 2198-6053
J9 COMPLEX INTELL SYST
JI COMPLEX INTELL. SYST.
PD JUN
PY 2022
VL 8
IS 3
SI SI
BP 2183
EP 2201
DI 10.1007/s40747-022-00647-3
EA JAN 2022
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1P7VN
UT WOS:000743847600003
PM 35070641
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Bonner, S
   Kirik, U
   Engkvist, O
   Tang, J
   Barrett, IP
AF Bonner, Stephen
   Kirik, Ufuk
   Engkvist, Ola
   Tang, Jian
   Barrett, Ian P.
TI Implications of topological imbalance for representation learning on
   biomedical knowledge graphs
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Article; Early Access
DE knowledge graph embeddings; disease gene prediction; drug target
   discovery
ID INFORMATION
AB Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KGs) that utilize the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modelling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modelling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.
C1 [Bonner, Stephen] AstraZeneca, Data Sci & Quantitat Biol, Discovery Sci, Cambridge, England.
   [Kirik, Ufuk] AstraZeneca, Gothenburg, Sweden.
   [Engkvist, Ola] AstraZeneca Gothenburg, Discovery Sci, Mol AI Dept, Gothenburg, Sweden.
   [Engkvist, Ola] Chalmers Univ Technol, Machine Learning & AI Mol Design, Gothenburg, Sweden.
   [Tang, Jian] Mila Quebec AI Inst, Montreal, PQ, Canada.
   [Tang, Jian] HEC Montreal Canada, Montreal, PQ, Canada.
   [Barrett, Ian P.] AstraZeneca, Data Sci & Quantitat Biol Dept, Discovery Sci, Cambridge, England.
RP Bonner, S (通讯作者)，AstraZeneca, R&D, Data Sci & Quantitat Biol, Discovery Sci, Cambridge, England.; Kirik, U (通讯作者)，AstraZeneca, R&D, Data Sci & Quantitat Biol, Discovery Sci, Gothenburg, Sweden.
EM stephen.bonner1@astrazeneca.com; ufuk.kirik@astrazeneca.com
CR Ali M., 2020, IEEE T PATTERN ANAL
   Arduini M., 2020, ARXIV
   Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509
   Bonner S., 2022, ARTIF INTELL
   Bonner S., 2021, ARXIV
   Bourli S, 2020, PR I-A I C AD S N A, P6, DOI 10.1109/ASONAM49781.2020.9381459
   Breit A, 2020, BIOINFORMATICS, V36, P4097, DOI 10.1093/bioinformatics/btaa274
   Buniello A, 2019, NUCLEIC ACIDS RES, V47, pD1005, DOI 10.1093/nar/gky1120
   Cook D, 2014, NAT REV DRUG DISCOV, V13, P419, DOI 10.1038/nrd4309
   Nguyen DT, 2017, NUCLEIC ACIDS RES, V45, pD995, DOI 10.1093/nar/gkw1072
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ebrahim A, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms13091
   Gaudelet T, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbab159
   Goodwin S, 2016, NAT REV GENET, V17, P333, DOI 10.1038/nrg.2016.49
   Hasin Y, 2017, GENOME BIOL, V18, DOI 10.1186/s13059-017-1215-1
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Kimura Y, 2010, J BIOCHEM, V147, P793, DOI 10.1093/jb/mvq044
   Kojaku S., 2021, 35 C NEURAL INFORM P
   Liu YS, 2021, LECT NOTES COMPUT SC, V12731, P375, DOI 10.1007/978-3-030-77385-4_22
   Liu ZM, 2021, KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1109, DOI 10.1145/3447548.3467276
   Liu ZM, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P975, DOI 10.1145/3340531.3411910
   Mahdisoltani F., 2014, 7 BIENN C INN DAT SY
   Mohamed A, 2020, PR MACH LEARN RES, V124, P1059
   Morgan P, 2018, NAT REV DRUG DISCOV, V17, P167, DOI 10.1038/nrd.2017.244
   Newman M., 2018, NETWORKS
   Paliwal S, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-74922-z
   Pinero J, 2017, NUCLEIC ACIDS RES, V45, pD833, DOI 10.1093/nar/gkw943
   Pletscher-Frankild S, 2015, METHODS, V74, P83, DOI 10.1016/j.ymeth.2014.11.020
   Rahman T, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3289
   Ran FA, 2013, NAT PROTOC, V8, P2281, DOI 10.1038/nprot.2013.143
   Ratajczak F., 2021, REPORT
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Ruffinelli D., 2019, INT C LEARNING REPRE
   Sun Z., 2019, 7 INT C LEARN REPR
   Tang XF, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P1435, DOI 10.1145/3340531.3411872
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang N., 2022, P ACM WEB C 2022, P1423
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Yang B., 2015, ICLR
   Zhang CX, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P793, DOI 10.1145/3292500.3330961
   Zietz Michael, 2020, MANUBOT TECH REP
NR 45
TC 0
Z9 0
U1 3
U2 3
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1467-5463
EI 1477-4054
J9 BRIEF BIOINFORM
JI Brief. Bioinform.
DI 10.1093/bib/bbac279
EA JUL 2022
PG 14
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA 3J7JF
UT WOS:000833569000001
PM 35880623
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Li, XY
   Lyu, MT
   Wang, ZX
   Chen, CH
   Zheng, P
AF Li, Xinyu
   Lyu, Mengtao
   Wang, Zuoxu
   Chen, Chun-Hsien
   Zheng, Pai
TI Exploiting knowledge graphs in industrial products and services: A
   survey of key aspects, challenges, and future perspectives
SO COMPUTERS IN INDUSTRY
LA English
DT Article
DE Knowledge graph; Product development; Service innovation; Knowledge
   management; Product-service systems; Review
ID SEMANTIC SIMILARITY; ONTOLOGY EVOLUTION; FRAMEWORK; CONSTRUCTION;
   NETWORK; BASE; MANAGEMENT; DESIGN; SYSTEM; AI
AB The rapid development of information and communication technologies has enabled a value co-creation paradigm for developing industrial products and services, where massive heterogeneous data and multidisciplinary knowledge are generated and leveraged. In this context, Knowledge Graph (KG) emerges as a promising tool to elicit, fuse, process, and utilize numerous entities and relationships embedded in products and services, as well as their stakeholders. Nevertheless, to the best of the authors' knowledge, there is scarcely any comprehensive and thorough discussion about making full use of KG's potentials to solve pain points of product development and service innovation in the industry. Aiming to fill this gap, this paper conducted a systematic survey of KG exploitations in industrial products and services and the customizations towards higher adaptability to practices. The authors selected 119 representative papers (up to 10/03/2021) together with other 29 supplementary works to summarize the technical and practical efforts and discuss the current challenges of exploiting KG in industrial products and services. Meantime, this work also highlights enhancing KG's availability and boosting its productivity in industrial products and services development as the core future perspectives to explore. It is hoped that this work can provide a basis for the explorations and implementations of KG-supported industrial product and services development, and attract more open discussions to the exploitation of KG-enabled industrial information systems.
   (c) 2021 Elsevier B.V. All rights reserved.
C1 [Li, Xinyu; Lyu, Mengtao; Wang, Zuoxu; Chen, Chun-Hsien] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore.
   [Li, Xinyu] Nanyang Technol Univ, Sch Elect & Elect Engn, Delta NTU Corp Lab Cyber Phys Syst, Singapore 639798, Singapore.
   [Zheng, Pai] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hung Hom, Hong Kong 999077, Peoples R China.
RP Li, XY (通讯作者)，Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore.
EM Cash.li@ntu.edu.sg; m200019@e.ntu.edu.sg; zuoxu001@e.ntu.edu.sg;
   Mchchen@ntu.edu.sg; Pai.zheng@polyu.edu.hk
RI LI, XINYU/S-5783-2019; Mengtao, Lyu/ABG-2369-2021; ZHENG,
   PAI/K-7989-2012
OI LI, XINYU/0000-0002-4117-9698; Mengtao, Lyu/0000-0003-1505-1970; ZHENG,
   PAI/0000-0002-2329-8634; Wang, Zuoxu/0000-0003-2524-1217
FU Delta Electronics Inc; National Research Foundation (NRF) Singapore
   under the Corporate Laboratory @ University Scheme (SCO-RP1) at Nanyang
   Technological University, Singapore [RCA-16/434]
FX This work is conducted within the DeltaNTU Corporate Lab for
   CyberPhysical Systems with funding support from Delta Electronics Inc
   and the National Research Foundation (NRF) Singapore under the Corporate
   Laboratory @ University Scheme (Ref. RCA-16/434; SCO-RP1) at Nanyang
   Technological University, Singapore.
CR Abad-Navarro F, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10030861
   Abraham SS, 2019, NEW GENERAT COMPUT, V37, P551, DOI 10.1007/s00354-019-00067-x
   Ai QY, 2020, ACM T INFORM SYST, V38, DOI 10.1145/3361738
   Alemu EN, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2020.102299
   [Anonymous], 2020, IEEE ACCESS, DOI DOI 10.3390/MATH8112090
   Bakhshi M, 2020, EXPERT SYST APPL, V146, DOI 10.1016/j.eswa.2020.113205
   Basawaraj, 2019, IEEE T NEUR NET LEAR, V30, P3505, DOI 10.1109/TNNLS.2019.2927106
   Bellomarini L, 2018, PROC VLDB ENDOW, V11, P975, DOI 10.14778/3213880.3213888
   Bhatt S, 2020, IEEE INTERNET COMPUT, V24, P66, DOI 10.1109/MIC.2020.2979620
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Chaves-Fraga D, 2020, INT J SOFTW ENG KNOW, V30, P785, DOI 10.1142/S0218194020400070
   Chekol MW, 2017, PROC VLDB ENDOW, V10, P1929, DOI 10.14778/3137765.3137811
   Chen H., 2019, ADV ENG INFORM, V42
   Chen J, 2018, J WEB SEMANT, V52-53, P33, DOI 10.1016/j.websem.2018.09.001
   Chen YQ, 2020, INFORM SCIENCES, V515, P341, DOI 10.1016/j.ins.2019.12.033
   Cudre-Mauroux P, 2020, SEMANT WEB, V11, P13, DOI 10.3233/SW-190371
   Dai YF, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9050750
   Daruna A, 2021, IEEE ROBOT AUTOM LET, V6, P1128, DOI 10.1109/LRA.2021.3056071
   Denaux R, 2019, SEMANT WEB, V10, P881, DOI 10.3233/SW-190361
   Dou JH, 2018, J VISUAL LANG COMPUT, V48, P19, DOI 10.1016/j.jvlc.2018.06.005
   Eibeck A, 2019, COMPUT CHEM ENG, V131, DOI 10.1016/j.compchemeng.2019.106586
   Fan S., 2020, SCI PROGRAM, V2020, P1
   Fang YL, 2020, APPL INTELL, V50, P4281, DOI 10.1007/s10489-020-01761-w
   Farazi F, 2020, ACS OMEGA, V5, P18342, DOI 10.1021/acsomega.0c02055
   Fellbaum C., 2005, ENCY LANGUAGE LINGUI, P665
   Fernandez-Tobias I, 2019, USER MODEL USER-ADAP, V29, P443, DOI 10.1007/s11257-018-9217-6
   Fionda V, 2017, PROC VLDB ENDOW, V10, P1913, DOI 10.14778/3137765.3137807
   Fuji M, 2019, FUJITSU SCI TECH J, V55, P58
   Galkin M, 2017, ICEIS: PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS - VOL 2, P88, DOI 10.5220/0006325200880098
   Gao JY, 2019, PROC VLDB ENDOW, V12, P1679, DOI 10.14778/3342263.3342642
   Gottschalk S, 2019, SEMANT WEB, V10, P1039, DOI 10.3233/SW-190355
   Guo S, 2021, IEEE SENS J, V21, P5151, DOI 10.1109/JSEN.2020.3029098
   Guo XB, 2020, INFORM SCIENCES, V540, P263, DOI 10.1016/j.ins.2020.06.041
   Haase P, 2019, SEMANT WEB, V10, P1109, DOI 10.3233/SW-190360
   Han X, 2020, THEOR COMPUT SCI, V803, P48, DOI 10.1016/j.tcs.2019.03.033
   Hastings J, 2013, NUCLEIC ACIDS RES, V41, pD456, DOI 10.1093/nar/gks1146
   He C., 2020, APPL SCI, V10
   Lan LTH, 2020, IEEE ACCESS, V8, P164899, DOI 10.1109/ACCESS.2020.3021097
   Huang WJ, 2020, WIREL NETW, DOI 10.1007/s11276-020-02387-z
   Huang WY, 2020, NEURAL COMPUT APPL, V32, P11343, DOI 10.1007/s00521-019-04630-x
   Huang ZY, 2020, COMPUT IND, V117, DOI 10.1016/j.compind.2020.103189
   Ji ZZ, 2020, IEICE T INF SYST, VE103D, P1362, DOI 10.1587/transinf.2019EDP7229
   Jia YT, 2018, KNOWL-BASED SYST, V151, P37, DOI 10.1016/j.knosys.2018.03.020
   Kazemi SM, 2020, J MACH LEARN RES, V21
   Kertkeidkachorn N, 2018, IEICE T INF SYST, VE101D, P90, DOI 10.1587/transinf.2017SWP0006
   Khoo L. P., 2019, ADV ENG INFORM, V42
   Kushida T, 2019, NEW GENERAT COMPUT, V37, P525, DOI 10.1007/s00354-019-00074-y
   Lawrynowicz A, 2020, SEMANT WEB, V11, P69, DOI 10.3233/SW-190377
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li K, 2017, VLDB J, V26, P177, DOI 10.1007/s00778-016-0446-1
   Li LF, 2020, ARTIF INTELL MED, V103, DOI 10.1016/j.artmed.2020.101817
   Li MD, 2021, NEUROCOMPUTING, V429, P77, DOI 10.1016/j.neucom.2020.12.012
   Li X, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100938
   Li XY, 2021, KNOWL-BASED SYST, V215, DOI 10.1016/j.knosys.2021.106739
   Li XY, 2021, J CLEAN PROD, V279, DOI 10.1016/j.jclepro.2020.123618
   Li XY, 2017, ADV ENG INFORM, V34, P17, DOI 10.1016/j.aei.2017.08.001
   Lin ZQ, 2017, J COMPUT SCI TECH-CH, V32, P242, DOI 10.1007/s11390-017-1718-y
   Liu BF, 2021, ROBOT CIM-INT MANUF, V70, DOI 10.1016/j.rcim.2021.102128
   Liu C., 2020, IEEE ACCESS, V8
   Liu D., 2020, DIVERSE CONTROLLABLE
   Liu JT, 2021, RELIAB ENG SYST SAFE, V207, DOI 10.1016/j.ress.2020.107352
   Liu Q, 2020, INCOMPLETE UTTERANA
   Liu WQ, 2018, NEUROCOMPUTING, V320, P12, DOI 10.1016/j.neucom.2018.08.070
   Liu Y., 2019, COMPLEXITY, V2019, P1
   Liu ZM, 2020, SYST RES BEHAV SCI, V37, P632, DOI 10.1002/sres.2706
   Long JW, 2020, APPL SOFT COMPUT, V91, DOI 10.1016/j.asoc.2020.106205
   Lu RQ, 2020, INFORM SCIENCES, V509, P87, DOI 10.1016/j.ins.2019.08.051
   Tosi MDL, 2022, J INF SCI, V48, P71, DOI 10.1177/0165551520937915
   Ma XG, 2020, COMPUT GEOSCI-UK, V145, DOI 10.1016/j.cageo.2020.104620
   Malik KM, 2020, EXPERT SYST APPL, V145, DOI 10.1016/j.eswa.2019.113120
   Mandisoltani F., 2015, YAG03 KNOWLEDGE BASE
   Miller J J, 2013, GRAPH DATABASE APPL
   Morton K, 2019, BIOINFORMATICS, V35, P5382, DOI 10.1093/bioinformatics/btz604
   Munoz E, 2019, BRIEF BIOINFORM, V20, P190, DOI 10.1093/bib/bbx099
   Nam D, 2018, COMPUTER, V51, P42, DOI 10.1109/MC.2018.2701635
   Nanduri J, 2020, INFORMS J APPL ANAL, V50, P64, DOI 10.1287/inte.2019.1017
   Natalicchio A, 2017, J KNOWL MANAG, V21, P1362, DOI 10.1108/JKM-11-2016-0516
   Navigli R, 2012, ARTIF INTELL, V193, P217, DOI 10.1016/j.artint.2012.07.001
   Nizzoli L, 2020, DECIS SUPPORT SYST, V136, DOI 10.1016/j.dss.2020.113346
   Osborne F, 2018, LECT NOTES COMPUT SC, V11136, P495, DOI 10.1007/978-3-030-00671-6_29
   Palumbo E., 2020, EXPERT SYST, V151, P18
   Pan J.Z., 2017, EXPLOITING LINKED DA
   Pernischova R., 2020, CHLIM VISUALIZIN ONT
   Pernischova R., 2020, CHLNVP VISUALIZING O
   Peroni S, 2017, J WEB SEMANT, V44, P37, DOI 10.1016/j.websem.2017.06.001
   Pessoa MVP, 2020, RES ENG DES, V31, P175, DOI 10.1007/s00163-020-00330-z
   Ponza M., 2020, KNOWLEDGE BASED SYST, V188, P22
   Purohit H, 2020, IEEE INTERNET COMPUT, V24, P48, DOI 10.1109/MIC.2020.3013683
   Putman TE, 2017, DATABASE-OXFORD, DOI 10.1093/database/bax025
   QUILLIAN MR, 1967, BEHAV SCI, V12, P410, DOI 10.1002/bs.3830120511
   Rahman MM, 2020, IEICE T INF SYST, VE103D, P958, DOI 10.1587/transinf.2019DAP0007
   Rasmussen M.H, 2019, AUTOM CONSTR, V108, P16
   RASMUSSEN MH, 2018, CEUR WORKSHOP P, V2159, P24
   Ristoski P, 2019, SEMANT WEB, V10, P721, DOI 10.3233/SW-180317
   Sarica S., 2020, EXPERT SYST APPL, V15, P142
   Shan SB, 2017, IET SOFTW, V11, P126, DOI 10.1049/iet-sen.2016.0189
   Sheth A, 2019, IEEE INTERNET COMPUT, V23, P67, DOI 10.1109/MIC.2019.2928449
   Shi C, 2021, FRONT COMPUT SCI-CHI, V15, DOI 10.1007/s11704-020-9240-8
   Shi H.-B., 2019, ENTERP MODEL INF SYS, P1
   Song MN, 2020, NEURAL COMPUT APPL, V32, P14963, DOI 10.1007/s00521-020-04851-5
   Tam CM, 2004, SAFETY SCI, V42, P569, DOI 10.1016/j.ssci.2003.09.001
   Tian HZ, 2020, J ADV MECH DES SYST, V14, DOI 10.1299/jamdsm.2020jamdsm0057
   Trouillon T, 2017, J MACH LEARN RES, V18
   Vogt L, 2019, DATABASE, V2019, P22
   Wan S, 2017, COMPUT IND, V84, P14, DOI 10.1016/j.compind.2016.11.002
   Wang FH, 2021, ADV ENG INFORM, V47, DOI 10.1016/j.aei.2020.101207
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang K, 2020, SIGNAL TRANSDUCT TAR, V5, DOI 10.1038/s41392-020-00426-x
   Wang Q, 2020, NEUROCOMPUTING, V399, P342, DOI 10.1016/j.neucom.2020.02.065
   Wang Q, 2020, KNOWL-BASED SYST, V197, DOI 10.1016/j.knosys.2020.105910
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang T, 2020, NEURAL COMPUT APPL, V32, P235, DOI 10.1007/s00521-018-3806-5
   Wang Y., 2020, KNOWLEDGE BASED SYST, V190, P13
   Wang Y, 2020, SCI PROGRAMMING-NETH, V2020, DOI 10.1155/2020/8812754
   Wang ZX, 2021, INT J PROD RES, V59, P635, DOI 10.1080/00207543.2019.1702227
   Wang ZX, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100983
   Wenige L, 2019, SEMANT WEB, V10, P1007, DOI 10.3233/SW-190353
   Wu MH, 2019, KNOWL INF SYST, V58, P1, DOI 10.1007/s10115-018-1282-y
   Wu QY, 2020, INT J SOFTW ENG KNOW, V30, P765, DOI 10.1142/S0218194020400069
   Wu TX, 2020, WORLD WIDE WEB, V23, P2671, DOI 10.1007/s11280-019-00719-4
   Wu ZY, 2018, COMPUT IND, V100, P43, DOI 10.1016/j.compind.2018.04.008
   Xie C, 2021, IEEE INTERNET THINGS, V8, P2635, DOI 10.1109/JIOT.2020.3019707
   Xiu XL, 2020, JMIR MED INF, V8, DOI 10.2196/18287
   Yang Y G, 2019, COMPLEXITY, V2019, P1
   Yin QY, 2018, IEEE T NEUR NET LEAR, V29, P5541, DOI 10.1109/TNNLS.2017.2786743
   Yuan JB, 2020, KNOWL INF SYST, V62, P317, DOI 10.1007/s10115-019-01351-4
   Zablith F, 2015, KNOWL ENG REV, V30, P45, DOI 10.1017/S0269888913000349
   Zeng WX, 2019, NEURAL PROCESS LETT, V50, P1861, DOI 10.1007/s11063-018-9966-6
   Zhang M, 2019, INTELL AUTOM SOFT CO, V25, P625, DOI 10.31209/2019.100000117
   Zhang Y., 2020, INF PROCESS MANAGE, V57
   Zhang Y, 2021, AD HOC NETW, V113, DOI 10.1016/j.adhoc.2020.102402
   Zhao F, 2020, COMPUT COMMUN, V159, P271, DOI 10.1016/j.comcom.2020.05.017
   Zhao YF, 2019, RELIAB ENG SYST SAFE, V186, P64, DOI 10.1016/j.ress.2019.02.014
   Zheng P, 2019, PROC CIRP, V83, P187, DOI 10.1016/j.procir.2019.02.129
   Zhou L, 2017, APPL ENERG, V204, P1284, DOI 10.1016/j.apenergy.2017.05.002
   Zhou XG, 2020, J COMPUT SCI TECH-CH, V35, P368, DOI 10.1007/s11390-020-9966-7
   Zhou XC, 2019, COMPUT CHEM ENG, V130, DOI 10.1016/j.compchemeng.2019.106577
   Zhu GG, 2018, EXPERT SYST APPL, V101, P8, DOI 10.1016/j.eswa.2018.02.011
   Zhu GG, 2017, KNOWL-BASED SYST, V130, P30, DOI 10.1016/j.knosys.2017.05.021
   Zhu Q, 2020, METHOD INFORM MED, V59, P131, DOI 10.1055/s-0040-1718940
   Zhu Y, 2020, LIBR HI TECH, V38, P741, DOI 10.1108/LHT-08-2019-0170
   Zhu YQ, 2017, SCI PROGRAMMING-NETH, V2017, DOI 10.1155/2017/5072427
NR 143
TC 20
Z9 20
U1 25
U2 61
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-3615
EI 1872-6194
J9 COMPUT IND
JI Comput. Ind.
PD AUG
PY 2021
VL 129
AR 103449
DI 10.1016/j.compind.2021.103449
EA APR 2021
PG 14
WC Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA RZ8WD
UT WOS:000648879500004
DA 2022-08-23
ER

PT J
AU Zhang, JY
   Liu, JX
   Chen, YQ
   Feng, XC
   Sun, ZL
AF Zhang, Jingyi
   Liu, Jiaxin
   Chen, Yaqi
   Feng, Xiaochun
   Sun, Zilai
TI Knowledge Mapping of Machine Learning Approaches Applied in Agricultural
   Management-A Scientometric Review with CiteSpace
SO SUSTAINABILITY
LA English
DT Review
DE emerging trends; visualization; research hotspot; Web of Science
ID EMERGING TRENDS; RANDOM FOREST; PREDICTION; REGRESSION; ALGORITHM;
   MODELS
AB With the continuous development of the Internet of Things, artificial intelligence, big data technology, and intelligent agriculture have become hot topics in agricultural science and technology research. Machine learning is one of the core topics in artificial intelligence, and its application has penetrated every aspect of human social life. In modern agricultural intelligent management and decision making, machine learning plays an important role in crop classification, crop disease and insect pest prediction, agricultural product price prediction, and other aspects of management and decision-making processes in agriculture. To detect and recognize the latest research developing features in a quantitative and visual way, and based on machine learning methods in agricultural management, the authors of this paper used CiteSpace bibliometric methods to analyze relevant studies on the development process and hot spots. High-value references, productive authors, country and institution distributions, journal visualizations, research topics, and emerging trends were reviewed and analyzed. According to the keyword visualization and high-value references, machine learning approaches focus on sustainable agriculture, water resources, remote sensing, and machine learning methods. The research mainly focuses on six topics: learning technology, land environment, reference evapotranspiration, decision support systems for river geography, soil management, and winter wheat, while learning technology has been the most popular in recent years.
C1 [Zhang, Jingyi; Liu, Jiaxin; Chen, Yaqi; Feng, Xiaochun; Sun, Zilai] Northwest A&F Univ, Coll Econ & Management, Xianyang 712100, Peoples R China.
RP Feng, XC (通讯作者)，Northwest A&F Univ, Coll Econ & Management, Xianyang 712100, Peoples R China.
EM 2018012805@nwafu.edu.cn; 2018013443@nwafu.edu.cn; chenyaqi@nwafu.edu.cn;
   xiaochunf@nwafu.edu.cn; sunzilai@mail.dlut.edu.cn
FU sub-project of the National Key Research and Development Program
   [2019YFD1101103]; Natural Science Foundation Research Project of Shaanxi
   Province [2020JQ-281]; Annual Social Science Foundation Project of
   Shaanxi Province [2020R002]; Scientific Research Startup Foundation of
   Northwest AF University [2452019167]; Chinese Universities Scientific
   Fund [2452020068]; Science Foundation for Distinguished Youth of Shaanxi
   Province [2021JC-21]
FX This study was supported by the sub-project of the National Key Research
   and Development Program (2019YFD1101103), Natural Science Foundation
   Research Project of Shaanxi Province (2020JQ-281), Annual Social Science
   Foundation Project of Shaanxi Province (2020R002), Scientific Research
   Startup Foundation of Northwest A&F University (2452019167), Chinese
   Universities Scientific Fund (2452020068),and Science Foundation for
   Distinguished Youth of Shaanxi Province (2021JC-21).
CR Abdullah SS, 2015, J HYDROL, V527, P184, DOI 10.1016/j.jhydrol.2015.04.073
   Akhter R, 2021, J KING SAUD U COMPUT, DOI [10.1016/j.jksuci.2021.05.013, DOI 10.1016/J.JKSUCI.2021.05.013]
   Ali M, 2018, ATMOS RES, V213, P450, DOI 10.1016/j.atmosres.2018.07.005
   Ayaz M, 2019, IEEE ACCESS, V7, P129551, DOI 10.1109/ACCESS.2019.2932609
   Belgiu M, 2016, ISPRS J PHOTOGRAMM, V114, P24, DOI 10.1016/j.isprsjprs.2016.01.011
   Berner LT, 2012, BIOGEOSCIENCES, V9, P3943, DOI 10.5194/bg-9-3943-2012
   Bhavsar H., 2012, INT J SOFT COMPUT EN, V2, P2231
   Biro K, 2013, LAND DEGRAD DEV, V24, P90, DOI 10.1002/ldr.1116
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen Y., 2015, STUDIES SCI SCI, V33, P242
   Cui NB, 2008, AGR WATER MANAGE, V95, P489, DOI 10.1016/j.agwat.2007.11.007
   Duarte-Carvajalino JM, 2018, REMOTE SENS-BASEL, V10, DOI 10.3390/rs10101513
   Elith J, 2008, J ANIM ECOL, V77, P802, DOI 10.1111/j.1365-2656.2008.01390.x
   Godfray HCJ, 2010, SCIENCE, V327, P812, DOI 10.1126/science.1185383
   Hu XP, 2020, FRONT ENG MANAG, V7, P309, DOI 10.1007/s42524-020-0107-3
   Jordan MI, 2015, SCIENCE, V349, P255, DOI 10.1126/science.aaa8415
   Kamilaris A, 2018, COMPUT ELECTRON AGR, V147, P70, DOI 10.1016/j.compag.2018.02.016
   Khanal S, 2021, SOIL TILL RES, V208, DOI 10.1016/j.still.2020.104880
   Kisi O, 2018, AGR FOREST METEOROL, V263, P41, DOI 10.1016/j.agrformet.2018.08.007
   LeCun Y., 2015, NATURE, V521, P436, DOI [10.1038/nature14539, DOI 10.1038/NATURE14539]
   Lee YC, 2016, APPL SCI-BASEL, V6, DOI 10.3390/app6010011
   Liakos KG, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18082674
   Mazzia V, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20092530
   Neshat A, 2014, ENVIRON EARTH SCI, V71, P3119, DOI 10.1007/s12665-013-2690-7
   Oliveira I, 2018, P IEEE INT C E-SCI, P423, DOI 10.1109/eScience.2018.00131
   Pradhan B, 2012, ENVIRON MONIT ASSESS, V184, P715, DOI 10.1007/s10661-011-1996-8
   Pradhan B, 2010, ENVIRON MODELL SOFTW, V25, P747, DOI 10.1016/j.envsoft.2009.10.016
   Prasad R, 2018, GEODERMA, V330, P136, DOI 10.1016/j.geoderma.2018.05.035
   Priya R, 2020, SUSTAIN COMPUT-INFOR, V28, DOI 10.1016/j.suscom.2020.100439
   Ruan JH, 2019, IEEE WIREL COMMUN, V26, P56, DOI 10.1109/MWC.001.1900096
   Sharma R, 2020, COMPUT OPER RES, V119, DOI 10.1016/j.cor.2020.104926
   Sonobe R, 2017, GISCI REMOTE SENS, V54, P918, DOI 10.1080/15481603.2017.1351149
   Sonobe R, 2014, INT J REMOTE SENS, V35, P7898, DOI 10.1080/01431161.2014.978038
   Synnestvedt Marie B, 2005, AMIA Annu Symp Proc, P724
   Tabari H, 2012, J HYDROL, V444, P78, DOI 10.1016/j.jhydrol.2012.04.007
   Tang DH, 2018, COMPUT ELECTRON AGR, V152, P375, DOI 10.1016/j.compag.2018.07.029
   Tao H, 2018, AGR WATER MANAGE, V208, P140, DOI 10.1016/j.agwat.2018.06.018
   Tilman D, 2011, P NATL ACAD SCI USA, V108, P20260, DOI 10.1073/pnas.1116437108
   Wuest T, 2016, PROD MANUF RES, V4, P23, DOI 10.1080/21693277.2016.1192517
NR 40
TC 0
Z9 0
U1 30
U2 93
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUL
PY 2021
VL 13
IS 14
AR 7662
DI 10.3390/su13147662
PG 15
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA TO4NK
UT WOS:000676890300001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Shahbaz, M
   Bashir, MF
   Bashir, MA
   Shahzad, L
AF Shahbaz, Muhammad
   Bashir, Muhammad Farhan
   Bashir, Muhammad Adnan
   Shahzad, Luqman
TI A bibliometric analysis and systematic literature review of
   tourism-environmental degradation nexus
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Review
DE Tourism; Bibliometric analysis; Massive literature data; Frequency and
   co-occurrence analysis; Research trends
ID LED GROWTH HYPOTHESIS; CO2 EMISSIONS; ECONOMIC-GROWTH;
   ENERGY-CONSUMPTION; INTERNATIONAL TOURISM; CLIMATE-CHANGE; ECOTOURISM;
   PANEL; INVESTMENT; RESOURCES
AB This study aims to provide a comprehensive knowledge map of tourism and environmental degradation literature review based on scientific articles published between 1999 and 2020. The study provides an overview of research, influential authors, and journals. The PR China has maintained the lead in academic research with the most contribution in academic research in recent years. Sustainability, the Chinese Academy of Sciences, and Katircioglu are the most productive journal, academic institution, and author in the field of tourism and environmental degradation, respectively. The degree of research contribution among researchers, institutional and national level, has an upward trend in recent years, but the research contribution among author level is higher than the institutional and national level. Furthermore, co-citation analysis suggests that research articles in the field are closely related. "Investigating the influence of tourism on economic growth and carbon emissions: Evidence from panel analysis of the European Union" authored by Lee is the most cited article. Our analysis of abstracts and keywords shows that climate change, ecotourism, carbon emissions, economic growth, and energy consumption are the hot spots of academic literature. We suggest that research collaboration between developed and developing nations should be promoted in creating sustainable tourism reforms.
C1 [Shahbaz, Muhammad] Beijing Inst Technol, Sch Management & Econ, Beijing, Peoples R China.
   [Shahbaz, Muhammad] Univ Econ Ho Chi Minh City, Inst Business Res, Ho Chi Minh City, Vietnam.
   [Bashir, Muhammad Farhan; Shahzad, Luqman] Cent South Univ, Business Sch, Changsha 410083, Hunan, Peoples R China.
   [Bashir, Muhammad Adnan] Nankai Univ, Sch Econ, Tianjin, Peoples R China.
RP Bashir, MF (通讯作者)，Cent South Univ, Business Sch, Changsha 410083, Hunan, Peoples R China.
EM muhdshahbaz77@gmail.com; farhan.paks89@gmail.com;
   adnanbashir2034@gmail.com; luqmanshahzad1975@gmail.com
RI Bashir, Muhammad Farhan/R-3086-2019
OI Bashir, Muhammad Farhan/0000-0001-5103-4639; Bahir, Muhammad
   Adnan/0000-0002-0342-5528; Shahzad, Luqman/0000-0001-9802-8519
CR Ahmad F, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10103520
   Aimin Z, 2008, MOD INF, V28, P30
   Al-Mulali U, 2015, ANATOLIA, V26, P230, DOI 10.1080/13032917.2014.934701
   [Anonymous], 2010, S ASIAN J TOUR HERIT
   Antonakakis N, 2016, ANN TOURISM RES, V61, P258, DOI 10.1016/j.annals.2016.09.018
   Apergis N, 2015, ECOL INDIC, V52, P16, DOI 10.1016/j.ecolind.2014.11.026
   Arbulu I, 2015, WASTE MANAGE, V46, P628, DOI 10.1016/j.wasman.2015.04.014
   Barkauskiene K., 2013, EC MANAGEMENT, V18, P449
   Bashir, 2021, EXPERT SYST APPL, V178
   Bashir MF, 2015, NEXUS, V13
   Bashir MF, 2018, BUS ECO J, V9, P2
   Bashir MA, 2020, GLOB LOCAL ECON REV, V24, P67
   Bashir MF, 2021, ENERGY, V226, DOI 10.1016/j.energy.2021.120366
   Bashir MF, 2021, ENVIRON SCI POLLUT R, V28, P37016, DOI 10.1007/s11356-021-14621-y
   Bashir MF, 2021, ENVIRON SCI POLLUT R, V28, P16982, DOI 10.1007/s11356-021-13110-6
   Bashir MF, 2021, ENVIRON SCI POLLUT R, V28, P20700, DOI 10.1007/s11356-020-12123-x
   Bashir MF, 2021, MANAG DECIS ECON, V42, P570, DOI 10.1002/mde.3255
   Bashir MF, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0242412
   Becken S., 2005, Tourism, recreation and climate change, P192
   Ben Jebli M, 2018, INT J SUST DEV WORLD, V25, P568, DOI 10.1080/13504509.2018.1434572
   Bobicki ER, 2012, PROG ENERG COMBUST, V38, P302, DOI 10.1016/j.pecs.2011.11.002
   Buckley R., 2004, Environmental impacts of ecotourism, DOI 10.1079/9780851998107.0000
   Bestard AB, 2007, TOURISM MANAGE, V28, P688, DOI 10.1016/j.tourman.2006.04.004
   BUTLER RW, 1991, ENVIRON CONSERV, V18, P201, DOI 10.1017/S0376892900022104
   CALLON M, 1991, SCIENTOMETRICS, V22, P155, DOI 10.1007/BF02019280
   Chen LJ, 2017, INT J PROD ECON, V194, P73, DOI 10.1016/j.ijpe.2017.04.005
   De Bakker F.G., 2005, BUS SOC, V44, P283, DOI DOI 10.1177/0007650305278086
   Dogan E, 2017, CURR ISSUES TOUR, V20, P1701, DOI 10.1080/13683500.2015.1119103
   Eshetu A., 2014, J ENV SCI WATER RESO, V3, P40
   Gautam B. P., 2011, NRB EC REV, V23, P18
   Golkarian, 2019, ECOPERSIA, V7, P115
   Gossling S., 2000, J SUSTAIN TOUR, V8, P410, DOI DOI 10.1080/09669580008667376
   Gossling S, 2018, J SUSTAIN TOUR, V26, P2071, DOI 10.1080/09669582.2018.1529770
   Gossling S, 2015, J SUSTAIN TOUR, V23, P639, DOI 10.1080/09669582.2015.1008500
   Gossling S, 2013, ENERG POLICY, V59, P433, DOI 10.1016/j.enpol.2013.03.058
   Hall CM., 2014, CLIMATE CHANGE TOURI, P27
   Hamilton JM, 2005, CLIM RES, V29, P245, DOI 10.3354/cr029245
   Hamilton JM, 2005, GLOBAL ENVIRON CHANG, V15, P253, DOI 10.1016/j.gloenvcha.2004.12.009
   Hedlund J, 2018, GLOBAL ENVIRON CHANG, V52, P75, DOI 10.1016/j.gloenvcha.2018.04.006
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Hussain M, 2021, WORLD J ENTREP MANAG, V17, P62, DOI 10.1108/WJEMSD-10-2019-0085
   Hye QMA, 2013, ASIA PAC J TOUR RES, V18, P303, DOI 10.1080/10941665.2012.658412
   Jalani JO, 2012, PROCD SOC BEHV, V57, P247, DOI 10.1016/j.sbspro.2012.09.1182
   Katircioglu ST, 2014, ECON MODEL, V41, P383, DOI 10.1016/j.econmod.2014.05.028
   Katircioglu ST, 2014, RENEW SUST ENERG REV, V36, P180, DOI 10.1016/j.rser.2014.04.058
   Khan M, 2003, ANN TOURISM RES, V30, P109, DOI 10.1016/S0160-7383(02)00032-4
   Kibat, 2016, TOUR LEIS GLOBAL CHA, V1, P38
   Lai, 2012, EFFECT TOURISM DEV E
   Laudati AA, 2010, SOC NATUR RESOUR, V23, P776, DOI 10.1080/08941920903278111
   Lee CC, 2008, TOURISM MANAGE, V29, P180, DOI 10.1016/j.tourman.2007.02.013
   Lee JW, 2013, TOURISM MANAGE, V38, P69, DOI 10.1016/j.tourman.2013.02.016
   Lenzen M, 2018, NAT CLIM CHANGE, V8, P522, DOI 10.1038/s41558-018-0141-x
   Leon CJ, 2014, APPL ECON LETT, V21, P1169, DOI 10.1080/13504851.2014.916376
   Lijia ZW, 2008, J INF, V5, P155
   Liu B, 2018, 2018 4 INT C ED TECH
   Liu JY, 2014, TOURISM MANAGE, V41, P190, DOI 10.1016/j.tourman.2013.08.016
   Liu JS., 2004, INTRO SOCIAL NETWORK, P92
   Malik MK, 2007, PAK DEV REV, V46, P985, DOI [10.30541/v46i4IIpp.985-995, DOI 10.30541/V46I4IIPP.985-995]
   Malik S., 2010, EUROPEAN J EC FINANC, V22, P21
   Mamatzakis EC, 2007, J POLICY MODEL, V29, P335, DOI 10.1016/j.jpolmod.2006.12.002
   Mao GZ, 2015, RENEW SUST ENERG REV, V48, P276, DOI 10.1016/j.rser.2015.03.094
   Martin RA, 2007, FISH RES, V84, P10, DOI 10.1016/j.fishres.2006.11.010
   Michailidou AV, 2016, TOURISM MANAGE, V55, P1, DOI 10.1016/j.tourman.2016.01.010
   Mikayilov JI, 2019, ENVIRON SCI POLLUT R, V26, P19389, DOI 10.1007/s11356-019-05269-w
   Mishra P. K., 2011, EUROPEAN J SOCIAL SC, V18, P518
   Mora C, 2018, NAT CLIM CHANGE, V8, P1062, DOI 10.1038/s41558-018-0315-6
   Mowforth M., 2015, TOURISM SUSTAINABILI, V4th
   Nepal SK, 2008, TOURISM MANAGE, V29, P89, DOI 10.1016/j.tourman.2007.03.024
   Ng TH, 2016, INT J SUST DEV WORLD, V23, P266, DOI 10.1080/13504509.2015.1117534
   Nyaupane GP, 2011, ANN TOURISM RES, V38, P1344, DOI 10.1016/j.annals.2011.03.006
   Paramati SR, 2017, J TRAVEL RES, V56, P712, DOI 10.1177/0047287516667848
   Paramati SR, 2017, TRANSPORT RES D-TR E, V50, P1, DOI 10.1016/j.trd.2016.10.034
   Priskin J, 2001, TOURISM MANAGE, V22, P637, DOI 10.1016/S0261-5177(01)00039-5
   Raza SA, 2017, CURR ISSUES TOUR, V20, P1768, DOI 10.1080/13683500.2016.1192587
   Reimer JK, 2013, TOURISM MANAGE, V34, P122, DOI 10.1016/j.tourman.2012.04.002
   Renlei YSZJ, 2011, J ACAD LIB, V4, DOI [10.25236/iemb.2021.025, DOI 10.25236/IEMB.2021.025]
   Rossello-Batle B, 2010, ENERG BUILDINGS, V42, P547, DOI 10.1016/j.enbuild.2009.10.024
   Scott D., 2008, CLIMATE CHANGE TOURI
   Scott D, 2016, J SUSTAIN TOUR, V24, P8, DOI 10.1080/09669582.2015.1062021
   Scott D, 2012, WIRES CLIM CHANGE, V3, P213, DOI 10.1002/wcc.165
   Scott D, 2010, J SUSTAIN TOUR, V18, P393, DOI 10.1080/09669581003653542
   Sghaier A, 2019, GEOJOURNAL, V84, P593, DOI 10.1007/s10708-018-9878-z
   Solarin SA, 2014, ANATOLIA, V25, P228, DOI 10.1080/13032917.2013.868364
   Srinivasan P, 2012, ENVIRON URBAN ASIA, V3, P397, DOI 10.1177/0975425312473234
   Stefan D, TOURISM CLIMATE CHAN
   Sutawa GK, 2012, PROC ECON FINANC, V4, P413, DOI 10.1016/S2212-5671(12)00356-5
   Sweeting, 2012, INTEGRATING BUSINESS
   Talbi B, 2022, J PUBLIC AFF, V22, DOI 10.1002/pa.2379
   Tang CF, 2016, GEOSYSTEM ENG, V19, P158, DOI 10.1080/12269328.2016.1162113
   Tang CF, 2011, INT J TOUR RES, V13, P97, DOI 10.1002/jtr.807
   Tang Z, 2015, TOURISM MANAGE, V46, P11, DOI 10.1016/j.tourman.2014.06.001
   Tugcu CT., 2018, THEOR APPL EC, V25, P125
   Un G, 2015, TRANSFORMING OUR WOR
   Vlavian-Gurmeza, 2010, STUDIES SCI RES EC E
   Wang Q, 2018, J CLEAN PROD, V175, P18, DOI 10.1016/j.jclepro.2017.11.055
   Weber O, 2014, BUS STRATEG ENVIRON, V23, P303, DOI 10.1002/bse.1785
   WTTC, 2017, TRAV TOUR EC IMP 201
   Xia WJ, 2022, PORT ECON J, V21, P239, DOI 10.1007/s10258-021-00193-9
   Yanda PZ, 2018, CONTRIBUTION WORKING
   Yu H, 2016, APPL ENERG, V184, P960, DOI 10.1016/j.apenergy.2016.07.129
   Zaman K, 2016, TOURISM MANAGE, V54, P275, DOI 10.1016/j.tourman.2015.12.001
   Zambrano A. M. A., 2010, Journal of Ecotourism, V9, P62, DOI 10.1080/14724040902953076
   Zhang K, 2016, RENEW SUST ENERG REV, V58, P297, DOI 10.1016/j.rser.2015.12.089
   Zhong LS, 2011, J ENVIRON MANAGE, V92, P2972, DOI 10.1016/j.jenvman.2011.07.011
   Zhou YB, 2013, BIOL CONSERV, V165, P18, DOI 10.1016/j.biocon.2013.05.007
   Zhu L, 2008, INFORM SCIENCES, V4
NR 106
TC 13
Z9 13
U1 13
U2 28
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
PD NOV
PY 2021
VL 28
IS 41
BP 58241
EP 58257
DI 10.1007/s11356-021-14798-2
EA JUN 2021
PG 17
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA WL1MQ
UT WOS:000659822300002
PM 34110589
DA 2022-08-23
ER

PT J
AU Morante-Carballo, F
   Montalvan-Burbano, N
   Carrion-Mero, P
   Jacome-Francis, K
AF Morante-Carballo, Fernando
   Montalvan-Burbano, Nestor
   Carrion-Mero, Paul
   Jacome-Francis, Kelly
TI Worldwide Research Analysis on Natural Zeolites as Environmental
   Remediation Materials
SO SUSTAINABILITY
LA English
DT Article
DE natural zeolites; adsorbent; environmental remediation; bibliometric
   analysis; knowledge mapping
ID ACID-MINE DRAINAGE; HEAVY-METALS REMOVAL; ION-EXCHANGE; WASTE-WATER;
   AQUEOUS-SOLUTIONS; FIXED-BED; BIBLIOMETRIC ANALYSIS; CONTAMINATED SOIL;
   RESEARCH TRENDS; PHENOL REMOVAL
AB Society faces a significant problem in regards to the spread of harmful products in the environment, primarily caused by accelerated growth and resource consumption. Consequently, there is a need for materials to be processed in less harmful ways and to remedy the contaminated sources they generate. Microporous materials have been studied for a long time and are used in waste treatment alternatives. Natural zeolites, on which this study is based, are attractive to the scientific and technological communities, due to their numerous applications as decontaminants and adsorption properties. This study analyzes the intellectual structures of publications related to natural zeolites in environmental remediation, using bibliometric methods to determine their volumes and trends. The methodology comprises of an analysis based on 1582 articles, using VOSviewer software, with data from 1974 to 2020, via the Scopus database. Results reflect a notable increase in publications from the end of the 1990s; the greatest contribution in the area comes from Eurasian countries. The study considers that development in this line of research will continue to increase and serve as a great contribution to preserve the environment in coming years, with themes that focus on water treatment (e.g., drinking water, wastewater, greywater), removal of heavy metals, ammonium, ammonia, and construction.
C1 [Morante-Carballo, Fernando; Montalvan-Burbano, Nestor; Carrion-Mero, Paul; Jacome-Francis, Kelly] ESPOL Polytech Univ, Ctr Invest & Proyectos Aplicados Ciencias Tierra, Guayaquil 09015863, Ecuador.
   [Morante-Carballo, Fernando] ESPOL Polytech Univ, Fac Ciencias Nat & Matemat FCNM, Guayaquil 09015863, Ecuador.
   [Morante-Carballo, Fernando] ESPOL Polytech Univ, Georecursos & Aplicac GIGA, Guayaquil 09015863, Ecuador.
   [Montalvan-Burbano, Nestor] Univ Almeria, Dept Econ & Business, Almeria 04120, Spain.
   [Carrion-Mero, Paul; Jacome-Francis, Kelly] ESPOL Polytech Univ, Fac Ingn Ciencias Tierra FICT, Guayaquil 09015863, Ecuador.
RP Morante-Carballo, F; Jacome-Francis, K (通讯作者)，ESPOL Polytech Univ, Ctr Invest & Proyectos Aplicados Ciencias Tierra, Guayaquil 09015863, Ecuador.; Morante-Carballo, F (通讯作者)，ESPOL Polytech Univ, Fac Ciencias Nat & Matemat FCNM, Guayaquil 09015863, Ecuador.; Morante-Carballo, F (通讯作者)，ESPOL Polytech Univ, Georecursos & Aplicac GIGA, Guayaquil 09015863, Ecuador.; Jacome-Francis, K (通讯作者)，ESPOL Polytech Univ, Fac Ingn Ciencias Tierra FICT, Guayaquil 09015863, Ecuador.
EM fmorante@espol.edu.ec; nmb218@inlumine.ual.es; pcarrion@espol.edu.ec;
   kjjacome@espol.edu.ec
RI Montalvan-Burbano, Nestor/AAR-1617-2020
OI Montalvan-Burbano, Nestor/0000-0002-6024-1629; Morante,
   Fernando/0000-0003-0374-0306; Carrion, Paul/0000-0002-9747-7547
FU ESPOL research project: "Register of geological and mining heritage and
   its incidence in the defense and preservation of geodiversity in
   Ecuador" [CIPAT-01-2018]; NOVA Science Research Associates
FX This work was made possible by the valuable collaboration of the ESPOL
   research project: "Register of geological and mining heritage and its
   incidence in the defense and preservation of geodiversity in Ecuador"
   under grant no. CIPAT-01-2018; and the support of NOVA Science Research
   Associates. The authors are grateful for the collaboration of the novel
   researcher Maria Fernanda Jaya for her help in the process of
   proofreading and editing the article.
CR Al Dwairi R.A, 2012, J U CHEM TECHNOL MET, V47, P69
   Alvarez-Ayuso E, 2003, WATER RES, V37, P4855, DOI 10.1016/j.watres.2003.08.009
   An W, 2014, J MEMBRANE SCI, V470, P431, DOI 10.1016/j.memsci.2014.07.054
   An WZ, 2011, J MEMBRANE SCI, V369, P414, DOI 10.1016/j.memsci.2010.12.025
   Andres A., 2009, MEAS ACAD RES, DOI [10.1533/9781780630182, DOI 10.1533/9781780630182]
   Asl M. K., 2016, Biosciences, Biotechnology Research Asia, V13, P1045
   Aznar-Sanchez JA, 2019, ECOL INDIC, V99, P310, DOI 10.1016/j.ecolind.2018.12.045
   Baas J, 2020, QUANT SCI STUD, V1, P377, DOI 10.1162/qss_a_00019
   Baier-Fuentes H, 2019, INT ENTREP MANAG J, V15, P385, DOI 10.1007/s11365-017-0487-y
   Baykara H, 2018, J POLYM ENVIRON, V26, P2566, DOI 10.1007/s10924-017-1153-8
   Bissen M, 2003, ACTA HYDROCH HYDROB, V31, P97, DOI 10.1002/aheh.200300485
   BLANCHARD G, 1984, WATER RES, V18, P1501, DOI 10.1016/0043-1354(84)90124-6
   Breck D.W., 1973, ZEOLITE MOL SIEVES S
   Briones-Bitar J, 2020, GEOSCIENCES, V10, DOI 10.3390/geosciences10100403
   Buenano X, 2017, ENVIRON SCI POLLUT R, V24, P6794, DOI 10.1007/s11356-016-8177-9
   Calvo B, 2009, J HAZARD MATER, V166, P619, DOI 10.1016/j.jhazmat.2008.11.087
   Cappelletti P, 2017, MICROPOR MESOPOR MAT, V250, P232, DOI 10.1016/j.micromeso.2015.05.048
   Carrion-Mero P, 2020, GEOSCIENCES, V10, DOI 10.3390/geosciences10090347
   Chandra Y, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0190228
   Chelishchev N.F., 1995, NATURAL ZEOLITES 93, P525
   Chen YQ, 2019, J SOIL SEDIMENT, V19, P3898, DOI 10.1007/s11368-019-02381-9
   Colella C, 1999, NATO ADV SCI I E-APP, V362, P207
   Contin M, 2019, J SOIL SEDIMENT, V19, P4052, DOI 10.1007/s11368-019-02359-7
   Costafreda J., 2018, ZEOLITAS NATURALES I
   Cruz-Guzman M., 2007, CONTAMINACION SUELOS
   Curkovic L, 1997, WATER RES, V31, P1379, DOI 10.1016/S0043-1354(96)00411-3
   del Rio-Rama MD, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12020724
   Diaz I, 2017, CATAL TODAY, V285, P29, DOI 10.1016/j.cattod.2017.01.045
   Dong DH, 2015, SCIENTOMETRICS, V105, P1111, DOI 10.1007/s11192-015-1769-1
   Doula MK, 2012, J HAZARD MATER, V207, P103, DOI 10.1016/j.jhazmat.2011.04.014
   Duran-Sanchez A, 2019, J ENTERP COMMUNITIES, V13, P84, DOI 10.1108/JEC-10-2018-0072
   Echevarria MS, 1997, ECLET QUIM, V22, P15, DOI 10.1590/S0100-46701997000100002
   Ellersdorfer M, 2018, WATER SCI TECHNOL, V77, P695, DOI 10.2166/wst.2017.561
   Erdem E, 2004, J COLLOID INTERF SCI, V280, P309, DOI 10.1016/j.jcis.2004.08.028
   Eva C., 2015, RES J CHEM ENVIRON, V19, P8
   Faghihian H, 1999, APPL RADIAT ISOTOPES, V50, P655, DOI 10.1016/S0969-8043(98)00134-1
   Fahimnia B, 2015, INT J PROD ECON, V162, P101, DOI 10.1016/j.ijpe.2015.01.003
   Ferretti G, 2020, J SOIL SCI PLANT NUT, V20, P973, DOI 10.1007/s42729-020-00184-3
   Ferretti G, 2017, APPL SOIL ECOL, V119, P138, DOI 10.1016/j.apsoil.2017.06.004
   Filatova EG, 2014, J WATER CHEM TECHNO+, V36, P303, DOI 10.3103/S1063455X14060083
   Fu HL, 2020, J HAZARD MATER, V393, DOI 10.1016/j.jhazmat.2020.122481
   Gaikwad RW, 2011, ARAB J GEOSCI, V4, P85, DOI 10.1007/s12517-009-0079-4
   Galletti C, 2021, ENVIRON SCI POLLUT R, V28, P24355, DOI 10.1007/s11356-020-08483-z
   Ghiaci M, 2004, SEP PURIF TECHNOL, V40, P217, DOI 10.1016/j.seppur.2004.03.001
   Guo XJ, 2013, J ENVIRON SCI-CHINA, V25, P954, DOI 10.1016/S1001-0742(12)60115-6
   Hailu Y, 2019, GROUNDWATER SUST DEV, V8, P457, DOI 10.1016/j.gsd.2019.01.009
   Han RP, 2009, CHEM ENG J, V145, P496, DOI 10.1016/j.cej.2008.05.003
   Herrera-Franco G, 2021, WATER-SUI, V13, DOI 10.3390/w13091283
   Herrera-Franco G, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13031175
   Herrera-Franco G, 2020, GEOSCIENCES, V10, DOI 10.3390/geosciences10100379
   Huang Z, 2014, FOOD CONTROL, V36, P248, DOI 10.1016/j.foodcont.2013.08.036
   Inglezakis VJ, 2003, J COLLOID INTERF SCI, V261, P49, DOI 10.1016/S0021-9797(02)00244-8
   Ivanova EP, 2010, ASIA-PAC J CHEM ENG, V5, P869, DOI 10.1002/apj.412
   Janos P, 2010, GEODERMA, V159, P335, DOI 10.1016/j.geoderma.2010.08.009
   Jorgensen TC, 2003, WATER RES, V37, P1723, DOI 10.1016/S0043-1354(02)00571-7
   Khan GF, 2015, SCIENTOMETRICS, V105, P959, DOI 10.1007/s11192-015-1712-5
   Kiurski J, 2012, CARPATH J EARTH ENV, V7, P5
   Kothandaraman V., 1969, JAWRA J AM WATER RES, V5, P73, DOI [10.1111/j.1752-1688.1969.tb04890.x, DOI 10.1111/J.1752-1688.1969.TB04890.X]
   Kragovic M, 2017, ADV SCI LETT, V23, P5862, DOI 10.1166/asl.2017.9051
   Li H, 2009, J HAZARD MATER, V169, P1106, DOI 10.1016/j.jhazmat.2009.04.067
   Li J, 2015, SCIENTOMETRICS, V105, P1273, DOI 10.1007/s11192-015-1739-7
   Lin SH, 2002, J HAZARD MATER, V92, P315, DOI 10.1016/S0304-3894(02)00026-2
   Liu XM, 2019, ADSORPTION, V25, P747, DOI 10.1007/s10450-019-00081-x
   Llanes-Monter MM, 2007, ENVIRON SCI POLLUT R, V14, P397, DOI 10.1065/espr2006.10.357
   LONDOÑO-FRANCO LUIS FERNANDO, 2016, Rev.Bio.Agro, V14, P145, DOI 10.18684/BSAA(14)145-153
   Malczyk P, 2009, FRESEN ENVIRON BULL, V18, P1090
   Malekian R, 2011, APPL CLAY SCI, V51, P323, DOI 10.1016/j.clay.2010.12.020
   Mansouri N, 2013, ENVIRON PROT ENG, V39, P139, DOI 10.5277/EPE130111
   Margeta K, 2015, APPL CLAY SCI, V116, P111, DOI 10.1016/j.clay.2015.08.021
   Markovic R, 2015, MATER TRANS, V56, P2053, DOI 10.2320/matertrans.M2015292
   Mazeikiene A, 2010, J ENVIRON ENG LANDSC, V18, P54, DOI 10.3846/jeelm.2010.07
   Meseguer-Sanchez V, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17134729
   Meshko V, 2001, WATER RES, V35, P3357, DOI 10.1016/S0043-1354(01)00056-2
   Meshko VD, 1998, J SERB CHEM SOC, V63, P891
   Mier MV, 2001, WATER RES, V35, P373, DOI 10.1016/S0043-1354(00)00270-0
   Montalvan-Burbano N, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13116039
   Montalvan-Burbano N, 2020, COGENT BUS MANAG, V7, DOI 10.1080/23311975.2020.1745043
   Morante F., 2004, ZEOLITAS COSTA ECUAD
   Moscou L., 1991, STUD SURF SCI CATAL, V58, P1
   Motsi T, 2009, INT J MINER PROCESS, V92, P42, DOI 10.1016/j.minpro.2009.02.005
   Moussavi G, 2011, CHEM ENG J, V171, P1159, DOI 10.1016/j.cej.2011.05.016
   Mumpton FA, 1999, P NATL ACAD SCI USA, V96, P3463, DOI 10.1073/pnas.96.7.3463
   Mustelier J.L.C., 2008, THESIS ETSI MADRID
   Nagaiah E, 2017, ENVIRON EARTH SCI, V76, DOI 10.1007/s12665-017-6873-5
   Najmi A, 2017, SCIENTOMETRICS, V110, P843, DOI 10.1007/s11192-016-2171-3
   Nasrabadi M, 2018, INT J ENVIRON TECHNO, V21, P174, DOI 10.1504/IJETM.2018.097917
   Ogawa T, 2017, MATERIALS, V10, DOI 10.3390/ma10121428
   Osmanlioglu AE, 2006, J HAZARD MATER, V137, P332, DOI 10.1016/j.jhazmat.2006.02.013
   PARHAM W, 1989, NAT RESOUR FORUM, V13, P107, DOI 10.1111/j.1477-8947.1989.tb00324.x
   Pepe F, 2013, CHEM ENG J, V219, P37, DOI 10.1016/j.cej.2012.12.075
   Peric J, 2004, WATER RES, V38, P1893, DOI 10.1016/j.watres.2003.12.035
   Pico-Saltos R, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13094625
   Polloni-Silva J, 2017, CHEMOSPHERE, V173, P267, DOI 10.1016/j.chemosphere.2017.01.029
   Pucarevic M, 2017, KUWAIT J SCI, V44, P99
   Rege SU, 2001, CHEM ENG SCI, V56, P2745, DOI 10.1016/S0009-2509(00)00531-5
   REHAKOVA M, 1994, J INCLUS PHENOM MOL, V20, P233
   Reyes Y., 2016, INGENIERIA INVESTIGA, V16, P66, DOI [10.19053/1900771X.v16.n2.2016.5447, DOI 10.19053/1900771X.V16.N2.2016.5447, 10.19053/1900771X, DOI 10.19053/1900771X]
   Ruban DA, 2019, GEOSCIENCES, V9, DOI 10.3390/geosciences9010014
   Salem A, 2011, CHEM ENG J, V174, P619, DOI 10.1016/j.cej.2011.09.075
   Saltali K, 2007, J HAZARD MATER, V141, P258, DOI 10.1016/j.jhazmat.2006.06.124
   Seliman AF, 2011, ENVIRON SCI POLLUT R, V18, P1098, DOI 10.1007/s11356-011-0459-7
   SEMMENS MJ, 1988, WATER RES, V22, P537, DOI 10.1016/0043-1354(88)90052-8
   Shavandi MA, 2012, J TAIWAN INST CHEM E, V43, P750, DOI 10.1016/j.jtice.2012.02.014
   Sheppard R.A., 1984, CHARACTERIZATION ZEO, P81
   SIMS RC, 1973, ENVIRON LETT, V4, P27, DOI 10.1080/00139307309435480
   Smical I., 2011, AACL Bioflux, V4, P481
   Swenson P, 2012, DESALINATION, V286, P442, DOI 10.1016/j.desal.2011.12.008
   Syamsiah S, 2004, SEP PURIF TECHNOL, V34, P125, DOI 10.1016/S1383-5866(03)00186-2
   Szollosi-Mota A, 2017, ENVIRON ENG MANAG J, V16, P1383, DOI 10.30638/eemj.2017.149
   Taheri-Sodejani H, 2015, DESALIN WATER TREAT, V54, P2723, DOI 10.1080/19443994.2014.904818
   Tian JJ, 2016, WATER SCI TECHNOL, V73, P2119, DOI 10.2166/wst.2016.055
   Tomeckova V, 2012, SPECTROSC LETT, V45, P447, DOI 10.1080/00387010.2011.637537
   Torma S, 2014, TURK J AGRIC FOR, V38, P739, DOI 10.3906/tar-1311-13
   Torosyan G, 2010, NATO SCI PEACE SECUR, P171, DOI 10.1007/978-94-007-0280-6_15
   Tranfield D, 2003, BRIT J MANAGE, V14, P207, DOI 10.1111/1467-8551.00375
   Van Eck N., 2014, MEASURING SCHOLARLY, P285, DOI [10.1007/978-3-319-10377-8_13, 10.1007/978-3-319-10377-8_13(InEng.)]
   van Eck NJ, 2017, SCIENTOMETRICS, V111, P1053, DOI 10.1007/s11192-017-2300-7
   van Eck NJ, 2010, SCIENTOMETRICS, V82, P581, DOI 10.1007/s11192-010-0173-0
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Varvara S, 2013, J ENVIRON PROT ECOL, V14, P1506
   Vdovina E.D., 1977, SOV RADIOCHEM, V19, P155
   Vrinceanu NO, 2019, CATENA, V176, P336, DOI 10.1016/j.catena.2019.01.015
   Waisberg M, 2003, TOXICOLOGY, V192, P95, DOI 10.1016/S0300-483X(03)00305-6
   Wang S, 2007, J COLLOID INTERF SCI, V314, P25, DOI 10.1016/j.jcis.2007.05.032
   Yousef RI, 2011, CHEM ENG J, V171, P1143, DOI 10.1016/j.cej.2011.05.012
   Yousefi A, 2018, SILICON-NETH, V10, P1737, DOI 10.1007/s12633-017-9663-5
   Zhou L, 2014, AQUACULTURE, V432, P252, DOI 10.1016/j.aquaculture.2014.05.019
   Zhu BK, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17082625
   Zolfaghari M, 2020, J WATER PROCESS ENG, V37, DOI 10.1016/j.jwpe.2020.101512
   Zou WH, 2009, CHINESE J CHEM ENG, V17, P585, DOI 10.1016/S1004-9541(08)60248-7
   Zupic I, 2015, ORGAN RES METHODS, V18, P429, DOI 10.1177/1094428114562629
NR 131
TC 9
Z9 9
U1 6
U2 17
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUN
PY 2021
VL 13
IS 11
AR 6378
DI 10.3390/su13116378
PG 21
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA SQ9YE
UT WOS:000660702400001
OA Green Published
DA 2022-08-23
ER

PT J
AU Zeng, RC
   Chini, A
AF Zeng, Ruochen
   Chini, Abdol
TI A review of research on embodied energy of buildings using bibliometric
   analysis
SO ENERGY AND BUILDINGS
LA English
DT Review
DE Embodied energy; Building; Bibliometric analysis; Life cycle assessment;
   Design
ID LIFE-CYCLE ASSESSMENT; CARBON; CONSTRUCTION; EMISSIONS; TRENDS
AB The share of embodied energy within the total life cycle of a building is expected to increase and become more significant as low energy and net-zero energy buildings become the norm. In the last two decades, many researchers have studied the embodied energy of buildings and published their findings. However, a comprehensive, quantitative analysis of the literature on the embodied energy of buildings is missing. In this study, in order to provide a better understanding of the field as a whole, a bibliometric approach is applied to create a knowledge map and discover more information about the topic. The primary knowledge structure and the topics for study were discovered by analyzing the keywords and abstract terms of 398 papers published from 1996 to 2015. The results show that life cycle assessment is the keyword with the greatest frequency of occurrence and design is the keyword that has the highest number of co-occurrence relationships with other keywords in the 398 papers. The major three research areas for the embodied energy of buildings are life cycle assessment (LCA), building design, and greenhouse gas emissions. By using the bibliometric method, this study analyzes previous studies to provide new insights into the embodied energy of buildings. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Zeng, Ruochen; Chini, Abdol] Univ Florida, ME Rinker Sr Sch Construct Management, Gainesville, FL 32611 USA.
RP Zeng, RC (通讯作者)，Univ Florida, ME Rinker Sr Sch Construct Management, Gainesville, FL 32611 USA.
EM r.zeng@ufl.edu
CR [Anonymous], 1992, APPL NEW PRODUCTION
   [Anonymous], 140402006 ISO
   Ban J., 2015, WORLD OIL OUTLOOK
   Basbagill J, 2013, BUILD ENVIRON, V60, P81, DOI 10.1016/j.buildenv.2012.11.009
   Bastian M., 2009, P INT AAAI C WEB SOC, P361, DOI 10.13140/2.1.1341.1520
   Bikas D., 2001, SCI ISSUE BUILD J A, P13
   Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106
   Breesch H, 2005, SOL ENERGY, V79, P682, DOI 10.1016/j.solener.2004.12.002
   Cabeza LF, 2013, RENEW SUST ENERG REV, V23, P536, DOI 10.1016/j.rser.2013.03.017
   Canadian Architect (CA), 2011, OP EN
   Chen C., 2014, THE CITESPACE MANUAL
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Cole RJ, 1999, BUILD ENVIRON, V34, P335, DOI 10.1016/S0360-1323(98)00020-1
   Cox PM, 2000, NATURE, V408, P184, DOI 10.1038/35041539
   Crowther R., 1999, 16 INT C PASS LOW EN, P22
   Dimoudi A, 2008, RESOUR CONSERV RECY, V53, P86, DOI 10.1016/j.resconrec.2008.09.008
   Dixit MK, 2012, RENEW SUST ENERG REV, V16, P3730, DOI 10.1016/j.rser.2012.03.021
   Dixit MK, 2010, ENERG BUILDINGS, V42, P1238, DOI 10.1016/j.enbuild.2010.02.016
   Dunning T., 1993, Computational Linguistics, V19, P61
   Global Construction Oxford Economics, 2011, GSM 3G MARK TECHN UP, P3
   Gonzalez MJ, 2006, BUILD ENVIRON, V41, P902, DOI 10.1016/j.buildenv.2005.04.006
   Griffin C.T., 2010, STRUCT ARCH ICSA 201, P373
   Griffith B., 2006, ASSESSMENT TECHNICAL
   Hamilton-MacLaren F., 2009, CALCULATION EMBODIED
   Hammond G.P., 2008, P I CIVIL ENG-ENERGY, V161, P87, DOI DOI 10.1680/ENER.2008.161.2.87
   He Q., 2016, INT J PROJECT MANAGE
   Houghton J. T., 1995, CLIMATE CHANGE 1994
   Ibn-Mohammed T, 2013, ENERG BUILDINGS, V66, P232, DOI 10.1016/j.enbuild.2013.07.026
   Kampf JH, 2010, ENERG BUILDINGS, V42, P807, DOI 10.1016/j.enbuild.2009.11.019
   Kaufman L., 1990, FINDING GROUPS DATA, P68, DOI [DOI 10.1002/9780470316801.CH2, 10.1002/9780470316801.ch2]
   Kua HW, 2012, ENERG BUILDINGS, V51, P6, DOI 10.1016/j.enbuild.2012.03.027
   Lashof D. A., 1990, RELATIVE CONTRIBUTIO
   Lippiatt BC, 2004, PROCEEDINGS OF THE INTERNATIONAL WORKSHOP ON SUSTAINABLE DEVELOPMENT AND CONCRETE TECHNOLOGY, P213
   Moncaster A.M., 2012, INT J SUSTAINABLE BU, V3, P26, DOI [10.1080/2093761X.2012.673915, DOI 10.1080/2093761X.2012.673915]
   Mpakati-Gama EC, 2016, INT J CONSTR MANAG, V16, P1, DOI 10.1080/15623599.2015.1110274
   Newman MEJ, 2006, P NATL ACAD SCI USA, V103, P8577, DOI 10.1073/pnas.0601602103
   Nisbet M., 1997, WORLD CEM, V28, P3
   Pollack J, 2015, INT J PROJ MANAG, V33, P236, DOI 10.1016/j.ijproman.2014.04.011
   Pollard D, 2015, EARTH PLANET SC LETT, V412, P112, DOI 10.1016/j.epsl.2014.12.035
   Ramesh T, 2010, ENERG BUILDINGS, V42, P1592, DOI 10.1016/j.enbuild.2010.05.007
   Reddy BVV, 2003, ENERG BUILDINGS, V35, P129
   Robertson AB, 2012, BUILDINGS, V2, P245, DOI 10.3390/buildings2030245
   Roisin B, 2008, ENERG BUILDINGS, V40, P514, DOI 10.1016/j.enbuild.2007.04.006
   ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7
   Saeli M, 2010, ENERG BUILDINGS, V42, P1666, DOI 10.1016/j.enbuild.2010.04.010
   SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220
   Shakun JD, 2012, NATURE, V484, P49, DOI 10.1038/nature10915
   Shrivastava S, 2016, J MANAGE ENG, V32, DOI 10.1061/(ASCE)ME.1943-5479.0000412
   Shrivastava S, 2012, INT J CONSTR MANAG, V12, P51, DOI 10.1080/15623599.2012.10773184
   Small H, 1999, J AM SOC INFORM SCI, V50, P799, DOI 10.1002/(SICI)1097-4571(1999)50:9<799::AID-ASI9>3.0.CO;2-G
   Solomon S, 2009, P NATL ACAD SCI USA, V106, P1704, DOI 10.1073/pnas.0812721106
   Staufer N., 2009, INNOVATIVE BUILDINGS
   Su HN, 2010, SCIENTOMETRICS, V85, P65, DOI 10.1007/s11192-010-0259-8
   Sukontasukkul P., 2009, P 1 INT C COMP TECHN
   Treloar G.J., 2000, CONSTRUCT MANAG EC, V18, P5, DOI DOI 10.1080/014461900370898
   Van den Heede P, 2012, CEMENT CONCRETE COMP, V34, P431, DOI 10.1016/j.cemconcomp.2012.01.004
   Yalcinkaya M, 2015, AUTOMAT CONSTR, V59, P68, DOI 10.1016/j.autcon.2015.07.012
   Yan H, 2010, BUILD ENVIRON, V45, P949, DOI 10.1016/j.buildenv.2009.09.014
   Bribian IZ, 2011, BUILD ENVIRON, V46, P1133, DOI 10.1016/j.buildenv.2010.12.002
   Zhang JL, 2014, INT J LIFE CYCLE ASS, V19, P745, DOI 10.1007/s11367-013-0689-7
   Zhang XB, 2007, NATURE, V448, P461, DOI 10.1038/nature06025
NR 62
TC 43
Z9 43
U1 5
U2 67
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0378-7788
EI 1872-6178
J9 ENERG BUILDINGS
JI Energy Build.
PD NOV 15
PY 2017
VL 155
BP 172
EP 184
DI 10.1016/j.enbuild.2017.09.025
PG 13
WC Construction & Building Technology; Energy & Fuels; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Energy & Fuels; Engineering
GA FL3FB
UT WOS:000414107200016
DA 2022-08-23
ER

PT J
AU Ravetz, J
AF Ravetz, Joe
TI State of the stock-What do we know about existing buildings and their
   future prospects?
SO ENERGY POLICY
LA English
DT Article
DE Housing stock; Non-domestic buildings; Maintenance and alterations
ID ENGLAND; ENERGY; MODEL; WALES
AB The UK building stock has seen major changes in the last 50 years, in its form, fabric and function. The context is the expansion of the building stock and built infrastructure, which takes place in most areas at 1-2% per year, with the implication that up to 75% of the dwellings of the year 2050 already exist now. This is a major challenge. The energy performance of much of this stock is generally low, while its economic, social and cultural values are often high.
   The purpose of this review is to provide a brief outline of the state of knowledge of the existing building stock, and of potential advances in that knowledge. We follow a knowledge mapping approach, set out on several axes. The first is an axis from buildings as physical forms, to buildings as containers of socio-economic activity. Another axis spans between existing buildings, renovations and adaptations, and new buildings. A third axis is that of scale, from building components to large-scale settlements. There are many possible combinations of these parameters. Here we focus on those that are most relevant to the SEMBE goals of sustainable energy management across the whole building stock. (C) 2008 Queen's Printer and Controller of HMSO. Published by Elsevier Ltd. All rights reserved.
C1 Univ Manchester, HBS, Sch Environm & Dev, Ctr Urban & Reg Ecol, Manchester M13 9PL, Lancs, England.
RP Ravetz, J (通讯作者)，Univ Manchester, HBS, Sch Environm & Dev, Ctr Urban & Reg Ecol, Oxford Rd, Manchester M13 9PL, Lancs, England.
EM joe.ravetz@manchester.ac.uk
CR Boardman B, 2007, HOME TRUTHS LOW CARB
   Boardman B., 2005, 40 HOUSE
   Boardman B, 2007, BUILD RES INF, V35, P363, DOI 10.1080/09613210701238276
   Brand Stanley, 1994, BUILDINGS LEARN WHAT
   Bretherton J, 2008, RESIDENTS VIEWS NEW
   Bruhns HR, 2000, ENVIRON PLANN B, V27, P641, DOI 10.1068/bst08
   *BUILD RES EST, 1991, ASS UK COMM IND BUIL
   *CHART I B UILD, 2005, CONSTR INF Q 4, V7
   *CLG, 2004, EG REV SKILLS SUST C
   *CLG, 2008, REV PROJ HOUS ENGL R
   CLG, 2006, REV SUST EX BUILD EN
   *CLG, 2008, ENGL HOUS COND SURV
   CLG, 2006, COD SUST HOM STEP CH
   *CLG ONS, 2008, STAT REL FLOORSP RAT
   Coaffee Jon, 2003, TERRORISM RISK CITY
   *DEFR, 2008, CARB RED COMM AN ORG
   *DEFR, 2006, ROUGH GUID IND CARB
   *DTI, 2007, CONSTR STAT ANN REP
   Empty Homes Agency, 2008, NEW TRICKS OLD BRICK
   Energy Saving Trust, 2006, RIS MACH REV EN US P
   EVANS R, 1989, ENERGY USE UK DOMEST
   Garreau J., 1991, EDGE CITY LIFE NEW F
   Glasze G., 2005, PRIVATE CITIES LOCAL
   Hand M., 2004, HOME CULT, V1, P235, DOI [10.2752/174063104778053464, DOI 10.2752/174063104778053464]
   Herring H., 1988, ENERGY USE ENERGY EF
   HOLMANS A, 2006, MORE HOUSEHOLDS HOUS
   Hudson-Smith A., 2008, DIGITAL GEOGRAPHY GE
   LOWE R, 2007, BUILD RES INF, V35, P399
   Mitchell W.J., 1996, CITY BITS SPACE PLAC
   Muthesius Stefan, 1982, ENGLISH TERRACED HOU
   Penman JM, 2000, ENVIRON PLANN B, V27, P1, DOI 10.1068/bst1
   Pout CH, 2000, ENVIRON PLANN B, V27, P721, DOI 10.1068/bst12
   RAVETZ A, 1995, PLACE HOME ENGLISH D
   Ravetz J, 2008, PROC INST CIV ENG-WA, V161, P51, DOI 10.1680/warm.2008.161.2.51
   Ravetz J., 2000, CITY REGION 2020 INT, DOI DOI 10.1108/IJSHE.2000.1.3.305.3
   RAVETZ J, 1998, TOWN COUNTRY PLANN, V67, P173
   RAVETZ J, 1999, ACHIEVING SUSTAINABL
   RCEP, 2007, URB ENV 26 REP
   *RECP, 2007, URB ENV 26 REP
   Schiller G, 2007, BUILD RES INF, V35, P399, DOI 10.1080/09613210701217171
   SHORROCK LD, 2003, 457 BR BUILD RES EST
   Sustainable Development Commission, 2006, STOCK TAK DEL IMPR E
   UK Green Building Council, 2007, REP CARB RED NEW NON
   Worthington J., 2005, REINVENTING WORKPLAC
NR 44
TC 89
Z9 90
U1 0
U2 23
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0301-4215
J9 ENERG POLICY
JI Energy Policy
PD DEC
PY 2008
VL 36
IS 12
BP 4462
EP 4470
DI 10.1016/j.enpol.2008.09.026
PG 9
WC Economics; Energy & Fuels; Environmental Sciences; Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Energy & Fuels; Environmental Sciences & Ecology
GA 383MZ
UT WOS:000261679000032
DA 2022-08-23
ER

PT J
AU Yang, K
   Qi, HY
AF Yang, Keng
   Qi, Hanying
TI Research on Health Disparities Related to the COVID-19 Pandemic: A
   Bibliometric Analysis
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE health disparities; health inequalities; COVID-19; public health
   emergency; public health regulatory policy; bibliometric analysis
ID EMERGING TRENDS; CARE; INEQUALITIES; INEQUITIES; PATTERNS; STATES
AB With the outbreak of the 2019 coronavirus (COVID-19) pandemic, the issue of increasing health disparities has received a great deal of attention from scholars and organizations. This study analyzes 2282 papers on COVID-19-related health disparities that have been retrieved from the WOS database, with 58,413 references. Using bibliometric analysis and knowledge mapping visualizations, the paper focuses on the academic structure and research trends by examining the research distribution of countries, journals and authors, keywords, highly cited articles, and reference co-citation. The results show that the United States has contributed the most, and the International Journal of Environmental Research and Public Health has published the largest number of papers on this topic. As for the core authors, Michael Marmot is the most productive. Issues such as racial health, mental health, and digital health disparities have been the trending topics of the COVID-19-related health disparities. The research directions include the features, factors, and interventions of health disparities under the influence of COVID-19. As such, this study provides literature support and suggestions to investigate COVID-19-related health disparities. The findings of the paper also remind public health regulators to consider factors of health disparities when developing long-term public health regulatory policies related to the pandemic.
C1 [Yang, Keng] Tsinghua Univ, Inst Econ, Beijing 100084, Peoples R China.
   [Yang, Keng] Tsinghua Univ, One Belt One Rd Strategy Inst, Beijing 100084, Peoples R China.
   [Qi, Hanying] Zhejiang Univ Finance & Econ, New Type Key Think Tank Zhejiang Prov Res Inst Re, Hangzhou 310018, Peoples R China.
   [Qi, Hanying] Zhejiang Univ Finance & Econ, China Inst Regulat Res, Hangzhou 310018, Peoples R China.
RP Qi, HY (通讯作者)，Zhejiang Univ Finance & Econ, New Type Key Think Tank Zhejiang Prov Res Inst Re, Hangzhou 310018, Peoples R China.; Qi, HY (通讯作者)，Zhejiang Univ Finance & Econ, China Inst Regulat Res, Hangzhou 310018, Peoples R China.
EM yangkengok@163.com; qihanying90@zufe.edu.cn
OI Qi, Hanying/0000-0002-5762-9024; Yang, Keng/0000-0001-7518-6004
FU Key Project of the National Social Science Foundation of China
   [21AGL025]
FX FundingThis research was funded by Key Project of the National Social
   Science Foundation of China grant number 21AGL025.
CR Abedi V, 2021, J RACIAL ETHN HEALTH, V8, P732, DOI [10.1101/2020.04.26.20079756, 10.1007/s40615-020-00833-4]
   Ahmed F, 2020, LANCET PUBLIC HEALTH, V5, pE240, DOI 10.1016/S2468-2667(20)30085-2
   Almeida-Filho N, 2003, AM J PUBLIC HEALTH, V93, P2037, DOI 10.2105/AJPH.93.12.2037
   [Anonymous], 2020, LANCET, DOI DOI 10.1016/S0140-6736(20)30183-5
   Arul K, 2017, J RACIAL ETHN HEALTH, V4, P854, DOI 10.1007/s40615-016-0288-y
   Azar KMJ, 2020, HEALTH AFFAIR, V39, P1253, DOI 10.1377/hlthaff.2020.00598
   Bailey ZD, 2017, LANCET, V389, P1453, DOI 10.1016/S0140-6736(17)30569-X
   Bambra C, 2020, J EPIDEMIOL COMMUN H, V74, P964, DOI 10.1136/jech-2020-214401
   Baqui P, 2020, LANCET GLOB HEALTH, V8, P1018, DOI 10.1016/S2214-109X(20)30285-0
   Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008
   Blumenthal D, 2020, NEW ENGL J MED, V383, P1483, DOI 10.1056/NEJMsb2021088
   Bombard Y, 2020, NAT REV GENET, V21, P505, DOI 10.1038/s41576-020-0260-x
   Bouchard L, 2015, SOC SCI MED, V141, P100, DOI 10.1016/j.socscimed.2015.07.022
   Braveman P, 2006, ANNU REV PUBL HEALTH, V27, P167, DOI 10.1146/annurev.publhealth.27.021405.102103
   Burwell-Naney K, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18126544
   Cash-Gibson L, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0191901
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen CM, 2014, J ASSOC INF SCI TECH, V65, P334, DOI 10.1002/asi.22968
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen JT, 2021, J PUBLIC HEALTH MAN, V27, pS43, DOI 10.1097/PHH.0000000000001263
   Chowkwanyun M, 2020, NEW ENGL J MED, V383, P201, DOI 10.1056/NEJMp2012910
   Cobo MJ, 2011, J AM SOC INF SCI TEC, V62, P1382, DOI 10.1002/asi.21525
   Collyer TA, 2020, SOC SCI MED, V264, DOI 10.1016/j.socscimed.2020.113330
   Cote D, 2021, AM J IND MED, V64, P551, DOI 10.1002/ajim.23256
   Douglas M, 2020, BMJ-BRIT MED J, V369, DOI 10.1136/bmj.m1557
   Egede LE, 2020, NEW ENGL J MED, V383, DOI 10.1056/NEJMp2023616
   Ellegaard O, 2015, SCIENTOMETRICS, V105, P1809, DOI 10.1007/s11192-015-1645-z
   Furstenau LB, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18030952
   Galvez-Sanchez FJ, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13063156
   Garg S, 2020, MMWR-MORBID MORTAL W, V69, P458, DOI 10.15585/mmwr.mm6915e3
   Gingras Y, 2014, BIBLIOMETRICS RES EV
   Hollander JE, 2020, NEW ENGL J MED, V382, P1679, DOI 10.1056/NEJMp2003539
   Holmes EA, 2020, LANCET PSYCHIAT, V7, P547, DOI 10.1016/S2215-0366(20)30168-1
   Hood WW, 2001, SCIENTOMETRICS, V52, P291, DOI 10.1023/A:1017919924342
   Ibrahim H, 2021, LANCET DIGIT HEALTH, V3, pE260, DOI 10.1016/S2589-7500(20)30317-4
   Karliner, 2020, NEJM CATAL INNOV CAR, P1, DOI 10.1056/CAT.20.0123
   Kim J, 2021, J MED INTERNET RES, V23, DOI 10.2196/24870
   Kim SJ, 2020, HEALTH EDUC BEHAV, V47, P509, DOI 10.1177/1090198120929677
   Pirtle WNL, 2020, HEALTH EDUC BEHAV, V47, P504, DOI 10.1177/1090198120922942
   Laurencin CT, 2020, J RACIAL ETHN HEALTH, V7, P398, DOI 10.1007/s40615-020-00756-0
   Liem A, 2020, LANCET PSYCHIAT, V7, pE20, DOI 10.1016/S2215-0366(20)30076-6
   Livingston G, 2020, LANCET, V396, P413, DOI 10.1016/S0140-6736(20)30367-6
   Logan RI, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17249223
   Malik AA, 2020, ECLINICALMEDICINE, V26, DOI 10.1016/j.eclinm.2020.100495
   Millett GA, 2020, ANN EPIDEMIOL, V47, P37, DOI 10.1016/j.annepidem.2020.05.003
   Moore JT, 2020, MMWR-MORBID MORTAL W, V69, P1122, DOI 10.15585/mmwr.mm6933e1
   Moreno C, 2020, LANCET PSYCHIAT, V7, P813, DOI 10.1016/S2215-0366(20)30307-2
   Muric G, 2021, J MED INTERNET RES, V23, DOI 10.2196/25379
   Nalimov V.V, MEASUREMENT SCI STUD
   Niedzwiedz CL, 2020, BMC MED, V18, DOI 10.1186/s12916-020-01640-8
   Ong PM, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18094829
   Osiecki KM, 2013, ENVIRON JUSTICE, V6, P81, DOI 10.1089/env.2013.0010
   Price-Haywood EG, 2020, NEW ENGL J MED, V382, P2534, DOI 10.1056/NEJMsa2011686
   PRITCHARD A, 1969, J DOC, V25, P348
   Raifman MA, 2020, AM J PREV MED, V59, P137, DOI 10.1016/j.amepre.2020.04.003
   Sanchez TH, 2020, AIDS BEHAV, V24, P2024, DOI 10.1007/s10461-020-02894-2
   Shadmi E, 2020, INT J EQUITY HEALTH, V19, DOI 10.1186/s12939-020-01218-z
   Tai DBG, 2021, CLIN INFECT DIS, V72, P703, DOI 10.1093/cid/ciaa815
   van Dorn A, 2020, LANCET, V395, P1243, DOI 10.1016/S0140-6736(20)30893-X
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Wadhera RK, 2020, JAMA-J AM MED ASSOC, V323, P2192, DOI 10.1001/jama.2020.7197
   Wagstaff A, 2012, J HEALTH ECON, V31, P406, DOI 10.1016/j.jhealeco.2012.03.002
   Wang LL, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16203825
   Wang Q, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18116053
   Wang ZC, 2020, NAT MED, V26, P458, DOI 10.1038/s41591-020-0823-6
   Webb Hooper M, 2020, JAMA-J AM MED ASSOC, V323, P2466, DOI 10.1001/jama.2020.8598
   Williams DR, 2020, JAMA-J AM MED ASSOC, V323, P2478, DOI 10.1001/jama.2020.8051
   Williams DR, 2019, ANNU REV PUBL HEALTH, V40, P105, DOI 10.1146/annurev-publhealth-040218-043750
   Yancy CW, 2020, JAMA-J AM MED ASSOC, V323, P1891, DOI 10.1001/jama.2020.6548
   Yao Q, 2014, SCIENTOMETRICS, V101, P751, DOI 10.1007/s11192-014-1383-7
   Zhai F, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17113766
   Zhou F, 2020, LANCET, V395, P1054, DOI 10.1016/S0140-6736(20)30566-3
NR 74
TC 1
Z9 1
U1 18
U2 19
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD FEB
PY 2022
VL 19
IS 3
AR 1220
DI 10.3390/ijerph19031220
PG 25
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA YY4PU
UT WOS:000754773200001
PM 35162243
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Shan, W
   Wang, JY
AF Shan, Wei
   Wang, Jingyi
TI Mapping the Landscape and Evolutions of Green Supply Chain Management
SO SUSTAINABILITY
LA English
DT Article
DE green supply chains; sustainable supply chains; bibliometrics; knowledge
   mapping
ID ENVIRONMENTAL PERFORMANCE; CLEANER PRODUCTION; HIERARCHY PROCESS;
   FRAMEWORK; IMPACT; SUSTAINABILITY; IMPLEMENTATION; COMPETITIVENESS;
   COLLABORATION; DETERMINANTS
AB There is a growing need to integrate environmental considerations into supply chain management research and practice. Conceptual papers are being updated all the time to gain a systematic view of the framework in green supply chain management. The purpose of this paper is to visualize the research to-date on green supply chain management according to the intellectual framework and hot topics. Using bibliometric analysis, our paper will present a comprehensive summary of previous research on the knowledge domain. Exploring 1145 papers mainly published from 2000 to May 2017 in international peer-reviewed journals from social sciences citation index (SSCI), the basic distribution of publications are analyzed, and nine prominent research clusters in green supply chain management are visualized by a co-citation network. Key issues are highlighted and analyzed. As for future inquiries, there are numerous opportunities for more advanced theoretically-grounded research and exploration of more multi-functional and systematic approaches. In order to gain broader views of research, a triple bottom line approach can be widely applied to evaluation, impact mechanisms, decision making, drivers, risks and carrier analysis, as well as system contributions. This also provides an integrated point of view to understand the landscape of green supply chain management both for researchers and practitioners.
C1 [Shan, Wei; Wang, Jingyi] Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China.
   [Shan, Wei] Minist Educ, Key Lab Complex Syst Anal & Management Decis, Beijing 100191, Peoples R China.
RP Shan, W (通讯作者)，Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China.; Shan, W (通讯作者)，Minist Educ, Key Lab Complex Syst Anal & Management Decis, Beijing 100191, Peoples R China.
EM shanwei@buaa.edu.cn; amanda1249@126.com
FU National Natural Science Foundation of China [71371025]; Beijing Natural
   Science Foundation [9182010]
FX The authors are very grateful for the insightful comments and
   suggestions of the anonymous reviewers and the editor, which have helped
   to significantly improve this article. Furthermore, this work was
   supported by the National Natural Science Foundation of China (No.
   71371025), and the Beijing Natural Science Foundation (No. 9182010).
CR Ahi P, 2013, J CLEAN PROD, V52, P329, DOI 10.1016/j.jclepro.2013.02.018
   [Anonymous], 2000, J CLEAN PROD, DOI DOI 10.1016/S0959-6526(00)00013-5
   Aragon-Correa JA, 1998, ACAD MANAGE J, V41, P556, DOI 10.2307/256942
   Arundel A., 1999, PATTERNS ADV MANUFAC, V12, P55
   Azevedo SG, 2011, TRANSPORT RES E-LOG, V47, P850, DOI 10.1016/j.tre.2011.05.017
   Bai CG, 2010, J CLEAN PROD, V18, P1200, DOI 10.1016/j.jclepro.2010.01.016
   Bowen FE, 2001, PROD OPER MANAG, V10, P174
   Brandenburg M, 2015, ANN OPER RES, V229, P213, DOI 10.1007/s10479-015-1853-1
   Brandenburg M, 2014, EUR J OPER RES, V233, P299, DOI 10.1016/j.ejor.2013.09.032
   Bras B, 2006, J CLEAN PROD, V14, P527, DOI 10.1016/j.jclepro.2005.03.019
   Brewerton P., 2001, ORGAN RES METHODS, P54
   Buyukozkan G, 2011, COMPUT IND, V62, P164, DOI 10.1016/j.compind.2010.10.009
   Carter CR, 1998, DECISION SCI, V29, P659, DOI 10.1111/j.1540-5915.1998.tb01358.x
   Carter CR, 2000, TRANSPORT RES E-LOG, V36, P219, DOI 10.1016/S1366-5545(99)00034-4
   Carter CR, 2011, INT J PHYS DISTR LOG, V41, P46, DOI 10.1108/09600031111101420
   Carter CR, 2008, INT J PHYS DISTR LOG, V38, P360, DOI 10.1108/09600030810882816
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Christmann P, 2001, J INT BUS STUD, V32, P439, DOI 10.1057/palgrave.jibs.8490976
   Coughlin DM, 2016, J ASSOC INF SCI TECH, V67, P518, DOI 10.1002/asi.23407
   Cousins PD, 2004, INT J OPER PROD MAN, V24, P554, DOI 10.1108/01443570410538104
   De Solla Price D.J., 1963, LITTLE SCI BIG SCI
   Demartini M, 2017, SMART INNOV SYST TEC, V68, P98, DOI 10.1007/978-3-319-57078-5_10
   Diabat A, 2011, RESOUR CONSERV RECY, V55, P659, DOI 10.1016/j.resconrec.2010.12.002
   Garfield E., 2001, BIBLIO COUPLING COCI
   Gavronski I, 2011, TRANSPORT RES E-LOG, V47, P872, DOI 10.1016/j.tre.2011.05.018
   Geffen CA, 2000, INT J OPER PROD MAN, V20, P166, DOI 10.1108/01443570010304242
   Gonzalez-Benito J, 2006, INT J PROD RES, V44, P1353, DOI 10.1080/00207540500435199
   Govindan K, 2014, INT J PROD ECON, V147, P555, DOI 10.1016/j.ijpe.2013.08.018
   Green KW, 2012, SUPPLY CHAIN MANAG, V17, P290, DOI 10.1108/13598541211227126
   Guan JC, 2007, RES POLICY, V36, P880, DOI 10.1016/j.respol.2007.02.004
   Handfield R, 2002, EUR J OPER RES, V141, P70, DOI 10.1016/S0377-2217(01)00261-2
   Handfield R. B., 1997, Journal of Operations Management, V15, P293, DOI 10.1016/S0272-6963(97)00004-1
   Handfield R. B., 1999, INTRO SUPPLY CHAIN M, P23
   Hassini E, 2012, INT J PROD ECON, V140, P69, DOI 10.1016/j.ijpe.2012.01.042
   Hervani AA, 2005, BENCHMARKING, V12, P330, DOI 10.1108/14635770510609015
   Kannan D, 2013, J CLEAN PROD, V47, P355, DOI 10.1016/j.jclepro.2013.02.010
   Khan Z, 2008, J CLEAN PROD, V16, P22, DOI 10.1016/j.jclepro.2006.06.007
   King AA, 2001, PROD OPER MANAG, V10, P244, DOI 10.1111/j.1937-5956.2001.tb00373.x
   Klassen RD, 2003, PROD OPER MANAG, V12, P336
   Kleinberg J., 2002, P 8 ACM SIGKDD C KNO
   Kumar V., 2016, INT J RES REV ENG SC, V5, P56
   Linton JD, 2007, J OPER MANAG, V25, P1075, DOI 10.1016/j.jom.2007.01.012
   Liu CC, 2015, SUSTAINABILITY-BASEL, V7, P2787, DOI 10.3390/su7032787
   Lu LYY, 2007, INT J PROD RES, V45, P4317, DOI 10.1080/00207540701472694
   Mayring P., 2003, QUALITATIVE CONTENT, P27
   Min H., 1997, INT J PURCH MAT MANA, V33, P10, DOI [10.1111/j.1745-493X.1997.tb00026.x, DOI 10.1111/J.1745-493X.1997.TB00026.X]
   Mitra S, 2014, INT J PROD RES, V52, P2085, DOI 10.1080/00207543.2013.849014
   Mollenkopf D, 2010, INT J PHYS DISTR LOG, V40, P14, DOI 10.1108/09600031011018028
   Nagel MH, 2003, J CLEAN PROD, V11, P11, DOI 10.1016/S0959-6526(02)00021-5
   Nicolaisen J, 2010, J AM SOC INF SCI TEC, V61, P205, DOI 10.1002/asi.21181
   Organisation for Economic Co- operation and Development (OECD), 2008, BIBL OECD GLOSS STAT
   Pagell M, 2014, J SUPPLY CHAIN MANAG, V50, P44, DOI 10.1111/jscm.12037
   Pilkington A, 2009, J OPER MANAG, V27, P185, DOI 10.1016/j.jom.2008.08.001
   PORTER ME, 1995, J ECON PERSPECT, V9, P97, DOI 10.1257/jep.9.4.97
   PORTER ME, 1995, HARVARD BUS REV, V73, P120
   Preuss L, 2001, J BUS ETHICS, V34, P345, DOI 10.1023/A:1012549318786
   Rao P, 2005, INT J OPER PROD MAN, V25, P898, DOI 10.1108/01443570510613956
   Rao P, 2002, INT J OPER PROD MAN, V22, P632, DOI 10.1108/01443570210427668
   ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7
   Rutkauskas AV, 2012, SUSTAINABILITY-BASEL, V4, P1135, DOI 10.3390/su4061135
   Sarkis J, 2003, J CLEAN PROD, V11, P397, DOI 10.1016/S0959-6526(02)00062-8
   Sarkis J, 1998, EUR J OPER RES, V107, P159, DOI 10.1016/S0377-2217(97)00160-4
   Sarkis J, 2012, SUPPLY CHAIN MANAG, V17, P202, DOI 10.1108/13598541211212924
   Sarkis J, 2010, CORP SOC RESP ENV MA, V17, P337, DOI 10.1002/csr.220
   Sarkis J, 2011, INT J PROD ECON, V130, P1, DOI 10.1016/j.ijpe.2010.11.010
   Sharma A, 2010, IND MARKET MANAG, V39, P330, DOI 10.1016/j.indmarman.2008.11.005
   Simpson DE, 2005, SUPPLY CHAIN MANAG, V10, P60, DOI 10.1108/13598540510578388
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Srivastava SK, 2007, INT J MANAG REV, V9, P53, DOI 10.1111/j.1468-2370.2007.00202.x
   Tate WL, 2010, J SUPPLY CHAIN MANAG, V46, P19, DOI 10.1111/j.1745-493X.2009.03184.x
   Tibely G, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0084133
   Tseng ML, 2013, J CLEAN PROD, V40, P22, DOI 10.1016/j.jclepro.2010.08.007
   Vachon S, 2008, INT J PROD ECON, V111, P299, DOI 10.1016/j.ijpe.2006.11.030
   Vachon S, 2006, INT J OPER PROD MAN, V26, P795, DOI 10.1108/01443570610672248
   van Hoof B, 2014, J CLEAN PROD, V67, P239, DOI 10.1016/j.jclepro.2013.12.030
   Walton S.V., 1998, J SUPPLY CHAIN MANAG, V34, P2, DOI DOI 10.1111/J.1745-493X.1998.TB00042.X
   Webb, 1994, RESOUR ENERGY ECON, V1, P14
   WEICK KE, 1995, ADMIN SCI QUART, V40, P385, DOI 10.2307/2393789
   Whitehead D, 2003, J ADV NURS, V44, P108, DOI 10.1046/j.1365-2648.2003.02790_1.x
   Zhu QH, 2004, J OPER MANAG, V22, P265, DOI 10.1016/j.jom.2004.01.005
   Zhu QH, 2005, INT J OPER PROD MAN, V25, P449, DOI 10.1108/01443570510593148
   Zhu QH, 2008, INT J PROD ECON, V111, P261, DOI 10.1016/j.ijpe.2006.11.029
   Zhu Q, 2007, INT J PROD RES, V45, P4333, DOI 10.1080/00207540701440345
   Zhu QH, 2013, J PURCH SUPPLY MANAG, V19, P106, DOI 10.1016/j.pursup.2012.12.001
   Zhu QH, 2012, INT J PROD RES, V50, P1377, DOI 10.1080/00207543.2011.571937
NR 86
TC 9
Z9 9
U1 0
U2 38
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAR
PY 2018
VL 10
IS 3
AR 597
DI 10.3390/su10030597
PG 23
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA GA8DA
UT WOS:000428567100024
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Gosalia, N
   Economides, AN
   Dewey, FE
   Balasubramanian, S
AF Gosalia, Nehal
   Economides, Aris N.
   Dewey, Frederick E.
   Balasubramanian, Suganthi
TI MAPPIN: a method for annotating, predicting pathogenicity and mode of
   inheritance for nonsynonymous variants
SO NUCLEIC ACIDS RESEARCH
LA English
DT Article
ID SINGLE-NUCLEOTIDE POLYMORPHISMS; HUMAN GENES; HUMAN GENOMES;
   HUMAN-DISEASE; MUTATIONS; FRAMEWORK; IDENTIFICATION; CHALLENGES;
   SEQUENCE; IMPACT
AB Nonsynonymous single nucleotide variants (nsSNVs) constitute about 50% of known disease-causing mutations and understanding their functional impact is an area of active research. Existing algorithms predict pathogenicity of nsSNVs; however, they are unable to differentiate heterozygous, dominant disease-causing variants from heterozygous carrier variants that lead to disease only in the homozygous state. Here, we present MAPPIN (Method for Annotating, Predicting Pathogenicity, and mode of Inheritance for Nonsynonymous variants), a prediction method which utilizes a random forest algorithm to distinguish between nsSNVs with dominant, recessive, and benign effects. We apply MAPPIN to a set of Mendelian disease-causing mutations and accurately predict pathogenicity for all mutations. Furthermore, MAPPIN predicts mode of inheritance correctly for 70.3% of nsSNVs. MAPPIN also correctly predicts pathogenicity for 87.3% of mutations from the Deciphering Developmental Disorders Study with a 78.5% accuracy for mode of inheritance. When tested on a larger collection of mutations from the Human Gene Mutation Database, MAPPIN is able to significantly discriminate between mutations in known dominant and recessive genes. Finally, we demonstrate that MAPPIN outperforms CADD and Eigen in predicting disease inheritance modes for all validation datasets. To our knowledge, MAPPIN is the first nsSNV pathogenicity prediction algorithm that provides mode of inheritance predictions, adding another layer of information for variant prioritization.
C1 [Gosalia, Nehal; Economides, Aris N.; Dewey, Frederick E.; Balasubramanian, Suganthi] Regeneron Genet Ctr, Tarrytown, NY 10591 USA.
   [Gosalia, Nehal; Economides, Aris N.] Regeneron Pharmaceut, Tarrytown, NY 10591 USA.
RP Balasubramanian, S (通讯作者)，Regeneron Genet Ctr, Tarrytown, NY 10591 USA.
EM suganthi.bala@regeneron.com
OI Balasubramanian, Suganthi/0000-0003-2243-5948
FU Regeneron Genetics Center
FX Funding for open access charge: Regeneron Genetics Center.
CR Adzhubei IA, 2010, NAT METHODS, V7, P248, DOI 10.1038/nmeth0410-248
   Altshuler DM, 2015, NATURE, V526, P68, DOI 10.1038/nature15393
   Altshuler DM, 2012, NATURE, V491, P56, DOI 10.1038/nature11632
   Bamshad MJ, 2011, NAT REV GENET, V12, P745, DOI 10.1038/nrg3031
   Berg JS, 2013, GENET MED, V15, P36, DOI 10.1038/gim.2012.112
   Blekhman R, 2008, CURR BIOL, V18, P883, DOI 10.1016/j.cub.2008.04.074
   Boone PM, 2013, GENOME RES, V23, P1383, DOI 10.1101/gr.156075.113
   Cargill M, 1999, NAT GENET, V22, P231, DOI 10.1038/10290
   Chasman D, 2001, J MOL BIOL, V307, P683, DOI 10.1006/jmbi.2001.4510
   Chong JX, 2015, AM J HUM GENET, V97, P199, DOI 10.1016/j.ajhg.2015.06.009
   Chun S, 2009, GENOME RES, V19, P1553, DOI 10.1101/gr.092619.109
   Cooper GM, 2005, GENOME RES, V15, P901, DOI 10.1101/gr.3577405
   Dweep H, 2011, J BIOMED INFORM, V44, P839, DOI 10.1016/j.jbi.2011.05.002
   Eldomery MK, 2017, GENOME MED, V9, DOI 10.1186/s13073-017-0412-6
   Fitzgerald TW, 2015, NATURE, V519, P223, DOI 10.1038/nature14135
   Flanagan SE, 2010, GENET TEST MOL BIOMA, V14, P533, DOI 10.1089/gtmb.2010.0036
   Flicek P, 2014, NUCLEIC ACIDS RES, V42, pD749, DOI 10.1093/nar/gkt1196
   Furney SJ, 2006, BMC GENOMICS, V7, DOI 10.1186/1471-2164-7-165
   Gonzalez-Perez A, 2011, AM J HUM GENET, V88, P440, DOI 10.1016/j.ajhg.2011.03.004
   Grimm DG, 2015, HUM MUTAT, V36, P513, DOI 10.1002/humu.22768
   Guo Y, 2013, AM J HUM GENET, V93, P78, DOI 10.1016/j.ajhg.2013.05.022
   Habegger L, 2012, BIOINFORMATICS, V28, P2267, DOI 10.1093/bioinformatics/bts368
   Hamosh A, 2005, NUCLEIC ACIDS RES, V33, pD514
   Hornbeck PV, 2012, NUCLEIC ACIDS RES, V40, pD261, DOI 10.1093/nar/gkr1122
   Huang N, 2010, PLOS GENET, V6, DOI 10.1371/journal.pgen.1001154
   Ionita-Laza I, 2016, NAT GENET, V48, P214, DOI 10.1038/ng.3477
   Itan Y, 2015, P NATL ACAD SCI USA, V112, P11426, DOI 10.1073/pnas.1515057112
   Jian XQ, 2014, NUCLEIC ACIDS RES, V42, P13534, DOI 10.1093/nar/gku1206
   Khurana E, 2013, PLOS COMPUT BIOL, V9, DOI 10.1371/journal.pcbi.1002886
   Kircher M, 2014, NAT GENET, V46, P310, DOI 10.1038/ng.2892
   Kumar P, 2009, NAT PROTOC, V4, P1073, DOI 10.1038/nprot.2009.86
   Landrum MJ, 2016, NUCLEIC ACIDS RES, V44, pD862, DOI 10.1093/nar/gkv1222
   Lek M, 2016, NATURE, V536, P285, DOI 10.1038/nature19057
   Levy S, 2007, PLOS BIOL, V5, P2113, DOI 10.1371/journal.pbio.0050254
   Li MX, 2013, PLOS GENET, V9, DOI 10.1371/journal.pgen.1003143
   Liu XM, 2016, HUM MUTAT, V37, P235, DOI 10.1002/humu.22932
   Lonsdale J, 2013, NAT GENET, V45, P580, DOI 10.1038/ng.2653
   Lopez-Bigas N, 2004, NUCLEIC ACIDS RES, V32, P3108, DOI 10.1093/nar/gkh605
   Lyon GJ, 2012, GENOME MED, V4, DOI 10.1186/gm359
   Ng PC, 2008, PLOS GENET, V4, DOI 10.1371/journal.pgen.1000160
   Pei BK, 2012, GENOME BIOL, V13, DOI 10.1186/gb-2012-13-9-r51
   Samocha KE, 2014, NAT GENET, V46, P944, DOI 10.1038/ng.3050
   Schug J, 2005, GENOME BIOL, V6, DOI 10.1186/gb-2005-6-4-r33
   Schwarz JM, 2014, NAT METHODS, V11, P361, DOI 10.1038/nmeth.2890
   Shannon CE., 1998, MATH THEORY COMMUNIC
   Stark C, 2006, NUCLEIC ACIDS RES, V34, pD535, DOI 10.1093/nar/gkj109
   Stenson PD, 2014, HUM GENET, V133, P1, DOI 10.1007/s00439-013-1358-4
   Stephens JC, 2001, SCIENCE, V293, P489, DOI 10.1126/science.1059431
   Sunyaev S, 2000, TRENDS GENET, V16, P198, DOI 10.1016/S0168-9525(00)01988-0
   Wang J, 2008, NATURE, V456, P60, DOI 10.1038/nature07484
   Ward JJ, 2004, BIOINFORMATICS, V20, P2138, DOI 10.1093/bioinformatics/bth195
   Wheeler DA, 2008, NATURE, V452, P872, DOI 10.1038/nature06884
NR 52
TC 9
Z9 9
U1 1
U2 6
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0305-1048
EI 1362-4962
J9 NUCLEIC ACIDS RES
JI Nucleic Acids Res.
PD OCT 13
PY 2017
VL 45
IS 18
BP 10393
EP 10402
DI 10.1093/nar/gkx730
PG 10
WC Biochemistry & Molecular Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology
GA FJ9OK
UT WOS:000413107400010
PM 28977528
OA Green Submitted, Green Published, gold
DA 2022-08-23
ER

PT J
AU Zheng, XC
   Lu, JZ
   Kiritsis, D
AF Zheng, Xiaochen
   Lu, Jinzhi
   Kiritsis, Dimitris
TI The emergence of cognitive digital twin: vision, challenges and
   opportunities
SO INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
LA English
DT Article; Early Access
DE Digital twin; cognitive digital twin; semantic modelling; knowledge
   graph; lifecycle management; systems engineering; ontology; model-based
   systems engineering
ID MANAGEMENT; INTERNET; DESIGN; PLM
AB As a key enabling technology of Industry 4.0, Digital Twin (DT) has been widely applied to various industrial domains covering different lifecycle phases of products and systems. To fully realize the Industry 4.0 vision, it is necessary to integrate multiple relevant DTs of a system according to a specific mission. This requires integrating all available data, information and knowledge related to the system across its entire lifecycle. It is a challenging task due to the high complexity of modern industrial systems. Semantic technologies such as ontology and knowledge graphs provide potential solutions by empowering DTs with augmented cognitive capabilities. The Cognitive Digital Twin (CDT) concept has been recently proposed which reveals a promising evolution of the current DT concept towards a more intelligent, comprehensive, and full lifecycle representation of complex systems. This paper reviews existing studies relevant to the CDT concept, and further explores its definitions and key features. To facilitate CDT development, a reference architecture is proposed based on the RAMI4.0 and some other existing architectures. Moreover, some key enabling technologies and several application scenarios of CDT are introduced. The challenges and opportunities are discussed in the end to boost future studies.
C1 [Zheng, Xiaochen; Lu, Jinzhi; Kiritsis, Dimitris] Ecole Polytech Fed Lausanne EPFL, ICT Sustainable Mfg, CH-1015 Lausanne, Switzerland.
RP Lu, JZ (通讯作者)，Ecole Polytech Fed Lausanne EPFL, ICT Sustainable Mfg, CH-1015 Lausanne, Switzerland.
EM jinzhi.lu@epfl.ch
OI Lu, Jinzhi/0000-0001-5044-2921
FU EUH2020 project FACTLOG [869951]; EU H2020 project QU4LITY [825030]
FX The work presented in this paper has been partially supported by the
   EUH2020 project FACTLOG(869951) -Energyaware Factory Analytics for
   Process Industries, and EU H2020 project QU4LITY (825030) -Digital
   Reality in Zero Defect Manufacturing.
CR Abburu S., 2020, P 12 INT C ADV COGN, P25
   Abburu Sailesh, 2020 IEEE INT C ENG, P1
   Adl AE, 2016, COGNITIVE DIGITAL TW
   Al Faruque MA, 2021, PROCEEDINGS OF THE 2021 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2021), P440, DOI 10.23919/DATE51398.2021.9474166
   Ali MI, 2021, IEEE INTELL SYST, V36, P96, DOI 10.1109/MIS.2021.3062437
   Alliance Open Mobile, 2012, NEXT GEN SERVICE INT
   [Anonymous], 2012, P 53 AIAAASMEASCEAHS, DOI DOI 10.2514/6.2012-1813
   [Anonymous], 2011, SYSTEMS ENG PRINCIPL
   Arp R., 2008, NATURE PRECEDINGS, V1, P1, DOI DOI 10.1038/NPRE.2008.1941.1
   Bachelor G, 2020, IEEE SYST J, V14, P1568, DOI 10.1109/JSYST.2019.2925627
   Banerjee A, 2017, PROCEEDINGS OF THE 2017 ACM WEB SCIENCE CONFERENCE (WEBSCI '17), P425
   Batenburg R., 2006, International Journal of Product Lifecycle Management, V1, P333, DOI 10.1504/IJPLM.2006.011053
   Boschert S., 2018, P TMCE PLAM GRAN CAN, P7, DOI DOI 10.17560/ATP.V60I10.2371
   Chandra A, 2013, IEEE INTERNET COMPUT, V17, P70, DOI 10.1109/MIC.2013.93
   Chen JJ, 2020, ENTROPY-SWITZ, V22, DOI 10.3390/e22010095
   Cho S, 2019, IEEE INT CONF DISTR, P347, DOI 10.1109/DCOSS.2019.00076
   Chowdhury GG, 2003, ANNU REV INFORM SCI, V37, P51, DOI 10.1002/aris.1440370103
   Denkena B, 2014, PROC TECH, V15, P406, DOI 10.1016/j.protcy.2014.09.095
   Ding K, 2019, INT J PROD RES, V57, P6315, DOI 10.1080/00207543.2019.1566661
   Duprez Jean, 2019, INCOSE International Symposium, V29, P22, DOI 10.1002/j.2334-5837.2019.00587.x
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Eirinakis P., 2020, P 2020 IEEE INT C EN, P1
   El Kadiri S, 2015, INT J PROD RES, V53, P5657, DOI 10.1080/00207543.2015.1052155
   Fariz Saracevic I.B.M., 2017, COGNITIVE DIGITAL TW
   FERNANDEZ F, 2019, INT WORK C INT NAT A, P23
   Grieves M., 2014, DIGITAL TWIN MANUFAC
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Haskins C, 2014, INSIGHT, V17, P63, DOI [10.1002/inst.201417263a, DOI 10.1002/INST.201417263A]
   Hewett A, 2009, ANN INFORM SYST, V5, P81, DOI 10.1007/978-1-4419-1081-3_5
   Nguyen HL, 2020, INFORM FUSION, V61, P56, DOI 10.1016/j.inffus.2020.03.014
   Hongwei Wang, 2019, New Knowledge in Information Systems and Technologies. Advances in Intelligent Systems and Computing (930), P426, DOI 10.1007/978-3-030-16181-1_40
   IBM, 2021, COGN MAN OV 4 APPL A
   International Organization for Standardization, 2021, 2324712021 ISO
   IOF, 2021, IOF TECHN PRINC
   ISO, 2005, IND AUT SYST INT PRO
   Jacoby M, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10186519
   Jinzhi Lu, 2020, Proceedings of 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing, (AMP 2020). Lecture Notes in Mechanical Engineering (LNME), P105, DOI 10.1007/978-3-030-46212-3_7
   Khan WZ, 2020, COMPUT ELECTR ENG, V81, DOI 10.1016/j.compeleceng.2019.106522
   Kharlamov E, 2018, IEEE INT CONF BIG DA, P4189, DOI 10.1109/BigData.2018.8622503
   Kiritsis D, 2011, COMPUT AIDED DESIGN, V43, P479, DOI 10.1016/j.cad.2010.03.002
   Knapp GL, 2017, ACTA MATER, V135, P390, DOI 10.1016/j.actamat.2017.06.039
   Kokar MM, 2012, IEEE INTELL SYST, V27, P91, DOI 10.1109/MIS.2012.61
   Liao YX, 2018, IEEE INTERNET THINGS, V5, P4515, DOI 10.1109/JIOT.2018.2834151
   Liu J, 2021, J MANUF SYST, V60, P93, DOI 10.1016/j.jmsy.2021.05.004
   Liu Y, 2015, INT J PROD RES, V53, P1019, DOI 10.1080/00207543.2014.932936
   Lu J, 2021, SMART SERV SUMM, P105
   Lu J., 2020, ARXIV200303472
   Lu Jinzhi, 2020, INT WORKSH SEM DIG T
   Masolo C., 2003, WONDER WEB PROJECT D, P75
   Matthews Sky, 2018, DESIGNING BETTER MAC
   Gomez-Berbis JM, 2019, COMM COM INF SC, V1124, P178, DOI 10.1007/978-3-030-34989-9_14
   Mohan N, 2016, 2016 CLOUDIFICATION OF THE INTERNET OF THINGS (CIOT)
   Muller, 2008, CISC VIS NETW IND GL
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Oliver D. W., 1993, P INCOSE INT S 1993, P97
   Ordieres-Mere Joaquin, 2019, Procedia Manufacturing, V39, P185, DOI 10.1016/j.promfg.2020.01.308
   Perin, 2020, CYB WORKSH EUR STEEL, P132
   Popov, 2018, REP
   Psarommatis F, 2021, J MANUF SYST, V59, P507, DOI 10.1016/j.jmsy.2021.03.021
   Qi QL, 2021, J MANUF SYST, V58, P3, DOI 10.1016/j.jmsy.2019.10.001
   Qi QL, 2019, IEEE ACCESS, V7, P86769, DOI 10.1109/ACCESS.2019.2923610
   Roman R, 2018, FUTURE GENER COMP SY, V78, P680, DOI 10.1016/j.future.2016.11.009
   Rozanec JM, 2022, INT J PROD RES, V60, P452, DOI 10.1080/00207543.2021.2002967
   Schleich B, 2017, CIRP ANN-MANUF TECHN, V66, P141, DOI 10.1016/j.cirp.2017.04.040
   Schweichhart, 2016, INTRODUCTION
   Scott Jackson, 2019, INCOSE
   Seshadri B.R., 2017, P 25 AIAA AHS AD STR, P1675
   Singhal A., 2012, OFFICIAL GOOGLE BLOG
   Soderberg R, 2017, CIRP ANN-MANUF TECHN, V66, P137, DOI 10.1016/j.cirp.2017.04.038
   Solutions Cisco Fog Computing, 2015, UNLEASH POWER INTERN
   Sun SJ, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20113046
   Tao F, 2019, INT J PROD RES, V57, P3935, DOI 10.1080/00207543.2018.1443229
   Tao F, 2019, IEEE T IND INFORM, V15, P2405, DOI 10.1109/TII.2018.2873186
   Tao Fei, 2017, Computer Integrated Manufacturing Systems, V23, P1, DOI 10.13196/j.cims.2017.01.001
   W3C-WoT, 2020, THINGS DESCR W3C REC
   Wang J, 2021, IEEE T EMERG TOP COM, V9, P484, DOI 10.1109/TETC.2019.2890867
   Wang JJ, 2019, INT J PROD RES, V57, P3920, DOI 10.1080/00207543.2018.1552032
   Wang XV, 2019, INT J PROD RES, V57, P3892, DOI 10.1080/00207543.2018.1497819
   Wymore, 2018, ENGINEERING, V3
   Yitmen I, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11094276
   Zhang YF, 2017, J CLEAN PROD, V159, P229, DOI 10.1016/j.jclepro.2017.04.172
   ZHENG X, 2020, IFIP INT C ADV PRODU, P222
   Zheng X., 2020, PROCEDIA MANUF, V51, P309
   Zheng X., 2021, FOOD SCI, P1, DOI [10.7506/spkx1002-6630-20210520-251, DOI 10.7506/SPKX1002-6630-20210520-251]
   Zhou GH, 2020, INT J PROD RES, V58, P1034, DOI 10.1080/00207543.2019.1607978
NR 85
TC 9
Z9 9
U1 31
U2 44
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0020-7543
EI 1366-588X
J9 INT J PROD RES
JI Int. J. Prod. Res.
DI 10.1080/00207543.2021.2014591
EA DEC 2021
PG 23
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA XU0XF
UT WOS:000733998500001
OA hybrid
DA 2022-08-23
ER

PT J
AU Luo, WB
   Tian, ZY
   Zhong, SH
   Lyu, QK
   Deng, MJ
AF Luo, Wenbing
   Tian, Ziyan
   Zhong, Shihu
   Lyu, Qinke
   Deng, Mingjun
TI Global Evolution of Research on Sustainable Finance from 2000 to 2021: A
   Bibliometric Analysis on WoS Database
SO SUSTAINABILITY
LA English
DT Review
DE sustainable finance; CiteSpace; knowledge mapping; bibliometric; Web of
   Science
ID ENVIRONMENTAL INVESTMENT; CLIMATE-CHANGE; AID; VULNERABILITY;
   PERSPECTIVES; GOVERNANCE; ALLOCATION; INNOVATION; TRENDS
AB The expanding international influence of sustainable finance has made it one of the most cutting-edge development trends in the financial field. Learning about the global evolution of research on sustainable finance can improve the understanding and evaluation of sustainable finance by scholars and practitioners. Based on the ISI Web of Science database, this paper used bibliometric methods to analyze 3786 articles related to sustainable finance published between 2000 and 2021, mastering their discipline co-occurrence, publication characteristics, partnership, influence, keyword co-occurrence, co-citations, and structural variation. The highlights of the results: socially responsible investment, climate change, corporate social responsibility, green finance, carbon credits, and renewable energy were the hotspots between 2000 and 2021; responsible investment, green bond, low-carbon transition, vulnerable countries, low-carbon investment, business model, financial development, supply chain, conventional investment dilemma, sustainable financing, environmental investment, and green credit policy were the hot research topics between 2016 and 2021; papers related to socially responsible investment were an important knowledge base for sustainable financial research between 2000 and 2021; the research topics of the articles with the strongest transformative potentials between 2016 and 2021 mainly involved green bonds, socially responsible mutual funds, ESG investors' preferences, and the impact of COVID-19.
C1 [Luo, Wenbing; Tian, Ziyan] Hunan Univ Sci & Technol, Sch Business, Xiangtan 411201, Peoples R China.
   [Luo, Wenbing] Hunan Univ Technol & Business, Sch Accounting, Changsha 410205, Peoples R China.
   [Zhong, Shihu] Shanghai Natl Accounting Inst, Shanghai 201799, Peoples R China.
   [Lyu, Qinke] Univ Amsterdam, Amsterdam Business Sch, NL-1000 GG Amsterdam, Netherlands.
   [Deng, Mingjun] Hunan Univ Sci & Technol, Big Data & Intelligent Decis Res Ctr, Xiangtan 411201, Peoples R China.
RP Deng, MJ (通讯作者)，Hunan Univ Sci & Technol, Big Data & Intelligent Decis Res Ctr, Xiangtan 411201, Peoples R China.
EM wbluo@hnust.edu.cn; 21011502006@mail.hnust.edu.cn;
   zhongshihu@163.sufe.edu.cn; lvqk0720@gmail.com; mjdeng@hnust.edu.cn
FU National Social Science Foundation of China [20BGL201]
FX This study is supported by the National Social Science Foundation of
   China (20BGL201).
CR An SM, 2021, EUR J OPER RES, V292, P125, DOI 10.1016/j.ejor.2020.10.025
   Ari I, 2021, ENERGY, V222, DOI 10.1016/j.energy.2021.119925
   Bernardo G, 2016, ENVIRON INNOV SOC TR, V21, P123, DOI 10.1016/j.eist.2016.04.006
   Betzold C, 2017, INT ENVIRON AGREEM-P, V17, P17, DOI 10.1007/s10784-016-9343-8
   Bilbao-Terol A, 2017, INT REV FINANC ANAL, V52, P213, DOI 10.1016/j.irfa.2017.06.003
   Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106
   Boukhatem J, 2021, ANN OPER RES, V297, DOI 10.1007/s10479-020-03519-6
   Brzeszczynski J, 2021, FINANC RES LETT, V42, DOI 10.1016/j.frl.2021.101939
   Mejia-Escobar JC, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12145648
   Chariri A, 2019, J ASIA BUS STUD, V13, P433, DOI 10.1108/JABS-05-2017-0061
   Chatzitheodorou K, 2019, SUSTAIN PROD CONSUMP, V19, P117, DOI 10.1016/j.spc.2019.03.006
   Chegut A, 2011, SUSTAIN DEV, V19, P77, DOI 10.1002/sd.509
   Chen Chaomei, 2020, Front Res Metr Anal, V5, P607286, DOI 10.3389/frma.2020.607286
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen CM, 2012, J AM SOC INF SCI TEC, V63, P431, DOI 10.1002/asi.21694
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Cheng YP, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10020403
   Chien FS, 2021, ENVIRON SCI POLLUT R, V28, P40957, DOI 10.1007/s11356-021-13423-6
   Cobo MJ, 2011, J AM SOC INF SCI TEC, V62, P1382, DOI 10.1002/asi.21525
   Cui LB, 2015, INT J CLIM CHANG STR, V7, P206, DOI 10.1108/IJCCSM-11-2013-0125
   D'Apice V, 2021, CORP SOC RESP ENV MA, V28, P1496, DOI 10.1002/csr.2178
   D'Orazio P, 2021, CLIM POLICY, V21, P1368, DOI 10.1080/14693062.2021.1975623
   D'Orazio P, 2022, ENVIRON SCI POLLUT R, V29, P7678, DOI 10.1007/s11356-021-15655-y
   D'Orazio P, 2019, ECOL ECON, V160, P25, DOI 10.1016/j.ecolecon.2019.01.029
   Daugaard D, 2020, ACCOUNT FINANC, V60, P1501, DOI 10.1111/acfi.12479
   Donthu N, 2021, J BUS RES, V133, P285, DOI 10.1016/j.jbusres.2021.04.070
   Dorfleitner G, 2017, J ASSET MANAG, V18, P81, DOI 10.1057/s41260-016-0011-x
   Doshi D, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12104308
   Ellegaard O, 2015, SCIENTOMETRICS, V105, P1809, DOI 10.1007/s11192-015-1645-z
   Escrig-Olmedo E, 2017, J CLEAN PROD, V162, P1334, DOI 10.1016/j.jclepro.2017.06.143
   Farza K, 2022, ENVIRON MODEL ASSESS, DOI 10.1007/s10666-022-09836-3
   Farza K, 2021, J ENVIRON MANAGE, V300, DOI 10.1016/j.jenvman.2021.113695
   Ferreiraet M.C.R.D.C., 2016, J SUSTAIN FINANC INV, V6, P112
   Cunha FAFD, 2021, BUS STRATEG ENVIRON, V30, P3821, DOI 10.1002/bse.2842
   Freeman OE, 2014, ENVIRON SCI TECHNOL, V48, P14112, DOI 10.1021/es503941u
   Fu JY, 2021, ENERGIES, V14, DOI 10.3390/en14113076
   Gao Y, 2021, CHINA FINANC REV INT, DOI 10.1108/CFRI-06-2021-0136
   Herrera M, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0010355
   Hood WW, 2001, SCIENTOMETRICS, V52, P291, DOI 10.1023/A:1017919924342
   Hu GQ, 2021, ENERG ECON, V98, DOI 10.1016/j.eneco.2021.105134
   Jayaram R, 2020, INT J INDIAN CULT BU, V21, P317, DOI 10.1504/IJICBM.2020.110452
   Jeriji M, 2022, BRIT J MANAGE, DOI 10.1111/1467-8551.12603
   Munoz-Torres MJ, 2019, CORP SOC RESP ENV MA, V26, P439, DOI 10.1002/csr.1695
   Khudyakova LS, 2019, MIROVAYA EKON MEZHD, V63, P16, DOI 10.20542/0131-2227-2019-63-7-16-22
   Klock C, 2018, ENVIRON POLIT, V27, P892, DOI 10.1080/09644016.2018.1480273
   Kolbel JF, 2020, ORGAN ENVIRON, V33, P554, DOI 10.1177/1086026620919202
   Lee CC, 2021, N AM J ECON FINANC, V55, DOI 10.1016/j.najef.2020.101309
   Leitao J, 2021, BUS STRATEG ENVIRON, V30, P2077, DOI 10.1002/bse.2733
   Lewis JI, 2010, ENERG POLICY, V38, P2875, DOI 10.1016/j.enpol.2010.01.020
   Li J, 2011, CLIMATIC CHANGE, V107, P567, DOI 10.1007/s10584-010-9970-v
   Pham L, 2021, GLOB FINANC J, V50, DOI 10.1016/j.gfj.2021.100669
   Liu CL, 2016, SCIENTOMETRICS, V109, P159, DOI 10.1007/s11192-016-2045-8
   Liu NN, 2021, J CLEAN PROD, V279, DOI 10.1016/j.jclepro.2020.123595
   Liu WJ, 2019, ECOL ECON, V156, P293, DOI 10.1016/j.ecolecon.2018.09.001
   Ma XM, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16050842
   Marti-Ballester CP, 2015, J CLEAN PROD, V106, P466, DOI 10.1016/j.jclepro.2014.06.058
   Marti-Ballester CP, 2015, MANAGE DECIS, V53, P571, DOI 10.1108/MD-04-2014-0207
   Martinez-Climent C, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11030934
   Mercure JF, 2019, CLIM POLICY, V19, P1019, DOI 10.1080/14693062.2019.1617665
   Mohsin M, 2021, FINANC RES LETT, V43, DOI 10.1016/j.frl.2020.101520
   Monasterolo I, 2019, ENERG POLICY, V124, P355, DOI 10.1016/j.enpol.2018.08.051
   Muganyi T, 2021, ENVIRON SCI ECOTECH, V7, DOI 10.1016/j.ese.2021.1001072666-4984/
   Naeem MA, 2021, ENERG POLICY, V153, DOI 10.1016/j.enpol.2021.112285
   Nemet GF, 2017, GLOBAL ENVIRON CHANG, V42, P47, DOI 10.1016/j.gloenvcha.2016.12.004
   Pedersen LH, 2021, J FINANC ECON, V142, P572, DOI 10.1016/j.jfineco.2020.11.001
   Pham L, 2021, ENERG ECON, V98, DOI 10.1016/j.eneco.2021.105257
   Pickering J, 2015, GLOBAL ENVIRON POLIT, V15, P39, DOI 10.1162/GLEP_a_00325
   Pickering J, 2015, WORLD DEV, V68, P149, DOI 10.1016/j.worlddev.2014.10.033
   Purdon M, 2015, WORLD DEV, V74, P462, DOI 10.1016/j.worlddev.2015.05.024
   Rathner S, 2013, J BUS ETHICS, V118, P349, DOI 10.1007/s10551-012-1584-z
   Reboredo JC, 2020, ENERG ECON, V86, DOI 10.1016/j.eneco.2019.104629
   Reboredo JC, 2020, ECON MODEL, V88, P25, DOI 10.1016/j.econmod.2019.09.004
   Rindefjall T, 2011, INT ENVIRON AGREEM-P, V11, P7, DOI 10.1007/s10784-011-9151-0
   Sciarelli M, 2021, TQM J, V33, P39, DOI 10.1108/TQM-08-2020-0180
   Setyowati AB, 2020, CLIM POLICY, DOI 10.1080/14693062.2020.1858741
   Sharma GD, 2021, ENERG POLICY, V156, DOI 10.1016/j.enpol.2021.112467
   Shen YJ, 2021, SCI TOTAL ENVIRON, V755, DOI 10.1016/j.scitotenv.2020.142538
   Sheraz M, 2021, ENVIRON SCI POLLUT R, V28, P35126, DOI 10.1007/s11356-021-13116-0
   Shi XT, 2018, ENERGIES, V11, DOI 10.3390/en11040983
   Shi YL, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11133716
   Sievanen R, 2013, J BUS ETHICS, V117, P137, DOI 10.1007/s10551-012-1514-0
   Sudmant AH, 2017, CLIM POLICY, V17, P299, DOI 10.1080/14693062.2015.1104498
   Tian JF, 2020, ADV CLIM CHANG RES, V11, P85, DOI 10.1016/j.accre.2020.05.003
   Tolliver C, 2020, J CLEAN PROD, V244, DOI 10.1016/j.jclepro.2019.118643
   Vasileiou E, 2022, RES POLICY, V51, DOI 10.1016/j.respol.2022.104530
   Viviers S, 2012, S AFR J BUS MANAG, V43, P1
   Weiler F, 2018, WORLD DEV, V104, P65, DOI 10.1016/j.worlddev.2017.11.001
   Widyawati L, 2020, BUS STRATEG ENVIRON, V29, P619, DOI 10.1002/bse.2393
   Yang YY, 2019, J CLEAN PROD, V225, P11, DOI 10.1016/j.jclepro.2019.03.242
   Yang YX, 2021, RESOUR POLICY, V74, DOI 10.1016/j.resourpol.2021.102445
   Yi YT, 2020, SCIENTOMETRICS, V123, P1051, DOI 10.1007/s11192-020-03416-6
   Yu XB, 2021, ADV CIV ENG, V2021, DOI 10.1155/2021/9934004
   Zafar A, 2021, ENVIRON SCI POLLUT R, V28, P50430, DOI 10.1007/s11356-021-14152-6
   Zhang DY, 2019, FINANC RES LETT, V29, P425, DOI 10.1016/j.frl.2019.02.003
   Zhang J, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13094847
   Zhang K, 2021, J ENVIRON MANAGE, V298, DOI 10.1016/j.jenvman.2021.113445
   Zhang SL, 2021, J ENVIRON MANAGE, V296, DOI 10.1016/j.jenvman.2021.113159
   Zhang YM, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18010033
   Zhao D., 2015, SYNTH LECT INF CONCE, V38, pe113
   Zhou Y, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17093082
   Zou F, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12093597
   Zupic I, 2015, ORGAN RES METHODS, V18, P429, DOI 10.1177/1094428114562629
NR 102
TC 0
Z9 0
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD AUG
PY 2022
VL 14
IS 15
AR 9435
DI 10.3390/su14159435
PG 23
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA 3T3LW
UT WOS:000840181200001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Xiong, H
   Zhao, Z
AF Xiong, H.
   Zhao, Z.
TI THE CORRELATION BETWEEN HAZE AND ECONOMIC GROWTH: BIBLIOMETRIC ANALYSIS
   BASED ON WOS DATABASE
SO APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH
LA English
DT Article
DE PM2.5; EKC; spatial spillover; knowledge map; CiteSpace; hot trend
ID AIR-POLLUTION; ENVIRONMENTAL-QUALITY; KUZNETS CURVE; EMISSIONS; ENERGY;
   EUROPE; TRADE; IMPACT
AB This paper takes 4,225 articles on the relationship between haze and economic growth in the Web of Science database from 1992 to 2017 as the research object, and uses CiteSpace to explore the spatial and temporal distribution, research topics and hot trends of the literature. The results show that: (1) The relevant literature volume keeps growing at a high speed. (2) The cooperative group of high yield authors represented by Huang GH and Streets DG is basically formed. (3) The research institutions are divided into the top international colleges and universities represented by Tsinghua University and the University of California, Berkeley, as well as the two major groups of international well-known scientific research institutes represented by the Chinese Academy of Sciences and the American Environmental Protection Agency. (4) The cited high literature is concentrated in the six international high level journals such as "Science". (5) The two major cooperation bodies of "the United States-China" and "Holland- France-Italy-Spain" have been formed. (6) The research topics are mainly about the economic mechanism of haze formation, its relationship with economic growth, spatial spillover effects and countermeasures. (7) The research hotspots show trends from "air pollution" and "health" to "PM2.5" and "CO2 emission".
C1 [Xiong, H.] Nanchang Univ, Sch Management, Nanchang 330031, Jiangxi, Peoples R China.
   [Zhao, Z.] Zhejiang Univ, Inst Chinas Sci Technol & Educ Policy, Hangzhou 310058, Peoples R China.
RP Xiong, H (通讯作者)，Nanchang Univ, Sch Management, Nanchang 330031, Jiangxi, Peoples R China.
EM xionghuanhuan@ncu.edu.cn
FU National Social Science Foundation of China [15CJL029]
FX This work is supported by the National Social Science Foundation of
   China (grant number 15CJL029).
CR Allan G, 2014, ECOL ECON, V100, P40, DOI 10.1016/j.ecolecon.2014.01.012
   [Anonymous], 1993, EMPIRICAL TESTS POLI
   Anselin L, 2001, AM J AGR ECON, V83, P705, DOI 10.1111/0002-9092.00194
   Ansuategi A, 2003, ENVIRON RESOUR ECON, V26, P305, DOI 10.1023/A:1026387431794
   Antonakakis N, 2017, RENEW SUST ENERG REV, V68, P808, DOI 10.1016/j.rser.2016.09.105
   Antweiler W, 2001, AM ECON REV, V91, P877, DOI 10.1257/aer.91.4.877
   ARROW K, 1995, SCIENCE, V268, P520, DOI 10.1126/science.268.5210.520
   Bovenberg A.L., 1993, J PUBL EC, V57, P369
   Chander P, 2018, SINGAP ECON REV, V63, P1085, DOI 10.1142/S0217590818500339
   Chen Y., 2014, PRINCIPLES APPL ANAL
   CHICHILNISKY G, 1994, AM ECON REV, V84, P851
   Dimitriou K., 2014, ATMOS ENVIRON, V135, P69
   Dinda S, 2004, ECOL ECON, V49, P431, DOI 10.1016/j.ecolecon.2004.02.011
   Feng X. L., 2017, J INTELLIGENCE, V36, P149
   Grossman G.M., 1995, EC GROWTH ENV
   GROSSMAN GM, 1995, Q J ECON, V110, P353, DOI 10.2307/2118443
   Hao Y., 2016, J CLEAN PROD, V112, P65
   He CF, 2012, URBAN STUD, V49, P1767, DOI 10.1177/0042098011415719
   He J, 2005, CHINA ECON REV, V16, P364, DOI 10.1016/j.chieco.2005.03.005
   Hosseini HM, 2013, ENERG POLICY, V56, P312, DOI 10.1016/j.enpol.2012.12.067
   [侯海燕 Hou Haiyan], 2009, [科研管理, Science Research Management], V30, P164
   Karki SK, 2005, ENERG POLICY, V33, P499, DOI 10.1016/j.enpol.2003.08.014
   Liu N. B., 2011, J MODERN INFORM, V31, P100
   Liu X. X., 2017, J S CHINA NORMAL U S, V5:, P86
   Lu H., 2015, FUJIAN FORUM HUMANIT, V9, P44
   Ma T, 2019, J ENVIRON SCI, V83, P8, DOI 10.1016/j.jes.2019.02.031
   Maddison D, 2007, OXFORD ECON PAP, V59, P726, DOI 10.1093/oep/gpm005
   Poon JPH, 2006, EURASIAN GEOGR ECON, V47, P568, DOI 10.2747/1538-7216.47.5.568
   Qiu J.P., 2007, INFORM METROLOGY
   Sarkodie SA, 2019, SCI TOTAL ENVIRON, V683, P489, DOI 10.1016/j.scitotenv.2019.05.278
   SELDEN TM, 1994, J ENVIRON ECON MANAG, V27, P147, DOI 10.1006/jeem.1994.1031
   Stern DI, 2004, WORLD DEV, V32, P1419, DOI 10.1016/j.worlddev.2004.03.004
   Sun R. Y., 2016, J MODERN INFORM, V36, P153
   Tamazian A, 2010, ENERG ECON, V32, P137, DOI 10.1016/j.eneco.2009.04.004
   Wang HJ, 2015, ATMOS OCEAN SCI LETT, V8, P1, DOI 10.3878/AOSL20140081
   Xiaocen M.L., 2016, OPEN ED RES, P90
   Xu Y, 2009, EUR J OPER RES, V198, P315, DOI 10.1016/j.ejor.2008.07.048
   Zhou Q., 2011, STAT INF FORUM, V26, P45
NR 38
TC 2
Z9 2
U1 8
U2 40
PU CORVINUS UNIV BUDAPEST
PI BUDAPEST
PA VILLANYI UT 29/43, BUDAPEST, H-1118, HUNGARY
SN 1589-1623
EI 1785-0037
J9 APPL ECOL ENV RES
JI Appl. Ecol. Environ. Res.
PY 2020
VL 18
IS 1
BP 59
EP 75
DI 10.15666/aeer/1801_059075
PG 17
WC Ecology; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA KS5WA
UT WOS:000518377600004
OA gold
DA 2022-08-23
ER

PT J
AU Guo, JQ
   Liu, SB
   Liu, X
AF Guo, Jianqiang
   Liu, ShuBin
   Liu, Xian
TI Construction of visual cognitive computation model for sports psychology
   based on knowledge atlas
SO COGNITIVE SYSTEMS RESEARCH
LA English
DT Article
DE Sports psychology; Sports; Knowledge mapping; Visualization; Knowledge
   atlas
AB A total of 584 thematic journals were included in the "Chinese Journal of Psychology" from 1996 to 2017 as the basic data set for this study, using bibliometrics, co-occurrence analysis, word frequency analysis, etc., and using CiteSpace III. Software mapping of knowledge. Visual analysis of published papers, research hotspots, research topic time zones, core authors, author co-occurrences, and research institutions of sports psychology research in China. It aims to fully grasp the dynamics of research on the development of sports psychology in China. The process provides a valuable reference for the theoretical research and practice of sports psychology in China. The results show that: Taking time as the axis, the amount of published documents can be divided into three stages: slow growth stage, rapid growth stage, and stable development stage; research topic time zone is also divided into three stages, and the basis for division of each stage is For the introduction of national sports reforms or new policies; 1996-2017, the focus of sports psychology disciplines is the application of sports psychology in physical education and sports training; the core authors in the field are representatives of Yan Jun, Wang Jin, and Yan. Gang Yan, etc.; The core organization of the volume of documents issued is based on professional sports colleges. The representative agency is Beijing Sport University. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Guo, Jianqiang; Liu, Xian] ChangZhou Univ, Sch Phys Educ, Changzhou, Peoples R China.
   [Liu, ShuBin] ChangZhou Univ, Huaide Coll, Jing Jiang, Peoples R China.
RP Guo, JQ (通讯作者)，ChangZhou Univ, Sch Phys Educ, Changzhou, Peoples R China.
EM gjq@cczu.edu.cn
FU Jiang Su social science fund project [16TYD001]
FX This paper is phased research achievements of Jiang Su social science
   fund project (Fund number: 16TYD001).
CR de Haan B, 2017, NEUROIMAGE, V163, P301, DOI 10.1016/j.neuroimage.2017.09.051
   Dmp J., 2017, OTOL NEUROTOL, V38, pc289
   Olivers C. N., 2017, FRONTIERS BIOSCIENCE, V13, P1182
   Stankov L., 2016, J CROSS CULTURAL PSY, V27, P9
   Suppa P, 2015, J ALZHEIMERS DIS, V46, P199, DOI 10.3233/JAD-142280
   Tavella E, 2015, GROUP DECIS NEGOT, V24, P451, DOI 10.1007/s10726-014-9398-2
   Wu SF, 2015, INT J BIFURCAT CHAOS, V25, DOI 10.1142/S0218127415400398
   Zhao HM, 2016, COGN COMPUT, V8, P246, DOI 10.1007/s12559-015-9357-5
NR 8
TC 7
Z9 7
U1 10
U2 65
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1389-0417
J9 COGN SYST RES
JI Cogn. Syst. Res.
PD DEC
PY 2018
VL 52
BP 521
EP 530
DI 10.1016/j.cogsys.2018.07.028
PG 10
WC Computer Science, Artificial Intelligence; Neurosciences; Psychology,
   Experimental
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Neurosciences & Neurology; Psychology
GA HB2GA
UT WOS:000450854400050
DA 2022-08-23
ER

PT J
AU Ahsan, MM
   Cheng, W
   Hussain, AB
   Chen, XF
   Wajid, BA
AF Ahsan, Muhammad Mubashir
   Cheng, Wei
   Hussain, Aqsa Bilal
   Chen, Xuefeng
   Wajid, Basit Ali
TI Knowledge mapping of research progress in vertical greenery systems
   (VGS) from 2000 to 2021 using CiteSpace based scientometric analysis
SO ENERGY AND BUILDINGS
LA English
DT Article
DE Vertical greenery systems; Green facade; Living wall; Bibliometric
   analysis; Knowledge structure; Systematic review
ID POLLUTION TOLERANCE INDEX; COST-BENEFIT-ANALYSIS; LIVING WALL SYSTEMS;
   THERMAL PERFORMANCE; GREENING SYSTEMS; ENERGY PERFORMANCE; PARTICULATE
   MATTER; URBAN-ENVIRONMENT; FACADES; PLANT
AB Due to the rapid research advancements in vertical greenery systems (VGS) recently, the findings of VGS research are prolific. This study intends to visualize the intellectual background, main research frontiers and state-of-the-art knowledge structure of entire VGS domain by conducting a scientometric analysis using CiteSpace. The original research articles published between Jan 2000 to Sep 2021 were extracted from the Web of Science core collection (WOSCC) database and visualization networks of co-authorship, co-citation, co-occurrence, and cluster analysis of VGS related knowledge were developed to categorize the major contributing bodies involved in the flourishment of VGS-related research, such as, most prolific publications, highly productive authors, top contributing institutions, active countries, highly influential journals, documents, and keywords. Furthermore, current research hotspots and recently emerging sub-fields were discovered by using citation burst analysis, whereas cluster analysis was performed to categorize the entire VGS literature into knowledge clusters to achieve state-of-the-art knowledge structure of VGS domain consisting of various research themes. This study can be signif-icantly helpful for scientists and engineers to distinguish among the most critical sub-domains of VGS literature and to identify the new research frontiers for focusing on the most significant aspects for future studies. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Ahsan, Muhammad Mubashir; Cheng, Wei; Chen, Xuefeng; Wajid, Basit Ali] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Shaanxi, Peoples R China.
   [Ahsan, Muhammad Mubashir; Cheng, Wei; Chen, Xuefeng; Wajid, Basit Ali] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R China.
   [Hussain, Aqsa Bilal] Xi An Jiao Tong Univ, Sch Management, Xian 710049, Shaanxi, Peoples R China.
RP Cheng, W (通讯作者)，Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R China.
EM chengw@xjtu.edu.cn
OI AHSAN, MUHAMMAD MUBASHIR/0000-0002-2973-6404
FU National Major Science and Technology Project [J2019-II-0013-0033];
   National Natural Science Foundation of China [52175118]; General Project
   of Joint Pre-research Fund for Equipment of Ministry of Education
   [6141A02022121]; Fundamental Research Funds for the Central
   Universities; Innovative Scientific Program of CNNC
FX This work was supported by the Projects of the National Major Science
   and Technology Project (No. J2019-II-0013-0033) , National Natural
   Science Foundation of China (No. 52175118) , the General Project of
   Joint Pre-research Fund for Equipment of Ministry of Education (No.
   6141A02022121) , the Fundamental Research Funds for the Central
   Universities, and Innovative Scientific Program of CNNC.
CR Al-Kayiem H.H, 2020, SUSTAINABILITY-BASEL, V12
   Alexandria E, 2008, BUILD ENVIRON, V43, P480, DOI 10.1016/j.buildenv.2006.10.055
   Ascione F, 2020, ENERGIES, V13, DOI 10.3390/en13092296
   Azkorra Z, 2015, APPL ACOUST, V89, P46, DOI 10.1016/j.apacoust.2014.09.010
   Behrend J, 2019, ACCOUNT HIST REV, V29, P103, DOI 10.1080/21552851.2019.1606721
   Besir AB, 2018, RENEW SUST ENERG REV, V82, P915, DOI 10.1016/j.rser.2017.09.106
   Bianco L, 2017, ENERG EFFIC, V10, P625, DOI 10.1007/s12053-016-9473-4
   Bustami RA, 2018, BUILD ENVIRON, V146, P226, DOI 10.1016/j.buildenv.2018.09.045
   Cameron RWF, 2014, BUILD ENVIRON, V73, P198, DOI 10.1016/j.buildenv.2013.12.005
   Cetin M, 2016, POL J ENVIRON STUD, V25, P973, DOI 10.15244/pjoes/61744
   Chafer M, 2021, SUSTAIN CITIES SOC, V65, DOI 10.1016/j.scs.2020.102608
   Charoenkit S, 2017, BUILD ENVIRON, V115, P67, DOI 10.1016/j.buildenv.2017.01.017
   Chen Q., 2013, INT J ENV SUSTAIN, V9, P67
   Chen QY, 2013, ENERG BUILDINGS, V61, P298, DOI 10.1016/j.enbuild.2013.02.030
   Chen XY, 2020, TRANSPORT POLICY, V85, P1, DOI 10.1016/j.tranpol.2019.10.004
   Cheng CY, 2010, BUILD ENVIRON, V45, P1779, DOI 10.1016/j.buildenv.2010.02.005
   Chiquet C, 2013, URBAN ECOSYST, V16, P453, DOI 10.1007/s11252-012-0277-9
   Coma J, 2017, BUILD ENVIRON, V111, P228, DOI 10.1016/j.buildenv.2016.11.014
   Dahanayake KWDKC, 2017, ENERG BUILDINGS, V138, P47, DOI 10.1016/j.enbuild.2016.12.002
   de Kluizenaar Y, 2016, BUILD ENVIRON, V102, P54, DOI 10.1016/j.buildenv.2016.03.013
   Dimoudi A, 2003, ENERG BUILDINGS, V35, P69, DOI 10.1016/S0378-7788(02)00081-6
   Dunnett N. P., 2008, PLANTING GREEN ROOFS
   Eumorfopoulo EA, 2009, BUILD ENVIRON, V44, P1024, DOI 10.1016/j.buildenv.2008.07.004
   Fernandez-Canero R, 2013, J ENVIRON MANAGE, V128, P106, DOI 10.1016/j.jenvman.2013.04.052
   Guillaume G, 2015, SCI TOTAL ENVIRON, V502, P22, DOI 10.1016/j.scitotenv.2014.08.111
   Gunawardena K, 2019, BUILD ENVIRON, V148, P478, DOI 10.1016/j.buildenv.2018.11.014
   He Y, 2017, ENERG BUILDINGS, V140, P324, DOI 10.1016/j.enbuild.2016.12.083
   Hesselink J.K, 2008, ONDERZOEK MET PLANTE
   Horoshenkov KV, 2013, J ACOUST SOC AM, V133, P2554, DOI 10.1121/1.4798671
   Hu CP, 2011, SCIENTOMETRICS, V86, P657, DOI 10.1007/s11192-010-0313-6
   Hui S. C. M., 2013, JT S 2013 INN TECHN, P1
   Hunter AM, 2014, ECOL ENG, V63, P102, DOI 10.1016/j.ecoleng.2013.12.021
   Irga PJ, 2017, URBAN FOR URBAN GREE, V24, P164, DOI 10.1016/j.ufug.2017.03.026
   Ismail M.R., 2010, EFFECT BUILDING DENS
   Ismail MR, 2013, FRONT ARCHIT RES, V2, P162, DOI 10.1016/j.foar.2013.02.002
   Koehler Manfred, 2008, Urban Ecosystems, V11, P423, DOI 10.1007/s11252-008-0063-x
   Kontoleon KJ, 2010, BUILD ENVIRON, V45, P1287, DOI 10.1016/j.buildenv.2009.11.013
   Kvesitadze G., 2006, BIOCHEMICAL MECH DET, P167
   Liao HC, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10010166
   Liu H, 2015, ENERG CONVERS MANAGE, V89, P1, DOI 10.1016/j.enconman.2014.09.060
   Liu ZG, 2015, SCIENTOMETRICS, V103, P135, DOI 10.1007/s11192-014-1517-y
   Lunain D., 2016, INTER NOISE NOISE CO, P1149
   Madre F, 2015, GLOB ECOL CONSERV, V3, P222, DOI 10.1016/j.gecco.2014.11.016
   Mangone G, 2014, BUILD ENVIRON, V81, P410, DOI 10.1016/j.buildenv.2014.07.019
   Manso M, 2021, RENEW SUST ENERG REV, V135, DOI 10.1016/j.rser.2020.110111
   Manso M, 2015, RENEW SUST ENERG REV, V41, P863, DOI 10.1016/j.rser.2014.07.203
   Manso S, 2014, SCI TOTAL ENVIRON, V481, P232, DOI 10.1016/j.scitotenv.2014.02.059
   Marchi M, 2015, ECOL MODEL, V306, P46, DOI 10.1016/j.ecolmodel.2014.08.013
   Martensson LM, 2016, URBAN FOR URBAN GREE, V15, P84, DOI 10.1016/j.ufug.2015.12.001
   Martensson LM, 2014, ECOL ENG, V71, P610, DOI 10.1016/j.ecoleng.2014.07.027
   Mazzali U, 2013, BUILD ENVIRON, V64, P57, DOI 10.1016/j.buildenv.2013.03.005
   MCCAIN KW, 1991, J AM SOC INFORM SCI, V42, P290, DOI 10.1002/(SICI)1097-4571(199105)42:4<290::AID-ASI5>3.0.CO;2-9
   Medl A, 2017, BUILD ENVIRON, V125, P227, DOI 10.1016/j.buildenv.2017.08.054
   Moya TA, 2019, INDOOR BUILT ENVIRON, V28, P298, DOI 10.1177/1420326X18783042
   Natarajan M, 2015, URBAN ECOSYST, V18, P1, DOI 10.1007/s11252-014-0378-8
   Ottele M, 2011, ENERG BUILDINGS, V43, P3419, DOI 10.1016/j.enbuild.2011.09.010
   Ottele M, 2010, ECOL ENG, V36, P154, DOI 10.1016/j.ecoleng.2009.02.007
   Pandey AK, 2016, ECOTOX ENVIRON SAFE, V134, P358, DOI 10.1016/j.ecoenv.2015.08.028
   Pandey AK, 2015, LANDSCAPE URBAN PLAN, V144, P119, DOI 10.1016/j.landurbplan.2015.08.014
   Perez G, 2017, APPL ENERG, V187, P424, DOI 10.1016/j.apenergy.2016.11.055
   Perez G, 2016, APPL ACOUST, V110, P218, DOI 10.1016/j.apacoust.2016.03.040
   Perez G, 2014, RENEW SUST ENERG REV, V39, P139, DOI 10.1016/j.rser.2014.07.055
   Perez G, 2011, APPL ENERG, V88, P4854, DOI 10.1016/j.apenergy.2011.06.032
   Perez-Urrestarazu L, 2017, URBAN FOR URBAN GREE, V24, P141, DOI 10.1016/j.ufug.2017.04.002
   Perez-Urrestarazu L, 2014, J IRRIG DRAIN ENG, V140, DOI 10.1061/(ASCE)IR.1943-4774.0000702
   Perini K, 2017, ENERG BUILDINGS, V143, P35, DOI 10.1016/j.enbuild.2017.03.036
   Perini K, 2017, ECOL ENG, V100, P268, DOI 10.1016/j.ecoleng.2016.12.032
   Perini K, 2013, BUILD ENVIRON, V70, P110, DOI 10.1016/j.buildenv.2013.08.012
   Perini K, 2013, URBAN ECOSYST, V16, P265, DOI 10.1007/s11252-012-0262-3
   Perini K, 2011, BUILD ENVIRON, V46, P2287, DOI 10.1016/j.buildenv.2011.05.009
   Poddar S, 2017, ENERG EFFIC, V10, P887, DOI 10.1007/s12053-016-9487-y
   Qian JW, 2019, SCAND J HOSP TOUR, V19, P192, DOI 10.1080/15022250.2018.1526113
   Qin J, 2014, INDOOR BUILT ENVIRON, V23, P709, DOI 10.1177/1420326X13481372
   Safikhani T, 2014, ENVIRON CLIM TECHNOL, V14, P5, DOI 10.1515/rtuect-2014-0007
   Safikhani T, 2014, RENEW SUST ENERG REV, V40, P450, DOI 10.1016/j.rser.2014.07.166
   Scarpa M, 2014, ENERG BUILDINGS, V79, P155, DOI 10.1016/j.enbuild.2014.04.014
   Seyam S, 2019, J BUILD ENG, V26, DOI 10.1016/j.jobe.2019.100887
   Shi YL, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11133716
   Singh P, 2020, MATER TODAY-PROC, V28, P1569, DOI 10.1016/j.matpr.2020.04.842
   Susorova I, 2014, BUILD ENVIRON, V76, P113, DOI 10.1016/j.buildenv.2014.03.011
   Susorova I, 2013, BUILD ENVIRON, V67, P1, DOI 10.1016/j.buildenv.2013.04.027
   Van Renterghem T, 2012, J SOUND VIB, V331, P2404, DOI 10.1016/j.jsv.2012.01.006
   Van Renterghem T, 2013, BUILD ENVIRON, V61, P34, DOI 10.1016/j.buildenv.2012.12.001
   Veisten K, 2012, INT J ENV RES PUB HE, V9, P3770, DOI 10.3390/ijerph9113770
   Wang XX, 2015, RENEW SUST ENERG REV, V47, P241, DOI 10.1016/j.rser.2015.02.047
   Weerakkody U, 2017, URBAN FOR URBAN GREE, V27, P173, DOI 10.1016/j.ufug.2017.07.005
   Weinmaster M, 2009, J GREEN BUILD, V4, P3, DOI 10.3992/jgb.4.4.3
   Wong NH, 2010, J URBAN PLAN D-ASCE, V136, P330, DOI 10.1061/(ASCE)UP.1943-5444.0000034
   Wong NH, 2010, BUILD ENVIRON, V45, P663, DOI 10.1016/j.buildenv.2009.08.005
   Wong NH, 2010, BUILD ENVIRON, V45, P411, DOI 10.1016/j.buildenv.2009.06.017
   Wong NH, 2009, ENERG BUILDINGS, V41, P1401, DOI 10.1016/j.enbuild.2009.08.010
   Yu DJ, 2017, INFORM SCIENCES, V418, P619, DOI 10.1016/j.ins.2017.08.031
NR 92
TC 1
Z9 2
U1 27
U2 27
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0378-7788
EI 1872-6178
J9 ENERG BUILDINGS
JI Energy Build.
PD FEB 1
PY 2022
VL 256
AR 111768
DI 10.1016/j.enbuild.2021.111768
EA DEC 2021
PG 18
WC Construction & Building Technology; Energy & Fuels; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Construction & Building Technology; Energy & Fuels; Engineering
GA YX3UD
UT WOS:000754031200001
DA 2022-08-23
ER

PT J
AU Ma, D
   Guan, BY
   Song, LX
   Liu, QY
   Fan, YX
   Zhao, L
   Wang, TX
   Zhang, ZH
   Gao, ZY
   Li, SM
   Xu, H
AF Ma, Dan
   Guan, Baoyi
   Song, Luxia
   Liu, Qiyu
   Fan, Yixuan
   Zhao, Lin
   Wang, Tongxin
   Zhang, Zihao
   Gao, Zhuye
   Li, Siming
   Xu, Hao
TI A Bibliometric Analysis of Exosomes in Cardiovascular Diseases From 2001
   to 2021
SO FRONTIERS IN CARDIOVASCULAR MEDICINE
LA English
DT Article
DE cardiovascular disease; CiteSpace; VOSviewer; bibliometrics;
   cardiovascular diseases
ID MESENCHYMAL STEM-CELL; CIRCULATING EXTRACELLULAR VESICLES; CARDIOMYOCYTE
   APOPTOSIS; MYOCARDIAL-INFARCTION; DRUG-DELIVERY; CARDIAC-FUNCTION;
   BIOMARKERS; THERAPY; MIRNAS; CANCER
AB Background: Exosomes in cardiovascular diseases (CVDs) have become an active research field with substantial value and potential. Nevertheless, there are few bibliometric studies in this field. We aimed to visualize the research hotspots and trends of exosomes in CVDs using a bibliometric analysis to help understand the future development of basic and clinical research.
   Methods: The articles and reviews regarding exosomes in the CVDs were culled from the Web of Science Core Collection, and knowledge maps were generated using CiteSpace and VOSviewer software.
   Results: A total of 1,039 articles were included. The number of exosome articles in the CVDs increased yearly. These publications came from 60 countries/regions, led by the US and China. The primary research institutions were Shanghai Jiao Tong University and Nanjing Medical University. Circulation Research was the journal and co-cited journal with the most studies. We identified 473 authors among which Lucio Barile had the most significant number of articles and Thery C was co-cited most often. After analysis, the most common keywords are myocardium infarction, microRNA and mesenchymal stem cells. Ischemic heart disease, pathogenesis, regeneration, stem cells, targeted therapy, biomarkers, cardiac protection, and others are current and developing areas of study.
   Conclusion: We identified the research hotspots and trends of exosomes in CVDs using bibliometric and visual methods. Research on exosomes is flourishing in the cardiovascular medicine. Regenerative medicine, exosome engineering, delivery vehicles, and biomarkers will likely become the focus of future research.
C1 [Ma, Dan; Guan, Baoyi; Gao, Zhuye; Li, Siming; Xu, Hao] China Acad Chinese Med Sci, Xiyuan Hosp, Beijing, Peoples R China.
   [Song, Luxia; Liu, Qiyu; Fan, Yixuan; Zhao, Lin; Wang, Tongxin; Zhang, Zihao] Beijing Univ Chinese Med, Grad Sch, Beijing, Peoples R China.
   [Gao, Zhuye; Li, Siming; Xu, Hao] China Acad Chinese Med Sci, Xiyuan Hosp, Natl Clin Res Ctr Chinese Med Cardiol, Beijing, Peoples R China.
RP Gao, ZY; Li, SM; Xu, H (通讯作者)，China Acad Chinese Med Sci, Xiyuan Hosp, Beijing, Peoples R China.; Gao, ZY; Li, SM; Xu, H (通讯作者)，China Acad Chinese Med Sci, Xiyuan Hosp, Natl Clin Res Ctr Chinese Med Cardiol, Beijing, Peoples R China.
EM zhuyegao@126.com; mingsiii99@163.com; xuhaotcm@hotmail.com
FU National natural science foundation of China [82004192, 82074215];
   Fundamental Research Funds for the Central public welfare research
   institutes [ZZ14-YQ-004]; CACMS Innovation fund [CI2021A00917]; National
   Clinical Research Center for Chinese Medicine Cardiology, Xiyuan
   Hospital, China Academy of Chinese Medical Sciences [CI2021A00920]
FX This study was supported by National natural science foundation of China
   (Nos. 82004192 and 82074215) and the Fundamental Research Funds for the
   Central public welfare research institutes (ZZ14-YQ-004). CACMS
   Innovation fund (CI2021A00917), and National Clinical Research Center
   for Chinese Medicine Cardiology, Xiyuan Hospital, China Academy of
   Chinese Medical Sciences (CI2021A00920).
CR Adamiak M, 2018, MOL THER, V26, P1635, DOI 10.1016/j.ymthe.2018.04.024
   Ahmadi M, 2021, CELL BIOCHEM FUNCT, V39, P60, DOI 10.1002/cbf.3602
   Akbari A, 2020, STEM CELL RES THER, V11, DOI 10.1186/s13287-020-01866-6
   Akbari A, 2020, LIFE SCI, V249, DOI 10.1016/j.lfs.2020.117447
   Andreadou I, 2015, BRIT J PHARMACOL, V172, P1587, DOI 10.1111/bph.12811
   Arslan F, 2013, STEM CELL RES, V10, P301, DOI 10.1016/j.scr.2013.01.002
   Babaei M, 2021, J TRANSL MED, V19, DOI 10.1186/s12967-021-02863-w
   Barile L, 2018, CARDIOVASC RES, V114, P992, DOI 10.1093/cvr/cvy055
   Barile L, 2014, CARDIOVASC RES, V103, P530, DOI 10.1093/cvr/cvu167
   Batrakova EV, 2016, WIRES NANOMED NANOBI, V8, P744, DOI 10.1002/wnan.1395
   Bei YH, 2017, ADV EXP MED BIOL, V998, P71, DOI 10.1007/978-981-10-4397-0_5
   Boltze J, 2015, FRONT NEUROL, V6, DOI 10.3389/fneur.2015.00155
   Burrello J, 2020, J CELL MOL MED, V24, P9945, DOI 10.1111/jcmm.15594
   Castellani C, 2020, J HEART LUNG TRANSPL, V39, P1136, DOI 10.1016/j.healun.2020.06.011
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen X, 2015, J THORAC DIS, V7, P890, DOI 10.3978/j.issn.2072-1439.2014.11.32
   Cheng YH, 2012, J MOL CELL CARDIOL, V53, P668, DOI 10.1016/j.yjmcc.2012.08.010
   Cheow ESH, 2016, MOL CELL PROTEOMICS, V15, P2628, DOI 10.1074/mcp.M115.055731
   Cyranoski D, 2010, NATURE, V468, P485, DOI 10.1038/468485a
   Davidson SM, 2019, CARDIOVASC RES, V115, P1156, DOI 10.1093/cvr/cvy314
   de Gonzalo-Calvo D, 2016, CLIN INVEST ARTERIOS, V28, P167, DOI 10.1016/j.arteri.2016.05.005
   Hoog VC, 2013, EUR HEART J-ACUTE CA, V2, P53, DOI 10.1177/2048872612471212
   Deddens JC, 2016, J CARDIOVASC TRANSL, V9, P291, DOI 10.1007/s12265-016-9705-1
   Devaux Y, 2015, J INTERN MED, V277, P260, DOI 10.1111/joim.12183
   Donnelly JP, 2017, EVAL PROGRAM PLANN, V60, P186, DOI 10.1016/j.evalprogplan.2016.08.010
   Emanueli C, 2015, VASC PHARMACOL, V71, P24, DOI 10.1016/j.vph.2015.02.008
   Farooqi AA, 2018, BIOTECHNOL ADV, V36, P328, DOI 10.1016/j.biotechadv.2017.12.010
   Femmino S, 2020, VASC PHARMACOL, V135, DOI 10.1016/j.vph.2020.106790
   Fu DL, 2020, EUR REV MED PHARMACO, V24, P10107, DOI 10.26355/eurrev_202010_23230
   Gao L, 2020, SCI TRANSL MED, V12, DOI 10.1126/scitranslmed.aay1318
   Guler AT, 2016, SCIENTOMETRICS, V107, P385, DOI 10.1007/s11192-016-1885-6
   Guo D, 2020, BIOMED RES INT, V2020, DOI 10.1155/2020/5424281
   Huang XQ, 2019, J TRANSL MED, V17, DOI 10.1186/s12967-019-1810-x
   Hur YH, 2020, STEM CELLS, V38, P469, DOI 10.1002/stem.3140
   Ibrahim A, 2016, ANNU REV PHYSIOL, V78, P67, DOI 10.1146/annurev-physiol-021115-104929
   Ibsen SD, 2017, ACS NANO, V11, P6641, DOI 10.1021/acsnano.7b00549
   Jansen F, 2014, J AM HEART ASSOC, V3, DOI 10.1161/JAHA.114.001249
   Jiang WY, 2019, REV CARDIOVASC MED, V20, P153, DOI 10.31083/j.rcm.2019.03.528
   Johnson J, 2021, TRENDS BIOTECHNOL, V39, P598, DOI 10.1016/j.tibtech.2020.10.004
   JOHNSTONE RM, 1992, BIOCHEM CELL BIOL, V70, P179, DOI 10.1139/o92-028
   Joo HS, 2020, INT J MOL SCI, V21, DOI 10.3390/ijms21030727
   Jung JW, 2013, YONSEI MED J, V54, P1293, DOI 10.3349/ymj.2013.54.5.1293
   Kahlert C, 2013, J MOL MED, V91, P431, DOI 10.1007/s00109-013-1020-6
   Kalluri R, 2020, SCIENCE, V367, P640, DOI 10.1126/science.aau6977
   Kalluri R, 2016, J CLIN INVEST, V126, P1208, DOI 10.1172/JCI81135
   Kanelidis AJ, 2017, CIRC RES, V120, P1139, DOI 10.1161/CIRCRESAHA.116.309819
   Khan M, 2015, CIRC RES, V117, P52, DOI 10.1161/CIRCRESAHA.117.305990
   Kourembanas S, 2015, ANNU REV PHYSIOL, V77, P13, DOI 10.1146/annurev-physiol-021014-071641
   Lai RC, 2015, SEMIN CELL DEV BIOL, V40, P82, DOI 10.1016/j.semcdb.2015.03.001
   Lai RC, 2013, BIOTECHNOL ADV, V31, P543, DOI 10.1016/j.biotechadv.2012.08.008
   Lakhal S, 2011, BIOESSAYS, V33, P737, DOI 10.1002/bies.201100076
   Li CW, 2012, CLIN BIOCHEM, V45, P727, DOI 10.1016/j.clinbiochem.2012.04.013
   Li H, 2018, THERANOSTICS, V8, P2079, DOI 10.7150/thno.21895
   Liang MM, 2020, ANN TRANSL MED, V8, DOI 10.21037/atm-20-1259
   Liao ZF, 2021, THERANOSTICS, V11, P268, DOI 10.7150/thno.47021
   Libby P, 2021, NATURE, V592, P524, DOI 10.1038/s41586-021-03392-8
   Liu JW, 2018, MOL THER-NUCL ACIDS, V11, P103, DOI 10.1016/j.omtn.2018.01.010
   Lovell-Badge R, 2021, STEM CELL REP, V16, P1398, DOI 10.1016/j.stemcr.2021.05.012
   Luther KM, 2018, J MOL CELL CARDIOL, V119, P125, DOI 10.1016/j.yjmcc.2018.04.012
   Ma CQ, 2021, FRONT ONCOL, V10, DOI 10.3389/fonc.2020.627891
   Martynov I, 2020, BMC CANCER, V20, DOI 10.1186/s12885-020-06974-3
   Mathieu M, 2019, NAT CELL BIOL, V21, P9, DOI 10.1038/s41556-018-0250-9
   Merino-Gonzalez C, 2016, FRONT PHYSIOL, V7, DOI 10.3389/fphys.2016.00024
   Nguyen PK, 2016, JAMA CARDIOL, V1, P953, DOI 10.1001/jamacardio.2016.2750
   Nikfarjam S, 2020, J TRANSL MED, V18, DOI 10.1186/s12967-020-02622-3
   Nouraee N, 2015, FRONT GENET, V6, DOI 10.3389/fgene.2015.00232
   O'Brien K, 2020, NAT REV MOL CELL BIO, V21, P585, DOI 10.1038/s41580-020-0251-y
   Ong SG, 2014, CIRCULATION, V130, pS60, DOI 10.1161/CIRCULATIONAHA.113.007917
   Pan QW, 2014, EXP BIOL MED, V239, P105, DOI 10.1177/1535370213506802
   Pegtel DM, 2019, ANNU REV BIOCHEM, V88, P487, DOI 10.1146/annurev-biochem-013118-111902
   Pittenger MF, 1999, SCIENCE, V284, P143, DOI 10.1126/science.284.5411.143
   Poe AJ, 2017, J MOL CELL CARDIOL, V111, P40, DOI 10.1016/j.yjmcc.2017.08.002
   Raposo G, 2013, J CELL BIOL, V200, P373, DOI 10.1083/jcb.201211138
   Rezaie J, 2019, J CELL PHYSIOL, V234, P21732, DOI 10.1002/jcp.28894
   Robson A, 2020, NAT REV CARDIOL, V17, P758, DOI 10.1038/s41569-020-00460-w
   Rosland GV, 2009, CANCER RES, V69, P5331, DOI 10.1158/0008-5472.CAN-08-4630
   Saha P, 2019, SCI TRANSL MED, V11, DOI 10.1126/scitranslmed.aau1168
   Sun DM, 2010, MOL THER, V18, P1606, DOI 10.1038/mt.2010.105
   Sun JC, 2020, STEM CELL RES THER, V11, DOI 10.1186/s13287-020-01881-7
   Synnestvedt Marie B, 2005, AMIA Annu Symp Proc, P724
   Tang JY, 2020, DRUG DES DEV THER, V14, P3765, DOI 10.2147/DDDT.S239546
   Tang K, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms2282
   Thankam FG, 2020, J CARDIOVASC TRANSL, V13, P686, DOI 10.1007/s12265-019-09952-8
   Tschope C, 2021, NAT REV CARDIOL, V18, P169, DOI 10.1038/s41569-020-00435-x
   Valadi H, 2007, NAT CELL BIOL, V9, P654, DOI 10.1038/ncb1596
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vandergriff A, 2018, THERANOSTICS, V8, P1869, DOI 10.7150/thno.20524
   Waldenstrom A, 2014, CIRC RES, V114, P315, DOI 10.1161/CIRCRESAHA.114.300584
   Wang C, 2021, THERANOSTICS, V11, P3996, DOI 10.7150/thno.56035
   Wang K, 2017, CELL DEATH DIFFER, V24, P1111, DOI 10.1038/cdd.2017.61
   Wang WT, 2018, CYTOTHERAPY, V20, P1204, DOI 10.1016/j.jcyt.2018.06.012
   Wang X, 2018, J AM HEART ASSOC, V7, DOI 10.1161/JAHA.118.008737
   Wang YJ, 2015, INT J CARDIOL, V192, P61, DOI 10.1016/j.ijcard.2015.05.020
   Wen D, 2016, J CONTROL RELEASE, V238, P166, DOI 10.1016/j.jconrel.2016.07.044
   Wu PP, 2018, CYTOTHERAPY, V20, P291, DOI 10.1016/j.jcyt.2017.11.002
   Wu Z, 2017, TRANSPL P, V49, P1656, DOI 10.1016/j.transproceed.2017.03.078
   Xiong YY, 2021, THERANOSTICS, V11, P1046, DOI 10.7150/thno.53326
   Yoshida S, 2018, MOL THER, V26, P2681, DOI 10.1016/j.ymthe.2018.08.012
   Zhang JH, 2021, FRONT NEUROSCI-SWITZ, V14, DOI 10.3389/fnins.2020.620555
   Zhang TC, 2020, J CANCER, V11, P4823, DOI 10.7150/jca.44126
   Zheng B, 2017, MOL THER, V25, P1279, DOI 10.1016/j.ymthe.2017.03.031
   Zheng DD, 2021, FRONT CELL DEV BIOL, V8, DOI 10.3389/fcell.2020.616161
   Zhou RF, 2020, IUBMB LIFE, V72, P2546, DOI 10.1002/iub.2396
NR 103
TC 5
Z9 5
U1 29
U2 50
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2297-055X
J9 FRONT CARDIOVASC MED
JI Front. Cardiovasc. Med.
PD AUG 25
PY 2021
VL 8
AR 734514
DI 10.3389/fcvm.2021.734514
PG 17
WC Cardiac & Cardiovascular Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Cardiovascular System & Cardiology
GA UU5GS
UT WOS:000698829200001
PM 34513962
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Yu, HQ
   Reiff-Marganiec, S
AF Yu, Hong Qing
   Reiff-Marganiec, Stephan
TI Learning Disease Causality Knowledge From the Web of Health Data
SO INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS
LA English
DT Article
DE Artificial Intelligent; Causality Analysis; Disease Diagnosis;
   Healthcare; Knowledge Graph; Natural Language Processing; Semantic Web
ID NAIVE BAYES; MODEL
AB Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. The authors introduce a novel approach that will develop causality-focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for inference (thinking) and accurate prediction later. In addition, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach is built on a causal probability description logic framework that combines natural language processing (NLP), causality analysis, and extended knowledge graph (KG) technologies. The experimental work has processed 801 diseases in total (from the UK NHS website linking with DBpedia datasets). As a result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.
C1 [Yu, Hong Qing; Reiff-Marganiec, Stephan] Univ Derby, Sch Comp & Engn, Derby, England.
RP Yu, HQ (通讯作者)，Univ Derby, Sch Comp & Engn, Derby, England.
OI Yu, Hong Qing/0000-0001-6330-2725
CR Agarap AFM, 2015, 2ND INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND SOFT COMPUTING (ICMLSC 2018), P5, DOI 10.1145/3184066.3184080
   Agrawal R., 1994, P 20 INT C VER LARG, V1215, P487
   [Anonymous], 2019, DAT QUAL ART INT MIT
   Boshnak H., 2019, INT J COMPUT SCI INF, V16, P68
   Chin J, 2011, J HEALTH COMMUN, V16, P222, DOI 10.1080/10810730.2011.604702
   Dasgupta I., 2019, ARXIV
   Ehsani-Moghaddam B, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0209018
   El-Sappagh S, 2018, BMC MED INFORM DECIS, V18, DOI 10.1186/s12911-018-0651-5
   Esteva A, 2017, NATURE, V542, P115, DOI 10.1038/nature21056
   Gutierrez-Basulto V, 2017, J ARTIF INTELL RES, V58, P1, DOI 10.1613/jair.5222
   Harini D. K., 2018, INT RES J ENG TECHNO, V5
   HOLLAND PW, 1986, J AM STAT ASSOC, V81, P945, DOI 10.2307/2289064
   Hongqing Yu, 2020, ICBRA 2020: 2020 7th International Conference on Bioinformatics Research and Applications, P49, DOI 10.1145/3440067.3440077
   Jiang F, 2017, STROKE VASC NEUROL, V2, P230, DOI 10.1136/svn-2017-000101
   Kaur C., 2019, INT J COMPUTER SCI E, V7, P1148
   Kibriya AM, 2004, LECT NOTES ARTIF INT, V3339, P488
   Kim H, 2020, EUR RADIOL, V30, P2346, DOI 10.1007/s00330-019-06589-8
   Knight W, 2017, TECHNOL REV, V120, P54
   Konys A, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10020300
   Krishnamurthy V., 2016, PARTIALLY OBSERVED M
   Lampropoulos AS, 2015, INTEL SYST REF LIBR, V92, P1, DOI 10.1007/978-3-319-19135-5
   Langarizadeh Mostafa, 2016, Acta Inform Med, V24, P364, DOI 10.5455/aim.2016.24.364-369
   Masud M, 2021, IEEE INTERNET THINGS, V8, P15694, DOI 10.1109/JIOT.2020.3047662
   Mate S, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0116656
   Oberkampf H, 2013, OGMS BASED MODEL CLI
   Papachristou N, 2016, 2016 IEEE HEALTHCARE INNOVATION POINT-OF-CARE TECHNOLOGIES CONFERENCE (HI-POCT), P162, DOI 10.1109/HIC.2016.7797722
   Pearl J, 2010, INT J BIOSTAT, V6, DOI 10.2202/1557-4679.1203
   Richens JG, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-17419-7
   Sheridan K, 2012, J RENAL NUTR, V22, pE27, DOI 10.1053/j.jrn.2012.02.002
   Sheth A, 2006, LECT NOTES COMPUT SC, V4273, P913
   Verma C, 2018, INT J COMPUTER APPL, V7, P255, DOI DOI 10.7753/IJCATR0707.1002
   Vorhies W, 2019, NEXT MOST IMPORTANT
   Wang Fei, 2019, Yearb Med Inform, V28, P16, DOI 10.1055/s-0039-1677908
   Warner J, 2008, EATING BREAKFAST MAY
   Wei W, 2011, J AM MED INFORM ASSN, V18, P370, DOI 10.1136/amiajnl-2011-000101
   Yekkala I., 2018, INT J BIG DATA ANALY, V3, P1, DOI [10.4018/IJBDAH.2018010101, DOI 10.4018/IJBDAH.2018010101]
   Yu H. Q., 2021, P FUT TECHN C FTC 20, V3, DOI [10.1007/978-3-030-63092-8_3, DOI 10.1007/978-3-030-63092-8_3]
   Zheng XB, 2020, GUT, V69, DOI 10.1136/gutjnl-2019-318374
NR 38
TC 1
Z9 1
U1 0
U2 0
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1552-6283
EI 1552-6291
J9 INT J SEMANT WEB INF
JI Int. J. Semant. Web Inf. Syst.
PY 2022
VL 18
IS 1
DI 10.4018/IJSWIS.297145
PG 19
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3G6ZO
UT WOS:000831500300019
OA gold
DA 2022-08-23
ER

PT J
AU Martin, F
   Gines, J
   Rodriguez-Lera, FJ
   Guerrero-Higueras, AM
   Olivera, VM
AF Martin, Francisco
   Gines, Jonatan
   Rodriguez-Lera, Francisco J.
   Guerrero-Higueras, Angel M.
   Matellan Olivera, Vicente
TI Client-Server Approach for Managing Visual Attention, Integrated in a
   Cognitive Architecture for a Social Robot
SO FRONTIERS IN NEUROROBOTICS
LA English
DT Article
DE visual attention; cognitive architectures; social robots; object-based
   visual attention; robotic cognition; robot vision
ID SALIENCY; MODEL
AB This paper proposes a novel system for managing visual attention in social robots. This system is based on a client/server approach that allows integration with a cognitive architecture controlling the robot. The core of this architecture is a distributed knowledge graph, in which the perceptual needs are expressed by the presence of arcs to stimuli that need to be perceived. The attention server sends motion commands to the actuators of the robot, while the attention clients send requests through the common knowledge representation. The common knowledge graph is shared by all levels of the architecture. This system has been implemented on ROS and tested on a social robot to verify the validity of the approach and was used to solve the tests proposed in RoboCup @ Home and SciROc robotic competitions. The tests have been used to quantitatively compare the proposal to traditional visual attention mechanisms.
C1 [Martin, Francisco; Gines, Jonatan] Univ Rey Juan Carlos, Intelligent Robot Lab, Fuenlabrada, Spain.
   [Rodriguez-Lera, Francisco J.; Guerrero-Higueras, Angel M.; Matellan Olivera, Vicente] Univ Leon, Grp Robot, Leon, Spain.
RP Olivera, VM (通讯作者)，Univ Leon, Grp Robot, Leon, Spain.
EM vicente.mateilan@unileon.es
RI Guerrero-Higueras, Ángel Manuel/AAE-1375-2022
OI Guerrero-Higueras, Ángel Manuel/0000-0001-8277-0700
FU Ministerio de Ciencia, Innovacion y Universidades [RTI2018-100683-B-I00]
FX This work has been partially funded by Ministerio de Ciencia, Innovacion
   y Universidades through grant RTI2018-100683-B-I00.
CR Aguero CE, 2012, INT J ADV ROBOT SYST, V9, DOI 10.5772/53571
   Aliasghari P, 2020, SN APPL SCI, V2, DOI 10.1007/s42452-020-2911-0
   Bachiller Pilar, 2008, Advances in Robotics - Automation and Control, P111
   Breazeal C, 1999, IJCAI-99: PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 & 2, P1146
   BROOKS RA, 1986, IEEE T ROBOTIC AUTOM, V2, P14, DOI 10.1109/JRA.1986.1087032
   Butko Nicholas J., 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), P2751, DOI 10.1109/CVPRW.2009.5206540
   Cashmore M, 2015, P I C AUTOMAT PLAN S, P333
   Garcia J.F., 2010, 5 WORKSH HUM SOCC RO, P1
   Goferman S, 2012, IEEE T PATTERN ANAL, V34, P1915, DOI 10.1109/TPAMI.2011.272
   Grotz M, 2017, IEEE INT C INT ROBOT, P1427, DOI 10.1109/IROS.2017.8205944
   Harel J., 2007, NEURAL INFORM PROCES, P545, DOI DOI 10.7551/MITPRESS/7503.003.0073
   Hashimoto S, 2002, AUTON ROBOT, V12, P25, DOI 10.1023/A:1013202723953
   Hou XD, 2007, PROC CVPR IEEE, P2280
   Itti L, 1998, IEEE T PATTERN ANAL, V20, P1254, DOI 10.1109/34.730558
   Kanda T., 2017, HUMAN ROBOT INTERACT
   Kawamura K, 2005, 2005 IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ROBOTICS AND AUTOMATION, PROCEEDINGS, P211, DOI 10.1109/CIRA.2005.1554279
   Manso LJ, 2018, COGN PROCESS, V19, P285, DOI 10.1007/s10339-017-0828-3
   Martin F., 2010, ROBOT SOCCER, P1
   Martin F, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10176067
   Martin F, 2016, ADV INTELL SYST, V418, P611, DOI 10.1007/978-3-319-27149-1_47
   Meger D, 2008, ROBOT AUTON SYST, V56, P503, DOI 10.1016/j.robot.2008.03.008
   Nguyen TV, 2018, INT J COMPUT VISION, V126, P86, DOI 10.1007/s11263-017-1042-6
   Ruesch J, 2008, IEEE INT CONF ROBOT, P962, DOI 10.1109/ROBOT.2008.4543329
   Scheier C, 1997, ISIE '97 - PROCEEDINGS OF THE IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS, VOLS 1-3, pSS48, DOI 10.1109/ISIE.1997.651734
   Stefanov K, 2019, ACM T HUM-ROBOT INTE, V8, DOI 10.1145/3323231
   Treue S, 2003, CURR OPIN NEUROBIOL, V13, P428, DOI 10.1016/S0959-4388(03)00105-3
   WOLFE JM, 1994, PSYCHON B REV, V1, P202, DOI 10.3758/BF03200774
NR 27
TC 0
Z9 0
U1 1
U2 3
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1662-5218
J9 FRONT NEUROROBOTICS
JI Front. Neurorobotics
PD SEP 9
PY 2021
VL 15
AR 630386
DI 10.3389/fnbot.2021.630386
PG 11
WC Computer Science, Artificial Intelligence; Robotics; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Robotics; Neurosciences & Neurology
GA UT9ZF
UT WOS:000698465100001
PM 34566618
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zheng, W
   Wang, Z
   Yao, QM
   Li, XL
AF Zheng, Wei
   Wang, Zhen
   Yao, Quanming
   Li, Xuelong
TI WRTRe: Weighted relative position transformer for joint entity and
   relation extraction
SO NEUROCOMPUTING
LA English
DT Article
DE Deep learning; Natural language processing; Entity and relation
   extraction; Transformer; Knowledge graph
AB Entity and relation extraction is a critical task of information extraction in natural language processing. With fast developments of deep learning, this area has attracted great research attention. In spite of these achievements, however, due to the limited feature extraction ability of previous models, extracting overlapping and multiple relation triplets from a sentence is still an enormous challenge. Aim to this issue, here we propose a sequence-to-sequence method, which includes a weighted relative position Transformer encoder to flexibly capture the semantic relationship between entities. To prove the effectiveness of this suggested method, we conduct experiments on two publicly available datasets NYT24 and NYT29. The experimental results show that the proposed approach outperforms previous methods and achieves state-of-the-art performance. Such a framework may shed novel light into knowledge graph construction under complex situations and its potential applications. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Zheng, Wei; Wang, Zhen; Li, Xuelong] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China.
   [Zheng, Wei; Wang, Zhen; Li, Xuelong] Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China.
   [Wang, Zhen] Northwestern Polytech Univ, Sch Mech Engn, Xian 710072, Peoples R China.
   [Yao, Quanming] 4Paradigm Inc, Beijing, Peoples R China.
RP Wang, Z; Yao, QM; Li, XL (通讯作者)，Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China.
EM w-zhen@nwpu.edu.cn; quanmingyao@gmail.com; li@nwpu.edu.cn
OI Yao, Quanming/0000-0001-8944-8618
FU National Key Research and Development Program of China [2018YFB4103501];
   National Natural Science Foundation for Distinguished Young Scholars
   [62025602]; National Natural Science Foundation of China [U1803263,
   11931015, 81961138010]; Key Technology Research and Development Program
   of Science and TechnologyScientific and Technological Innovation Team of
   Shaanxi Province [2020TD-013]; Fok Ying-Tong Education Foundation, China
   [171105]; Key Area R&D Program of Guangdong Province [2019B010137004]
FX This work was supported in part by the National Key Research and
   Development Program of China (No. 2018YFB4103501), National Natural
   Science Foundation for Distinguished Young Scholars (No. 62025602),
   National Natural Science Foundation of China (Nos. U1803263, 11931015,
   81961138010), Key Technology Research and Development Program of Science
   and TechnologyScientific and Technological Innovation Team of Shaanxi
   Province (No. 2020TD-013), Fok Ying-Tong Education Foundation, China
   (No. 171105), Key Area R&D Program of Guangdong Province (No.
   2019B010137004).
CR Bai CY, 2020, NEUROCOMPUTING, V377, P325, DOI 10.1016/j.neucom.2019.09.097
   Bekoulis G, 2018, EXPERT SYST APPL, V114, P34, DOI 10.1016/j.eswa.2018.07.032
   Cai R, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P756
   Chan Y. S., 2011, P 49 ANN M ASS COMP, P551
   Cho K., 2014, ARXIV14061078, P1724
   Cui L, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P407
   Dai Z., ARXIV PREPRINT ARXIV
   Dat Quoc Nguyen, 2019, Advances in Information Retrieval. 41st European Conference on IR Research, ECIR 2019. Proceedings: Lecture Notes in Computer Science (LNCS 11437), P729, DOI 10.1007/978-3-030-15712-8_47
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Fu G., 2017, P2017 C EMP METH NAT, P1730, DOI DOI 10.18653/V1/D17-1182
   Fu TJ, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1409
   Hashimoto K., 2013, P 2013 C EMP METH NA, P1372
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Hong Y, 2020, NEUROCOMPUTING, V411, P302, DOI 10.1016/j.neucom.2020.06.061
   Hu L., 2019, PROC 9 INT JOINT C N, P3812
   Katiyar A, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P919
   [刘峤 Liu Qiao], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P582
   Lukovnikov D, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1211, DOI 10.1145/3038912.3052675
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Miwa M, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1105
   Nayak T, 2020, AAAI CONF ARTIF INTE, V34, P8528
   Niklaus C., 2018, P 27 INT C COMP LING, P3866
   Pang N, 2020, NEUROCOMPUTING, V418, P21, DOI 10.1016/j.neucom.2020.07.077
   Peters M., 2018, IMPROVING LANGUAGE U
   Pujara J, 2013, LECT NOTES COMPUT SC, V8218, P542, DOI 10.1007/978-3-642-41335-3_34
   Shaw P., ARXIV PREPRINT ARXIV
   Takanobu R, 2019, AAAI CONF ARTIF INTE, P7072
   Tang J, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16 COMPANION), P373, DOI 10.1145/2872518.2890513
   Vaswani A, 2017, ADV NEUR IN, V30
   Wu F, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P118
   Yan H., ARXIV PREPRINT ARXIV
   Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205
   Zeng D., 2015, P C EMP METH NAT LAN, P1753, DOI DOI 10.18653/V1/D15-1203
   Zeng DJ, 2020, AAAI CONF ARTIF INTE, V34, P9507
   Zeng XR, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P506
   Zhang JC, 2019, NEUROCOMPUTING, V364, P245, DOI 10.1016/j.neucom.2019.06.087
   Zhang YQ, 2020, PROC INT CONF DATA, P433, DOI 10.1109/ICDE48307.2020.00044
   Zhang Yongqi, 2020, ADV NEURAL INFORM PR, V33, P10030
   Zhang Yongqi, INT J VERY LARGE DAT
   Zheng SC, 2017, NEUROCOMPUTING, V257, P59, DOI 10.1016/j.neucom.2016.12.075
NR 44
TC 1
Z9 1
U1 6
U2 11
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD OCT 12
PY 2021
VL 459
BP 315
EP 326
DI 10.1016/j.neucom.2021.06.071
EA JUL 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WM4QE
UT WOS:000711070700008
DA 2022-08-23
ER

PT J
AU Qi, F
   Yang, XS
   Xu, CS
AF Qi, Fan
   Yang, Xiaoshan
   Xu, Changsheng
TI Emotion Knowledge Driven Video Highlight Detection
SO IEEE TRANSACTIONS ON MULTIMEDIA
LA English
DT Article
DE Visualization; Training data; Predictive models; Training; Semantics;
   Emotion recognition; Computational modeling; Deep ranking; knowledge
   graph; video highlight detection
ID RETRIEVAL
AB This paper addresses video highlight detection which aims to select a small subset of frames according to user's major or special interest. The performances of conventional methods highly depend on large-scale manually labeled training data which are time-consuming and labor-intensive to collect. To deal with this problem, we trace back to the original problem definition and find that whether a user is interested in a specific video segment heavily depends on human's subjective emotions. Leveraging this insight, we introduce an emotion knowledge driven video detection framework for modeling human's general emotion and inferencing highlight strength. Firstly, we obtain the concept-level representation of the video clip with a front-end network. The concepts are used as nodes to build an emotion-related knowledge graph, and their relationships in the graph are modeled via external public knowledge graphs. Then we adopt Siamese GCNs to model the dependencies between nodes in the graph and propagate messages along the edges. Finally, we compute the emotion-aware representation of the video clip based on the GCN layers and further use it to predict the highlight score. Our framework, including the front-end network, graph convolution layers and the highlight mapping network, can be trained in an end-to-end manner with the constraint of a ranking loss. Experiments on two benchmark datasets show that our proposed method performs favorably against the state-of-the-art methods.
C1 [Qi, Fan] HeFei Univ Technol, Comp Sci, Hefei 230009, Peoples R China.
   [Qi, Fan; Yang, Xiaoshan; Xu, Changsheng] Peng Cheng Lab, Shenzhen 518055, Peoples R China.
   [Yang, Xiaoshan; Xu, Changsheng] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China.
   [Yang, Xiaoshan; Xu, Changsheng] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China.
RP Xu, CS (通讯作者)，Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China.; Xu, CS (通讯作者)，Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China.
EM fanqi@mail.hfut.edu.cn; xiaoshan.yang@nlpr.ia.ac.cn; csxu@nlpr.ia.ac.cn
FU National Key Research and Development Program of China [2018AAA0100604];
   National Natural Science Foundation of China [61720106006, 62072455,
   61702511, 61751211, 61620106003, 61532009, U1836220, U1705262,
   61872424]; Key Research Program of Frontier Sciences of CAS
   [QYZDJSSWJSC039]; Research Program of National Laboratory of Pattern
   Recognition [Z-2018007]
FX This work was supported by the National Key Research and Development
   Program of China under Grant 2018AAA0100604, in part by the
   NationalNatural Science Foundation of China under Grants 61720106006,
   62072455, 61702511, 61751211, 61620106003, 61532009, U1836220, U1705262,
   and 61872424, in part by the Key Research Program of Frontier Sciences
   of CAS under Grant QYZDJSSWJSC039, and in part by the Research Program
   of National Laboratory of Pattern Recognition under Grant Z-2018007.
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bond Francis, 2013, P 51 ANN M ASS COMP, V1, P1352
   Bruna J., 2013, P 2 INT C LEARN REPR
   Cambria E, 2018, AAAI CONF ARTIF INTE, P1795
   Carreira J, 2017, PROC CVPR IEEE, P4724, DOI 10.1109/CVPR.2017.502
   Chen YT, 2019, IEEE T MULTIMEDIA, V21, P704, DOI 10.1109/TMM.2018.2865860
   Chu WS, 2015, PROC CVPR IEEE, P3584, DOI 10.1109/CVPR.2015.7298981
   Chung, 1997, SPECTRAL GRAPH THEOR
   Dagtas S, 2004, MULTIMEDIA SYST, V9, P586, DOI 10.1007/s00530-003-0130-3
   Defferrard M., 2016, 30 INT C NEURAL INFO, P3844
   Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
   Fang Y, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1661
   Gao H., 2019, PR MACH LEARN RES, P2083
   Gao JY, 2021, IEEE T MULTIMEDIA, V23, P3203, DOI 10.1109/TMM.2020.3021980
   Gao JY, 2018, PROCEEDINGS OF THE 2018 ACM MULTIMEDIA CONFERENCE (MM'18), P690, DOI 10.1145/3240508.3240566
   Gao JY, 2019, AAAI CONF ARTIF INTE, P8303
   Gygli M, 2016, PROC CVPR IEEE, P1001, DOI 10.1109/CVPR.2016.114
   Gygli M, 2014, LECT NOTES COMPUT SC, V8695, P505, DOI 10.1007/978-3-319-10584-0_33
   Hanjalic A, 2005, IEEE T MULTIMEDIA, V7, P1114, DOI 10.1109/TMM.2005.858397
   Hanjalic A, 1999, IEEE T CIRC SYST VID, V9, P1280, DOI 10.1109/76.809162
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Jiao YF, 2018, IEEE T MULTIMEDIA, V20, P2693, DOI 10.1109/TMM.2018.2815998
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Kossaifi J, 2021, IEEE T PATTERN ANAL, V43, P1022, DOI 10.1109/TPAMI.2019.2944808
   Liu W, 2015, PROC CVPR IEEE, P3707, DOI 10.1109/CVPR.2015.7298994
   Marino K, 2017, PROC CVPR IEEE, P20, DOI 10.1109/CVPR.2017.10
   Mettes P, 2017, IEEE I CONF COMP VIS, P4453, DOI 10.1109/ICCV.2017.476
   Mettes P, 2016, ICMR'16: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, P175, DOI 10.1145/2911996.2912036
   NEPAL S., 2001, P ACM MULT, P261, DOI DOI 10.1145/500141.500181
   Ortony A., 1988, COGNITIVE STRUCTURE
   Panda R, 2017, IEEE I CONF COMP VIS, P3677, DOI 10.1109/ICCV.2017.395
   Ping Q, 2017, P WORKSH NEW FRONT S, P1
   Poria S, 2013, IEEE INTELL SYST, V28, P31, DOI 10.1109/MIS.2013.4
   Potapov D, 2014, LECT NOTES COMPUT SC, V8694, P540, DOI 10.1007/978-3-319-10599-4_35
   Rui Y., 2000, Proceedings ACM Multimedia 2000, P105, DOI 10.1145/354384.354443
   Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
   Scherer KR, 2005, SOC SCI INFORM, V44, P695, DOI 10.1177/0539018405058216
   Soomro K, 2012, UCF101 DATASET 101 H
   Speer R., 2013, PEOPLES WEB MEETS NL, P161, DOI DOI 10.1007/978-3-642-35085-6_6
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Sun M, 2014, LECT NOTES COMPUT SC, V8689, P787, DOI 10.1007/978-3-319-10590-1_51
   Szegedy C, 2015, PROC CVPR IEEE, P1, DOI 10.1109/CVPR.2015.7298594
   Thomee B, 2016, COMMUN ACM, V59, P64, DOI 10.1145/2812802
   Valstar M., 2014, P 4 INT WORKSH AUD V, P3
   Wolf W, 1996, INT CONF ACOUST SPEE, P1228, DOI 10.1109/ICASSP.1996.543588
   Wu F, 2019, PR MACH LEARN RES, V97
   Xiong B, 2019, PROC CVPR IEEE, P1258, DOI 10.1109/CVPR.2019.00135
   Yang H, 2015, IEEE I CONF COMP VIS, P4633, DOI 10.1109/ICCV.2015.526
   Yang XS, 2016, IEEE T MULTIMEDIA, V18, P1832, DOI 10.1109/TMM.2016.2582379
   Yao T, 2016, PROC CVPR IEEE, P982, DOI 10.1109/CVPR.2016.112
   Yeh MC, 2016, MULTIMEDIA SYST, V22, P287, DOI 10.1007/s00530-015-0457-6
   Yuan F, 2017, ARXIV171101714
   Zhang HJ, 1997, PATTERN RECOGN, V30, P643, DOI 10.1016/S0031-3203(96)00109-4
   Zhang YY, 2020, AAAI CONF ARTIF INTE, V34, P12902
   Zhong P., 2020, IEEE T AFFECT COMPUT, DOI [10.1109/TAFFC.2020.2994159, DOI 10.1109/TAFFC.2020.2994159]
   Zhou J, 2018, ARTIF CELL NANOMED B, V46, pS1016, DOI 10.1080/21691401.2018.1442841
   Zhu Y, 2015, ARXIV150705670
NR 57
TC 4
Z9 4
U1 0
U2 0
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1520-9210
EI 1941-0077
J9 IEEE T MULTIMEDIA
JI IEEE Trans. Multimedia
PY 2021
VL 23
BP 3999
EP 4013
DI 10.1109/TMM.2020.3035285
PG 15
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA XA2YU
UT WOS:000720519900007
DA 2022-08-23
ER

PT J
AU Zhang, XH
   Ma, HF
   Gao, ZH
   Li, ZX
   Chang, L
AF Zhang, Xiaohui
   Ma, Huifang
   Gao, Zihao
   Li, Zhixin
   Chang, Liang
TI Exploiting cross-session information for knowledge-aware session-based
   recommendation via graph attention networks
SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
LA English
DT Article; Early Access
DE attentive network; cross-session; knowledge graph; session-based
   recommendation; similar sessions
AB Session-based recommendation (SBR) aims to predict the next item based on anonymous behavior session, which has become increasingly essential in various online services. Prior efforts mainly focus on modeling user preference based on the current session. Although some of them have been proven effective, they fail to address two main challenges in SBR. First, SBR suffers more from the problem of data sparsity due to the very limited user-item interactions, and hence it cannot sufficiently capture complicated item dependency relationships. Second, most of the user-item interaction sequences may be with noisy preference signals due to the uncertainty of user's behaviors, and it is difficult to distill high-quality item for recommendation. In this study, we propose a novel SBR model that exploits Cross-session information for Knowledge-aware Session-based Recommendation (CKSR) to address these two issues. Specifically, cross-session graph and knowledge graph are combined to model a cross-session knowledge graph, based on which a knowledge-aware attention mechanism is performed to capture the complicated transition pattern among interacted items. Each session is then represented as the composition of the global preference and the current interest of that session. Moreover, we leverage the similar sessions for the target session to establish a similar session referral circle and apply an influence coupler to judge the significance of different session referrals. An attentive network is designed to distill session preferences from its unique session referral circle. It dynamically extracts high-quality item from noisy session. Experiments on two benchmark data sets demonstrate that CKSR outperforms the state-of-the-art methods consistently.
C1 [Zhang, Xiaohui; Ma, Huifang; Gao, Zihao] Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou, Peoples R China.
   [Ma, Huifang; Li, Zhixin] Guangxi Normal Univ, Coll Comp Sci & Informat Technol, Guilin, Peoples R China.
   [Chang, Liang] Guilin Univ Elect Technol, Sch Comp Sci & Informat Secur, Guilin, Peoples R China.
RP Ma, HF (通讯作者)，Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou, Peoples R China.
EM mahuifang@yeah.net
OI Li, Zhixin/0000-0002-5313-6134
FU National Natural Science Foundation of China [61762078, 61363058,
   61966004]; Northwest Normal University Young Teachers Research Capacity
   Promotion Plan [NWNULKQN2019-2]; Natural Science Foundation of Gansu
   Province [21JR7RA114]; Northwest Normal University Postgraduate Research
   Funding Project [2021KYZZ02107]
FX National Natural Science Foundation of China, Grant/Award Numbers:
   61762078, 61363058, 61966004; Northwest Normal University Young Teachers
   Research Capacity Promotion Plan, Grant/Award Number: NWNULKQN2019-2;
   Natural Science Foundation of Gansu Province, Grant/Award Number:
   21JR7RA114; Northwest Normal University Postgraduate Research Funding
   Project, Grant/Award Number: 2021KYZZ02107
CR Chang JX, 2021, SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P378, DOI 10.1145/3404835.3462968
   Chen TW, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1172, DOI 10.1145/3394486.3403170
   Chen Tianwen, P 14 ACM INT C WEB S, P400
   Hamilton WL, 2017, ADV NEUR IN, V30
   Hsu CC, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P771, DOI 10.1145/3018661.3018668
   Kingma D. P., 2017, ARXIV
   Kipf T. N, 2017, INT C LEARN REPR
   Kuang D, 2015, J GLOBAL OPTIM, V62, P545, DOI 10.1007/s10898-014-0247-2
   Li J, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1419, DOI 10.1145/3132847.3132926
   LIN Y, 2015, P 29 AAAI C ART INT, P1
   Liu Q, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1831, DOI 10.1145/3219819.3219950
   Liu Y., 2021, IEEE T KNOWL DATA EN
   Meng WJ, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P1091, DOI 10.1145/3397271.3401098
   Qiu JZ, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2110, DOI 10.1145/3219819.3220077
   Qiu RH, 2020, ACM T INFORM SYST, V38, DOI 10.1145/3382764
   Ren PJ, 2019, AAAI CONF ARTIF INTE, P4806
   Rendle S., 2010, P 19 INT C WORLD WID, P811, DOI [/10.1145 /1772690.1772773, DOI 10.1145/1772690.1772773]
   Saxe A.M., 2013, 2 INT C LEARN REPR I
   Tikk D, 2016, ICLR
   Velickovic P., 2017, P 6 INT C LEARN REPR
   Wang PF, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P403, DOI 10.1145/2766462.2767694
   Wang W, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P3056, DOI 10.1145/3366423.3380077
   Wang X, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P878, DOI 10.1145/3442381.3450133
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wang Z, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P219, DOI 10.1145/3397271.3401141
   Wang ZY, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P169, DOI 10.1145/3397271.3401142
   Wu S, 2019, AAAI CONF ARTIF INTE, P346
   Xu CF, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3940
   Ye R, 2020, INT CONF DAT MIN WOR, P220, DOI 10.1109/ICDMW51313.2020.00039
   Zhang X, 2022, PHYSIOTHER THEOR PR, DOI [10.1080/09593985.2022.2040668, 10.1145/3468270]
NR 30
TC 0
Z9 0
U1 5
U2 5
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0884-8173
EI 1098-111X
J9 INT J INTELL SYST
JI Int. J. Intell. Syst.
DI 10.1002/int.22896
EA APR 2022
PG 24
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0U5LT
UT WOS:000787693100001
DA 2022-08-23
ER

PT J
AU Xue, YJ
   Chen, LX
   Mu, Y
   Zeng, LF
   Rezaeibagha, F
   Deng, RH
AF Xue, Yujie
   Chen, Lanxiang
   Mu, Yi
   Zeng, Lingfang
   Rezaeibagha, Fatemeh
   Deng, Robert H.
TI Structured encryption for knowledge graphs
SO INFORMATION SCIENCES
LA English
DT Article
DE Structured encryption; Knowledge search; Knowledge graph;
   Multi-relational graph; Property graph
ID SEARCHABLE SYMMETRIC-ENCRYPTION
AB We investigate the problem of structured encryption (STE) for knowledge graphs (KGs) where the knowledge of data can be efficiently and privately queried. Presently, the application of natural language processing (NLP) for knowledge-based search is gradually emerging. Compared with the traditional search based only on keywords of documents-symmetric searchable encryption (SSE), the knowledge-based search system transforms the latent knowledge contained in documents into a semantic network as a knowledge base, which greatly improves the accuracy and relevance of search results. In order to develop a knowledge-based search, the contents of documents are analyzed and extracted using KG techniques (e.g. multi-relational graph (MG) and property graph (PG)), and then all encrypted nodes and edges in a KG constitute the entire index table and database. This paper proposes the first STE for KGs with CQA2-security to search on protected knowledge, where KGs include MGs and PGs. In general, the latter is more complex than the former, but it can represent more abundant knowledge. Experimental results show that the index construction time of our schemes is about 1.9s and the query time is about 190 ms. Our sensitivity analysis shows that the performance of our proposed schemes is greatly influenced by the number of edges and nodes, but less by the number of properties. (C) 2022 Elsevier Inc. All rights reserved.
C1 [Xue, Yujie; Chen, Lanxiang] Fujian Normal Univ, Coll Comp & Cyber Secur, Fujian Prov Key Lab Network Secur & Cryptol, Fuzhou, Peoples R China.
   [Chen, Lanxiang; Zeng, Lingfang] Zhejiang Lab, Hangzhou, Peoples R China.
   [Mu, Yi] City Univ Macau, Fac Data Sci, Macau, Peoples R China.
   [Rezaeibagha, Fatemeh] Murdoch Univ, Discipline Informat Technol, Perth, WA, Australia.
   [Deng, Robert H.] Singapore Management Univ, Sch Informat Syst, Singapore, Singapore.
RP Chen, LX (通讯作者)，Fujian Normal Univ, Coll Comp & Cyber Secur, Fujian Prov Key Lab Network Secur & Cryptol, Fuzhou, Peoples R China.
EM lxiangchen@fjnu.edu.cn
FU National Natural Science Foundation of China [62072105, 61872087,
   U1805263]; Zhejiang provincial "Ten Thousand Talents Program"
   [2021R52007]; Center-initiated Research Project of Zhejiang Lab
   [2021DA0AM01]; Natural Science Foundation of Fujian Province
   [2019J01274]; Social Science Planning Project of Fujian Province
   [FJ202TWFB04]
FX This work was supported by the National Natural Science Foundation of
   China under Grant No.62072105, No.61872087 and No.U1805263, Zhejiang
   provincial ``Ten Thousand Talents Program"(2021R52007) and
   Center-initiated Research Project of Zhejiang Lab (No.2021DA0AM01),
   Natural Science Foundation of Fujian Province under Grant No.2019J01274
   and Social Science Planning Project of Fujian Province under Grant
   No.FJ202TWFB04.
CR Amjad Ghous, 2019, Proceedings on Privacy Enhancing Technologies, V2019, P245, DOI 10.2478/popets-2019-0014
   Asharov G, 2021, J CRYPTOL, V34, DOI 10.1007/s00145-020-09370-z
   Bost R, 2017, CCS'17: PROCEEDINGS OF THE 2017 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P1465, DOI 10.1145/3133956.3133980
   Bost R, 2016, CCS'16: PROCEEDINGS OF THE 2016 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P1143, DOI 10.1145/2976749.2978303
   Cao N, 2011, INT CON DISTR COMP S, P393, DOI 10.1109/ICDCS.2011.84
   Cash D, 2013, LECT NOTES COMPUT SC, V8042, P353, DOI 10.1007/978-3-642-40041-4_20
   Chase M, 2010, LECT NOTES COMPUT SC, V6477, P577, DOI 10.1007/978-3-642-17373-8_33
   Collarana D, 2016, LECT NOTES COMPUT SC, V10033, P752, DOI 10.1007/978-3-319-48472-3_47
   Cui S., 2021, ACM T PRIV SECUR, V24, P1
   Curtmola R, 2011, J COMPUT SECUR, V19, P895, DOI 10.3233/JCS-2011-0426
   Du MX, 2021, IEEE T ENG MANAGE, V68, P1677, DOI 10.1109/TEM.2020.2966832
   Fu ZJ, 2017, IEEE T INF FOREN SEC, V12, P1874, DOI 10.1109/TIFS.2017.2692728
   Geong Sen Poh, 2012, Advances in Information and Computer Security. 7th International Workshop on Security, IWSEC 2012. Proceedings, P105, DOI 10.1007/978-3-642-34117-5_7
   Goh E. -J., 2003, IACR CRYPTOL EPRINT, V2003
   Golle P, 2004, LECT NOTES COMPUT SC, V3089, P31, DOI 10.1007/978-3-540-24852-1_3
   He K, 2021, IEEE T INF FOREN SEC, V16, P1538, DOI 10.1109/TIFS.2020.3033412
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   Hua W., 2013, P 6 ACM INT C WEB SE, P93, DOI DOI 10.1145/2433396.2433410
   Jurafsky D., 2000, SPEECH LANGUAGE PROC
   Kamara S, 2019, LECT NOTES COMPUT SC, V11477, P183, DOI 10.1007/978-3-030-17656-3_7
   Kamara S, 2018, LECT NOTES COMPUT SC, V10991, P339, DOI 10.1007/978-3-319-96884-1_12
   Li J, 2021, IEEE T DEPEND SECURE, V18, P460, DOI [10.1109/TPAMI.2019.2929036, 10.1109/TDSC.2019.2894411]
   Liu C, 2017, IEEE T SUST COMPUT, V2, P371, DOI 10.1109/TSUSC.2017.2704163
   Liu XQ, 2020, IEEE T DEPEND SECURE, V17, P1322, DOI 10.1109/TDSC.2018.2876831
   Oya S, 2021, PROCEEDINGS OF THE 30TH USENIX SECURITY SYMPOSIUM, P127
   Shang ZH, 2021, POLYM ADVAN TECHNOL, V32, P4745, DOI 10.1002/pat.5467
   Shen M, 2018, IEEE T INF FOREN SEC, V13, P940, DOI 10.1109/TIFS.2017.2774451
   Singhal A, OFFICIAL GOOGLE BLOG
   Song DXD, 2000, P IEEE S SECUR PRIV, P44, DOI 10.1109/SECPRI.2000.848445
   Song Q, 2018, IEEE T KNOWL DATA EN, V30, P1887, DOI 10.1109/TKDE.2018.2807442
   Song QY, 2021, IEEE T INF FOREN SEC, V16, P1795, DOI 10.1109/TIFS.2020.3042058
   Song XF, 2020, IEEE T DEPEND SECURE, V17, P912, DOI 10.1109/TDSC.2018.2822294
   Wang JF, 2018, LECT NOTES COMPUT SC, V11099, P83, DOI 10.1007/978-3-319-98989-1_5
   Wang Q, 2017, LECT NOTES COMPUT SC, V10322, P79, DOI 10.1007/978-3-319-70972-7_5
   Zhang ZJ, 2019, LECT NOTES COMPUT SC, V11736, P304, DOI 10.1007/978-3-030-29962-0_15
NR 35
TC 0
Z9 0
U1 3
U2 3
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD AUG
PY 2022
VL 605
BP 43
EP 70
DI 10.1016/j.ins.2022.05.015
PG 28
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2H7NX
UT WOS:000814478300003
DA 2022-08-23
ER

PT J
AU Zhu, CY
   Yang, ZH
   Xia, XQ
   Li, N
   Zhong, F
   Liu, L
AF Zhu, Chaoyu
   Yang, Zhihao
   Xia, Xiaoqiong
   Li, Nan
   Zhong, Fan
   Liu, Lei
TI Multimodal reasoning based on knowledge graph embedding for specific
   diseases
SO BIOINFORMATICS
LA English
DT Article
ID REPRESENTATION; GENE
AB Motivation: Knowledge Graph (KG) is becoming increasingly important in the biomedical field. Deriving new and reliable knowledge from existing knowledge by KG embedding technology is a cutting-edge method. Some add a variety of additional information to aid reasoning, namely multimodal reasoning. However, few works based on the existing biomedical KGs are focused on specific diseases.
   Results: This work develops a construction and multimodal reasoning process of Specific Disease Knowledge Graphs (SDKGs). We construct SDKG-11, a SDKG set including five cancers, six non-cancer diseases, a combined Cancer5 and a combined Diseases11, aiming to discover new reliable knowledge and provide universal pre-trained knowledge for that specific disease field. SDKG-11 is obtained through original triplet extraction, standard entity set construction, entity linking and relation linking. We implement multimodal reasoning by reverse-hyperplane projection for SDKGs based on structure, category and description embeddings. Multimodal reasoning improves pre-existing models on all SDKGs using entity prediction task as the evaluation protocol. We verify the model's reliability in discovering new knowledge by manually proofreading predicted drug-gene, gene-disease and disease-drug pairs. Using embedding results as initialization parameters for the biomolecular interaction classification, we demonstrate the universality of embedding models.
   Availability and implementation: The constructed SDKG-11 and the implementation by TensorFlow are available from . Supplementary information are available at Bioinformatics online.
C1 [Zhu, Chaoyu; Xia, Xiaoqiong; Zhong, Fan; Liu, Lei] Fudan Univ, Shanghai Med Coll, Inst Biomed Sci, Shanghai 200032, Peoples R China.
   [Zhu, Chaoyu; Xia, Xiaoqiong; Zhong, Fan; Liu, Lei] Fudan Univ, Shanghai Med Coll, Sch Basic Med Sci, Shanghai 200032, Peoples R China.
   [Yang, Zhihao; Li, Nan] Dalian Univ Technol, Coll Comp Sci & Technol, Dalian 116024, Peoples R China.
   [Liu, Lei] Jihua Lab, Engn Res Ctr Intelligent Robot, Guangzhou 510000, Peoples R China.
RP Zhong, F; Liu, L (通讯作者)，Fudan Univ, Shanghai Med Coll, Inst Biomed Sci, Shanghai 200032, Peoples R China.; Zhong, F; Liu, L (通讯作者)，Fudan Univ, Shanghai Med Coll, Sch Basic Med Sci, Shanghai 200032, Peoples R China.; Liu, L (通讯作者)，Jihua Lab, Engn Res Ctr Intelligent Robot, Guangzhou 510000, Peoples R China.
EM zonefan@163.com; liulei_sibs@163.com
OI Zhu, Chaoyu/0000-0003-2132-2060
FU Key Area Support Plan of Guangdong Province for Jihua Laboratory
   [X190051TB190]; S&T Program of Hebei [21377734D]
FX This work was supported by Key Area Support Plan of Guangdong Province
   for Jihua Laboratory [X190051TB190]; and S&T Program of Hebei
   [21377734D].
CR Al-Saleem J, 2021, J CHEM INF MODEL, V61, P4058, DOI 10.1021/acs.jcim.1c00642
   Alshahrani M, 2021, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.341
   Amberger JS, 2015, NUCLEIC ACIDS RES, V43, pD789, DOI 10.1093/nar/gku1205
   Bateman A, 2006, NUCLEIC ACIDS RES, V34, pD1, DOI 10.1093/nar/gkj150
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Boontanrart M, 2016, J NEUROIMMUNOL, V292, P126, DOI 10.1016/j.jneuroim.2016.01.015
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Brown GR, 2015, NUCLEIC ACIDS RES, V43, pD36, DOI 10.1093/nar/gku1055
   Carbon S, 2019, NUCLEIC ACIDS RES, V47, pD330, DOI 10.1093/nar/gky1055
   Che MX, 2021, FUTURE INTERNET, V13, DOI 10.3390/fi13010013
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Fang YL, 2019, IEEE ACCESS, V7, P46004, DOI 10.1109/ACCESS.2019.2909069
   Glorot X., 2010, JMLR WRKSHOP C P, P249
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Guan NN, 2019, KNOWL-BASED SYST, V164, P38, DOI 10.1016/j.knosys.2018.10.008
   Hastings J, 2016, NUCLEIC ACIDS RES, V44, pD1214, DOI 10.1093/nar/gkv1031
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Kang HY, 2020, JMIR MED INF, V8, DOI 10.2196/20291
   King DB, 2015, ACS SYM SER, V1214, P1
   Kiperwasser E., 2016, T ASSOC COMPUT LING, V4, P313, DOI [DOI 10.1162/TACL_A_00101, 10.1162/tacl_a_00101]
   Kohler S, 2019, NUCLEIC ACIDS RES, V47, pD1018, DOI 10.1093/nar/gky1105
   Kozomara A, 2019, NUCLEIC ACIDS RES, V47, pD155, DOI 10.1093/nar/gky1141
   Lee J, 2020, BIOINFORMATICS, V36, P1234, DOI 10.1093/bioinformatics/btz682
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li LF, 2020, ARTIF INTELL MED, V103, DOI 10.1016/j.artmed.2020.101817
   Luo L, 2018, BIOINFORMATICS, V34, P1381, DOI 10.1093/bioinformatics/btx761
   Mair P, 2020, BEHAV RES METHODS, V52, P464, DOI 10.3758/s13428-019-01246-w
   Moghimi Mansour, 2018, Asian Pac J Cancer Prev, V19, P3353, DOI 10.31557/APJCP.2018.19.12.3353
   Mohamed SK, 2021, BRIEF BIOINFORM, V22, P1679, DOI 10.1093/bib/bbaa012
   Mohamed SK, 2020, BIOINFORMATICS, V36, P603, DOI 10.1093/bioinformatics/btz600
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Nguyen D. Q., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Nie BL, 2019, FUTURE GENER COMP SY, V91, P426, DOI 10.1016/j.future.2018.09.040
   Rodchenkov I, 2020, NUCLEIC ACIDS RES, V48, pD489, DOI 10.1093/nar/gkz946
   Role F, 2019, J STAT SOFTW, V88, P1, DOI 10.18637/jss.v088.i07
   Sangrak L, 2018, DATABASE, V2018
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Su Gang, 2014, Curr Protoc Bioinformatics, V47, DOI 10.1002/0471250953.bi0813s47
   Sung HH, 2022, CLIN EXP HYPERTENS, V44, P146, DOI 10.1080/10641963.2021.2007943
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Tang X, 2019, INFORM PROCESS MANAG, V56, P809, DOI 10.1016/j.ipm.2019.01.005
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang X, 2019, BIOINFORMATICS, V35, P1745, DOI 10.1093/bioinformatics/bty869
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   World Health Organization, 2016, WORLD HLTH STAT 2016
   Wu YY, 2017, CANCERS, V9, DOI 10.3390/cancers9100144
   Xiao H, 2017, AAAI CONF ARTIF INTE, P3104
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xie RB, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3140
   Yang K, 2019, IEEE J BIOMED HEALTH, V23, P1805, DOI 10.1109/JBHI.2018.2870728
   Zhang F, 2021, BMC MED INFORM DECIS, V21, DOI 10.1186/s12911-021-01402-3
   Zhang R, 2021, J BIOMED INFORM, V115, DOI 10.1016/j.jbi.2021.103696
   Zhao DS, 2019, PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 3 (BIOINFORMATICS), P123, DOI 10.5220/0007688101230130
   Zheng SJ, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbaa344
NR 58
TC 0
Z9 0
U1 25
U2 25
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD APR 12
PY 2022
VL 38
IS 8
BP 2235
EP 2245
DI 10.1093/bioinformatics/btac085
EA FEB 2022
PG 11
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA 0L4DY
UT WOS:000759661200001
PM 35150235
OA Green Published
DA 2022-08-23
ER

PT J
AU Mosa, MA
AF Atef Mosa, Mohamed
TI Predicting Semantic Categories in Text Based on Knowledge Graph Combined
   with Machine Learning Techniques
SO APPLIED ARTIFICIAL INTELLIGENCE
LA English
DT Article
ID OPTIMIZATION
AB The Quran and the Sunnah are the two principal elements of the Islamic religion, and the hadith is an interpreter of the Quran. Hadith is everything that the Messenger Muhammad said, whether it was a word, an action, or a good adjective of the Prophet. Given the status of the hadith of Muslims everywhere in the world, digging into it is the m ain perspective to evoke the guiding principles and institutions that Muslims must follow. The mining of the hadith has received much attention in recent times, but so far, the work has not been fully implemented. This study focuses on predicting the semantic categories of the unclassified hadith text based on its text. The model can distinguish between several categories to predict the optimal one such as ablution, fasting, Hajj, and Zakat. To achieve this goal, a Knowledge-Graphic (KG) prediction model was developed to improve machine learning classifiers from the standpoint of two unique traits. 1) Define pivotal terms that have high values. II) Taking into account all the paths of those pivotal terms through their convergence with the categories in the Knowledge-Graphic of all the paths that link them. We rely on six books with more than 30,000 hadith and 120 classifications. Empirically, we found optimistic results in combining the KG model and machine learning classifiers.
C1 [Atef Mosa, Mohamed] Inst Publ Adm, Informat Technol Sector, Riyadh, Saudi Arabia.
RP Mosa, MA (通讯作者)，Inst Publ Adm, Informat Technol Sector, Riyadh, Saudi Arabia.
EM mosamo@ipa.edu.sa
CR Abdelaal HM, 2019, IEEE ACCESS, V7, P152379, DOI 10.1109/ACCESS.2019.2948159
   Abu Bakar MY, 2018, INT CONF ASIAN LANG, P344, DOI 10.1109/IALP.2018.8629263
   Adeleke A.O., 2017, INT J ADV SCI ENG IN, V7, P1419
   Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170035.170072
   AL-Kabi Mohammed., 2015, INT J ISLAM APPL COM, V3, P13
   [Anonymous], 2020, TRENDS APPL TEXT SUM, DOI DOI 10.4018/978-1-5225-9373-7.CH004
   Bahassine S, 2020, J KING SAUD UNIV-COM, V32, P225, DOI 10.1016/j.jksuci.2018.05.010
   El Salam Al Hajjar Abd, 2010, Proceedings of the Fifth International Conference on Internet and Web Applications and Services (ICIW 2010), P506, DOI 10.1109/ICIW.2010.98
   El-Fishawy N, 2014, AIN SHAMS ENG J, V5, P411, DOI 10.1016/j.asej.2013.11.002
   El-khair I. A., 2006, INT J COMPUTING INFO, V4, P119
   Elghazel H, 2016, EXPERT SYST APPL, V57, P1, DOI 10.1016/j.eswa.2016.03.041
   Fei Dong, 2016, P 2016 C EMP METH NA, P1072, DOI DOI 10.18653/V1/D16-1115
   Flachsbart B., 1994, P 1994 ACM S APPL CO, P292, DOI DOI 10.1145/326619.326750
   Ghazizadeh M, 2008, ADVANCES IN COMPUTER AND INFORMATIOM SCIENCES AND ENGINEERING, P354, DOI 10.1007/978-1-4020-8741-7_64
   Harrag F, 2009, NDT: 2009 FIRST INTERNATIONAL CONFERENCE ON NETWORKED DIGITAL TECHNOLOGIES, P110, DOI 10.1109/NDT.2009.5272214
   Harrag F, 2009, 2009 SECOND INTERNATIONAL CONFERENCE ON THE APPLICATIONS OF DIGITAL INFORMATION AND WEB TECHNOLOGIES (ICADIWT 2009), P778, DOI 10.1109/ICADIWT.2009.5273841
   Hassanat A.B., 2014, ARXIV14090919
   KAMSIN A, 2014, 5 INT C INF COMM TEC, P1
   Maazouzi F., 2012, INT J BUSINESS INTEL, V7, P274, DOI [10.1504/IJBIDM.2012.051712, DOI 10.1504/IJBIDM.2012.051712]
   Maraoui H., 2018, INT C ADV INT SYST I, P252
   Mosa, 2017, CAN ANTS EXTRACT ESS
   Mosa MA, 2020, APPL SOFT COMPUT, V90, DOI 10.1016/j.asoc.2020.106189
   Mosa MA, 2019, EXPERT SYST APPL, V137, P117, DOI 10.1016/j.eswa.2019.06.065
   Mosa MA, 2019, KNOWL-BASED SYST, V163, P518, DOI 10.1016/j.knosys.2018.09.008
   Mosa MA, 2017, EXPERT SYST APPL, V74, P115, DOI 10.1016/j.eswa.2017.01.010
   Mosa MA, 2017, KNOWL-BASED SYST, V118, P105, DOI 10.1016/j.knosys.2016.11.009
   Rostam NAP, 2021, J KING SAUD UNIV-COM, V33, P658, DOI 10.1016/j.jksuci.2019.03.007
   Saloot MA, 2016, ARTIF INTELL REV, V46, P113, DOI 10.1007/s10462-016-9458-x
   Suryana Nanna, 2018, Journal of Theoretical and Applied Information Technology, V96, P568
   Zainol Z, 2016, 2016 PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY (ICICTM), P104, DOI 10.1109/ICICTM.2016.7890785
NR 30
TC 2
Z9 2
U1 1
U2 3
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0883-9514
EI 1087-6545
J9 APPL ARTIF INTELL
JI Appl. Artif. Intell.
PD OCT 15
PY 2021
VL 35
IS 12
BP 933
EP 951
DI 10.1080/08839514.2021.1966883
EA AUG 2021
PG 19
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA WA5KZ
UT WOS:000686473000001
OA Bronze
DA 2022-08-23
ER

PT J
AU Fionda, V
   Pirro, G
AF Fionda, Valeria
   Pirro, Giuseppe
TI Explaining and Querying Knowledge Graphs by Relatedness
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB We demonstrate RECAP, a tool that explains relatedness between entities in Knowledge Graphs (KGs) and implements a query by relatedness paradigm that allows to retrieve entities related to those in input. One of the peculiarities of RECAP is that it does not require any data preprocessing and can combine knowledge from multiple KGs. The underlying algorithmic techniques are reduced to the execution of SPARQL queries plus some local refinement. This makes the tool readily available on a large variety of KGs accessible via SPARQL endpoints. To show the general applicability of the tool, we will cover a set of use cases drawn from a variety of knowledge domains (e.g., biology, movies, co-authorship networks) and report on the concrete usage of RECAP in the SENSE4US FP7 project. We will underline the technical aspects of the system and give details on its implementation. The target audience of the demo includes both researchers and practitioners and aims at reporting on the benefits of RECAP in practical knowledge discovery applications.
C1 [Fionda, Valeria] Univ Calabria, Via Pietro Bucci 30-B, Rend, CS, Italy.
   [Pirro, Giuseppe] CNR, ICAR, Via Pietro Bucci 41-C, Rend, CS, Italy.
RP Fionda, V (通讯作者)，Univ Calabria, Via Pietro Bucci 30-B, Rend, CS, Italy.
EM fionda@mat.unical.it; pirro@icar.cnr.it
RI Pirrò, Giuseppe/AAZ-4500-2020; Pirrò, Giuseppe/AAF-1993-2019
OI Pirrò, Giuseppe/0000-0002-7499-5798
CR Bonifati A, 2014, PROC VLDB ENDOW, V7, P1541, DOI 10.14778/2733004.2733025
   Diaz G, 2016, PROC VLDB ENDOW, V9, P1533, DOI 10.14778/3007263.3007302
   Fang L, 2011, PROC VLDB ENDOW, V5, P241, DOI 10.14778/2078331.2078339
   Katz L., 1953, PSYCHOMETRIKA, Vvol. 18, ppp. 39, DOI DOI 10.1007/BF02289026
   Metzger S., JIIS, P1
   Mottin D, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P901, DOI 10.1145/2588555.2594529
   Mottin D, 2016, VLDB J, V25, P741, DOI 10.1007/s00778-016-0429-2
   Nakashole N, 2012, PROC VLDB ENDOW, V5, P1982, DOI 10.14778/2367502.2367553
   PIRRO G, 2015, ISWC, V9366, P622, DOI DOI 10.1007/978-3-319-25007-6_36
   Pirro G, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16 COMPANION), P235, DOI 10.1145/2872518.2890533
   Pirro Giuseppe, 2012, P 26 AAAI C ART INT, P129
NR 11
TC 8
Z9 8
U1 1
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD AUG
PY 2017
VL 10
IS 12
BP 1913
EP 1916
DI 10.14778/3137765.3137807
PG 4
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FO1CJ
UT WOS:000416494000040
OA Green Published
DA 2022-08-23
ER

PT J
AU Shan, Y
   Li, MD
   Chen, Y
AF Shan, Yi
   Li, Mingda
   Chen, Yi
TI Constructing target-aware results for keyword search on knowledge graphs
SO DATA & KNOWLEDGE ENGINEERING
LA English
DT Article
DE Knowledge graphs; Entity relationship model; Conceptual modeling;
   Meta-data; Semi-structured data; XML; Keyword search; Query semantics;
   INEX
AB Existing work of processing keyword searches on graph data focuses on efficiency of result generation. However, being oblivious to user search intention, a query result may contain multiple instances of user search target, and multiple query results may contain information for the same instance of user search target. With the misalignment between query results and search targets, a ranking function is unable to effectively rank the instances of search targets. In this paper we propose the concept of target-aware query results driven by inferred user search intention. We leverage the Information Theory and develop a general probability model to infer search targets by analyzing return specifiers, modifiers, relatedness relationships, and query keywords' information gain. Then we propose two important properties for a target-aware result: atomicity and intactness. We develop techniques to efficiently generate target-aware results. Extensive experimental evaluation shows the effectiveness and efficiency of our approach.
C1 [Shan, Yi] Elect Arts Inc, 2401 4th Ave,Suite 330, Seattle, WA 98121 USA.
   [Li, Mingda; Chen, Yi] New Jersey Inst Technol, Newark, NJ 07102 USA.
RP Shan, Y (通讯作者)，Elect Arts Inc, 2401 4th Ave,Suite 330, Seattle, WA 98121 USA.; Li, MD (通讯作者)，New Jersey Inst Technol, Newark, NJ 07102 USA.
EM yishan@ea.com; ml456@njit.edu; yi.chen@njit.edu
FU NSF CAREER [IIS-0845647]; Leir Charitable Foundations; Google Research
   Award; Google Cloud Service
FX This work is partially supported by NSF CAREER Award IIS-0845647, Google
   Research Award, Google Cloud Service and the Leir Charitable
   Foundations.
CR Aditya B., 2002, VLDB
   [Anonymous], [No title captured]
   [Anonymous], 2005, VLDB
   [Anonymous], 2011, P ACM SIGMOD INT C M
   [Anonymous], 2000, LECT INFORM RETRIEVA
   [Anonymous], [No title captured]
   BAO Z, 2009, ICDE
   BHALOTIA G, 2002, ICDE
   Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X
   Chen, 2010, ACM T DATABASE SYST, V35
   Cheng T., 2007, P 33 INT C VER LARG, P387
   Cheng T., 2007, CIDR 2007, P108
   Coffman J., 2010, P 19 ACM INT C INF K, P729, DOI DOI 10.1145/1871437.1871531
   Cohen S., 2003, VLDB, V29, P45, DOI DOI 10.1016/B978-012722442-8/50013-6
   Daiv BB, 2008, PROC VLDB ENDOW, V1, P1189
   Demidova E, 2010, PROC INT CONF DATA, P349, DOI 10.1109/ICDE.2010.5447929
   Ding Bolin, 2007, ICDE
   Golenberg K., 2008, SIGMOD, P927, DOI DOI 10.1145/1376616.1376708
   Guo JF, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/1571941.1571989
   GUO L, 2003, SIGMOD
   Hristidis V., 2003, VLDB, P850, DOI DOI 10.1016/13978-012722442-8/50080-X
   Hristidis Vagelis, 2003, ICDE
   Jiayin Li, 2010, Proceedings 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT), P561, DOI 10.1109/WI-IAT.2010.30
   Kamps J, 2007, INEX, P24
   Kargar M., 2011, VLDB ENDOW
   Kasneci G, 2009, PROC INT CONF DATA, P868, DOI 10.1109/ICDE.2009.64
   KAUSHIK R, 2002, ICDE
   Kong L., 2009, EDBT
   Li G., 2008, P 2008 ACM SIGMOD IN, P903, DOI DOI 10.1145/1376616.1376706
   Li YJ, 2004, VTC2004-FALL: 2004 IEEE 60TH VEHICULAR TECHNOLOGY CONFERENCE, VOLS 1-7, P72
   Lin RR, 2011, SIGMOD REC, V40, P5, DOI 10.1145/2007206.2007208
   Liu Z., 2007, P ACM SIGMOD INT C M, P329
   Liu ZY, 2015, LECT NOTES COMPUT SC, V9381, P441, DOI 10.1007/978-3-319-25264-3_33
   Liu ZY, 2010, PROC INT CONF DATA, P1101, DOI 10.1109/ICDE.2010.5447880
   Mass Y., 2011, CIDR, P38
   Mingda Y.C., TECHNICAL REPORT TAR
   Nandi A., 2009, CIDR
   Pu KQ, 2008, PROC VLDB ENDOW, V1, P909, DOI 10.14778/1453856.1453955
   Sadikov E., 2010, WWW
   Tarjan R., 1971, SWITCHING AUTOMATA T
   Thou Z. B. Junfeng, 2012, ICDE
   Yang M, 2014, PROC VLDB ENDOW, V7, P1809, DOI 10.14778/2733085.2733088
   Yin X., 2010, P 19 INT C WORLD WID, P1001, DOI DOI 10.1145/1772690.1772792
   Yu X., 2009, P 1 INT WORKSH KEYW, P21, DOI DOI 10.1145/1557670.1557680
   Zeng Z., 2012, KEYS
NR 45
TC 4
Z9 6
U1 0
U2 15
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0169-023X
EI 1872-6933
J9 DATA KNOWL ENG
JI Data Knowl. Eng.
PD JUL
PY 2017
VL 110
BP 1
EP 23
DI 10.1016/j.datak.2017.02.001
PG 23
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FD4SP
UT WOS:000407522400001
DA 2022-08-23
ER

PT J
AU Zhao, Y
   Feng, HL
   Zhou, H
   Yang, YR
   Chen, XY
   Xie, RB
   Zhuang, FZ
   Li, Q
AF Zhao, Yu
   Feng, Huali
   Zhou, Han
   Yang, Yanruo
   Chen, Xingyan
   Xie, Ruobing
   Zhuang, Fuzhen
   Li, Qing
TI EIGAT: Incorporating global information in local attention for knowledge
   representation learning
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Graph Neural Networks; Knowledge Graphs; Knowledge representation
   learning
ID NETWORK
AB Graph Attention Networks (GATs) have proven a promising model that takes advantage of localized attention mechanism to perform knowledge representation learning (KRL) on graph-structure data, e.g., Knowledge Graphs (KGs). While such approaches model entities' local pairwise importance, they lack the capability to model global importance relative to other entities of KGs. This causes such models to miss critical information in tasks where global information is also a significant component for the task, such as in knowledge representation learning. To address the issue, we allow the proper incorporation of global information into the GAT family of models through the use of scaled entity importance, which is calculated by an attention-based global random walk algorithm. In the context of KRL, incorporating global information boosts performance significantly. Experimental results on KG entity prediction against the state-of-the-arts sufficiently demonstrate the effectiveness of our proposed model.
   (c) 2021 Elsevier B.V. All rights reserved.
C1 [Zhao, Yu; Feng, Huali; Chen, Xingyan; Li, Qing] Southwestern Univ Finance & Econ, Fintech Innovat Ctr, Financial Intelligence & Financial Engn Key Lab, Chengdu, Peoples R China.
   [Zhou, Han; Yang, Yanruo] Baidu Inc, Shenzhen, Peoples R China.
   [Xie, Ruobing] Tencent, WeChat Search Applicat Dept, Beijing, Peoples R China.
   [Zhuang, Fuzhen] Beihang Univ, Inst Artificial Intelligence, Beijing 100191, Peoples R China.
   [Zhuang, Fuzhen] Beihang Univ, Sch Comp Sci, SKLSDE, Beijing 100191, Peoples R China.
RP Chen, XY (通讯作者)，Southwestern Univ Finance & Econ, Fintech Innovat Ctr, Financial Intelligence & Financial Engn Key Lab, Chengdu, Peoples R China.
EM xychen@swufe.edu.cn
OI Zhao, Yu/0000-0002-8454-0025; Chen, Xingyan/0000-0003-0861-2100
FU National Natural Science Foundation of China [61906159, 62176014,
   U1836206, 71671141, 71873108, 62072379]; Fundamental Research Funds for
   the Central Universities, China [kjcx20210102, 220810004005040246]
FX Acknowledgments The authors would like to thank all anonymous reviewers
   for their insightful comments. This work was supported by the National
   Natural Science Foundation of China under Grant No. 61906159, 62176014,
   U1836206, 71671141, 71873108, 62072379, and in part supported by the
   Fundamental Research Funds for the Central Universities, China under
   Grant No. kjcx20210102, 220810004005040246.
CR Abu-El-Haija S, 2018, ADV NEUR IN, V31
   [Anonymous], 2018, ARXIV PREPRINT ARXIV
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Balkir E., 2019, P EMNLP, P3591, DOI [10. 18653/v1/D19-1368, DOI 10.18653/V1/D19-1368]
   Bansal T, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4387
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X
   Busbridge D., 2019, ARXIV PREPRINT ARXIV, DOI [10.18653/v1/2020.acl-main.295, DOI 10.18653/V1/2020.ACL-MAIN.295]
   Chami I., 2020, P 58 ANN M ASS COMP, P6901, DOI [10.18653/v1/2020.acl-main.617, DOI 10.18653/V1/2020.ACL-MAIN.617]
   Chaturvedi I, 2016, KNOWL-BASED SYST, V108, P144, DOI 10.1016/j.knosys.2016.07.019
   Che FH, 2020, AAAI CONF ARTIF INTE, V34, P2774
   Chen GB, 2017, IEEE IJCNN, P2377, DOI 10.1109/IJCNN.2017.7966144
   COLEMAN TF, 1983, SIAM J NUMER ANAL, V20, P187, DOI 10.1137/0720013
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ebisu T., 2019, P NAACL, P988, DOI [10.18653/v1/N19-1104, DOI 10.18653/V1/N19-1104]
   Fan CJ, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P559, DOI 10.1145/3357384.3357979
   Florescu C, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1105, DOI 10.18653/v1/P17-1102
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Han X, 2018, AAAI CONF ARTIF INTE, P4832
   Haveliwala T.H., 2002, P 11 INT C WORLD WID, P517, DOI DOI 10.1145/511446.511513
   Hong HT, 2020, AAAI CONF ARTIF INTE, V34, P4132
   Hou Y, 2020, ARXIV PREPRINT ARXIV
   Hu Linmei, 2019, P 2019 C EMP METH NA, P4820, DOI DOI 10.18653/V1/D19-1488
   Hu ZN, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P2704, DOI 10.1145/3366423.3380027
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Jing Y, 2014, SYST APPL TECHN C LI, P1, DOI DOI 10.1109/ICME.2014.6890130
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140
   Lee JB, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P499, DOI 10.1145/3357384.3357880
   Lee JB, 2019, ACM T KNOWL DISCOV D, V13, DOI 10.1145/3363574
   Lee JB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1666, DOI 10.1145/3219819.3219980
   Li A, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P2703, DOI 10.1145/3357384.3357820
   Li P., 2012, P 8 IEEE INT C WIR C, P1
   Li XB, 2012, PRZ ELEKTROTECHNICZN, V88, P141
   Li Y, 2018, INFORM SCIENCES, V450, P301, DOI 10.1016/j.ins.2018.03.050
   Lin X.V., 2018, ARXIV180810568
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu Q, 2017, ACM T KNOWL DISCOV D, V11, DOI 10.1145/3046941
   Lu LY, 2016, PHYS REP, V650, P1, DOI 10.1016/j.physrep.2016.06.007
   Lu YJ, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P505
   Ma X., 2021, IEEE TKDE
   Ma YK, 2018, COGN COMPUT, V10, P639, DOI 10.1007/s12559-018-9549-x
   Makris C, 2012, J AM SOC INF SCI TEC, V63, P1581, DOI 10.1002/asi.22685
   Mihalcea R., 2004, P 2004 C EMP METH NA, P404, DOI DOI 10.3115/1219044.1219064
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Ng M. K.-P., 2011, P 17 ACM SIGKDD INT, P1217
   Nguyen D.Q., 2019, P NAACL HLT 2019, P2180, DOI DOI 10.18653/V1/N19-1226
   Nguyen D. Q., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Ogryczak W, 2009, SOCINFO 2009: INTERNATIONAL WORKSHOP ON SOCIAL INFORMATICS, PROCEEDINGS, P57, DOI 10.1109/SocInfo.2009.8
   Park N, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P596, DOI 10.1145/3292500.3330855
   Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
   Prasad A., 2019, P NAACL, P1837, DOI [10.18653/v1/N19-1182, DOI 10.18653/V1/N19-1182]
   Qian W, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4286
   Saxena A, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P4498
   Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Shi C, 2017, IEEE T KNOWL DATA EN, V29, P17, DOI 10.1109/TKDE.2016.2598561
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Su X, 2022, IEEE T NEUR NET LEAR, DOI 10.1109/TNNLS.2021.3137396
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Toutanova K., 2015, P 2015 C EMP METH NA, P1499, DOI DOI 10.18653/V1/D15-1174
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vashishth S., 2020, INT C LEARN REPR
   Vashishth S, 2020, AAAI CONF ARTIF INTE, V34, P3009
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang D, 2020, P 58 ANN M ASS COMP, P6209, DOI DOI 10.18653/V1/2020.ACL-MAIN.553
   Wang H., 2019, P EMNLP, P2623, DOI [10.18653/v1/D19-1264, DOI 10.18653/V1/D19-1264]
   Wang K., 2020, DROPPED REF, DOI [10.18653/v1/2020.acl-main.295, DOI 10.18653/V1/2020.ACL-MAIN.295]
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wang X, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2022, DOI 10.1145/3308558.3313562
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weng Jianshu, 2010, P 3 ACM INT C WEB SE, P261
   Wu F, 2019, PR MACH LEARN RES, V97
   Wu J, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P406, DOI 10.1145/3292500.3330950
   Xiao H, 2017, AAAI CONF ARTIF INTE, P3104
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xie Z, 2020, P 58 ANN M ASS COMP, P5929
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Xu K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3156
   Xu S, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P1355
   Yang B., 2015, P INT C LEARN REPR I
   Yang YD, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4099
   Zhang CX, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P793, DOI 10.1145/3292500.3330961
   Zhang JN, 2018, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, P339
   Zhang K., 2020, P ICLR
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
   Zhang Z, 2020, AAAI CONF ARTIF INTE, V34, P9612
   Zhao W, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1549
   Zhao Y., 2020, P 58 ANN M ASS COMP, P6419
   Zhou H, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4623
   Zhou J, 2018, ARTIF CELL NANOMED B, V46, pS1016, DOI 10.1080/21691401.2018.1442841
NR 95
TC 0
Z9 0
U1 6
U2 6
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD FEB 15
PY 2022
VL 237
AR 107909
DI 10.1016/j.knosys.2021.107909
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V5PO
UT WOS:000788395300013
DA 2022-08-23
ER

PT J
AU Vizcarra, J
   Kozaki, K
   Ruiz, MT
   Quintero, R
AF Vizcarra, Julio
   Kozaki, Kouji
   Torres Ruiz, Miguel
   Quintero, Rolando
TI Knowledge-Based Sentiment Analysis and Visualization on Social Networks
SO NEW GENERATION COMPUTING
LA English
DT Article
DE Sentiment analysis; Knowledge engineering; Conceptual similarity;
   Knowledge graph; Disambiguation
AB A knowledge-based methodology is proposed for sentiment analysis on social networks. The work was focused on semantic processing taking into account the content handling the public user's opinions as excerpts of knowledge. Our approach implements knowledge graphs, similarity measures, graph theory algorithms, and a disambiguation process. The results obtained were compared with data retrieved from Twitter and users' reviews in Amazon. We measured the efficiency of our contribution with precision, recall, and the F-measure, comparing it with the traditional method of looking up concepts in dictionaries which usually assign averages. Moreover, an analysis was carried out to find the best performance for the classification by using polarity, sentiment, and a polarity-sentiment hybrid. A study is presented for arguing the advantage of using a disambiguation process in knowledge processing. A visualization system presents the social graphs to display the sentiment information of each comment as well as the social structure and communications in the network.
C1 [Vizcarra, Julio; Kozaki, Kouji] Osaka Univ, Inst Sci & Ind Res ISIR, Mihogaoka 8-1, Osaka 5670047, Japan.
   [Kozaki, Kouji] Osaka Electrocommun Univ, Dept Informat Engn, Fac Informat & Commun Engn, 12-16 Hayakocho, Neyagawa, Osaka 5720837, Japan.
   [Torres Ruiz, Miguel; Quintero, Rolando] UPALM Zacatenco, Ctr Invest Computac CIC, Inst Politecn Nacl, CIC Bldg, Mexico City 07738, DF, Mexico.
RP Vizcarra, J (通讯作者)，Osaka Univ, Inst Sci & Ind Res ISIR, Mihogaoka 8-1, Osaka 5670047, Japan.
EM kaivzro@gmail.com
RI QUINTERO, ROLANDO/AGN-4493-2022; Torres-Ruiz, Miguel/AAU-9308-2021
OI QUINTERO, ROLANDO/0000-0003-4454-8791; Torres-Ruiz,
   Miguel/0000-0001-8289-6979; vizcarra, julio/0000-0002-4201-3083
FU CONACYT; JSPS KAKENHI [JP17H01789]
FX This work was supported by CONACYT and JSPS KAKENHI Grant Number
   JP17H01789.
CR Baccianella S, 2010, LREC, P2200, DOI DOI citeulike-article-id:9238846
   Bond Francis, 2012, P 6 GLOB WORDNET C G, P56
   Bond Francis, 2013, P 51 ANN M ASS COMP, V1, P1352
   Calheiros AC, 2017, J HOSP MARKET MANAG, V26, P675, DOI 10.1080/19368623.2017.1310075
   Campos V, 2017, IMAGE VISION COMPUT, V65, P15, DOI 10.1016/j.imavis.2017.01.011
   Deng SG, 2017, IEEE T NEUR NET LEAR, V28, P1164, DOI 10.1109/TNNLS.2016.2514368
   Farooq U, 2017, J COMPUT, V12, P470, DOI 10.17706/jcp.12.5.470-478
   FREDMAN ML, 1987, J ACM, V34, P596, DOI 10.1145/28869.28874
   Garcia-Pablos A, 2018, EXPERT SYST APPL, V91, P127, DOI 10.1016/j.eswa.2017.08.049
   Georgiou T., 2017, P 2017 ACM C COMP SU
   Ghosh S., 2017, ARXIV170701184
   Gubichev A., 2012, P 21 ACM INT C INF K, P1497
   Hong K, 2018, PATTERN RECOGN, V77, P140, DOI 10.1016/j.patcog.2017.12.013
   Huang LW, 2020, FUTURE GENER COMP SY, V108, P1119, DOI 10.1016/j.future.2017.12.020
   Isahara H., 2008, 6 INT C LANG RES EV
   Jiang M, 2014, IEEE T KNOWL DATA EN, V26, P2789, DOI 10.1109/TKDE.2014.2300487
   Krovetz R., 1993, SIGIR Forum, P191
   Ladd D. Robert, 1992, SEMANTICS LINGUISTIC, P237, DOI DOI 10.3765/SALT.V2I0.3030
   Leskovec J., 2014, SNAP STANFORD NETWOR
   Mantyla MV, 2018, COMPUT SCI REV, V27, P16, DOI 10.1016/j.cosrev.2017.10.002
   McCandless M., 2010, LUCENE ACTION COVERS
   Mohammad S. M., 2010, P NAACL HLT 2010 WOR, P26, DOI DOI 10.5555/1860631.1860635
   Mohammad SM, 2013, COMPUT INTELL-US, V29, P436, DOI 10.1111/j.1467-8640.2012.00460.x
   Quintero R, 2019, KNOWL INF SYST, V59, P33, DOI 10.1007/s10115-018-1200-3
   Rudat A, 2015, COMPUT HUM BEHAV, V46, P75, DOI 10.1016/j.chb.2015.01.005
   Sehgal D., 2018, SOFT COMPUTING THEOR, P765, DOI [10.1007/978-981-10-5699-4_72, DOI 10.1007/978-981-10-5699-4_72]
   Srivastava S, 2015, IEEE DATA MINING, P985, DOI 10.1109/ICDM.2015.16
   Tian Y., 2017, P 5 INT WORKSH NAT L, P11
   Vizcarra J., 2018, CEUR WORKSHOP P, V2293, P55
   Wang S., 2018, ARXIV180205818
   Wang Y., 2016, P 2016 C EMP METH NA, P1359
   Zhang X., 2015, 150201710 ARXIV
NR 32
TC 4
Z9 4
U1 6
U2 25
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0288-3635
EI 1882-7055
J9 NEW GENERAT COMPUT
JI New Gener. Comput.
PD APR
PY 2021
VL 39
IS 1
SI SI
BP 199
EP 229
DI 10.1007/s00354-020-00103-1
EA AUG 2020
PG 31
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RP7BN
UT WOS:000564534800001
DA 2022-08-23
ER

PT J
AU Sarazin, A
   Bascans, J
   Sciau, JB
   Song, JF
   Supiot, B
   Montarnal, A
   Lorca, X
   Truptil, S
AF Sarazin, Alexandre
   Bascans, Jeremy
   Sciau, Jean-Baptiste
   Song, Jiefu
   Supiot, Bruno
   Montarnal, Aurelie
   Lorca, Xavier
   Truptil, Sebastien
TI Expert system dedicated to condition-based maintenance based on a
   knowledge graph approach: Application to an aeronautic system
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE CBM; Expert System; Knowledge graph
ID BAYESIAN HIERARCHICAL-MODELS; PROGNOSTICS; DIAGNOSTICS; MANAGEMENT
AB Condition Based Maintenance (CBM) has become the focus of many research topics over the past decades. This is mostly related to the development of new machine learning algorithms and the ever increasing capacity to collect data allowing failures to be detected and the system's remaining lifetime to be estimated while requiring few or no expert knowledge. However, current machine learning based CBM solutions have limitations. They require an extensive and relevant data set to train on and are performed at the component level, not systemwide. Conversely, Expert Systems (ES) do not have these restrictions but should be used on systems with available expert knowledge and are currently suffering from efficiency, scalability and applicability limits. In this paper, an ES solution for CBM based on an heterogeneous information network will be presented to address the efficiency, scalability and applicability issues of modern ES. An application to an aircraft system will be used as case study to illustrate the process and performance of this solution for anomaly detection and diagnostics.
C1 [Sarazin, Alexandre; Bascans, Jeremy] APSYS, Data Proc & AI Div, Blagnac, France.
   [Sarazin, Alexandre; Montarnal, Aurelie; Lorca, Xavier] IMT Mines Albi, Ctr Genie Industriel, Albi, France.
   [Sciau, Jean-Baptiste; Song, Jiefu; Supiot, Bruno] ACTIVUS, Rech & Dev, Toulouse, France.
   [Truptil, Sebastien] CEA Tech Occitanie, CEA, Labege, France.
RP Sarazin, A (通讯作者)，APSYS, Data Proc & AI Div, Blagnac, France.
EM alexandre.sarazin@apsys-airbus.com; jeremy.bascans@apsys-airbus.com;
   jeanbaptiste.sciau@activus-group.fr; jiefu.song@activus-group.fr;
   bruno.supiot@activus-group.fr; aurelie.montarnal@mines-albi.fr;
   xavier.lorca@mines-albi.fr; sebastien.truptil@cea.fr
OI Truptil, Sebastien/0000-0002-8431-8010; Lorca,
   Xavier/0000-0002-6534-8644; Bascans, Jeremy/0000-0002-7352-7968
CR Abu-Nasser B, 2017, MED EXPERT SYSTEMS S
   Agrawal R, 2000, LECT NOTES COMPUT SC, V1777, P365
   Angelova R, 2012, WORLD WIDE WEB, V15, P139, DOI 10.1007/s11280-011-0126-4
   Association Francaise de NORmalisation (AFNOR), 2018, EN13306 AFNOR
   Bayoumi A., 2008, AHS INT SPEC M COND
   Beleulmi S, 2014, PHYSCS PROC, V55, P75, DOI 10.1016/j.phpro.2014.07.012
   Chein M., 1998, KR, P524
   Cheng SF, 2010, SENSORS-BASEL, V10, P5774, DOI 10.3390/s100605774
   Corset F., 2003, QUALITA
   Couallier V., 2016, 20E C MAITR RISQ SUR, V4, P1
   Davis D, 2011, 2011 INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2011), P281, DOI 10.1109/ASONAM.2011.107
   Guillen AJ, 2016, COMPUT IND, V82, P170, DOI 10.1016/j.compind.2016.07.003
   Gunduz D, 2019, IEEE J SEL AREA COMM, V37, P2184, DOI 10.1109/JSAC.2019.2933969
   Hanachi H, 2018, IEEE T RELIAB, V67, P1340, DOI 10.1109/TR.2018.2822702
   HURVICH CM, 1989, BIOMETRIKA, V76, P297, DOI 10.1093/biomet/76.2.297
   Huynh KT, 2020, EUR J OPER RES, V280, P152, DOI 10.1016/j.ejor.2019.07.007
   Jackson P., 1998, INTRO EXPERT SYSTEMS
   Jouin M, 2013, INT J HYDROGEN ENERG, V38, P15307, DOI 10.1016/j.ijhydene.2013.09.051
   Lai CD, 2014, SPRINGERBRIEF STAT, P1, DOI 10.1007/978-3-642-39106-4
   Lee J, 2014, MECH SYST SIGNAL PR, V42, P314, DOI 10.1016/j.ymssp.2013.06.004
   Levine P., 1990, SYSTEMES INTERACTIFS
   LIEBOWITZ J, 1995, ENG FRACT MECH, V50, P601, DOI 10.1016/0013-7944(94)E0047-K
   Liu XX, 2019, IEEE ACCESS, V7, P62251, DOI 10.1109/ACCESS.2019.2915987
   Numanoglu M. F., 2020, J INT TRADE LOGISTIC, V6, P126
   Nwobi FN., 2014, METODOLOSKI ZVEZKI, V11, P65
   Puggina N, 2012, J ENG GAS TURB POWER, V134, DOI 10.1115/1.4004185
   Rajabi M., 2019, ARXIV190908794CS
   Roemer MJ, 2000, AEROSP CONF PROC, P345, DOI 10.1109/AERO.2000.877909
   Roussey C, 2001, INFORSID, P419
   Salameh JP, 2018, MECH SYST SIGNAL PR, V111, P251, DOI 10.1016/j.ymssp.2018.03.052
   Sheng VS, 2019, AAAI CONF ARTIF INTE, P9837
   Sowa JF, 1983, CONCEPTUAL STRUCTURE
   Sun Y., 2012, SYNTH LECT DATA MIN, V3, P1, DOI DOI 10.2200/S00433ED1V01Y201207DMK005
   Xie M, 1996, RELIAB ENG SYST SAFE, V52, P87, DOI 10.1016/0951-8320(95)00149-2
   Yanase J, 2019, EXPERT SYST APPL, V138, DOI 10.1016/j.eswa.2019.112821
   Zaidan MA, 2016, MECH SYST SIGNAL PR, V70-71, P120, DOI 10.1016/j.ymssp.2015.09.014
   Zaidan MA, 2015, EXPERT SYST APPL, V42, P539, DOI 10.1016/j.eswa.2014.08.007
   Zhang LF, 2008, SPRING SER RELIAB EN, P57, DOI 10.1007/978-1-84800-113-8_3
NR 38
TC 2
Z9 3
U1 5
U2 18
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD DEC 30
PY 2021
VL 186
AR 115767
DI 10.1016/j.eswa.2021.115767
EA SEP 2021
PG 10
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA WE3LM
UT WOS:000705531600009
OA Green Published
DA 2022-08-23
ER

PT J
AU Orogat, A
   El-Roby, A
AF Orogat, Abdelghny
   El-Roby, Ahmed
TI CBench: Demonstrating Comprehensive Evaluation of Question Answering
   Systems over Knowledge Graphs Through Deep Analysis of Benchmarks
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article; Proceedings Paper
CT 47th International Conference on Very Large Data Bases (VLDB)
CY AUG 16-20, 2021
CL Copenhagen, DENMARK
AB A plethora of question answering (QA) systems that retrieve answers to natural language questions from knowledge graphs have been developed in recent years. However, choosing a benchmark to accurately assess the quality of a question answering system is a challenging task due to the high degree of variations among the available benchmarks with respect to their fine-grained properties.
   In this demonstration, we introduce CBench, an extensible, and more informative benchmarking suite for analyzing benchmarks and evaluating QA systems. CBench can be used to analyze existing benchmarks with respect to several fine-grained linguistic, syntactic, and structural properties of the questions and queries in the benchmarks. Moreover, CBench can be used to facilitate the evaluation of QA systems using a set of popular benchmarks that can be augmented with other user-provided benchmarks. CBench not only evaluates a QA system based on popular single-number metrics but also gives a detailed analysis of the linguistic, syntactic, and structural properties of answered and unanswered questions to help the developers of QA systems to better understand where their system excels and where it struggles.
C1 [Orogat, Abdelghny; El-Roby, Ahmed] Carleton Univ, Ottawa, ON, Canada.
RP Orogat, A (通讯作者)，Carleton Univ, Ottawa, ON, Canada.
EM abdelghny.orogat@carleton.ca; ahmed.elroby@carleton.ca
CR El-Roby A., 2021, P VLDB ENDOWMENT PVL, V14
   Hoffner K, 2017, SEMANT WEB, V8, P895, DOI 10.3233/SW-160247
   Su Y., 2016, P C EMP METH NAT LAN
   Trivedi P., 2017
   Usbeck R., 2018, JOINT WORKSH NAT LAN
   Usbeck R, 2019, SEMANT WEB, V10, P293, DOI 10.3233/SW-180312
   W3C, 2013, SPARQL 1 1 QUERY LAN
NR 7
TC 0
Z9 0
U1 2
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD AUG
PY 2021
VL 14
IS 12
BP 2711
EP 2714
DI 10.14778/3476311.3476326
PG 4
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA YH1AX
UT WOS:000742908500015
DA 2022-08-23
ER

PT J
AU Li, QY
   Bu, ZH
   Xue, MT
   Huang, AL
   Tu, WJ
   Xu, GH
AF Li, Qingyun
   Bu, Zihan
   Xue, Mengting
   Huang, Anle
   Tu, Wenjing
   Xu, Guihua
TI Visual Analysis of Research Hotspots in Geriatric Nursing Education in
   China and Abroad
SO JOURNAL OF HEALTHCARE ENGINEERING
LA English
DT Article
ID CARE; RESIDENTS; DEMENTIA; DELIRIUM; QUALITY; STAFF
AB Background. This study aimed to conduct a literature search to determine research hotspots in the field of gerontological care education in China and abroad. A knowledge of the focus of research conducted abroad may assist Chinese educators in determining the shape of gerontological care education in the future. Methods. The "Web of Science Core Collection" and "CNKI" databases were searched for literature on gerontological care education published from 2010 to 2020. CiteSpace software was used to display the knowledge map of co-occurrence of keywords, and an evolution trend map of research hotspots in recent 10 years was constructed. Results. From 2010 to 2020, the focus of foreign gerontological nursing education research was on the training of gerontological nursing personnel; the development of elderly care services; and education and training in dealing with patients with senile delirium and dementia. The focus of gerontological nursing education research in China was primary care education, training of senior elderly nursing personnel, talent training mode reform, training of nursing staff, and career development paths for geriatric nursing staff. Conclusions. Foreign geriatric nursing education research focuses on cultivating talents, mental health services for the elderly, innovating the mode of pension, and the care of patients with disorders such as dementia and delirium, while in China, the emphasis remains on gerontological nursing personnel training. Attention to research developments in other countries may assist Chinese educators to promote the development of geriatric nursing education in our country.
C1 [Li, Qingyun; Bu, Zihan; Xue, Mengting; Huang, Anle; Tu, Wenjing; Xu, Guihua] Nanjing Univ Chinese Med, Nanjing, Peoples R China.
RP Xu, GH (通讯作者)，Nanjing Univ Chinese Med, Nanjing, Peoples R China.
EM xgh_88@163.com
FU National Natural Science Foundation of China [716749]
FX AcknowledgmentsThis study was funded by the National Natural Science
   Foundation of China (no. 716749).
CR AACN, 2010, J PROF NURS, V28, P23
   [Anonymous], 2016, OFFICE NATL WORKING
   Brown PB, 2016, HEALTH SERV RES, V51, P418, DOI 10.1111/1475-6773.12431
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen Y., 2011, NURS RES, V25, P3117
   Chen YM, 2016, INT J NURS PRACT, V22, P239, DOI 10.1111/ijn.12401
   Gaba DM, 2010, BRIT J ANAESTH, V105, P3, DOI 10.1093/bja/aeq124
   Gray-Miceli D, 2014, J PROF NURS, V30, P447, DOI 10.1016/j.profnurs.2014.05.001
   Guo J., 2016, NURS RES, V30, P2366
   Jackson TA, 2016, INT J GERIATR PSYCH, V31, P396, DOI 10.1002/gps.4344
   Kang J, 2016, STRATEGIC MANAGE J, V37, P389, DOI 10.1002/smj.2234
   Krause K., 2011, ALZHEIMERS DEMENT, V7, pS291, DOI [10.1016/j.jalz.2011.05.845, DOI 10.1016/J.JALZ.2011.05.845]
   [李春玉 Li Chunyu], 2014, [护理学杂志, Journal of Nursing Science], V29, P1
   Li H., 2019, NURS RES, V33, P3956
   Li Y., 2015, CHINESE J PRACTICAL, V31, P549
   Luan C., 2009, SCI RES, V127, P240
   National Health and Family Planning Commission of the Peoples Republic of China, 2016, CIRC NATL HLTH FAM P
   National Health and Family Planning Commission of the Peoples Republic of China, 2012, GUID MIN HLTH IMPL H
   Passalacqua SA, 2012, CLIN GERONTOLOGIST, V35, P425, DOI 10.1080/07317115.2012.702655
   Pepper GA, 2014, J PROF NURS, V30, P443, DOI 10.1016/j.profnurs.2014.10.001
   Smith ER, 2011, INT PSYCHOGERIATR, V23, P256, DOI 10.1017/S1041610210001845
   State Council, 2017, CIRC PRINT ISS PLAN
   State Council, 2013, SEV OP ACC PENS SERV
   Tyler R, 2013, BASIC PRINCIPLES CUR
   van de Steeg L, 2014, BMC GERIATR, V14, DOI 10.1186/1471-2318-14-69
   Wang Y., 2013, CHINESE NURSING ED, V10, P304
   Wang Z, 2015, CHINESE J NURSING, V50, P457
   Welford Claire, 2014, Nurs Times, V110, P22
   World Health Organization, 2022, WHO EUR GER NURS CUR
   Yan S., 2014, GLOBAL ED OUTLOOK, V43, P64
   Yasuda M, 2017, AGING MENT HEALTH, V21, P991, DOI 10.1080/13607863.2016.1191056
   Zhang K., 2011, RES DISABLED PEOPLE, V2, P11
   Zhou Y, 2019, HLTH VOCATIONAL ED, V37, P3
NR 33
TC 0
Z9 0
U1 16
U2 16
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 2040-2295
EI 2040-2309
J9 J HEALTHC ENG
JI J. Healthc. Eng.
PD APR 18
PY 2022
VL 2022
AR 1796485
DI 10.1155/2022/1796485
PG 7
WC Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Health Care Sciences & Services
GA 1C9BC
UT WOS:000793405200009
PM 35480150
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ren, JL
   Lyu, PH
   Wu, XM
   Ma, FC
   Wang, ZZ
   Yang, G
AF Ren, Jun-Lin
   Lyu, Peng-Hui
   Wu, Xin-Mu
   Ma, Fei-Cheng
   Wang, Zhi-Zhang
   Yang, Gang
TI An Informetric Profile of Water Resources Management Literatures
SO WATER RESOURCES MANAGEMENT
LA English
DT Article
DE Water resource management; Informetric study; Research trend; Web of
   Science; Knowledge mapping
ID SYSTEM
AB The main purpose of this paper is to evaluate the global performance and to assess the current trends in research of water resource management. The methods of informetric analysis were used to survey water resource management related articles in the Science Citation Index (SCI) and Social Science Citation Index (SSCI) during the past decades. The publication records, subjects, journals, countries, institutes, authors, citations and keywords were analyzed respectively for each paper. The number of papers related to water resource management in 2012 was approximately 8 times that of the year 2000 and hundreds of times more than early 1990s. Water resource management related papers were distributed unevenly by countries. The USA, P.R. China, Australia and UK were the top contributing countries, also present normalized by dividing with population that published most SCI papers as well as SSCI papers. The largest water resource management research center is located in the USA according to the number of publications and citations, with P.R. China becoming more proficient in water resource management according to the data from country and institute. In addition, the quality of papers produced by developed countries is more advanced than developing countries. All these efforts contributed to the indication in trends of water resource management research on a global scale. Earlier water resource management research appeared and was originally concentrated on engineering, irrigation and geography. Issues gradually transferred to management, economics and regime recently.
C1 [Ren, Jun-Lin; Wu, Xin-Mu] Wuhan Univ, Sch Econ & Management, Wuhan 430071, Peoples R China.
   [Lyu, Peng-Hui; Ma, Fei-Cheng] Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China.
   [Wang, Zhi-Zhang; Yang, Gang] Southwest Univ, Sch Econ & Management, Chongqing 400715, Peoples R China.
RP Lyu, PH (通讯作者)，Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China.
EM ph@nimte.ac.cn
RI LYU, Penghui/A-6864-2013
OI LYU, Penghui/0000-0002-7542-4591
FU Natural Science Foundation of China [71173249]
FX This work is supported by the Natural Science Foundation of China
   (Grant. No 71173249). The authors are grateful to Jie Li, Qian Cao and
   Gerard Joseph White for their helpful discussions and suggestions. The
   authors would also like to thank anonymous reviewers for their valuable
   comments.
CR Asian Development Bank, 2007, KEY IND 2007, V38
   Biswas AK, 2004, WATER INT, V29, P248, DOI 10.1080/02508060408691775
   Biswas AK, 2008, INT J WATER RESOUR D, V24, P5, DOI 10.1080/07900620701871718
   Cao SY, 2010, J HYDRO-ENVIRON RES, V4, P3, DOI 10.1016/j.jher.2009.09.003
   Foster S, 2012, HYDROGEOL J, V20, P415, DOI 10.1007/s10040-012-0831-9
   Giupponi C, 2007, ENVIRON MODELL SOFTW, V22, P248, DOI 10.1016/j.envsoft.2005.07.024
   GLEICK PH, 1993, INT SECURITY, V18, P79, DOI 10.2307/2539033
   Hoekstra AY, 2005, GLOBAL ENVIRON CHANG, V15, P45, DOI 10.1016/j.gloenvcha.2004.06.004
   Karamouz M, 2004, J IRRIG DRAIN ENG, V130, P391, DOI 10.1061/(ASCE)0733-9437(2004)130:5(391)
   Loukas A, 2007, WATER RESOUR MANAG, V21, P1673, DOI 10.1007/s11269-006-9120-5
   Mohammadi T, 2003, DESALINATION, V158, P267, DOI 10.1016/S0011-9164(03)00462-4
   Mysiak J, 2005, ENVIRON MODELL SOFTW, V20, P203, DOI 10.1016/j.envsoft.2003.12.019
   Partnership GW, 2000, TAC BACKGROUND PAPER, P4
   Qin BQ, 2010, ENVIRON MANAGE, V45, P105, DOI 10.1007/s00267-009-9393-6
   Renwick ME, 1998, LAND ECON, V74, P343, DOI 10.2307/3147117
   Rojas-Sola JI, 2011, TECNOL CIENC AGUA, V2, P195
   Seckler D., 1999, International Journal of Water Resources Development, V15, P29, DOI 10.1080/07900629948916
   Teele RP, 1900, J POLIT ECON, V8, P524, DOI 10.1086/250696
   Tenge AJ, 2005, APPL GEOGR, V25, P348, DOI 10.1016/j.apgeog.2005.08.003
   Vidyasagar D, 2007, J PERINATOL, V27, P56, DOI 10.1038/sj.jp.7211629
   Vorosmarty CJ, 2010, NATURE, V467, P555, DOI 10.1038/nature09440
   Wang JZ, 2002, PUBLIC ADMIN DEVELOP, V22, P369, DOI 10.1002/pad.241
   Wang MH, 2011, DESALIN WATER TREAT, V28, P353, DOI 10.5004/dwt.2011.2412
   WEINBERG M, 1993, AM J AGR ECON, V75, P278, DOI 10.2307/1242912
   Yao Q, 2012, J VECTOR DIS, V49, P205
NR 25
TC 9
Z9 10
U1 1
U2 68
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0920-4741
EI 1573-1650
J9 WATER RESOUR MANAG
JI Water Resour. Manag.
PD OCT
PY 2013
VL 27
IS 13
BP 4679
EP 4696
DI 10.1007/s11269-013-0435-8
PG 18
WC Engineering, Civil; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Water Resources
GA 216FI
UT WOS:000324267000016
DA 2022-08-23
ER

PT J
AU Gagliardi, AR
   Wright, FC
   Grunfeld, E
   Davis, D
AF Gagliardi, Anna R.
   Wright, Frances C.
   Grunfeld, Eva
   Davis, Dave
TI Colorectal cancer care knowledge mapping: identifying priorities for
   knowledge translation research
SO CANCER CAUSES & CONTROL
LA English
DT Review
DE colorectal neoplasms; quality indicators; health care; health services
   research; health priorities; diffusion of innovation
ID FECAL OCCULT BLOOD; HOSPITAL PROCEDURE VOLUME; OUTCOME FOLLOWING
   SURGERY; HEALTH-SERVICES RESEARCH; RECTAL-CANCER; COLON-CANCER;
   UNITED-STATES; FOLLOW-UP; SCREENING COLONOSCOPY; NATIONAL-SURVEY
AB Objective We do not know the extent and nature of knowledge translation (KT) in oncology. This study examined colorectal cancer (CRC) health services research, and engaged researchers and decision makers in prioritizing KT research gaps.
   Methods MEDLINE was searched from 1996 to 2006 for CRC health services research in Canada, Australia, the United Kingdom, and United States. Studies were tabulated by indicator, type of research and country to reveal gaps. Researchers and decision makers prioritized gaps via questionnaire, then generated research questions for top-ranked gaps at a one-day workshop.
   Results A total of 132 articles were categorized and 29 individuals attended the workshop. We lack knowledge about factors influencing rates of many indicators. Researchers and decision makers prioritized KT research on factors that could either influence the utilization of screening or enhance the quality of surgical outcomes. They acknowledged lack of research capacity and policy support as barriers, and confusion about the concept of KT.
   Conclusions Several opportunities were revealed for improving the quality of CRC screening and surgery. Greater coordination of, and support for KT research is required to address these gaps. Further research should evaluate different methods of achieving KT between researchers and decision makers for research planning.
C1 [Gagliardi, Anna R.; Wright, Frances C.] Univ Toronto, Sunnybrook Hlth Sci Ctr, Fac Med, Dept Surg, Toronto, ON M4N 3M5, Canada.
   [Gagliardi, Anna R.; Wright, Frances C.] Univ Toronto, Sunnybrook Hlth Sci Ctr, Fac Med, Dept Hlth Policy, Toronto, ON M4N 3M5, Canada.
   [Gagliardi, Anna R.; Wright, Frances C.] Univ Toronto, Sunnybrook Hlth Sci Ctr, Fac Med, Dept Management & Evaluat, Toronto, ON M4N 3M5, Canada.
   [Grunfeld, Eva] Dalhousie Univ, Dept Med, Div Med Oncol, Canc Care Nova Scotia,Canc Outcomes Res Program, Halifax, NS, Canada.
   [Davis, Dave] Assoc Amer Med Coll, Washington, DC USA.
RP Gagliardi, AR (通讯作者)，Univ Toronto, Sunnybrook Hlth Sci Ctr, Fac Med, Dept Surg, 2075 Bayview Ave,Room T2054, Toronto, ON M4N 3M5, Canada.
EM anna.gagliardi@sunnybrook.ca
CR [Anonymous], 2000, Health Policy Plan, V15, P130
   Asano TK, 2004, CAN J SURG, V47, P104
   *ASS OFF SEED CERT, 2001, GEN CROP STAND ASS O, P1
   Baig N, 2003, AM J GASTROENTEROL, V98, P2078, DOI 10.1016/S0002-9270(03)00424-6
   Ball JK, 1996, MED CARE, V34, P970, DOI 10.1097/00005650-199609000-00008
   Bampton PA, 2002, MED J AUSTRALIA, V176, P155, DOI 10.5694/j.1326-5377.2002.tb04344.x
   Barnsley J, 1998, HEALTH CARE MANAGE R, V23, P18, DOI 10.1097/00004010-199801000-00003
   Battat AC, 2004, ANN SURG ONCOL, V11, P970, DOI 10.1245/ASO.2004.03.047
   Bensing JM, 2003, INT J TECHNOL ASSESS, V19, P604, DOI 10.1017/S0266462303000564
   Benson R, 2001, INT J RADIAT ONCOL, V50, P1309, DOI 10.1016/S0360-3016(01)01545-0
   Brenes GA, 2000, PREV MED, V31, P410, DOI 10.1006/pmed.2000.0729
   Bressler B, 2004, GASTROINTEST ENDOSC, V60, P921, DOI 10.1016/S0016-5107(04)02231-X
   Brown ML, 2003, AM J MED, V115, P129, DOI 10.1016/S0002-9343(03)00297-3
   Busch Sabita, 2003, ABNF J, V14, P99
   Campbell MK, 2004, HEALTH PSYCHOL, V23, P492, DOI 10.1037/0278-6133.23.5.492
   Carson N, 2000, Aust Health Rev, V23, P46
   Christie Jennifer, 2005, J Natl Med Assoc, V97, P1361
   Christman LK, 2004, J HEALTH CARE POOR U, V15, P281, DOI 10.1353/hpu.2004.0021
   Church TR, 2004, JNCI-J NATL CANCER I, V96, P770, DOI 10.1093/jnci/djh134
   Cibula Donald A, 2003, J Public Health Manag Pract, V9, P315
   Cockburn J, 2002, AUST NZ J PUBL HEAL, V26, P236, DOI 10.1111/j.1467-842X.2002.tb00680.x
   Cooney A, 2004, Colorectal Dis, V6, P418, DOI 10.1111/j.1463-1318.2004.00710.x
   Cooper GS, 1999, CANCER-AM CANCER SOC, V85, P2124, DOI 10.1002/(SICI)1097-0142(19990515)85:10<2124::AID-CNCR5>3.0.CO;2-L
   Corbett M, 2004, MED J AUSTRALIA, V181, P423, DOI 10.5694/j.1326-5377.2004.tb06365.x
   Corrrigan M, 2006, BMC HEALTH SERV RES, V6, DOI 10.1186/1472-6963-6-90
   Coughlin SS, 2004, J RURAL HEALTH, V20, P118, DOI 10.1111/j.1748-0361.2004.tb00017.x
   Cronin DP, 2006, AM J GASTROENTEROL, V101, P2308, DOI 10.1111/j.1572-0241.2006.00775.x
   Davis D, 2006, J CONTIN EDUC HEALTH, V26, P5, DOI 10.1002/chp.46
   Davis D, 2003, BMJ-BRIT MED J, V327, P33, DOI 10.1136/bmj.327.7405.33
   Davis DA, 2006, JAMA-J AM MED ASSOC, V296, P1094, DOI 10.1001/jama.296.9.1094
   Dimick JB, 2003, J SURG RES, V114, P50, DOI 10.1016/S0022-4804(03)00207-5
   Dulai GS, 2004, CANCER, V100, P1843, DOI 10.1002/cncr.20209
   Duxbury MS, 2003, EUR J SURG ONCOL, V29, P400, DOI 10.1053/ejso.2002.1426
   Earle CC, 2003, ANN ONCOL, V14, P400, DOI 10.1093/annonc/mdg101
   Easson AM, 2002, ANN SURG ONCOL, V9, P380, DOI 10.1007/BF02573873
   Ebener S, 2006, B WORLD HEALTH ORGAN, V84, P636, DOI 10.2471/BLT.06.029736
   Erban S, 2001, Eff Clin Pract, V4, P10
   Estabrooks CA, 2006, J CONTIN EDUC HEALTH, V26, P25, DOI 10.1002/chp.48
   Estabrooks CA, 2004, NURS RES, V53, P293, DOI 10.1097/00006199-200409000-00003
   Farmer KC, 2002, ANZ J SURG, V72, P265, DOI 10.1046/j.1445-2197.2002.02371.x
   Fleurence RL, 2004, HEALTH POLICY, V69, P1, DOI 10.1016/j.healthpol.2003.11.002
   Frew E, 2001, J Health Serv Res Policy, V6, P85, DOI 10.1258/1355819011927279
   Gagliardi A, 2007, BREAST CANCER RES TR, V103, P131, DOI 10.1007/s10549-006-9359-9
   Gagliardi AR, 2005, CAN J SURG, V48, P441
   Ganz PA, 2003, CANCER EPIDEM BIOMAR, V12, P824
   Gattellari M, 2001, AUST NZ J SURG, V71, P147, DOI 10.1046/j.1440-1622.2001.02059.x
   Gennarelli M, 2005, MT SINAI J MED, V72, P36
   Gipsh Katherine, 2004, Gastroenterol Nurs, V27, P262, DOI 10.1097/00001610-200411000-00004
   Goldberg D, 2004, AM J PREV MED, V26, P431, DOI 10.1016/j.amepre.2004.02.009
   Govindarajan R, 2003, CANCER, V97, P493, DOI 10.1002/cncr.11067
   Graham ID, 2006, J CONTIN EDUC HEALTH, V26, P13, DOI 10.1002/chp.47
   Greenhalgh T, 2004, MILBANK Q, V82, P581, DOI 10.1111/j.0887-378X.2004.00325.x
   Grimshaw JM, 2004, HEALTH TECHNOL ASSES, V8, P1, DOI 10.3310/hta8060
   Guillem JG, 2000, DIS COLON RECTUM, V43, P18, DOI 10.1007/BF02237238
   Harewood GC, 2003, AM J GASTROENTEROL, V98, P2312, DOI 10.1016/S0002-9270(03)00662-2
   Haward R, 2003, BRIT J CANCER, V89, P15, DOI 10.1038/sj.bjc.6601073
   Hilsden RJ, 2005, CAN J SURG, V48, P434
   Hodgson DC, 2003, J NATL CANCER I, V95, P708, DOI 10.1093/jnci/95.10.708
   Hyman N, 2002, ARCH SURG-CHICAGO, V137, P413, DOI 10.1001/archsurg.137.4.413
   Hysong SJ, 2006, IMPLEMENT SCI, V1, DOI 10.1186/1748-5908-1-9
   Ioannou GN, 2003, AM J GASTROENTEROL, V98, P2082, DOI 10.1016/S0002-9270(03)00423-4
   Jamtvedt G, 2006, QUAL SAF HEALTH CARE, V15, P433, DOI 10.1136/qshc.2006.018549
   Janda Monika, 2003, Asia Pac J Public Health, V15, P50, DOI 10.1177/101053950301500109
   Johnson PM, 2002, J GASTROINTEST SURG, V6, P883, DOI 10.1016/S1091-255X(02)00131-2
   Johnston GM, 2004, CLIN INVEST MED, V27, P142
   Kerrigan M, 2005, MED CARE, V43, P1043, DOI 10.1097/01.mlr.0000178213.76463.cb
   Kim Jane, 2005, BMC Med Inform Decis Mak, V5, P36, DOI 10.1186/1472-6947-5-36
   Kim YE, 2000, SOC SCI MED, V50, P1099, DOI 10.1016/S0277-9536(99)00358-5
   Klabunde CN, 2004, AM J MANAG CARE, V10, P273
   Klabunde CN, 2005, MED CARE, V43, P939, DOI 10.1097/01.mlr.0000173599.67470.ba
   Klabunde CN, 2003, PREV MED, V36, P352, DOI 10.1016/S0091-7435(02)00066-X
   Klabunde CN, 2002, AM J ROENTGENOL, V179, P1419, DOI 10.2214/ajr.179.6.1791419
   Larkey L, 2006, J COMMUN HEALTH, V31, P69, DOI 10.1007/s10900-005-8190-2
   Lee-Feldstein A, 2002, MED CARE, V40, P362, DOI 10.1097/00005650-200205000-00002
   Lemieux-Charles L, 2006, MED CARE RES REV, V63, P263, DOI 10.1177/1077558706287003
   Lipkus IM, 2003, J HEALTH COMMUN, V8, P213, DOI 10.1080/10810730305684
   Lomas J, 2003, MILBANK Q, V81, P363, DOI 10.1111/1468-0009.t01-1-00060
   Longo WE, 1998, ANN SURG, V228, P64, DOI 10.1097/00000658-199807000-00010
   Longo WE, 2000, DIS COLON RECTUM, V43, P83, DOI 10.1007/BF02237249
   Luke CG, 2005, CLIN ONCOL-UK, V17, P372, DOI 10.1016/j.clon.2005.04.005
   Ma R, 2001, CAN J SURG, V44, P377
   Malin JL, 2006, J CLIN ONCOL, V24, P626, DOI 10.1200/JCO.2005.03.3365
   McArdle CS, 2002, BRIT MED BULL, V64, P119, DOI 10.1093/bmb/64.1.119
   McArdle CS, 2002, BRIT J CANCER, V86, P331, DOI 10.1038/sj.bjc.6600120
   McDonald PW, 2007, HEALTH PROMOT PRACT, V8, P140, DOI 10.1177/1524839906298494
   McGlynn EA, 2003, NEW ENGL J MED, V348, P2635, DOI 10.1056/NEJMsa022615
   McGory ML, 2006, J NATL CANCER I, V98, P1623, DOI 10.1093/jnci/djj438
   McGrath DR, 2005, ANZ J SURG, V75, P901, DOI 10.1111/j.1445-2197.2005.03543.x
   McGrath DR, 2004, ANZ J SURG, V74, P55, DOI 10.1046/j.1445-1433.2003.02891.x
   McGregor SE, 2005, CAN J GASTROENTEROL, V19, P343, DOI 10.1155/2005/359243
   McMullen TPW, 2005, CAN J SURG, V48, P19
   Menon Devidas, 2005, Healthc Manage Forum, V18, P26
   Merrill RM, 1999, MED CARE RES REV, V56, P177, DOI 10.1177/107755879905600204
   Meyerhardt JA, 2003, ANN INTERN MED, V139, P649, DOI 10.7326/0003-4819-139-8-200310210-00008
   Minsky BD, 1998, J CLIN ONCOL, V16, P2542, DOI 10.1200/JCO.1998.16.7.2542
   Morris AM, 2004, ARCH SURG-CHICAGO, V139, P151, DOI 10.1001/archsurg.139.2.151
   Morris E, 2006, BRIT J CANCER, V95, P979, DOI 10.1038/sj.bjc.6603372
   Myers RE, 2001, PREV MED, V33, P249, DOI 10.1006/pmed.2001.0878
   Nadel MR, 2005, ANN INTERN MED, V142, P86, DOI 10.7326/0003-4819-142-2-200501180-00007
   Nash D, 2006, J URBAN HEALTH, V83, P231, DOI 10.1007/s11524-006-9029-6
   Neutel CI, 2005, CANCER CAUSE CONTROL, V16, P1261, DOI 10.1007/s10552-005-0379-2
   Ng VV, 2006, ANN ROY COLL SURG, V88, P181, DOI 10.1308/003588406X94913
   O'Brien MA, 2007, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD000409.pub2
   O'Connell JB, 2004, DIS COLON RECTUM, V47, P2064, DOI 10.1007/s10350-004-0738-1
   O'Malley AS, 2005, ARCH INTERN MED, V165, P2129, DOI 10.1001/archinte.165.18.2129
   Olson DW, 2002, CAN J SURG, V45, P31
   Olynyk JK, 1998, MED J AUSTRALIA, V168, P331, DOI 10.5694/j.1326-5377.1998.tb138961.x
   Ong S, 2005, CAN J SURG, V48, P225
   Papagrigoriadis S, 2001, EUR J SURG ONCOL, V27, P541, DOI 10.1053/ejso.2001.1106
   Paszat LF, 1999, INT J RADIAT ONCOL, V45, P1185, DOI 10.1016/S0360-3016(99)00327-2
   Phang PT, 2003, AM J SURG, V185, P450, DOI 10.1016/S0002-9610(03)00058-8
   Platell C, 2003, WORLD J GASTROENTERO, V9, P961
   Pope Catherine, 2006, Healthc Manage Forum, V19, P27
   Porter GA, 2005, WORLD J SURG, V29, P1444, DOI 10.1007/s00268-005-7955-1
   Porter GA, 1998, ANN SURG, V227, P157, DOI 10.1097/00000658-199802000-00001
   Prajapati DN, 2003, AM J GASTROENTEROL, V98, P194
   Prystowsky JB, 2002, SURGERY, V132, P663, DOI 10.1067/msy.2002.127550
   Purves H, 2005, DIS COLON RECTUM, V48, P195, DOI 10.1007/s10350-004-0793-7
   Rabeneck L, 2004, AM J GASTROENTEROL, V99, P1141, DOI 10.1111/j.1572-0241.2004.30623.x
   Rabeneck L, 2006, AM J GASTROENTEROL, V101, P1098, DOI 10.1111/j.1572-0241.2006.00488.x
   Rao RS, 2004, MED CARE, V42, P789, DOI 10.1097/01.mlr.0000132838.29236.7e
   Retchin SM, 1997, ARCH INTERN MED, V157, P1878, DOI 10.1001/archinte.157.16.1878
   Richards C, 1998, AM J PREV MED, V15, P246, DOI 10.1016/S0749-3797(98)00075-0
   Roetzheim RG, 2000, AM J PUBLIC HEALTH, V90, P1746, DOI 10.2105/AJPH.90.11.1746
   Roetzheim RG, 2001, J FAM PRACTICE, V50, P1027
   Rozmovits Linda, 2004, J Health Serv Res Policy, V9, P159, DOI 10.1258/1355819041403231
   Salkeld GP, 2003, ANZ J SURG, V73, P128, DOI 10.1046/j.1445-2197.2003.02650.x
   Sansbury LB, 2003, AM J PREV MED, V25, P179, DOI 10.1016/S0749-3797(03)00203-4
   Sassi F, 2003, HEALTH POLICY, V63, P141, DOI 10.1016/S0168-8510(02)00061-1
   Schrag D, 2000, JAMA-J AM MED ASSOC, V284, P3028, DOI 10.1001/jama.284.23.3028
   Schrag D, 2002, J CLIN ONCOL, V20, P3999, DOI 10.1200/JCO.2002.11.084
   Schroy PC, 2005, AM J PREV MED, V28, P208, DOI 10.1016/j.amepre.2004.10.008
   Schroy PC, 2001, PREV MED, V33, P381, DOI 10.1006/pmed.2001.0903
   Schumacher KL, 2005, RES NURS HEALTH, V28, P268, DOI 10.1002/nur.20080
   Seeff LC, 2004, GASTROENTEROLOGY, V127, P1670, DOI 10.1053/j.gastro.2004.09.051
   Semmens JB, 2000, AUST NZ J SURG, V70, P11, DOI 10.1046/j.1440-1622.2000.01734.x
   Shah SA, 2006, J AM COLL SURGEONS, V202, P468, DOI 10.1016/j.jamcollsurg.2005.11.008
   Simunovic M, 2005, CAN J SURG, V48, P137
   Simunovic M, 2001, CAN MED ASSOC J, V165, P421
   Simunovic M, 2000, J GASTROINTEST SURG, V4, P324, DOI 10.1016/S1091-255X(00)80083-9
   Simunovic MI, 2004, DIS COLON RECTUM, V47, P1594, DOI 10.1007/s10350-004-0658-0
   Singh SM, 2004, CAN MED ASSOC J, V171, P461, DOI 10.1503/cmaj.1031921
   Smith AJ, 2003, J CANCER EDUC, V18, P81, DOI 10.1207/S15430154JCE1802_09
   Stamatakis Katherine A, 2002, Mo Med, V99, P279
   Taylor V, 2003, J NATL MED ASSOC, V95, P806
   Tyldesley S, 2000, INT J RADIAT ONCOL, V47, P469, DOI 10.1016/S0360-3016(00)00440-5
   Urbach DR, 2004, QUAL SAF HEALTH CARE, V13, P379, DOI 10.1136/qhc.13.5.379
   Wakefield SE, 1996, J ROY COLL SURG EDIN, V41, P374
   Walsh JME, 2004, J GEN INTERN MED, V19, P156, DOI 10.1111/j.1525-1497.2004.30263.x
   Walsh S, 2002, ANN ROY COLL SURG, V84, P386, DOI 10.1308/003588402760978166
   Warsi AA, 2002, DIGEST SURG, V19, P205, DOI 10.1159/000064214
   *WHO, 2006, CANC FACT SHEETS, V297
   Whynes DK, 2003, PUBLIC HEALTH, V117, P389, DOI 10.1016/S0033-3506(03)00146-X
   Wolf Michael S, 2005, Prev Chronic Dis, V2, pA11
   Woodrow Chris, 2006, BMC Fam Pract, V7, P53
   Wright FC, 2006, BMC HEALTH SERV RES, V6, DOI 10.1186/1472-6963-6-4
   Wright FC, 2003, ANN SURG ONCOL, V10, P903, DOI 10.1245/ASO.2003.01.012
   Wright Frances C, 2004, J Contin Educ Health Prof, V24, P213, DOI 10.1002/chp.1340240405
   Yabroff KR, 2003, MED CARE RES REV, V60, P294, DOI 10.1177/1077558703254698
   Youl Philippa H, 2006, Aust Fam Physician, V35, P547
   Young JM, 2006, MED EDUC, V40, P423, DOI 10.1111/j.1365-2929.2006.02443.x
   Yusoff IF, 2002, MED J AUSTRALIA, V176, P151, DOI 10.5694/j.1326-5377.2002.tb04343.x
   Zimmerman RK, 2006, J URBAN HEALTH, V83, P221, DOI 10.1007/s11524-005-9028-z
   Zitzelsberger Louise, 2004, BMC Fam Pract, V5, P25, DOI 10.1186/1471-2296-5-25
NR 164
TC 4
Z9 4
U1 0
U2 7
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0957-5243
EI 1573-7225
J9 CANCER CAUSE CONTROL
JI Cancer Causes Control
PD AUG
PY 2008
VL 19
IS 6
BP 615
EP 630
DI 10.1007/s10552-008-9126-9
PG 16
WC Oncology; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Oncology; Public, Environmental & Occupational Health
GA 321SR
UT WOS:000257327300008
PM 18270797
DA 2022-08-23
ER

PT J
AU Dong, GZ
   Zhang, WZ
   Yadav, R
   Mu, X
   Zhou, ZL
AF Dong, Guozhong
   Zhang, Weizhe
   Yadav, Rahul
   Mu, Xin
   Zhou, Zhili
TI OWGC-HMC: An Online Web Genre Classification Model Based on Hierarchical
   Multilabel Classification
SO SECURITY AND COMMUNICATION NETWORKS
LA English
DT Article
AB Web genre plays an important role in focused crawling, web link analysis, and contextual advertising. In this paper, web genre is defined as the functional purpose and the information type contained in the website. The intelligent classification of web genre can predict the content and functional type of website. However, there are several critical challenges to solve the web genre classification problem: lack of web genre classification dataset and efficient web genre classification mechanism. To improve web genre classification performance, we crawled Chinese websites of different web genres and converted crawled data into a hierarchical multilabel classification dataset. A website knowledge graph is constructed based on the relationship of website and meta tag features. Using entity features extracted from the knowledge graph, we propose an online web genre classification model based on hierarchical multilabel classification (OWGC-HMC) to mine the functional purpose of the corresponding website. Experimental results show that our OWGC-HMC model can mine hierarchical multilabel structure of web genre and outperform other web genre classification methods.
C1 [Dong, Guozhong; Zhang, Weizhe; Yadav, Rahul] Peng Cheng Lab, Cyberspace Secur Res Ctr, Shenzhen 518000, Peoples R China.
   [Zhang, Weizhe] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China.
   [Mu, Xin] Peng Cheng Lab, Frontier Res Ctr, Shenzhen 518000, Peoples R China.
   [Zhou, Zhili] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China.
RP Zhang, WZ (通讯作者)，Peng Cheng Lab, Cyberspace Secur Res Ctr, Shenzhen 518000, Peoples R China.; Zhang, WZ (通讯作者)，Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China.; Zhou, ZL (通讯作者)，Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China.
EM weizhe.zhang@pcl.ac.cn; zhou_zhili@163.com
OI zhou, zhili/0000-0002-5641-7169
FU Key-Area Research and Development Program of Guangdong Province
   [2020B0101360001]; Peng Cheng Laboratory Project [PCL2021A02]; National
   Natural Science Foundation of China [62106114]
FX This study was supported in part by the Key-Area Research and
   Development Program of Guangdong Province (2020B0101360001), the Peng
   Cheng Laboratory Project (grant no. PCL2021A02), and the National
   Natural Science Foundation of China (62106114).
CR Abramson M., 2012, P AAAI WORKSH INT TE, P2
   Buber E, 2019, PROCEDIA COMPUT SCI, V154, P62, DOI 10.1016/j.procs.2019.06.011
   Chen GY, 2008, APPLIED COMPUTING 2008, VOLS 1-3, P2353
   Deri L, 2015, 2015 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT), VOL 1, P85, DOI 10.1109/WI-IAT.2015.112
   Elizabeth S., 2005, P 20 NAT C ART INT J, P1596
   Fang ZJ, 2019, CMC-COMPUT MATER CON, V60, P351, DOI 10.32604/cmc.2019.07106
   Gong WW, 2022, IEEE T IND INFORM, V18, P6379, DOI [10.1080/08830185.2021.1962860, 10.1109/TII.2021.3133614]
   Hashemi M, 2020, MULTIMED TOOLS APPL, V79, P11921, DOI 10.1007/s11042-019-08373-8
   Janakiraman B, 2020, INTELL AUTOM SOFT CO, V26, P269, DOI 10.31209/2019.100000150
   Jebari Chaker, 2016, Polibits, V0, P43, DOI 10.17562/PB-53-4
   Jebari C, 2016, J INF KNOWL MANAG, V15, DOI 10.1142/S0219649216500088
   Jebari C, 2012, 2012 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2012), VOL 1, P578, DOI 10.1109/ICMLA.2012.106
   Joulin A., 2017, P C EUR CHAPT ASS CO, V2, P427, DOI [DOI 10.18653/V1/E17-2068, 10.18653/v1/e17-2068]
   Kalaivaani PCD, 2020, INTELL AUTOM SOFT CO, V26, P71, DOI 10.31209/2019.100000152
   KENNEDY A, 2005, P 38 HAW INT C SYST
   Kim Y., 2014, P 2014 C EMP METH NA, P1746
   Kim Y., 2008, P 41 ANN HAW INT C S, P132
   Lim CS, 2005, INFORM PROCESS MANAG, V41, P1263, DOI 10.1016/j.ipm.2004.06.004
   Ma JL, 2020, CMC-COMPUT MATER CON, V64, P455, DOI 10.32604/cmc.2020.09780
   Madjarov G, 2019, INFORM SCIENCES, V503, P551, DOI 10.1016/j.ins.2019.07.009
   Madjarov G, 2015, LECT NOTES COMPUT SC, V9375, P9, DOI 10.1007/978-3-319-24834-9_2
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Vidulin V., 2007, P INT WORKSH GENR EN, P45
   Vidulin V., 2009, J LANGUAGE TECHNOLOG, V24, P97
   Wang RS, 2019, IEEE IJCNN
   Wang T, 2022, IEEE T IND INFORM, V18, P4981, DOI 10.1109/TII.2021.3103547
   Xue Y, 2020, APPL SOFT COMPUT, V88, DOI 10.1016/j.asoc.2019.106031
   Yang KH, 2020, CMC-COMPUT MATER CON, V64, P557, DOI 10.32604/cmc.2020.09907
   Zhou ZL, 2020, ACM T MULTIM COMPUT, V16, DOI 10.1145/3383582
NR 30
TC 0
Z9 0
U1 1
U2 1
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1939-0114
EI 1939-0122
J9 SECUR COMMUN NETW
JI Secur. Commun. Netw.
PD MAR 29
PY 2022
VL 2022
AR 7549880
DI 10.1155/2022/7549880
PG 9
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 2I6CI
UT WOS:000815063800006
OA gold
DA 2022-08-23
ER

PT J
AU Wang, C
   Wang, JR
   Wei, LK
   Wang, XQ
   Yang, CY
   Zhang, LF
AF Wang, Chong
   Wang, Jiren
   Wei, Like
   Wang, Xiangqian
   Yang, Chaoyu
   Zhang, Lifang
TI Transfer Learning on Knowledge Graph Construction: A Case Study of
   Investigating Gas-Mining Risk Report
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
AB This study addressed the problem of automated Knowledge Graph (KG) construction from unstructured documents, with the assistance of transfer learning. Despite a large amount of effort made to discover KG, how to explore unknown KGs from existing knowledge remains a challenge. In this paper, we firstly formulate the KG detection process as a transfer-learning problem, which consists of two main steps. At first, we pretrain a backbone model using the source domain. Due to sufficient samples from the source domain, this backbone model can be trained better. Second, we migrate this model (from the known domain) to the target domain by fine-tuning key parameters. The fine-tuning operation only requires less computation, which is very efficient. As such, the backbone model can be successfully transferred into the target domain, even with limit training samples. Experimental evaluations using one real-world dataset of gas-mining reports demonstrate the advantages of utilizing the proposed algorithm to construct KG using transferable information.
C1 [Wang, Chong; Wang, Jiren] Liaoning Tech Univ, Coll Safety Sci & Engn, Fuxing 123000, Peoples R China.
   [Wang, Chong; Wang, Jiren] Minist Educ, Key Lab Mine Thermodynam Disasters & Control, Huludao 125105, Liaoning, Peoples R China.
   [Wang, Chong; Wei, Like] Minist Emergency Management, Informat Res Inst, Beijing 100029, Peoples R China.
   [Wang, Xiangqian; Yang, Chaoyu] Anhui Univ Sci & Technol, Sch Econ & Management, Huainan 232033, Peoples R China.
   [Zhang, Lifang] China Jiliang Univ, Hangzhou 310000, Zhejiang, Peoples R China.
RP Yang, CY (通讯作者)，Anhui Univ Sci & Technol, Sch Econ & Management, Huainan 232033, Peoples R China.
EM yangchy@aust.edu.cn
OI Wei, Like/0000-0002-6067-7721
FU National Natural Science Foundation of China [61873004, 51474007]
FX This work was partially supported by the National Natural Science
   Foundation of China under Grant Nos. 61873004 and 51474007.
CR Bian JX, 2021, IEEE ACCESS, V9, P66052, DOI 10.1109/ACCESS.2021.3076533
   Chen M., 2019, INT JOINT C NAT LANG, P4208
   Guo ZJ, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P241
   Han XM, 2021, COMPLEXITY, V2021, DOI 10.1155/2021/6696064
   Huang WM, 2020, EURASIP J IMAGE VIDE, V2020, DOI 10.1186/s13640-020-00539-x
   Huang ZH, 2015, COMPUT INTEL NEUROSC, V2015, DOI 10.1155/2015/685404
   Li ZR, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4377
   Long MS, 2017, PR MACH LEARN RES, V70
   Poster DD, 2021, IEEE ACCESS, V9, P52759, DOI 10.1109/ACCESS.2021.3070233
   Qian T, 2021, IEEE-ACM T AUDIO SPE, V29, P1438, DOI 10.1109/TASLP.2021.3069295
   Xiong WH, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4258
   Yang J, 2020, KNOWL-BASED SYST, V191, DOI 10.1016/j.knosys.2019.105214
   Yang J, 2020, INT J HUM-COMPUT INT, V36, P211, DOI 10.1080/10447318.2019.1625570
   Yang J, 2019, EXPERT SYST APPL, V116, P255, DOI 10.1016/j.eswa.2018.08.038
   Zhang N., 2019, TRANSFER LEARNING RE
   Zhong Zexuan, 2021, NAACL, P50
NR 16
TC 0
Z9 0
U1 5
U2 5
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1024-123X
EI 1563-5147
J9 MATH PROBL ENG
JI Math. Probl. Eng.
PD MAR 14
PY 2022
VL 2022
AR 9947098
DI 10.1155/2022/9947098
PG 7
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA 0H0SL
UT WOS:000778450000001
OA gold
DA 2022-08-23
ER

PT J
AU Hoyt, CT
   Domingo-Fernandez, D
   Aldisi, R
   Xu, LL
   Kolpeja, K
   Spalek, S
   Wollert, E
   Bachman, J
   Gyori, BM
   Greene, P
   Hofmann-Apitius, M
AF Hoyt, Charles Tapley
   Domingo-Fernandez, Daniel
   Aldisi, Rana
   Xu, Lingling
   Kolpeja, Kristian
   Spalek, Sandra
   Wollert, Esther
   Bachman, John
   Gyori, Benjamin M.
   Greene, Patrick
   Hofmann-Apitius, Martin
TI Re-curation and rational enrichment of knowledge graphs in Biological
   Expression Language
SO DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
LA English
DT Article
ID ALZHEIMERS-DISEASE; EFFECT MODELS; DATABASE; PATHWAYS; APOPTOSIS;
   NETWORKS; OMICS
AB The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich KGs. We have developed two workflows: one for re-curating a given KG to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the KGs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full-text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment.
   Database URL: https://github. com/bel-enrichment/results
C1 [Hoyt, Charles Tapley; Domingo-Fernandez, Daniel; Aldisi, Rana; Xu, Lingling; Kolpeja, Kristian; Spalek, Sandra; Wollert, Esther; Hofmann-Apitius, Martin] Fraunhofer Inst Algorithms & Sci Comp SCAI, Dept Bioinformat, D-53754 St Augustin, Germany.
   [Hoyt, Charles Tapley; Domingo-Fernandez, Daniel; Aldisi, Rana; Xu, Lingling; Hofmann-Apitius, Martin] Rhein Freidrich Wilhelms Univ Bonn, Bonn Aachen Int Ctr Informat Technol, D-53115 Bonn, Germany.
   [Bachman, John; Gyori, Benjamin M.; Greene, Patrick] Harvard Med Sch, Lab Syst Pharmacol, 200 Longwood Ave, Boston, MA 02115 USA.
RP Hoyt, CT (通讯作者)，Fraunhofer Inst Algorithms & Sci Comp SCAI, Dept Bioinformat, D-53754 St Augustin, Germany.; Hoyt, CT (通讯作者)，Rhein Freidrich Wilhelms Univ Bonn, Bonn Aachen Int Ctr Informat Technol, D-53115 Bonn, Germany.
EM charles.hoyt@scai.fraunhofer.de
RI Domingo-Fernández, Daniel/AGH-3497-2022; Hoyt, Charles
   Tapley/N-6315-2019
OI Domingo-Fernández, Daniel/0000-0002-2046-6145; Hoyt, Charles
   Tapley/0000-0003-4423-4370; Gyori, Benjamin/0000-0001-9439-5346; Greene,
   Patrick/0000-0001-7052-0608; Kolpeja, Kristian/0000-0001-9661-5277;
   Aldisi, Rana/0000-0002-3034-9970
FU Fraunhofer Society under the MAVO Project; Human Brain Pharmacome;
   EU/EFPIA Innovative Medicines Initiative Joint Undertaking under
   AETIONOMY [115568]; European Union's Seventh Framework Programme
   (FP7/2007-2013); EFPIA
FX Fraunhofer Society under the MAVO Project; Human Brain Pharmacome;
   EU/EFPIA Innovative Medicines Initiative Joint Undertaking under
   AETIONOMY (115568 to D.D.F.); European Union's Seventh Framework
   Programme (FP7/2007-2013); EFPIA.
CR Bachman JA, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2211-5
   Belleau F, 2008, J BIOMED INFORM, V41, P706, DOI 10.1016/j.jbi.2008.03.004
   Carbon S, 2017, NUCLEIC ACIDS RES, V45, pD331, DOI 10.1093/nar/gkw1108
   Caspi R, 2016, NUCLEIC ACIDS RES, V44, pD471, DOI 10.1093/nar/gkv1164
   Catlett NL, 2013, BMC BIOINFORMATICS, V14, DOI 10.1186/1471-2105-14-340
   Cerami EG, 2011, NUCLEIC ACIDS RES, V39, pD685, DOI 10.1093/nar/gkq1039
   Cote RG, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-97
   Demir E, 2010, NAT BIOTECHNOL, V28, P1308, DOI 10.1038/nbt1210-1308c
   Domingo-Fernandez D, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-2863-9
   Domingo-Fernandez D, 2018, NPJ SYST BIOL APPL, V4, DOI [10.1038/s41540-018-0078-8, 10.1038/s41540-018-0078]
   Domingo-Fernandez D, 2017, BIOINFORMATICS, V33, P3679, DOI 10.1093/bioinformatics/btx399
   Emon MAEK, 2017, J ALZHEIMERS DIS, V56, P677, DOI 10.3233/JAD-160222
   Fournier N, 2016, BBA-MOL CELL BIOL L, V1861, P331, DOI 10.1016/j.bbalip.2016.01.002
   Gaulton A, 2017, NUCLEIC ACIDS RES, V45, pD945, DOI 10.1093/nar/gkw1074
   Glont M, 2018, NUCLEIC ACIDS RES, V46, pD1248, DOI 10.1093/nar/gkx1023
   Goncalves RS, 2017, LECT NOTES COMPUT SC, V10588, P103, DOI 10.1007/978-3-319-68204-4_10
   Guryanova S, 2017, METHODS MOL BIOL, V1613, P21, DOI 10.1007/978-1-4939-7027-8_2
   Gyori BM, 2017, MOL SYST BIOL, V13, DOI 10.15252/msb.20177651
   Hastings J, 2013, NUCLEIC ACIDS RES, V41, pD456, DOI 10.1093/nar/gks1146
   Hofmann-Apitius M, 2015, INT J MOL SCI, V16, P29179, DOI 10.3390/ijms161226148
   Hoyt CT, 2018, DATABASE-OXFORD, DOI 10.1093/database/bay126
   Hoyt CT, 2018, DATABASE-OXFORD, DOI 10.1093/database/bay050
   Hoyt CT, 2018, BIOINFORMATICS, V34, P703, DOI 10.1093/bioinformatics/btx660
   Hucka M, 2003, BIOINFORMATICS, V19, P524, DOI 10.1093/bioinformatics/btg015
   Irin AK, 2015, J IMMUNOL RES, V2015, DOI 10.1155/2015/737168
   Juty N, 2012, NUCLEIC ACIDS RES, V40, pD580, DOI 10.1093/nar/gkr1097
   Kamburov A, 2013, NUCLEIC ACIDS RES, V41, pD793, DOI 10.1093/nar/gks1055
   Kandasamy K, 2010, GENOME BIOL, V11, DOI 10.1186/gb-2010-11-1-r3
   Karki R, 2017, J ALZHEIMERS DIS, V60, P721, DOI 10.3233/JAD-170440
   Kim S, 2016, NUCLEIC ACIDS RES, V44, pD1202, DOI 10.1093/nar/gkv951
   Kodamullil AT, 2015, ALZHEIMERS DEMENT, V11, P1329, DOI 10.1016/j.jalz.2015.02.006
   Kohler S, 2019, NUCLEIC ACIDS RES, V47, pD1018, DOI 10.1093/nar/gky1105
   Laibe C, 2007, BMC SYST BIOL, V1, DOI 10.1186/1752-0509-1-58
   Lovat PE, 2003, CANCER RES, V63, P7310
   Madan S, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw136
   Malone J, 2010, BIOINFORMATICS, V26, P1112, DOI 10.1093/bioinformatics/btq099
   McDonald DD, 2000, NATURAL LANGUAGE PROCESSING AND KNOWLEDGE REPRESENTATION, P77
   Meldal BHM, 2015, NUCLEIC ACIDS RES, V43, pD479, DOI 10.1093/nar/gku975
   Mihindukulasooriya N., 2017, CEUR WORKSHOP P, V1963, P1
   Morelli MB, 2014, BMC CANCER, V14, DOI 10.1186/1471-2407-14-921
   Naz M, 2016, BRIEF BIOINFORM, V17, P505, DOI 10.1093/bib/bbv063
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Perfetto L, 2016, NUCLEIC ACIDS RES, V44, pD548, DOI 10.1093/nar/gkv1048
   Pilalis E, 2015, COMPUT STRUCT BIOTEC, V13, P248, DOI 10.1016/j.csbj.2015.03.009
   Pon A, 2015, NUCLEIC ACIDS RES, V43, pW552, DOI 10.1093/nar/gkv399
   Pratt D, 2015, CELL SYST, V1, P302, DOI 10.1016/j.cels.2015.10.001
   Pujara J., 2017, C EMP METH NAT LANG, P1752
   Rausanu S, 2015, NEURAL COMPUT APPL, V26, P299, DOI 10.1007/s00521-014-1617-x
   Rinaldi Fabio, 2016, Database (Oxford), V2016, DOI 10.1093/database/baw067
   Rodriguez-Esteban R, 2015, DATABASE-OXFORD, DOI 10.1093/database/bav116
   ROGERS FB, 1963, B MED LIBR ASSOC, V51, P114
   Saqi M, 2019, BRIEF BIOINFORM, V20, P609, DOI 10.1093/bib/bby025
   Sarntivijai S, 2014, J BIOMED SEMANT, V5, DOI 10.1186/2041-1480-5-37
   Schriml LM, 2019, NUCLEIC ACIDS RES, V47, pD955, DOI 10.1093/nar/gky1032
   Slater T, 2014, DRUG DISCOV TODAY, V19, P193, DOI 10.1016/j.drudis.2013.12.011
   Slenter DN, 2018, NUCLEIC ACIDS RES, V46, pD661, DOI 10.1093/nar/gkx1064
   Stoklosa T, 2008, CANCER RES, V68, P2576, DOI 10.1158/0008-5472.CAN-07-6858
   Szostak J, 2015, DATABASE-OXFORD, DOI 10.1093/database/bav057
   Tripathi S, 2015, BMC SYST BIOL, V9, DOI 10.1186/s12918-015-0181-z
   Valenzuela-Escarcega MA, 2018, DATABASE-OXFORD, DOI 10.1093/database/bay098
   Valenzuela-Escarcega MA, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (ACL-IJCNLP 2015): SYSTEM DEMONSTRATIONS, P127
   Van Landeghem Sofie, 2012, Advances in Bioinformatics, V2012, P582765, DOI 10.1155/2012/582765
   Wadi L, 2016, NAT METHODS, V13, P705, DOI 10.1038/nmeth.3963
   Wang L, 2009, BIOMED MATER, V4, DOI 10.1088/1748-6041/4/2/025003
   Williams AJ, 2012, DRUG DISCOV TODAY, V17, P1188, DOI 10.1016/j.drudis.2012.05.016
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD608, DOI 10.1093/nar/gkx1089
   Yates B, 2017, NUCLEIC ACIDS RES, V45, pD619, DOI 10.1093/nar/gkw1033
   Young P, 2005, MOL CELL NEUROSCI, V30, P238, DOI 10.1016/j.mcn.2005.07.015
   Yugi K, 2016, TRENDS BIOTECHNOL, V34, P276, DOI 10.1016/j.tibtech.2015.12.013
NR 69
TC 8
Z9 8
U1 0
U2 8
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1758-0463
J9 DATABASE-OXFORD
JI Database
PD JUN 21
PY 2019
AR baz068
DI 10.1093/database/baz068
PG 13
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA IF1BA
UT WOS:000472810900001
PM 31225582
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Liu, WH
   Yin, L
   Wang, C
   Liu, FL
   Ni, ZY
AF Liu, Wanheng
   Yin, Ling
   Wang, Cong
   Liu, Fulin
   Ni, Zhiyu
TI Multitask Healthcare Management Recommendation System Leveraging
   Knowledge Graph
SO JOURNAL OF HEALTHCARE ENGINEERING
LA English
DT Article
AB In this paper, a novel multitask healthcare management recommendation system leveraging the knowledge graph is proposed, which is based on deep neural network and 5G network, and it can be applied in mobile and terminal device to free up medical resources and provide treatment programs. The technique we applied is referred to as KG-based recommendation system. When several experiments have been carried out, it is demonstrated that it is more intelligent and precise in disease prediction and treatment recommendation, similar to the state of the art. Also, it works well in the accuracy and comprehension, which is much higher and highly consistent with the predictions of the theoretical model. The fact that our work involves studies of multitask healthcare management recommendation system, which can contribute to the smart healthcare development, proves to be promising and encouraging.
C1 [Liu, Wanheng; Wang, Cong] Beijing Univ Posts & Telecommun, Beijing, Peoples R China.
   [Yin, Ling] Natl Populat Hlth Data Ctr, Changping, Peoples R China.
   [Liu, Fulin; Ni, Zhiyu] Hebei Univ, Affliated Hosp, Baoding, Hebei, Peoples R China.
RP Yin, L (通讯作者)，Natl Populat Hlth Data Ctr, Changping, Peoples R China.
EM liuwanheng301@163.com; lwhlcf@163.com; wangc@bupt.edu.cn;
   lful666@sina.com; chncrq@163.com
OI liu, fulin/0000-0001-5176-1625
FU Key R&D Program of Hebei Province, People's Livelihood Science and
   Technology Project [20277716D]
FX This work was supported by Key R&D Program of Hebei Province, People's
   Livelihood Science and Technology Project, Project no. 20277716D
   (Project name: Study on the pathological characteristics and pathogenic
   mechanism of Covid-19).
CR Ahire S.B., 2015, INT J COMPUTER APPL, V110, P24, DOI [10.5120/19282-0700, DOI 10.5120/19282-0700]
   Ali F, 2018, COMPUT COMMUN, V119, P138, DOI 10.1016/j.comcom.2017.10.005
   Archenaa J., 2017, J MANAGEMENT SCI BUS, V2, DOI [10.5281/zenodo.10.5281/zenodo.835606, DOI 10.5281/ZENODO.10.5281/ZENODO.835606]
   Biswas S, 2021, IEEE ACM T COMPUT BI, V18, P708, DOI 10.1109/TCBB.2019.2927310
   Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
   Guzman G, 2018, J MED BIOL ENG, V38, P1026, DOI 10.1007/s40846-018-0421-y
   Hongwei Wang, 2019, ACM Transactions on Information Systems, V37, DOI 10.1145/3312738
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Huang Y., 2020, ACM T MULTIM COMPUT, V16, DOI [10.1145/3382180, DOI 10.1145/3382180]
   Hussein A.S., 2012, GLOBALIZATION HLTH 1, P113
   Kaur H, 2018, FUTURE GENER COMP SY, V86, P297, DOI 10.1016/j.future.2018.03.017
   Lops P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P73, DOI 10.1007/978-0-387-85820-3_3
   Nagaraj P., 2020, ELECT GOVT INT J, V16, DOI [10.1504/eg.2020.105256, DOI 10.1504/EG.2020.105256]
   Noshad M., CLIN RECOMMENDER SYS
   Ochoa JGD, 2021, BMC MED INFORM DECIS, V21, DOI 10.1186/s12911-021-01553-3
   Paramonov I., 2016, P 2016 19 C OP INN A, DOI [10.23919/fruct.2016.7892199, DOI 10.23919/FRUCT.2016.7892199]
   Pitchai R., 2021, MATER TODAY-PROC, DOI [10.1016/j.matpr.2021.03.342, DOI 10.1016/J.MATPR.2021.03.342]
   Ponselvakumar A. P., 2021, IOP Conference Series: Materials Science and Engineering, V1055, DOI 10.1088/1757-899X/1055/1/012110
   Qian SS, 2021, ACM T MULTIM COMPUT, V17, DOI 10.1145/3451215
   Rathi M, 2019, INT J KNOWL SYST SCI, V10, P38, DOI 10.4018/IJKSS.2019010103
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Roy S.N., 2019, J INFORM TECHNOLOGY, V19
   Saad A, 2021, J AMB INTEL HUM COMP, DOI 10.1007/s12652-021-02927-1
   Sahoo AK, 2019, COMPUTATION, V7, DOI 10.3390/computation7020025
   Sarwar B, 2001, P 10 INT C WORLD WID, P285
   Somarathna U., 2018, P 2018 IISE ANN C MA
   Wang H., 2017, P 11 ACM INT C WEB S
   Wang H., 2018, P 27 ACM INT C OCT 2
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2000, DOI 10.1145/3308558.3313411
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Xiao Y., 2014, P 7 ACM INT C WEB SE
   Zaman N, 2014, INT CON ADV INFO NET, P765, DOI 10.1109/AINA.2014.93
   Zhang F., 2016, P 22 ACM SIGKDD INT
NR 34
TC 1
Z9 1
U1 9
U2 9
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 2040-2295
EI 2040-2309
J9 J HEALTHC ENG
JI J. Healthc. Eng.
PD NOV 5
PY 2021
VL 2021
AR 1233483
DI 10.1155/2021/1233483
PG 12
WC Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services
GA ZK1MK
UT WOS:000762759900001
PM 34777727
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Yaseen, Q
   Panda, B
AF Yaseen, Qussai
   Panda, Brajendra
TI Insider threat mitigation: preventing unauthorized knowledge acquisition
SO INTERNATIONAL JOURNAL OF INFORMATION SECURITY
LA English
DT Article
DE Database; Security; Insider threat; Dependencies; Dependency graph;
   Knowledge graph; Modification graph
AB This paper investigates insider threat in relational database systems. It discusses the problem of inferring unauthorized information by insiders and proposes methods to prevent such threats. The paper defines various types of dependencies as well as constraints on dependencies that may be used by insiders to infer unauthorized information. It introduces the constraint and dependency graph (CDG) that represents dependencies and constraints. In addition, CDG shows the paths that insiders can follow to acquire unauthorized knowledge. Moreover, the paper presents the knowledge graph (KG) that demonstrates the knowledgebase of an insider and the amount of information that the insider has about data items. To predict and prevent insider threat, the paper defines and uses the threat prediction graph (TPG). A TPG shows the threat prediction value (TPV) of each data item in insiders' KG, where TPV is used to raise an alert when an insider threat occurs. The paper provides solutions to prevent insider threat without limiting the availability of data items. Algorithms, theorems, proofs and experiments are provided to show the soundness, the completeness and the effectiveness of the proposed approaches.
C1 [Yaseen, Qussai; Panda, Brajendra] Univ Arkansas, Dept Comp Sci & Comp Engn, Fayetteville, AR 72701 USA.
RP Yaseen, Q (通讯作者)，Univ Arkansas, Dept Comp Sci & Comp Engn, Fayetteville, AR 72701 USA.
EM qyaseen@uark.edu; bpanda@uark.edu
OI Yaseen, Qussai/0000-0003-0777-1871
FU US AFOSR [FA 9550-08-1-0255]
FX This work has been supported in part by US AFOSR under grant FA
   9550-08-1-0255. We are thankful to Dr. Robert. L. Herklotz for his
   support, which made this work possible.
CR Althebyan Q., 2007, IEEE WORKSH INF ASS
   Bishop M., 2008, 4 ANN WORKSH CYB SEC
   Brackney R., 2004, 2004 WORKSH
   Brodsky A, 2000, IEEE T KNOWL DATA EN, V12, P900, DOI 10.1109/69.895801
   Chagarlamudi M., 2009, 6 INT C INF TECHN
   Farkas C., 2007, ACM SIGKDD EXPLORATI
   Farkas C., 2001, 15 IFIP WG11 3 WORK
   Gordon L., COMPUTER CRIME SECUR
   Jabbour G., 2009, INT C INF SEC PRIV
   Jabbour G., 2009, INT C COMP SCI ENG
   MAYBURY M, 2005, INT C INT AN
   Murata T., 1989, P IEEE
   RSA, INS SEC THREAT I T F
   Spitzner L., 2003, 19 ANN COMP SEC APPL
   Tan L., ASIA WORRIED INSIDER
   Yaseen Q., 2011, 7 INT C DISTR COMP I
   Yaseen Q., 2010, 4 WORKSH INF SEC THE
   Yaseen Q., 2010, 4 INT C NETW SYST SE
   Yaseen Q., 2009, INT WORKSH SOFTW SEC
   Yip R., 1998, 11 COMP SEC FDN WORK
NR 20
TC 9
Z9 9
U1 0
U2 19
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1615-5262
J9 INT J INF SECUR
JI Int. J. Inf. Secur.
PD AUG
PY 2012
VL 11
IS 4
SI SI
BP 269
EP 280
DI 10.1007/s10207-012-0165-6
PG 12
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 977RU
UT WOS:000306682900005
DA 2022-08-23
ER

PT J
AU Munne, RF
   Ichise, R
AF Munne, Rumana Ferdous
   Ichise, Ryutaro
TI Entity alignment via summary and attribute embeddings
SO LOGIC JOURNAL OF THE IGPL
LA English
DT Article; Early Access
DE Knowledge graph; entity alignment; embedding model
AB Entity alignment is the task of integrating heterogeneous knowledge among different knowledge graphs (KGs). KG is a popular way of storing facts about real-world entities. Unfortunately, a very limited number of the entities stored in different KGs are aligned. This paper presents an embedding-based entity alignment method that finds entity alignment by measuring the similarities between entity embeddings. Existing methods mainly focus on the relational structures and attributes information for the alignment process. Such methods fail while the entities have a fewer number of attributes or when the relational structure could not capture the meaningful representation of the entities. To address this problem, we propose EASAE, an entity alignment method using summary and attribute embeddings. We exploit the entity summary information available in KGs for entities' summary embedding. To learn the semantics of the entity summary, we employ bidirectional encoder representations from transformers (BERT). Our model learns the representations of entities by using relational triples, attribute triples and summary as well. We perform experiments on real-world datasets, and the results indicate that the proposed approach outperformed the state-of-the-art models for entity alignment.
C1 [Munne, Rumana Ferdous; Ichise, Ryutaro] Grad Univ Adv Studies, Natl Inst Informat, SOKENDAI, Tokyo, Japan.
RP Munne, RF (通讯作者)，Grad Univ Adv Studies, Natl Inst Informat, SOKENDAI, Tokyo, Japan.
EM rfmunne@nii.ac.jp; ichise@nii.ac.jp
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen MH, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5169
   Chen MH, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3998
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Hao YC, 2016, COMM COM INF SC, V650, P3, DOI 10.1007/978-981-10-3168-7_1
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Munne Rumana Ferdous, 2020, Hybrid Artificial Intelligent Systems. 15th International Conference, HAIS 2020. Proceedings. Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science (LNAI 12344), P107, DOI 10.1007/978-3-030-61705-9_10
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Tang XB, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3174
   Trisedya BD, 2019, AAAI CONF ARTIF INTE, P297
   Vrandeic D., 2014, COMMUN ACM
   Wu YT, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5278
   Wu Yuting, 2020, ACL, P6477
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Xu K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3156
   Zeng W., 2020, P ICDE
   Zhang QH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5429
   Zhu H, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4258
NR 20
TC 0
Z9 0
U1 5
U2 5
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-0751
EI 1368-9894
J9 LOG J IGPL
JI Log. J. IGPL
DI 10.1093/jigpal/jzac021
EA FEB 2022
PG 11
WC Mathematics, Applied; Mathematics; Logic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA ZC9MG
UT WOS:000757834100001
OA hybrid
DA 2022-08-23
ER

PT J
AU Sun, Z
   Guo, Q
   Yang, J
   Fang, H
   Guo, GB
   Zhan, J
   Burke, R
AF Sun, Zhu
   Guo, Qing
   Yang, Jie
   Fang, Hui
   Guo, Guibing
   Zhan, Jie
   Burke, Robin
TI Research commentary on recommendations with side information: A survey
   and research directions
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Research commentary; Recommender systems; Side information; Memory-based
   methods; Latent factor models; Representation learning; Deep learning;
   Flat features; Social networks; Feature hierarchies; Knowledge graphs
ID HETEROGENEOUS INFORMATION; SYSTEMS; NETWORK; TRUST; REGULARIZATION;
   FRAMEWORK; SPARSITY
AB Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and nonstructural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.
C1 [Sun, Zhu; Guo, Qing; Zhan, Jie] Nanyang Technol Univ, Singapore, Singapore.
   [Yang, Jie] Amason Res, Seattle, WA USA.
   [Fang, Hui] Shanghai Univ Finance & Econ, Shanghai, Peoples R China.
   [Guo, Guibing] Northeastern Univ, Shenyang, Liaoning, Peoples R China.
   [Burke, Robin] Univ Colorado, Boulder, CO 80309 USA.
RP Fang, H (通讯作者)，Shanghai Univ Finance & Econ, Shanghai, Peoples R China.
EM zhu.sun@ntu.edu.sg; qguo006@ntu.edu.sg; jiy@amazon.com;
   fang.hui@mail.shufe.edu.cn; guogb@swc.neu.edu.cn; zhangj@ntu.edu.sg;
   robin.burke@colorado.edu
RI Burke, Robin/AAV-5406-2021
OI Burke, Robin/0000-0001-5766-6434; SUN, ZHU/0000-0002-3350-7022
FU Delta Electronics Inc.; National Research Foundation (NRF) Singapore
   under the Corp Lab@University Scheme; BMW Tech Office Singapore;
   National Natural Science Foundation of China [71601104, 71601116,
   71771141, 61702084]; Fundamental Research Funds for the Central
   Universities in China [N181705007]
FX This work was partly conducted within the Delta-NTU Corporate Lab for
   Cyber-Physical Systems with funding support from Delta Electronics Inc.
   and the National Research Foundation (NRF) Singapore under the Corp
   Lab@University Scheme, and also supported by the funding awarded to Dr.
   Jie Zhang by the BMW Tech Office Singapore. We also gratefully
   acknowledge the support of National Natural Science Foundation of China
   (Grant No. 71601104, 71601116, 71771141 and 61702084) and the support of
   the Fundamental Research Funds for the Central Universities in China
   under Grant No. N181705007.
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
   Adomavicius G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P769, DOI 10.1007/978-0-387-85820-3_24
   Alashkar T, 2017, AAAI CONF ARTIF INTE, P941
   [Anonymous], 2015, P 1 INT WORKSH MACH
   Baltrusaitis T., 2017, CVPR, P6730
   Bao J, 2012, P 20 INT C ADV GEOGR, P199, DOI DOI 10.1145/2424321.2424348
   Bao Y, 2014, AAAI CONF ARTIF INTE, P2
   Barkan O., 2016, 2016 IEEE 26 INT WOR, P1, DOI DOI 10.1109/MLSP.2016.7738886
   Battaglia P.W., 2018, ARXIV 180601261
   Bellogin A, 2013, ACM T INTEL SYST TEC, V4, DOI 10.1145/2414425.2414439
   Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223
   Berkovsky S, 2007, LECT NOTES ARTIF INT, V4511, P355
   Bhargava P, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P130, DOI 10.1145/2736277.2741077
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bobadilla J, 2013, KNOWL-BASED SYST, V46, P109, DOI 10.1016/j.knosys.2013.03.012
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bruno V., 2019, ELECTRON COMMER R A, V34
   Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Cao Yixin, 2019, ARXIV190206236
   Catherine R, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P288, DOI 10.1145/3109859.3109878
   Catherine R, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P325, DOI 10.1145/2959100.2959131
   Chen JY, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P335, DOI 10.1145/3077136.3080797
   Chen LQ, 2016, ISTFA 2016: CONFERENCE PROCEEDINGS FROM THE 42ND INTERNATIONAL SYMPOSIUM FOR TESTING AND FAILURE ANALYSIS, P299
   Chen TQ, 2012, J MACH LEARN RES, V13, P3619
   Cheng B, 2010, 2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), P4584, DOI 10.1109/WCICA.2010.5554118
   Cheng H.T., 2016, P 1 WORKSH DEEP LEAR, P7
   Cho K., 2014, P SSST 8 8 WORKSH SY, P103
   Cho K., 2014, ARXIV14061078, P1724
   Chu WT, 2017, WORLD WIDE WEB, V20, P1313, DOI 10.1007/s11280-017-0437-1
   Collobert R, 2011, J MACH LEARN RES, V12, P2493
   Covington P, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P191, DOI 10.1145/2959100.2959190
   Davidson James, 2010, RECSYS
   Desrosiers C, 2011, RECOMMENDER SYSTEMS HANDBOOK, P107, DOI 10.1007/978-0-387-85820-3_4
   Ding DZ, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1479, DOI 10.1145/3132847.3132941
   Dong X, 2017, AAAI CONF ARTIF INTE, P1309
   Duvenaud David K, 2015, ADV NEURAL INFORM PR, P2224, DOI DOI 10.1021/ACS.JCIM.5B00572
   Ekstrand Michael D., 2010, Foundations and Trends in Human-Computer Interaction, V4, P81, DOI 10.1561/1100000009
   Fan W, 2019, ARXIV190207243
   Fan WQ, 2018, AAAI CONF ARTIF INTE, P8075
   Fang H, 2014, AAAI CONF ARTIF INTE, P30
   Fang H, 2015, DECIS SUPPORT SYST, V71, P37, DOI 10.1016/j.dss.2015.01.005
   Feng J, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1459, DOI 10.1145/3178876.3186058
   Feng SS, 2017, AAAI CONF ARTIF INTE, P102
   Fernandez-Tobias I, 2012, SPANISH C INFORM RET, P1
   Forsati R, 2014, ACM T INFORM SYST, V32, DOI 10.1145/2641564
   Gao HJ, 2015, AAAI CONF ARTIF INTE, P1721
   Gomez-Uribe CA, 2016, ACM TRANS MANAG INF, V6, DOI 10.1145/2843948
   Grbovic M, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1809, DOI 10.1145/2783258.2788627
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Guibing Guo, 2012, User Modeling, Adaptation, and Personalization. Proceedings 20th International Conference, UMAP 2012, P361, DOI 10.1007/978-3-642-31454-4_36
   Guibing Guo, 2012, User Modeling, Adaptation, and Personalization. Proceedings 20th International Conference, UMAP 2012, P114, DOI 10.1007/978-3-642-31454-4_10
   Guo G., 2013, P 7 ACM C REC SYST, P451
   Guo GB, 2015, AAAI CONF ARTIF INTE, P123
   Guo GB, 2015, KNOWL-BASED SYST, V74, P14, DOI 10.1016/j.knosys.2014.10.016
   Guo GB, 2014, KNOWL-BASED SYST, V57, P57, DOI 10.1016/j.knosys.2013.12.007
   Guo Q, 2019, LECT NOTES COMPUT SC, V11496, P569, DOI 10.1007/978-3-030-19274-7_46
   Guo X, 2017, QUANTITATIVE TRADING: ALGORITHMS, ANALYTICS, DATA, MODELS, OPTIMIZATION, P17
   Gurrin C, 2016, INT C MULT MOD, P361
   Han J, 2013, IJCAI HINA, P27
   He R., 2016, P 25 INT JOINT C ART, P3740
   He RN, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P309, DOI 10.1145/2959100.29.59152
   He RN, 2016, AAAI CONF ARTIF INTE, P144
   He RN, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P507, DOI 10.1145/2872427.2883037
   He X., 2018, ARXIV180803912
   He X., 2015, P 24 ACM INT C INF K, P1661
   He XN, 2018, ACM/SIGIR PROCEEDINGS 2018, P355, DOI 10.1145/3209978.3209981
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   Hershey JR, 2007, INT CONF ACOUST SPEE, P317, DOI 10.1109/icassp.2007.366913
   Hidasi B., 2015, ARXIV151106939
   Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hongwei Wang, 2019, ACM Transactions on Information Systems, V37, DOI 10.1145/3312738
   Hosseini S., 2018, IEEE T KNOWLEDGE DAT
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Hu LK, 2014, SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P345, DOI 10.1145/2600428.2609593
   Hu ZT, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P1292
   Hwang WS, 2012, P 21 ACM INT C INF K, P2355
   Jamali M., 2010, P 4 ACM C REC SYST, P135, DOI DOI 10.1145/1864708.1864736
   Jenatton R., 2010, P 27 INT C INT C MAC, P487
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Jia YQ, 2014, PROCEEDINGS OF THE 2014 ACM CONFERENCE ON MULTIMEDIA (MM'14), P675, DOI 10.1145/2647868.2654889
   Jing H, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P515, DOI 10.1145/3018661.3018719
   Kadie C., 1998, 14 C UNC ART INT SAN, P43
   Kaiming He, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), P770, DOI 10.1109/CVPR.2016.90
   Kanagal B, 2012, PROC VLDB ENDOW, V5, P956, DOI 10.14778/2336664.2336669
   Kang WC, 2018, IEEE DATA MINING, P197, DOI 10.1109/ICDM.2018.00035
   Karatzoglou A., 2010, P 4 ACM C REC SYST, P79, DOI DOI 10.1145/1864708.1864727
   Ke Ji, 2014, Advances in Knowledge Discovery and Data Mining. 18th Pacific-Asia Conference, PAKDD 2014. Proceedings: LNCS 8444, P13, DOI 10.1007/978-3-319-06605-9_2
   Kim C, 2003, IEEE/WIC INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, PROCEEDINGS, P524
   Kim S, 2010, P 27 INT C MACH LEAR, P543
   King I, 2008, P 17 ACM C INF KNOWL, P931
   Kipf T.N., 2016, ARXIV PREPRINT ARXIV
   Koenigstein N., 2011, P 5 ACM C REC SYST, P165, DOI [10.1145/2043932.2043964, DOI 10.1145/2043932.2043964]
   Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944
   Koren Y, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P447
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Krizhevsky A., 2012, ADV NEURAL INF PROCE, V25, P1097, DOI [10.1145/3065386, DOI 10.1145/3065386]
   Le Q.V., 2014, COMPUT RES REPOS COR, P1, DOI [DOI 10.1145/2740908.2742760, 10.1145/2740908.2742760]
   Leal F, 2019, ELECTRON COMMER R A, V33, DOI 10.1016/j.elerap.2018.11.004
   LeCun Y., 1995, CONVOLUTIONAL METWOR
   Lei CY, 2016, PROC CVPR IEEE, P2545, DOI 10.1109/CVPR.2016.279
   Lei T, 2013, PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON ENERGY, P213
   Li HY, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P975, DOI 10.1145/2939672.2939767
   Li L., 2010, P 19 INT C WORLD WID, P661, DOI [DOI 10.1145/1772690.1772758, 10.1145/1772690.1772758]
   Li LJ, 2011, METAGENOMICS OF THE HUMAN BODY, P297, DOI 10.1007/978-1-4419-7089-3_14
   Li PJ, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P345, DOI 10.1145/3077136.3080822
   Lian DF, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P831, DOI 10.1145/2623330.2623638
   Liang DW, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P59, DOI 10.1145/2959100.2959182
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344
   Lippert C., 2008, P NIPS 2008 WORKSH S, P1
   Liu Q, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P841, DOI 10.1145/3077136.3080658
   Liu Q, 2016, AAAI CONF ARTIF INTE, P194
   Liu X., 2016, IJCAI, P1188
   Liu X, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P733
   Lops P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P73, DOI 10.1007/978-0-387-85820-3_3
   Lu YC, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P4, DOI 10.1145/3240323.3240365
   Luo C, 2014, IEEE DATA MINING, P917, DOI 10.1109/ICDM.2014.64
   Luong M., 2015, P C EMP METH NAT LAN
   Ma H, 2009, P 3 ACM C REC SYST, P189, DOI DOI 10.1145/1639714.1639746
   Ma H, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P73
   Ma H, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961201
   Ma H, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P203, DOI 10.1145/1571941.1571978
   Masci J, 2011, LECT NOTES COMPUT SC, V6791, P52, DOI 10.1007/978-3-642-21735-7_7
   McAuley J., 2013, P 7 ACM C REC SYST, P165, DOI DOI 10.1145/2507157.2507163
   McAuley J, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P43, DOI 10.1145/2766462.2767755
   Medina CP, 2003, P 1 INSTR C MACH LEA, P133, DOI [10.15804/tner.2015.42.4.03, DOI 10.15804/TNER.2015.42.4.03]
   Melville P, 2002, EIGHTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-02)/FOURTEENTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE (IAAI-02), PROCEEDINGS, P187
   Menon A. K., 2011, P 17 ACM SIGKDD INT, P141, DOI DOI 10.1145/2020408.2020436
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Mnih A., 2012, ADV NEURAL INFORM PR, P2816
   Mnih Andriy, 2011, P 2011 INT C KDD CUP, P169
   Morschheuser B, 2016, P ANN HICSS, P4375, DOI 10.1109/HICSS.2016.543
   Nair V., 2010, INT C INT C MACH LEA, V32, P807
   Niepert M, 2016, PR MACH LEARN RES, V48
   Niu W, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P423, DOI 10.1145/3159652.3159728
   Okura S, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1933, DOI 10.1145/3097983.3098108
   Ostapuk N, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1398, DOI 10.1145/3308558.3313620
   Pappas N, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P773
   Park S., 2006, P 12 ACM SIGKDD INT, P699, DOI DOI 10.1145/1150402.1150490
   Pasricha R, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P63, DOI 10.1145/3240323.3240356
   Paterek A., 2007, P KDD CUP WORKSH, P5, DOI DOI 10.1145/1557019.1557072
   Pei WJ, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1459, DOI 10.1145/3132847.3133005
   Pero Stefan, 2013, User Modeling, Adaptation, and Personalization. 21th International Conference, UMAP 2013. Proceedings., P1, DOI 10.1007/978-3-642-38844-6_1
   Pourgholamali F, 2017, ELECTRON COMMER R A, V25, P70, DOI 10.1016/j.elerap.2017.08.001
   Rendle Steffen, 2010, Proceedings 2010 10th IEEE International Conference on Data Mining (ICDM 2010), P995, DOI 10.1109/ICDM.2010.127
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Rendle S., 2010, P 19 INT C WORLD WID, P811, DOI [/10.1145 /1772690.1772773, DOI 10.1145/1772690.1772773]
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1
   Rumelhart D. E., 1985, TECHNICAL REPORT
   Salakhutdinov R, 2007, ADV NEURAL INFORM PR, P1257
   Sarwar B, 2001, P 10 INT C WORLD WID, P285
   Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
   Schafer J. B., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P291
   Schmidhuber J, 2015, NEURAL NETWORKS, V61, P85, DOI 10.1016/j.neunet.2014.09.003
   Sedhain S, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P111, DOI 10.1145/2740908.2742726
   Seo S, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P297, DOI 10.1145/3109859.3109890
   Sharma M, 2011, LECT NOTES COMPUT SC, V7108, P137, DOI 10.1007/978-3-642-25731-5_12
   Shi C., 2015, P 24 ACM INT C INF K, P453
   Shi C, 2016, KNOWL INF SYST, V49, P835, DOI 10.1007/s10115-016-0925-0
   Shi Y, 2014, ACM COMPUT SURV, V47, DOI 10.1145/2556270
   Shi Y, 2011, LECT NOTES COMPUT SC, V6787, P305, DOI 10.1007/978-3-642-22362-4_26
   Singh A, 2008, INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, P650
   Smola AJ, 2003, LECT NOTES ARTIF INT, V2777, P144, DOI 10.1007/978-3-540-45167-9_12
   Socher R, 2011, P 28 INT C MACH LEAR, P129, DOI DOI 10.1007/978-3-540-87479-9
   Su X., 2009, ADV ARTIF INTELL, V2009, P395
   Sun Z., 2017, INT C US MOD AD PERS, P389
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Sun Z, 2017, AAAI CONF ARTIF INTE, P189
   Sun Z, 2015, LECT NOTES COMPUT SC, V9146, P397, DOI 10.1007/978-3-319-20267-9_37
   Sun ZH, 2017, INT CONF AGRO-GEOINF, P280
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Symeonidis P, 2010, IEEE T KNOWL DATA EN, V22, P179, DOI 10.1109/TKDE.2009.85
   Tan HS, 2012, PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION APPLICATIONS (ICCIA 2012), P1267
   Tang JX, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P565, DOI 10.1145/3159652.3159656
   Tay Y, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2309, DOI 10.1145/3219819.3220086
   Terzi M, 2014, LECT NOTES COMPUT SC, V8538, P195
   TUAN TX, 2017, P 11 ACM C REC SYST, P138, DOI DOI 10.1145/3109859.3109900
   Vasile F, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P225, DOI 10.1145/2959100.2959160
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Velickovic P., 2018, P INT C LEARN REPR A
   Vincent P., 2008, P 25 INT C MACH LEAR, P1096, DOI 10.1145/1390156.1390294
   Vincent P, 2010, J MACH LEARN RES, V11, P3371
   Wang H., 2019, ARXIV190504413
   Wang H., 2019, ARXIV190412575
   Wang H, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1135, DOI 10.1145/3097983.3098122
   Wang H, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1235, DOI 10.1145/2783258.2783273
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang Hongwei, 2019, ARXIV190108907
   Wang J, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P515, DOI 10.1145/3077136.3080786
   Wang PF, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P403, DOI 10.1145/2766462.2767694
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang SH, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P391, DOI 10.1145/3038912.3052638
   Wang SH, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1813
   Wang W., 2016, P 2016 C EMP METH NA, P616
   Wang X., 2018, ARXIV181104540
   Wang Xiang, 2019, ARXIV190507854
   Wang YY, 2017, MULTIMEDIA SYST, V23, P703, DOI 10.1007/s00530-015-0502-5
   Weng LT, 2008, PROC INT C TOOLS ART, P113, DOI 10.1109/ICTAI.2008.97
   Wibowo A.T., 2017, P RECSYS 2017 WORKSH, P1
   Wu C.Y., 2017, WORKSH INT C LEARN R, P4
   Wu CY, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P495, DOI 10.1145/3018661.3018689
   Xin X., 2019, ARXIV190412796
   Xiong L., 2010, P SIAM INT C DAT MIN, P211
   Xu K, 2015, PR MACH LEARN RES, V37, P2048
   Xu XY, 2018, ELECTRON COMMER R A, V29, P113, DOI 10.1016/j.elerap.2018.04.002
   Xue HJ, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3203
   Yan SC, 2007, IEEE T PATTERN ANAL, V29, P40, DOI 10.1109/TPAMI.2007.250598
   Yang B., 2015, P INT C LEARN REPR
   Yang B., 2013, INT JOINT C ART INT, P1633
   Yang C, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1245, DOI 10.1145/3097983.3098094
   Yang D., 2013, P 24 ACM C HYP SOC M, P119, DOI DOI 10.1145/2481492.2481505
   Yang DQ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2147, DOI 10.1145/3308558.3313635
   Yang J, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P23, DOI 10.1145/3178876.3186033
   Yang J, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P51, DOI 10.1145/2959100.2959159
   Yang ZJ, 2016, ADV SOC SCI EDUC HUM, V64, P1480
   Yao D, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2411, DOI 10.1145/3132847.3133056
   Yin HZ, 2017, IEEE T KNOWL DATA EN, V29, P2537, DOI 10.1109/TKDE.2017.2741484
   Yu F, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P729, DOI 10.1145/2911451.2914683
   Yu WH, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P649, DOI 10.1145/3178876.3186146
   Yu X, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P283, DOI 10.1145/2556195.2556259
   Yu X, 2013, IEEE INT CONF CON AU, P340
   Yun HK, 2012, 2012 8TH INTERNATIONAL CONFERENCE ON COMPUTING AND NETWORKING TECHNOLOGY (ICCNT, INC, ICCIS AND ICMIC), P395
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhang JD, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P443, DOI 10.1145/2766462.2767711
   Zhang S., 2018, ARXIV180806414
   Zhang S, 2017, ARXIV170707435
   Zhang S, 2006, SIAM PROC S, P549
   Zhang SA, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3285029
   Zhang YF, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1449, DOI 10.1145/3132847.3132892
   Zhang YF, 2014, SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P83, DOI 10.1145/2600428.2609579
   Zhang YF, 2014, SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1027, DOI 10.1145/2600428.2609501
   Zhang Z., 2017, ARXIV170404576
   Zhao XX, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1411, DOI 10.1145/2505515.2505690
   Zhao ZL, 2017, ELECTRON COMMER R A, V26, P109, DOI 10.1016/j.elerap.2017.10.007
   Zheng GY, 2011, INT C COMP AID IND D, P287
   Zheng J., 2017, INT J DATA SCI ANALY, V3, P35
   Zheng L, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P425, DOI 10.1145/3018661.3018665
   Ziegler C.-N., 2004, P 13 ACM INT C INF K, P406, DOI DOI 10.1145/1031171.1031252
NR 242
TC 58
Z9 63
U1 4
U2 69
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD SEP-OCT
PY 2019
VL 37
AR 100879
DI 10.1016/j.elerap.2019.100879
PG 29
WC Business; Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA JC1QV
UT WOS:000489053400007
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhou, TP
   Xu, YH
   Xu, WD
AF Zhou, Tianping
   Xu, Yihong
   Xu, Weidong
TI Emerging research trends and foci of studies on the meniscus: A
   bibliometric analysis
SO JOURNAL OF ORTHOPAEDIC SURGERY
LA English
DT Article
DE bibliometrics; CiteSpace V; hot spots; meniscus
AB Introduction: Meniscus has always been the focus of sport medicine with thousands of articles published annually. Few study has tried to analyze these papers with bibliometric methods. The purpose of this study is to statistically analyze the output of meniscus research and determine emerging research trends and hot spots. Methods: Papers related to the meniscus published from 2010 to 2019 were downloaded from the Web of Science Core Collection. Information about annual publications and journal distribution was analyzed by Excel 2016. Co-occurrence analysis of the countries/regions, institutions, authors, and keywords were performed with CiteSpace V, which was also used to perform a co-cited analysis of the references and generate corresponding knowledge maps as well as detect burst keywords. Results: A total of 10,066 articles regarding meniscus were published between 2010 and 2019. The number of articles annual about meniscus increased from 786 to 1300. Knee Surgery Sports Traumatology Arthroscopy, the United States, University of Pittsburgh, and LaPrade RF were journal, country, institution, and author with the most publications, respectively. Makris EA et al. in 2011 was the most cited articles, with a citation of 198. Osteoarthritis, tear repair, anterior cruciate ligament, and articular cartilage were keywords with occurrence of more than 500. Meniscal extrusion, scaffold, and tissue engineering were terms with most burst strength. Conclusions: Meniscus-related publications showed a gradual rising trend from 2010 to 2019. Osteoarthritis, tear repairs, and the anterior cruciate ligament are the current research hot spots. Extrusion and scaffolds may be the frontiers of meniscus research in the next few years. Level of evidence: IV.yy
C1 [Zhou, Tianping; Xu, Yihong; Xu, Weidong] Second Mil Med Univ, Changhai Hosp, Dept Joint Surg & Sports Med, 168 Changhai Rd, Shanghai 200433, Peoples R China.
RP Xu, WD (通讯作者)，Second Mil Med Univ, Changhai Hosp, Dept Joint Surg & Sports Med, 168 Changhai Rd, Shanghai 200433, Peoples R China.
EM xuweidongch@163.com
OI Xu, Weidong/0000-0002-1643-0449
CR Beaufils P, 2017, JOINT BONE SPINE, V84, P389, DOI 10.1016/j.jbspin.2016.12.007
   Bin SI, 2016, KNEE, V23, P246, DOI 10.1016/j.knee.2015.07.003
   Brophy RH, 2017, AM J SPORT MED, V45, P114, DOI 10.1177/0363546516664889
   Clayton RAE, 2008, INJURY, V39, P1338, DOI 10.1016/j.injury.2008.06.021
   Driban JB, 2020, BMC MUSCULOSKEL DIS, V21, DOI 10.1186/s12891-020-03367-2
   Ellegaard O, 2015, SCIENTOMETRICS, V105, P1809, DOI 10.1007/s11192-015-1645-z
   Englund M, 2008, NEW ENGL J MED, V359, P1108, DOI 10.1056/NEJMoa0800777
   Fox AJS, 2015, CLIN ANAT, V28, P269, DOI 10.1002/ca.22456
   Fu Weili, 2010, Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, V27, P458
   Gee SM, 2020, CLIN SPORT MED, V39, P13, DOI 10.1016/j.csm.2019.08.008
   Herrlin S, 2007, KNEE SURG SPORT TR A, V15, P393, DOI 10.1007/s00167-006-0243-2
   Herrlin SV, 2013, KNEE SURG SPORT TR A, V21, P358, DOI 10.1007/s00167-012-1960-3
   Houck DA, 2018, KNEE SURG SPORT TR A, V26, P2259, DOI 10.1007/s00167-018-4838-1
   Katz JN, 2013, NEW ENGL J MED, V368, P1675, DOI 10.1056/NEJMoa1301408
   Kennedy MI, 2020, CLIN SPORT MED, V39, P57, DOI 10.1016/j.csm.2019.08.009
   Kirkley A, 2008, NEW ENGL J MED, V359, P1097, DOI 10.1056/NEJMoa0708333
   Kwon H, 2019, NAT REV RHEUMATOL, V15, P550, DOI 10.1038/s41584-019-0255-1
   Lin DD, 2017, CURR STEM CELL RES T, V12, P348, DOI 10.2174/1574888X11666160429123110
   Lohmander LS, 2007, AM J SPORT MED, V35, P1756, DOI 10.1177/0363546507307396
   Lyman S, 2013, AM J SPORT MED, V41, P2772, DOI 10.1177/0363546513503444
   Masferrer-Pino A, 2019, INT ORTHOP, V43, P2549, DOI 10.1007/s00264-019-04398-8
   Masferrer-Pino A, 2018, ARTHROSCOPY, V34, P1879, DOI 10.1016/j.arthro.2018.01.017
   Mehl J, 2019, ARCH ORTHOP TRAUM SU, V139, P819, DOI 10.1007/s00402-019-03128-4
   Moseley JB, 2002, NEW ENGL J MED, V347, P81, DOI 10.1056/NEJMoa013259
   Noyes FR, 2015, KNEE SURG SPORT TR A, V23, P290, DOI 10.1007/s00167-014-3329-2
   Parker BR, 2016, AM J SPORT MED, V44, P1717, DOI 10.1177/0363546516638082
   Paxton ES, 2011, ARTHROSCOPY, V27, P1275, DOI 10.1016/j.arthro.2011.03.088
   Pillai MM, 2018, CURR OSTEOPOROS REP, V16, P224, DOI 10.1007/s11914-018-0436-x
   Poulsen E, 2019, BRIT J SPORT MED, V53, P1454, DOI 10.1136/bjsports-2018-100022
   Sapsford H, 2016, SCOT MED J, V61, P84, DOI 10.1177/0036933015608365
   Sihvonen R, 2013, NEW ENGL J MED, V369, P2515, DOI 10.1056/NEJMoa1305189
   Stein T, 2010, AM J SPORT MED, V38, P1542, DOI 10.1177/0363546510364052
   Synnestvedt Marie B, 2005, AMIA Annu Symp Proc, P724
   Wyatt RWB, 2013, AM J SPORT MED, V41, P2766, DOI 10.1177/0363546513503287
   Yasuda T, 2018, INT J RHEUM DIS, V21, P2104, DOI 10.1111/1756-185X.13074
NR 35
TC 4
Z9 4
U1 6
U2 21
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1022-5536
EI 2309-4990
J9 J ORTHOP SURG-HONG K
JI J. Orthop. Surg.
PD SEP
PY 2020
VL 28
IS 3
AR 2309499020947286
DI 10.1177/2309499020947286
PG 7
WC Orthopedics; Surgery
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Orthopedics; Surgery
GA QC4OS
UT WOS:000614813100001
PM 32806989
DA 2022-08-23
ER

PT J
AU Zhao, WB
   Li, YH
   Fan, TR
   Wu, F
AF Zhao, Wenbin
   Li, Yahui
   Fan, Tongrang
   Wu, Feng
TI A novel embedding learning framework for relation completion and
   recommendation based on graph neural network and multi-task learning
SO SOFT COMPUTING
LA English
DT Article; Early Access
DE Knowledge graph; Relation completion; Relation recommendation; Graph
   neural network; Multi-task learning
AB At present, the relation completion mainly study the influence of single-path or first-order information, but ignoring the more complex relation information widely existing between entities. Meanwhile, recommendation systems based on collaborative filtering algorithms are susceptible to data sparsity issues, leading to a cold start of the system. Therefore, a novel embedding learning framework for relation completion and recommendation based on graph neural network and multi-task learning is proposed. The graph neural network relation completion model GNNRC predicts the relation between entities by embedding learning, which fuses the high-order semantic features of the two target entities' subgraph based on graph neural network. A comparison with TransD model on embeding learning for relation completion is reported. On this basic, the multi-task learning relation recommendation model MLRR add the bridging unit into deep end-to-end recommendation model, which realizes the alternating learning of knowledge graph embedding and recommendation algorithm. Experimental results show that the performance of the proposed recommendation model is significantly better than other baselines. Moreover, strong recommendation performance can be maintained in cold start scenarios where data are sparse.
C1 [Zhao, Wenbin; Li, Yahui; Fan, Tongrang] Shijiazhuang Tiedao Univ, Sch Informat Sci & Technol, Shijiazhuang, Hebei, Peoples R China.
   [Wu, Feng] Hebei Inst Sci & Technol Informat, Hebei Sci & Technol Informat Proc Lab, Shijiazhuang, Hebei, Peoples R China.
RP Fan, TR (通讯作者)，Shijiazhuang Tiedao Univ, Sch Informat Sci & Technol, Shijiazhuang, Hebei, Peoples R China.
EM zhaowb2013@stdu.edu.cn; 15588552293@163.com; fantr@stdu.edu.cn;
   hebwf@126.com
FU National Natural Science Foundation of China [61373160]; Hebei science
   and technology information processing laboratory [QN2020197]; 
   [F2021210003]
FX National Natural Science Foundation of China, 61373160, Research on
   recognition method of knowledge evolution path for sequential associated
   text based on graph neural network, F2021210003, Knowledge Graph
   Construction of MultiSource Domain data based on Knowledge
   Representation learning, QN2020197, research project of Hebei science
   and technology information processing laboratory.
CR Cao S, 2020, INT CONF SIGN PROCES, P156, DOI 10.1109/ICSP48669.2020.9321009
   Cen YK, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1358, DOI 10.1145/3292500.3330964
   Chen X., 2021, J INTELL SYST, V16, P729
   [陈新元 Chen Xinyuan], 2021, [中文信息学报, Journal of Chinese Information Processing], V35, P54
   Nguyen DQ, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3429
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   He Y, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P639, DOI 10.1145/3357384.3358061
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Imran Mubashir, 2020, Database Systems for Advanced Applications. 25th International Conference, DASFAA 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12114), P425, DOI 10.1007/978-3-030-59419-0_26
   Lu YF, 2019, AAAI CONF ARTIF INTE, P4456
   Lv Y., 2020, COMP DIG ENG, V48, P638
   [孟小艳 Meng Xiaoyan], 2021, [计算机应用研究, Application Research of Computers], V38, P39
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Song HQ, 2022, CURR ISSUES TOUR, V25, P1570, DOI 10.1080/13683500.2021.1926441
   Teru Komal K, 2019, ARXIV1911
   Tong Z., 2020, INF TECH INF, V4, P187
   Velikovi P., 2017, PREPRINT
   Wang HW, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P968, DOI 10.1145/3292500.3330836
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2000, DOI 10.1145/3308558.3313411
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang X, 2019, AAAI CONF ARTIF INTE, P5337
   Wang YQ, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0251162
   Yu X, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P283, DOI 10.1145/2556195.2556259
   Zhang CX, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P690, DOI 10.1145/3289600.3291001
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhao WB, 2021, PATTERN RECOGN LETT, V144, P54, DOI 10.1016/j.patrec.2021.01.007
   [朱艳丽 Zhu Yanli], 2019, [中文信息学报, Journal of Chinese Information Processing], V33, P73
NR 28
TC 0
Z9 0
U1 12
U2 12
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1432-7643
EI 1433-7479
J9 SOFT COMPUT
JI Soft Comput.
DI 10.1007/s00500-021-06617-0
EA JAN 2022
PG 13
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YG1PQ
UT WOS:000742268100002
DA 2022-08-23
ER

PT J
AU Huang, FC
   Li, ZX
   Wei, HY
   Zhang, CL
   Ma, HF
AF Huang, Feicheng
   Li, Zhixin
   Wei, Haiyang
   Zhang, Canlong
   Ma, Huifang
TI Boost image captioning with knowledge reasoning
SO MACHINE LEARNING
LA English
DT Article
DE Image captioning; Word attention; Visual attention; Knowledge graph;
   Reinforcement learning
AB Automatically generating a human-like description for a given image is a potential research in artificial intelligence, which has attracted a great of attention recently. Most of the existing attention methods explore the mapping relationships between words in sentence and regions in image, such unpredictable matching manner sometimes causes inharmonious alignments that may reduce the quality of generated captions. In this paper, we make our efforts to reason about more accurate and meaningful captions. We first propose word attention to improve the correctness of visual attention when generating sequential descriptions word-by-word. The special word attention emphasizes on word importance when focusing on different regions of the input image, and makes full use of the internal annotation knowledge to assist the calculation of visual attention. Then, in order to reveal those incomprehensible intentions that cannot be expressed straightforwardly by machines, we introduce a new strategy to inject external knowledge extracted from knowledge graph into the encoder-decoder framework to facilitate meaningful captioning. Finally, we validate our model on two freely available captioning benchmarks: Microsoft COCO dataset and Flickr30k dataset. The results demonstrate that our approach achieves state-of-the-art performance and outperforms many of the existing approaches.
C1 [Huang, Feicheng; Li, Zhixin; Wei, Haiyang; Zhang, Canlong] Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China.
   [Ma, Huifang] Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou 730070, Peoples R China.
RP Li, ZX (通讯作者)，Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China.
EM lizx@gxnu.edu.cn
RI zhang, hanyun/ABH-7128-2020
OI Li, Zhixin/0000-0002-5313-6134
FU National Natural Science Foundation of China [61966004, 61663004,
   61866004, 61762078]; Guangxi Natural Science Foundation
   [2019GXNSFDA245018, 2018GXNSFDA281009, 2017GXNSFAA198365]; Guangxi
   "Bagui Scholar" Teams for Innovation and Research Project; Guangxi
   Talent Highland Project of Big Data Intelligence and Application;
   Guangxi Collaborative Innovation Center of Multi-source Information
   Integration and Intelligent Processing
FX This work is supported by the National Natural Science Foundation of
   China (Nos. 61966004, 61663004, 61866004, 61762078), the Guangxi Natural
   Science Foundation (Nos. 2019GXNSFDA245018, 2018GXNSFDA281009,
   2017GXNSFAA198365), the Guangxi "Bagui Scholar" Teams for Innovation and
   Research Project, the Guangxi Talent Highland Project of Big Data
   Intelligence and Application, Guangxi Collaborative Innovation Center of
   Multi-source Information Integration and Intelligent Processing.
CR Anderson P, 2018, PROC CVPR IEEE, P6077, DOI 10.1109/CVPR.2018.00636
   Bai S, 2018, NEUROCOMPUTING, V311, P291, DOI 10.1016/j.neucom.2018.05.080
   Bengio S., 2015, ADV NEURAL INFORM PR, P1171, DOI DOI 10.5555/2969239.2969370
   Biswas P, 2005, I CONF VLSI DESIGN, P651
   Cai DL, 2018, LEC NO MULTI IND ENG, P499, DOI 10.1007/978-3-319-59280-0_40
   Chen L, 2017, PROC CVPR IEEE, P6298, DOI 10.1109/CVPR.2017.667
   Chen M, 2017, 31 AAAI C ART INT
   Cornia M, 2018, ACM T MULTIM COMPUT, V14, DOI 10.1145/3177745
   Gu HY, 2017, PR IEEE INT CONF TEA, P229, DOI 10.1109/TALE.2017.8252338
   Gu JX, 2019, PROC CVPR IEEE, P1969, DOI 10.1109/CVPR.2019.00207
   Hendricks LA, 2016, PROC CVPR IEEE, P1, DOI 10.1109/CVPR.2016.8
   Hossain MZ, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3295748
   Huang L, 2019, IEEE I CONF COMP VIS, P4633, DOI 10.1109/ICCV.2019.00473
   Jia X, 2015, IEEE I CONF COMP VIS, P2407, DOI 10.1109/ICCV.2015.277
   Jiang W, 2018, 32 AAAI C ART INT
   Johnson J, 2016, PROC CVPR IEEE, P4565, DOI 10.1109/CVPR.2016.494
   Kim B, 2018, P EUR C COMP VIS ECC
   Li G, 2017, ARXIV COMPUTER VISIO
   Li L, 2017, 31 AAAI C ART INT
   Li XY, 2019, AAAI CONF ARTIF INTE, P8650
   Li Y, 2019, P IEEE C COMP VIS PA, P12497
   Lin CY, 2003, HLT-NAACL 2003: HUMAN LANGUAGE TECHNOLOGY CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE MAIN CONFERENCE, P150
   Liu CX, 2017, AAAI CONF ARTIF INTE, P4176
   Liu S, 2016, ARXIV1612003705
   Liu SQ, 2017, IEEE I CONF COMP VIS, P873, DOI 10.1109/ICCV.2017.100
   Liu Y, 2017, INT C COMP SUPP COOP, P521, DOI 10.1109/CSCWD.2017.8066748
   Lu J., 2017, P IEEE C COMPUTER VI, P375
   Mathews A.P, 2016, 30 AAAI C ART INT
   Papineni K, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P311, DOI 10.3115/1073083.1073135
   Park CC, 2017, P IEEE C COMP VIS PA, P895, DOI DOI 10.1109/CVPR.2017.681
   Pedersoli M, 2017, IEEE I CONF COMP VIS, P1251, DOI 10.1109/ICCV.2017.140
   Qin Y, 2019, PROC CVPR IEEE, P8359, DOI 10.1109/CVPR.2019.00856
   Ramanishka V, 2017, P IEEE C COMP VIS PA, P7206
   Ranzato M., 2015, INT C LEARN REPR
   Ren SQ, 2017, IEEE T PATTERN ANAL, V39, P1137, DOI 10.1109/TPAMI.2016.2577031
   Rennie S.J., 2017, P IEEE C COMP VIS PA, P7008
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Vedantam R, 2015, PROC CVPR IEEE, P4566, DOI 10.1109/CVPR.2015.7299087
   Vinyals O, 2015, PROC CVPR IEEE, P3156, DOI 10.1109/CVPR.2015.7298935
   Wang YM, 2017, IEEE IMAGE PROC, P2726, DOI 10.1109/ICIP.2017.8296778
   Wu Q, 2018, IEEE T PATTERN ANAL, V40, P1367, DOI 10.1109/TPAMI.2017.2708709
   Wu Z, 2016, ARXIV160507912
   Xu K, 2015, PR MACH LEARN RES, V37, P2048
   Yao T., 2017, P IEEE INT C COMP VI, P4894
   Yao T., 2017, P IEEE C COMP VIS PA, P6580
   Yao T, 2019, IEEE I CONF COMP VIS, P2621, DOI 10.1109/ICCV.2019.00271
   You QZ, 2016, PROC CVPR IEEE, P4651, DOI 10.1109/CVPR.2016.503
   Zhou YM, 2019, IEEE WINT CONF APPL, P283, DOI 10.1109/WACV.2019.00036
NR 48
TC 4
Z9 5
U1 3
U2 16
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0885-6125
EI 1573-0565
J9 MACH LEARN
JI Mach. Learn.
PD DEC
PY 2020
VL 109
IS 12
SI SI
BP 2313
EP 2332
DI 10.1007/s10994-020-05919-y
EA OCT 2020
PG 20
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OS5ZF
UT WOS:000584670500001
OA Bronze, Green Submitted
DA 2022-08-23
ER

PT J
AU Vilches-Blazquez, LM
   Saavedra, J
AF Vilches-Blazquez, Luis M.
   Saavedra, Jhonny
TI A graph-based representation of knowledge for managing land
   administration data from distributed agencies - A case study of Colombia
SO GEO-SPATIAL INFORMATION SCIENCE
LA English
DT Article
DE Land administration; knowledge graph; ontology; heterogeneity; data
   integration
ID DATA INTEGRATION; DOMAIN MODEL; HARMONIZATION; INFORMATION; VOCABULARY;
   FRAMEWORK; SERVICES; SCALE
AB Multiple efforts have been performed worldwide around diverse aspects of land administration. However, land administration data and systems' notorious heterogeneity remains a longstanding challenge to develop a harmonized vision. In this sense, the traditional Spatial Data Infrastructures adoption is not enough to overcome this challenge since data sources' heterogeneity implies needs related to harmonization interoperability, sharing, and integration in land administration development. This paper proposes a graph-based representation of knowledge for integrating multiple and heterogeneous data sources (tables, shapefiles, geodatabases, and WFS services) belonging to two Colombian agencies within a decentralized land administration scenario. These knowledge graphs are developed on an ontology-based knowledge representation using national and international standards for land administration. Our approach aims to prevent data isolation, enable cross-datasets integration, accomplish machine-processable data, and facilitate the reuse and exploitation of multi-jurisdictional datasets in a single approach. A real case study demonstrates the applicability of the land administration data cycle deployed.
C1 [Vilches-Blazquez, Luis M.] Inst Politecn Nacl, Ctr Invest Computac, Mexico City, DF, Mexico.
   [Saavedra, Jhonny] Univ Politecn Madrid, Sch Land Surveying Geodesy & Mapping Engn, Madrid, Spain.
RP Vilches-Blazquez, LM (通讯作者)，Inst Politecn Nacl, Ctr Invest Computac, Mexico City, DF, Mexico.
EM lmvilches@cic.ipn.mx
OI Vilches-Blazquez, Luis M./0000-0001-5799-469X
FU Ministerio de Tecnologias de la Informacion y las Comunicaciones de
   Colombia, CYTED program [520RT0010, SIP-IPN 20210677]; Colfuturo
FX This work was partially supported by Colfuturo and Ministerio de
   Tecnologias de la Informacion y las Comunicaciones de Colombia, CYTED
   program 520RT0010 [Red GeoLIBERO -Consolidacion de una red de geomatica
   libre aplicada a las necesidades de Iberoamerica], and SIP-IPN 20210677
   [Generacion de grafos de conocimiento sobre eventos meteorologicos
   urbanos].
CR Bajo, 2017, FIG WORKING WEEK 201
   Bank, 2019, COLOMBIA MULTIPURPOS
   Bellomarini L., 2020, KNOWLEDGE GRAPHS BIG, P20
   Berners-Lee T, 2006, LINKED DATA
   Bizer C, 2011, LECT NOTES COMPUT SC, V7051, P1, DOI 10.1007/978-3-642-24577-0_1
   Borzacchiello MT, 2013, COMPUT ENVIRON URBAN, V41, P276, DOI 10.1016/j.compenvurbsys.2012.05.004
   Cagdas V, 2015, SURV REV, V47, P49, DOI 10.1179/1752270614Y.0000000093
   Cagdas V, 2015, LAND USE POLICY, V49, P668, DOI 10.1016/j.landusepol.2014.12.017
   Chen YQ, 2018, COMPUT ENVIRON URBAN, V72, P177, DOI 10.1016/j.compenvurbsys.2018.06.009
   Cudre-Mauroux P, 2020, SEMANT WEB, V11, P13, DOI 10.3233/SW-190371
   Daniel L., 2019, 2019 WORLD BANK C LA
   Diaz A. A., 2014, INSPIRE C AALB
   Doan AnHai, 2012, PRINCIPLEDATA INTE
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   FGDC (Federal Geographic Data Committee), 2008, CAD DAT CONT STAND N
   FGDC (Federal Geographic Data Committee), 2008, GEOGR INF FRAM DAT 1
   FIG-International Federation of Surveyors, 1998, CADASTRE 2014 VISION
   Fonseca F., 2002, T GIS, V6, P231, DOI DOI 10.1111/1467-9671.00109
   Giuliani G, 2017, T GIS, V21, P811, DOI 10.1111/tgis.12209
   Gmez-PUrez, 2010, DEVELOPMENT, P1
   Golshan B, 2017, PODS'17: PROCEEDINGS OF THE 36TH ACM SIGMOD-SIGACT-SIGAI SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS, P101, DOI 10.1145/3034786.3056124
   Granell, 2014, CONNECTING DIGITAL E
   Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081
   Guo W., 2012, ISPRS ANN PHOTOGRAMM, VI-2, P209, DOI 10.5194/isprsannals-I-2-209-2012
   Hakimi O, 2020, ADV FUNCT MATER, V30, DOI 10.1002/adfm.201909910
   Hakimpour F., 2001, P 4 AGILE C GEOGR IN, P385
   Hess C, 2006, COMPUT ENVIRON URBAN, V30, P529, DOI 10.1016/j.compenvurbsys.2005.08.008
   Huang WM, 2020, INT J DIGIT EARTH, V13, P976, DOI 10.1080/17538947.2019.1604835
   INSPIRE, 2014, DAT SPEC CAD PARC TE
   ISO, 2005, 19110 ISO
   ISO, 2015, 1915022015 ISO
   ISO, 2012, 191522012 ISO
   Jung CT, 2013, COMPUT ENVIRON URBAN, V38, P45, DOI 10.1016/j.compenvurbsys.2012.10.008
   Kalantari M, 2015, LAND USE POLICY, V49, P552, DOI 10.1016/j.landusepol.2014.12.019
   Kejriwal M., 2019, SPRINGERBRIEFS COMPU, P1
   Krotzsch M, 2016, LECT NOTES COMPUT SC, V9981, P376, DOI 10.1007/978-3-319-46523-4_23
   Larsen R.L., 2017, ENCY LIB INFORM SCI, V4th ed., P2536
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lenzerini M., 2002, P 21 ACM SIGMOD SIGA, V21, P233, DOI DOI 10.1145/543613.543644
   Ling Shi, 2017, On the Move to Meaningful Internet Systems. OTM 2017 Conferences. Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017. Proceedings: LNCS 10574, P445, DOI 10.1007/978-3-319-69459-7_30
   Lutz M, 2009, COMPUT GEOSCI-UK, V35, P739, DOI 10.1016/j.cageo.2007.09.017
   Noy N, 2019, COMMUN ACM, V62, P36, DOI 10.1145/3331166
   Noy NF, 2004, SIGMOD REC, V33, P65
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Perry M., 2012, GEOSPARQL GEOGRAPHIC
   Poveda-Villalon M, 2014, INT J SEMANT WEB INF, V10, P7, DOI 10.4018/ijswis.2014040102
   Psyllidis A, 2015, COMM COM INF SC, V527, P21, DOI 10.1007/978-3-662-47386-3_2
   Radovanovic M., 2018, P 8 INT C WEB INT MI, DOI [10.1145/3227609.3227661, DOI 10.1145/3227609.3227661]
   Rajabifard, 2017, WORLD BANK C LAND PO
   Rector A, 2004, LECT NOTES COMPUT SC, V3257, P63
   Rietveld L., 2018, INT ARCH PHOTOGRAMM, VXLII-4/W8, P49, DOI [10.5194/isprs-archives-XLII-4-W8-49-2018, DOI 10.5194/ISPRS-ARCHIVES-XLII-4-W8-49-2018]
   Rocha, 2019, GEOGRAPHICAL INFORM, P936, DOI [10.1007/978-3-030-06010-7_7, DOI 10.1007/978-3-030-06010-7_7]
   Ronzhin S, 2019, INFORMATION, V10, DOI 10.3390/info10100310
   Salamanca S., 2017, FIG WORKING WEEK 201
   Seneviratne O, 2018, LECT NOTES COMPUT SC, V11137, P223, DOI 10.1007/978-3-030-00668-6_14
   Siricharoen WV, 2014, INT CONF DIGIT INFO, P180, DOI 10.1109/DICTAP.2014.6821679
   Sladic D, 2015, COMPUT SCI INF SYST, V12, P1033, DOI 10.2298/CSIS141031009S
   Soheil S., 2016, GSDI15 WORLD C
   Soon K.H., 2013, 5 LAND ADM DOM MOD W, P20
   Suarez-Figueroa M.C., 2012, ONTOLOGY ENG NETWORK, P21, DOI [10.1007/978-3-642-24794-1, DOI 10.1007/978-3-642-24794-1, 10.1007/978-3-642-24794-1_2, DOI 10.1007/978-3-642-24794-1_2]
   Tandy J., 2017, SPATIAL DATA WEB BES
   UNECE, 1996, LAND ADM GUID SPEC R
   Unger, 2018, OGC WHITE PAPER LAND
   V/gele, 1999, P WORKSH INT METH HA
   van den Brink L, 2017, COMPUT ENVIRON URBAN, V62, P233, DOI 10.1016/j.compenvurbsys.2016.12.002
   van Oosterom P, 2006, COMPUT ENVIRON URBAN, V30, P627, DOI 10.1016/j.compenvurbsys.2005.12.002
   van Oosterom P, 2015, LAND USE POLICY, V49, P527, DOI 10.1016/j.landusepol.2015.09.032
   Verlic, 2012, P 8 INT C SEM SYST I, P31
   Vilches-Blazquez LM, 2019, T GIS, V23, P22, DOI 10.1111/tgis.12496
   Vilches-Blazquez LM, 2014, INT J DIGIT EARTH, V7, P554, DOI 10.1080/17538947.2013.783127
   Wache H, 2001, IJCAI 01 WORKSH ONT, V2001, P108
   Williamson I., 2010, LAND ADM SUSTAINABLE
   Zhou X., 2017, ENCY GIS, P2504, DOI [10.1007/978-3-319-17885-1_1479, DOI 10.1007/978-3-319-17885-1_1479]
NR 73
TC 0
Z9 0
U1 1
U2 2
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1009-5020
EI 1993-5153
J9 GEO-SPAT INF SCI
JI Geo-Spat. Inf. Sci.
PD APR 3
PY 2022
VL 25
IS 2
BP 259
EP 277
DI 10.1080/10095020.2021.2015250
EA JAN 2022
PG 19
WC Remote Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Remote Sensing
GA 2H7TN
UT WOS:000740107800001
OA gold
DA 2022-08-23
ER

PT J
AU Liu, Y
   Song, R
   Lu, WB
   Xiao, YH
AF Liu, Ye
   Song, Rui
   Lu, Wenbin
   Xiao, Yanghua
TI A Probit Tensor Factorization Model For Relational Learning
SO JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
LA English
DT Article; Early Access
DE Alternating least square; EM algorithm; Link prediction;
   Multi-relational data; Open-world assumption; Probit model
ID LINK-PREDICTION; DECOMPOSITION; NETWORK; WEB
AB With the proliferation of knowledge graphs, modeling data with complex multi-relational structure has gained increasing attention in the area of statistical relational learning. One of the most important goals of statistical relational learning is link prediction, that is, predicting whether certain relations exist in the knowledge graph. A large number of models and algorithms have been proposed to perform link prediction, among which tensor factorization method has proven to achieve state-of-the-art performance in terms of computation efficiency and prediction accuracy. However, a common drawback of the existing tensor factorization models is that the missing relations and nonexisting relations are treated in the same way, which results in a loss of information. To address this issue, we propose a binary tensor factorization model with probit link, which not only inherits the computation efficiency from the classic tensor factorization model but also accounts for the binary nature of relational data. Our proposed probit tensor factorization (PTF) model shows advantages in both the prediction accuracy and interpretability. Supplementary files for this article are available online.
C1 [Liu, Ye; Song, Rui; Lu, Wenbin] North Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA.
   [Xiao, Yanghua] Fudan Univ, Dept Comp Sci, Shanghai, Peoples R China.
RP Song, R (通讯作者)，North Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA.
EM rsong@ncsu.edu
RI XIAO, YANG/GPW-5529-2022
OI Song, Rui/0000-0003-1875-2115
FU National Key Research and Development Project [NSF-DMS-1555244,
   2113637]; Shanghai Science and Technology Innovation Action Plan
   [2020AAA0109302]; Shanghai Municipal Science and Technology Major
   Project [19511120400];  [2021SHZDZX0103]
FX We thank the AE and the referees for their constructive comments. Rui
   Song's research is partially supported by NSF-DMS-1555244 and 2113637.
   Yanghua Xiao's work is partially supported by National Key Research and
   Development Project (No. 2020AAA0109302), Shanghai Science and
   Technology Innovation Action Plan (No.19511120400) and Shanghai
   Municipal Science and Technology Major Project (No.2021SHZDZX0103).
CR Aidini A., 2018, ELECT IMAGING, V2018, P261, DOI [10.2352/ISSN.2470-1173.2018.13.IPAS-261, DOI 10.2352/ISSN.2470-1173.2018.13.IPAS-261]
   Al Hasan M, 2011, SOCIAL NETWORK DATA ANALYTICS, P243
   Almansoori W, 2012, NETW MODEL ANAL HLTH, V1, P27, DOI 10.1007/s13721-012-0005-7
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bader BW, 2007, IEEE DATA MINING, P33, DOI 10.1109/ICDM.2007.54
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Carlson Andrew, 2010, P 24 C ART INT AAAI
   Chang Kai-Wei, 2014, P 2014 C EMP METH NA
   Cong FY, 2015, J NEUROSCI METH, V248, P59, DOI 10.1016/j.jneumeth.2015.03.018
   DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1, DOI 10.1111/j.2517-6161.1977.tb01600.x
   Denham W.W., 1979, AM ETHNOL, V6, P1, DOI [10.1525/ae.1979.6.1.02a00010, DOI 10.1525/AE.1979.6.1.02A00010]
   Drumond L., 2016, ARXIV PREPRINT ARXIV
   Durante D, 2014, BIOMETRIKA, V101, P883, DOI 10.1093/biomet/asu040
   Fan M., 2014, P 28 PAC AS C LANG I
   Feng J, 2016, FIFTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P557
   Foulds J., 2011, P 14 INT C ART INT S, V15, P287
   Franz T, 2009, LECT NOTES COMPUT SC, V5823, P213, DOI 10.1007/978-3-642-04930-9_14
   Garcia-Duran A, 2016, J ARTIF INTELL RES, V55, P715, DOI 10.1613/jair.5013
   Ghadermarzy N, 2019, IEEE T SIGNAL PROCES, V67, P29, DOI 10.1109/TSP.2018.2879031
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   HARSHMAN RA, 1994, COMPUT STAT DATA AN, V18, P39, DOI 10.1016/0167-9473(94)90132-5
   Hitchcock F. L., 1927, J MATH PHYS, V6, P164, DOI 10.1002/sapm192761164
   Hoff PD, 2016, BAYESIAN ANAL, V11, P627, DOI 10.1214/14-BA934
   Hoff PD, 2015, ANN APPL STAT, V9, P1169, DOI 10.1214/15-AOAS839
   Hoff PD, 2011, COMPUT STAT DATA AN, V55, P530, DOI 10.1016/j.csda.2010.05.020
   Hu CW, 2016, JMLR WORKSH CONF PRO, V51, P1133
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Karami A, 2012, IEEE J-STARS, V5, P444, DOI 10.1109/JSTARS.2012.2189200
   Kemp C., 2006, LEARNING SYSTEMS CON, V3, P5
   Kok S., 2007, P 24 INT C MACH LEAR, P433, DOI DOI 10.1145/1273496.1273551
   Krivitsky Pavel N, 2008, J Stat Softw, V24, DOI 10.18637/jss.v024.i05
   Krompass D., 2013, TENS METH MACH LEARN
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   London B., 2013, ARXIV PREPRINT ARXIV
   Mazumder R, 2010, J MACH LEARN RES, V11, P2287
   McCray AT, 2001, STUD HEALTH TECHNOL, V84, P216
   Minhas S, 2016, J PEACE RES, V53, P491, DOI 10.1177/0022343316630783
   Nguyen D, 2017, ARXIV PREPRINT ARXIV, P8
   Nguyen D.Q., 2016, P 2016 C N AM CHAPT, P460, DOI DOI 10.18653/V1/N16-1054
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M., 2013, ARXIV PREPRINT ARXIV
   Peng JJ, 2020, CURR PHARM DESIGN, V26, P3076, DOI 10.2174/1381612826666200116145057
   Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
   Rai P, 2014, PR MACH LEARN RES, V32, P1800
   Rendle S., 2010, P 3 ACM INT C WEB SE, P81, DOI DOI 10.1145/1718487.1718498
   Rummel R.J., 1976, 5409 INT U CONS POL
   Sarkar P., 2005, ACM SIGKDD EXPLOR NE, V18, P1145, DOI DOI 10.1145/1117454.1117459
   Sarkar P., 2007, P INT C ART INT STAT, V2, P420
   Schein A, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1045, DOI 10.1145/2783258.2783414
   SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136
   Sewell DK, 2015, J AM STAT ASSOC, V110, P1646, DOI 10.1080/01621459.2014.988214
   Shi C., 2019, J MACH LEARN RES, V20, P809
   Singhal Amit, 2012, OFFICIAL GOOGLE BLOG
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sulaimany S, 2018, INT J DATA MIN BIOIN, V20, P161, DOI 10.1504/IJDMB.2018.093684
   Sun WW, 2017, J R STAT SOC B, V79, P899, DOI 10.1111/rssb.12190
   TRIVEDI R, 2017, ARXIV PREPRINT ARXIV
   TUCKER LR, 1966, PSYCHOMETRIKA, V31, P279, DOI 10.1007/BF02289464
   Vervliet N, 2019, SIAM J SCI COMPUT, V41, pA789, DOI 10.1137/17M1152371
   Wang DX, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1225, DOI 10.1145/2939672.2939753
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Westveld AH, 2011, ANN APPL STAT, V5, P843, DOI 10.1214/10-AOAS403
   Xiao H., 2015, COMPUTER SCI
   Xu KS, 2014, IEEE J-STSP, V8, P552, DOI 10.1109/JSTSP.2014.2310294
   Xu ZL, 2015, IEEE T PATTERN ANAL, V37, P475, DOI 10.1109/TPAMI.2013.201
NR 68
TC 0
Z9 0
U1 5
U2 5
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 1061-8600
EI 1537-2715
J9 J COMPUT GRAPH STAT
JI J. Comput. Graph. Stat.
DI 10.1080/10618600.2021.2003204
EA MAR 2022
PG 10
WC Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA ZR4IB
UT WOS:000767747700001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, LS
   Kang, Z
   Sun, XX
   Sun, H
   Zhang, BZ
   Pu, DB
AF Zhang, Lisa
   Kang, Zhe
   Sun, Xiaoxin
   Sun, Hong
   Zhang, Bangzuo
   Pu, Dongbing
TI KCRec: Knowledge-aware representation Graph Convolutional Network for
   Recommendation
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Graph Convolutional Network; Attention mechanism;
   Recommender system
AB Collaborative filtering (CF) usually suffers from data sparsity and cold-start problems in real recommendation scenarios, therefore, side information like social networks and contexts have been introduced to improve its performance. In this paper, we consider the knowledge graph (KG) as a source of side information and propose a novel framework, Knowledge-aware representation Graph Convolutional Network for Recommendation (KCRec), that is an end-to-end framework that captures the inter-user and inter-item relatedness effectively. For exploring the potential long-distance interests of the user, we aggregate the item features and get the representation of the user preferences by propagating the relationships in KG between their neighborhood, and further integrates with the graph convolution network. Furthermore, we employ similarity features in different users to construct a user-adjacency graph, and utilize the user-item interaction features to establish a user-feature graph, to obtain the high-order representation of users. Extensive experiments on two real-world datasets demonstrate that our proposed method has substantially improved, which outperforms several state-of-the-art baselines. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Zhang, Lisa; Kang, Zhe; Sun, Xiaoxin; Sun, Hong; Zhang, Bangzuo; Pu, Dongbing] Northeast Normal Univ, Sch Informat Sci & Technol, Changchun 130117, Jilin, Peoples R China.
RP Zhang, BZ; Pu, DB (通讯作者)，Northeast Normal Univ, Sch Informat Sci & Technol, Changchun 130117, Jilin, Peoples R China.
EM Zhangbz@nenu.edu.cn; Pudb@nenu.edu.cn
OI Zhang, Bangzuo/0000-0002-3438-6795
FU National Natural Science Foundation of China [61977015]; Jilin Province
   Development and Reform Commission Project of China [2020C017-3]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61977015, and in part by the Jilin
   Province Development and Reform Commission Project of China under Grant
   2020C017-3.
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Hu Yifan, 2021, WWW 21
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Luo C, 2014, IEEE DATA MINING, P917, DOI 10.1109/ICDM.2014.64
   Ma WZ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1210, DOI 10.1145/3308558.3313607
   McMahan HB, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P1222
   Ni YB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P596, DOI 10.1145/3219819.3219828
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Paudel B, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P2046, DOI 10.1145/3442381.3449970
   [秦川 Qin Chuan], 2020, [中国科学. 信息科学, Scientia Sinica Informationis], V50, P937
   Sun Y, 2017, ACM T INFORM SYST, V35, DOI 10.1145/3041659
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Tang XL, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1891, DOI 10.1145/3292500.3330705
   van der Merwe R, 2019, ARCH REC, V40, P239, DOI 10.1080/23257962.2017.1388224
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang HW, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P592, DOI 10.1145/3159652.3159666
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang S, 2021, INT J APPL LINGUIST, V31, P283, DOI 10.1111/ijal.12334
   Wang X, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P165, DOI 10.1145/3331184.3331267
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wang Xiang, 2021, AAAI 21
   Wu ZH, 2021, IEEE T NEUR NET LEAR, V32, P4, DOI 10.1109/TNNLS.2020.2978386
   Xiangnan He, 2020, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, P639, DOI 10.1145/3397271.3401063
   Yang B., 2014, EMBEDDING ENTITIES R
   Ying R, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P974, DOI 10.1145/3219819.3219890
   Yu X., 2013, RECSYS, P347
   Yu X, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P283, DOI 10.1145/2556195.2556259
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhou J., 2020, ARXIV PREPRINT ARXIV, V1, P57, DOI 10.1016/j.aiopen.2021.01.001
   Zhou Ke, 2012, P 18 ACM SIGKDD INT, P498
NR 35
TC 5
Z9 5
U1 11
U2 26
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD OCT 27
PY 2021
VL 230
AR 107399
DI 10.1016/j.knosys.2021.107399
EA AUG 2021
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UR8PO
UT WOS:000697004300021
DA 2022-08-23
ER

PT J
AU Gao, JL
   Liu, XY
   Chen, YB
   Xiong, F
AF Gao, Jianliang
   Liu, Xiangyue
   Chen, Yibo
   Xiong, Fan
TI MHGCN: Multiview highway graph convolutional network for cross-lingual
   entity alignment
SO TSINGHUA SCIENCE AND TECHNOLOGY
LA English
DT Article
DE Semantics; Road transportation; Knowledge engineering; Task analysis;
   Social networking (online); Aggregates; Fuses; knowledge graph; entity
   alignment; graph convolutional network
AB Knowledge graphs (KGs) provide a wealth of prior knowledge for the research on social networks. Cross-lingual entity alignment aims at integrating complementary KGs from different languages and thus benefits various knowledge-driven social network studies. Recent entity alignment methods often take an embedding-based approach to model the entity and relation embedding of KGs. However, these studies mostly focus on the information of the entity itself and its structural features but ignore the influence of multiple types of data in KGs. In this paper, we propose a new embedding-based framework named multiview highway graph convolutional network (MHGCN), which considers the entity alignment from the views of entity semantic, relation semantic, and entity attribute. To learn the structural features of an entity, the MHGCN employs a highway graph convolutional network (GCN) for entity embedding in each view. In addition, the MHGCN weights and fuses the multiple views according to the importance of the embedding from each view to obtain a better entity embedding. The alignment entities are identified based on the similarity of entity embeddings. The experimental results show that the MHGCN consistently outperforms the state-of-the-art alignment methods. The research also will benefit knowledge fusion through cross-lingual KG entity alignment.
C1 [Gao, Jianliang; Liu, Xiangyue; Xiong, Fan] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.
   [Chen, Yibo] State Grid Hunan Elect Power Co Ltd, Informat & Commun Branch, Changsha 410004, Peoples R China.
RP Gao, JL (通讯作者)，Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.
EM gaojianliang@csu.edu.cn; liuxiangyue@csu.edu.cn; chenyibo8224@gmail.com;
   xiongfan@csu.edu.cn
FU National Natural Science Foundation of China [61873288]; Research on Key
   Technologies and Application for the Time Series Data of State Grid
   Hunan Electirc Power Company [5216A00036]; Hunan Key Laboratory for
   Internet of Things in Electricity [2019TP1016]; CAAI-Huawei MindSpore
   Open Fund
FX This work was supported by the National Natural Science Foundation of
   China (No. 61873288), Research on Key Technologies and Application for
   the Time Series Data of State Grid Hunan Electirc Power Company (No.
   5216A00036), the Hunan Key Laboratory for Internet of Things in
   Electricity (No. 2019TP1016), and CAAI-Huawei MindSpore Open Fund.
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cao YX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1452
   Chen MH, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1511
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Li Chengjiang, 2019, P 2019 C EMPIRICAL M, P2723
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Marcheggiani D, 2017, P 2017 C EMP METH NA, DOI DOI 10.18653/V1/D17-1159
   Qian JW, 2019, IEEE T DEPEND SECURE, V16, P679, DOI 10.1109/TDSC.2017.2697854
   Sarasua Cristina, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P525, DOI 10.1007/978-3-642-35176-1_33
   Shi Xiaofei, 2019, EMNLP IJCNLP, P813
   Srivatsan R, 2015, SEVA, SAVIOUR AND STATE: CASTE POLITICS, TRIBAL WELFARE AND CAPITALIST DEVELOPMENT, P1
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   [王雪鹏 Wang Xuepeng], 2017, [计算机学报, Chinese Journal of Computers], V40, P701
   Wang Z., 2018, P 27 INT JOINT C ART, P1021
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Yang K, 2021, TSINGHUA SCI TECHNOL, V26, P208, DOI 10.26599/TST.2019.9010059
   Zhang CH, 2017, TSINGHUA SCI TECHNOL, V22, P185
   Zhang JN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4264
   Zhang QH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5429
   Zhao X, 2021, TSINGHUA SCI TECHNOL, V26, P536, DOI 10.26599/TST.2020.9010024
   Zhu QN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1943
NR 27
TC 1
Z9 1
U1 20
U2 26
PU TSINGHUA UNIV PRESS
PI BEIJING
PA B605D, XUE YAN BUILDING, BEIJING, 100084, PEOPLES R CHINA
SN 1007-0214
EI 1878-7606
J9 TSINGHUA SCI TECHNOL
JI Tsinghua Sci. Technol.
PD AUG
PY 2022
VL 27
IS 4
BP 719
EP 728
DI 10.26599/TST.2021.9010056
PG 10
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA XM6ZL
UT WOS:000728972400008
OA gold
DA 2022-08-23
ER

PT J
AU Kuegler, P
   Marian, M
   Dorsch, R
   Schleich, B
   Wartzack, S
AF Kuegler, Patricia
   Marian, Max
   Dorsch, Rene
   Schleich, Benjamin
   Wartzack, Sandro
TI A Semantic Annotation Pipeline towards the Generation of Knowledge
   Graphs in Tribology
SO LUBRICANTS
LA English
DT Article
DE tribo-testing; tribo-informatics; machine learning; artificial
   intelligence; natural language processing; tribAIn; BERT
ID NEURAL-NETWORKS; DESIGN; PRINCIPLES; LUBRICANT; ONTOLOGY
AB Within the domain of tribology, enterprises and research institutions are constantly working on new concepts, materials, lubricants, or surface technologies for a wide range of applications. This is also reflected in the continuously growing number of publications, which in turn serve as guidance and benchmark for researchers and developers. Due to the lack of suited data and knowledge bases, knowledge acquisition and aggregation is still a manual process involving the time-consuming review of literature. Therefore, semantic annotation and natural language processing (NLP) techniques can decrease this manual effort by providing a semi-automatic support in knowledge acquisition. The generation of knowledge graphs as a structured information format from textual sources promises improved reuse and retrieval of information acquired from scientific literature. Motivated by this, the contribution introduces a novel semantic annotation pipeline for generating knowledge in the domain of tribology. The pipeline is built on Bidirectional Encoder Representations from Transformers (BERT)-a state-of-the-art language model-and involves classic NLP tasks like information extraction, named entity recognition and question answering. Within this contribution, the three modules of the pipeline for document extraction, annotation, and analysis are introduced. Based on a comparison with a manual annotation of publications on tribological model testing, satisfactory performance is verified.
C1 [Kuegler, Patricia; Dorsch, Rene; Schleich, Benjamin; Wartzack, Sandro] Friedrich Alexander Univ Erlangen Nurnberg FAU, Engn Design, Martensstr 9, D-91058 Erlangen, Germany.
   [Marian, Max] Pontificia Univ Catolica Chile, Sch Engn, Dept Mech & Met Engn, Santiago 6904411, Chile.
RP Kuegler, P (通讯作者)，Friedrich Alexander Univ Erlangen Nurnberg FAU, Engn Design, Martensstr 9, D-91058 Erlangen, Germany.
EM kuegler@mfk.fau.de; max.marian@ing.puc.cl; rene.dorsch@fau.de;
   schleich@mfk.fau.de; wartzack@mfk.fau.de
RI Marian, Max/AAB-3558-2019
OI Marian, Max/0000-0003-2045-6649; Kugler, Patricia/0000-0003-1639-0931;
   Schleich, Benjamin/0000-0002-3638-4179
FU German Research Foundation (DFG) [WA 2913/22-2]; Pontificia Universidad
   Catolica de Chile
FX P. Kugler, B. Schleich and S. Wartzack gratefully acknowledge the
   financial support of project WA 2913/22-2 within the Priority Program
   1921 by the German Research Foundation (DFG). M. Marian acknowledges the
   support from Pontificia Universidad Catolica de Chile.
CR Akbik A., 2019, P 2019 C N AM CHAPT, P54, DOI [10.18653/v1/N19- 4010, DOI 10.18653/V1/N19-4010]
   Alammar J., BLOGPOST
   Ashburner M, 2001, GENOME RES, V11, P1425
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Baader F., 2003, DESCRIPTION LOGIC HD
   Baboukani BS, 2020, TRIBOL LETT, V68, DOI 10.1007/s11249-020-01294-w
   Beltagy I., 2019, P 2019 C EMPIRICAL M, P3615
   Bhaumik S, 2019, TRIBOL INT, V140, DOI 10.1016/j.triboint.2019.06.006
   Bhaumik S, 2019, FUEL, V241, P733, DOI 10.1016/j.fuel.2018.12.094
   Bodenreider O, 2008, Yearb Med Inform, P67
   Boidi G, 2020, SURF TOPOGR-METROL, V8, DOI 10.1088/2051-672X/abae13
   Byeong-Choon G, 2017, MATERIALS, V10, DOI 10.3390/ma10070701
   Cetinel H, 2012, IND LUBR TRIBOL, V64, P288, DOI 10.1108/00368791211249674
   Chandrasegaran SK, 2013, COMPUT AIDED DESIGN, V45, P204, DOI 10.1016/j.cad.2012.08.006
   Cho YJ, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-3321-4
   Cullen J., 1988, Expert Systems, V5, P216, DOI 10.1111/j.1468-0394.1988.tb00065.x
   Czichos H., 2015, TRIBOLOGIE HDB, Vfourth, P767
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Domingue J, 2011, HANDBOOK OF SEMANTIC WEB TECHNOLOGIES: FOUNDATIONS AND TECHNOLOGIES, VOLS 1 AND 2, P1, DOI 10.1007/978-3-540-92913-0
   Esnaola-Gonzalez I., 2021, P ESWC WORKSH DORIC
   FEIGENBAUM EA, 1984, ANN NY ACAD SCI, V426, P91, DOI 10.1111/j.1749-6632.1984.tb16513.x
   Ghelani S., WORD EMBEDDINGS PR 2
   Giorgi JM, 2018, BIOINFORMATICS, V34, P4087, DOI 10.1093/bioinformatics/bty449
   Goldberg Y., 2017, SYNTH LECT HUM LANG, V10, P1, DOI [DOI 10.2200/S00762ED1V01Y201703HLT037, 10.2200/S00762ED1V01Y201703HLT037]
   Gruber T.R, 2017, TED TALK
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081
   Hasan MS, 2022, J TRIBOL-T ASME, V144, DOI 10.1115/1.4050525
   Hinton Geoffrey E., 2012, ABS12070580 CORR
   Hoekstra R, 2010, SEMANT WEB, V1, P111, DOI 10.3233/SW-2010-0004
   Holmberg K, 2017, FRICTION, V5, P263, DOI 10.1007/s40544-017-0183-5
   Hu J., OVERVIEW TEXT REPRES
   Joshi M, 2020, T ASSOC COMPUT LING, V8, P64, DOI 10.1162/tacl_a_00300
   Jurafsky D., SPEECH LANGUAGE PROC, V3rd
   Koroteev M.V., 2021, ARXIV210311943
   Kugler P, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10134421
   Lee J, 2020, BIOINFORMATICS, V36, P1234, DOI 10.1093/bioinformatics/btz682
   Li XX, 2018, RSC ADV, V8, P14129, DOI 10.1039/c7ra12756a
   Marian M., 2021, NUMERISCHE AUSLEGUNG, DOI [10.25593/978-3-96147-440-0, DOI 10.25593/978-3-96147-440-0]
   Marian M, 2022, ADV MATER INTERFACES, V9, DOI 10.1002/admi.202101622
   Marian M, 2021, APPL MATER TODAY, V25, DOI 10.1016/j.apmt.2021.101202
   Marian M, 2021, LUBRICANTS, V9, DOI 10.3390/lubricants9090086
   Marian M, 2020, APPL SURF SCI, V531, DOI 10.1016/j.apsusc.2020.147311
   Marian M, 2020, APPL SURF SCI, V523, DOI 10.1016/j.apsusc.2020.146503
   McKie J.X, PYMUPDF 1 19 0
   Mekgwe GN, 2019, IOP CONF SER-MAT SCI, V499, DOI 10.1088/1757-899X/499/1/012011
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Morik K., 1991, Knowledge Acquisition, V3, P137, DOI 10.1016/1042-8143(91)90002-5
   Navigli R., 2008, ONTOLOGY LEARNING PO
   Peters M.E., 2018, P 2018 C N AM CHAPT, P2227, DOI 10.18653/v1/N18-1202
   Prost J, 2021, LUBRICANTS, V9, DOI 10.3390/lubricants9050050
   Radford A., IMPROVING LANGUAGE U
   Rosenkranz A, 2021, LUBRICANTS, V9, DOI 10.3390/lubricants9010002
   Russell S., 2021, ARTIF INTELL, Vfourth
   Sathiya R, 2009, J MATER PROCESS TECH, V209, P2576, DOI 10.1016/j.jmatprotec.2008.06.030
   Schuster M, 2012, INT CONF ACOUST SPEE, P5149, DOI 10.1109/ICASSP.2012.6289079
   Soldatova LN, 2006, J R SOC INTERFACE, V3, P795, DOI 10.1098/rsif.2006.0134
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   Stipcevic M, 2015, OPT EXPRESS, V23, P11619, DOI 10.1364/OE.23.011619
   Studer R, 1998, DATA KNOWL ENG, V25, P161, DOI 10.1016/S0169-023X(97)00056-6
   Subrahmanyam M, 1997, TRIBOL INT, V30, P739, DOI 10.1016/S0301-679X(97)00056-X
   TALLIAN TE, 1989, J TRIBOL-T ASME, V111, P238, DOI 10.1115/1.3261896
   TALLIAN TE, 1987, J TRIBOL-T ASME, V109, P381, DOI 10.1115/1.3261450
   Vakis AI, 2018, TRIBOL INT, V125, P169, DOI 10.1016/j.triboint.2018.02.005
   Vaswani A, 2017, ADV NEUR IN, V30
   Verhagen WJC, 2012, ADV ENG INFORM, V26, P5, DOI 10.1016/j.aei.2011.06.004
   Vinoth A, 2020, J COMPOS MATER, V54, P179, DOI 10.1177/0021998319859924
   Wyatt BC, 2021, ADV MATER, V33, DOI 10.1002/adma.202007973
   Zeyong Wang, 2018, IOP Conference Series: Materials Science and Engineering, V382, DOI 10.1088/1757-899X/382/2/022032
   Zhang ZN, 2021, FRICTION, V9, P642, DOI 10.1007/s40544-020-0457-3
NR 71
TC 1
Z9 1
U1 7
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-4442
J9 LUBRICANTS
JI Lubricants
PD FEB
PY 2022
VL 10
IS 2
AR 18
DI 10.3390/lubricants10020018
PG 25
WC Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA ZL5LD
UT WOS:000763717600001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Hu, Y
   Wen, GH
   Chapman, A
   Yang, P
   Luo, MN
   Xu, YX
   Dai, D
   Hall, W
AF Hu, Yang
   Wen, Guihua
   Chapman, Adriane
   Yang, Pei
   Luo, Mingnan
   Xu, Yingxue
   Dai, Dan
   Hall, Wendy
TI Graph-Based Visual-Semantic Entanglement Network for Zero-Shot Image
   Recognition
SO IEEE TRANSACTIONS ON MULTIMEDIA
LA English
DT Article
DE Visualization; Semantics; Pipelines; Knowledge engineering; Load
   modeling; Task analysis; Couplings; Zero-shot learning; visual-semantic
   modeling; graph convolutional network; semantic knowledge graph;
   attribute word embedding
AB Zero-shot learning uses semantic attributes to connect the search space of unseen objects. In recent years, although the deep convolutional network brings powerful visual modeling capabilities to the ZSL task, its visual features have severe pattern inertia and lack of representation of semantic relationships, which leads to severe bias and ambiguity. In response to this, we propose the Graph-based Visual-Semantic Entanglement Network to conduct graph modeling of visual features, which is mapped to semantic attributes by using a knowledge graph, it contains several novel designs: 1. it establishes a multi-path entangled network with the convolutional neural network (CNN) and the graph convolutional network (GCN), which input the visual features from CNN to GCN to model the implicit semantic relations, then GCN feedback the graph modeled information to CNN features; 2. it uses attribute word vectors as the target for the graph semantic modeling of GCN, which forms a self-consistent regression for graph modeling and supervise GCN to learn more personalized attribute relations; 3. it fuses and supplements the hierarchical visual-semantic features refined by graph modeling into visual embedding. Our method outperforms state-of-the-art approaches on multiple representative ZSL datasets: AwA2, CUB, and SUN by promoting the semantic linkage modelling of visual features.
C1 [Hu, Yang; Wen, Guihua; Yang, Pei; Luo, Mingnan; Xu, Yingxue; Dai, Dan] South China Univ Technol, Guangzhou 510006, Peoples R China.
   [Hu, Yang; Chapman, Adriane; Hall, Wendy] Univ Southampton, Southampton SO17 1BJ, Hants, England.
   [Dai, Dan] Univ Lincoln, Lincoln LN6 7TS, England.
RP Hu, Y (通讯作者)，South China Univ Technol, Guangzhou 510006, Peoples R China.
EM superhy199148@hotmail.com; crghwen@scut.edu.cn;
   Adriane.Chapman@soton.ac.uk; yangpei@scut.edu.cn; 770160905@qq.com;
   845947406@qq.com; daidanjune@hotmail.com; wh@ecs.soton.ac.uk
OI Hall, Wendy/0000-0003-4327-7811; Hu, Yang/0000-0002-4856-5014; Chapman,
   Adriane/0000-0002-3814-2587
FU National Science Foundation of China [60973083, 61273363]; Science and
   Technology Planning Project of Guangdong Province [2014A010103009,
   2015A020217002]; Guangzhou Science and Technology Planning Project
   [201604020179, 201803010088]; Guangdong Province Key Area RD Plan
   [2020B1111120001]
FX This article was supported by National Science Foundation of China under
   Grants 60973083 and 61273363, Science and Technology Planning Project of
   Guangdong Province under Grants 2014A010103009 and 2015A020217002), and
   Guangzhou Science and Technology Planning Project under Grants
   201604020179 and 201803010088, and Guangdong Province Key Area R&D Plan
   under Project 2020B1111120001.
CR Akata Z, 2013, PROC CVPR IEEE, P819, DOI 10.1109/CVPR.2013.111
   Bojanowski P., 2017, T ASSOC COMPUT LING, V5, P135, DOI [DOI 10.1162/TACL_A_00051, 10.1162/tacl_a_00051]
   Bouma Gerlof, 2009, P GSCL, P31
   Bronstein MM, 2017, IEEE SIGNAL PROC MAG, V34, P18, DOI 10.1109/MSP.2017.2693418
   Chao WL, 2016, LECT NOTES COMPUT SC, V9906, P52, DOI 10.1007/978-3-319-46475-6_4
   Chen JJ, 2020, AAAI CONF ARTIF INTE, V34, P10542
   Chen L, 2017, PROC CVPR IEEE, P6298, DOI 10.1109/CVPR.2017.667
   Chen R, 2020, P 34 AAAI C ART INT
   Chen TS, 2019, IEEE I CONF COMP VIS, P522, DOI 10.1109/ICCV.2019.00061
   Chen TS, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P627
   Cui P, 2018, IEEE T MULTIMEDIA, V20, P198, DOI 10.1109/TMM.2017.2724843
   Cuturi M., 2013, ADV NEURAL INFORM PR, P2292
   Huynh D, 2020, PROC CVPR IEEE, P4482, DOI 10.1109/CVPR42600.2020.00454
   Dat Huynh, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P8773, DOI 10.1109/CVPR42600.2020.00880
   Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Farhadi A, 2009, PROC CVPR IEEE, P1778, DOI 10.1109/CVPRW.2009.5206772
   Frome Andrea, 2013, ADV NEURAL INFORM PR, P2121
   Fu YW, 2015, IEEE T PATTERN ANAL, V37, P2332, DOI 10.1109/TPAMI.2015.2408354
   Gao JY, 2020, IEEE T MULTIMEDIA, V22, P3088, DOI 10.1109/TMM.2020.2969787
   Gao R, 2020, IEEE T IMAGE PROCESS, V29, P3665, DOI 10.1109/TIP.2020.2964429
   Guo JC, 2021, IEEE T MULTIMEDIA, V23, P524, DOI 10.1109/TMM.2020.2984091
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Jiang HJ, 2018, LECT NOTES COMPUT SC, V11214, P121, DOI 10.1007/978-3-030-01249-6_8
   Jiang HJ, 2019, IEEE I CONF COMP VIS, P9764, DOI 10.1109/ICCV.2019.00986
   Kingma D. P., 2017, ARXIV
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Kodirov E, 2017, PROC CVPR IEEE, P4447, DOI 10.1109/CVPR.2017.473
   Lampert CH, 2009, PROC CVPR IEEE, P951, DOI 10.1109/CVPRW.2009.5206594
   Li GH, 2019, IEEE I CONF COMP VIS, P9266, DOI 10.1109/ICCV.2019.00936
   Li KP, 2019, IEEE I CONF COMP VIS, P4653, DOI 10.1109/ICCV.2019.00475
   Li XY, 2019, IEEE T MULTIMEDIA, V21, P2117, DOI 10.1109/TMM.2019.2896516
   Li Y, 2018, PROC CVPR IEEE, P7463, DOI 10.1109/CVPR.2018.00779
   Lin T.Y., 2017, PROC CVPR IEEE, DOI DOI 10.1109/CVPR.2017.106
   Liu K, 2021, IEEE T MULTIMEDIA, V23, P64, DOI 10.1109/TMM.2020.2974323
   Liu L, 2020, AAAI CONF ARTIF INTE, V34, P4868
   Liu Y, 2019, IEEE I CONF COMP VIS, P6697, DOI 10.1109/ICCV.2019.00680
   Lu LH, 2020, IEEE T MULTIMEDIA, V22, P524, DOI 10.1109/TMM.2019.2930344
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Morgado P, 2017, PROC CVPR IEEE, P2037, DOI 10.1109/CVPR.2017.220
   Ni J, 2019, ADV NEUR IN, V32
   Niu L, 2019, IEEE T IMAGE PROCESS, V28, P965, DOI 10.1109/TIP.2018.2872916
   Patterson G, 2012, PROC CVPR IEEE, P2751, DOI 10.1109/CVPR.2012.6247998
   Pennington J., 2014, EMNLP, P1532
   Rohrbach  Marcus, 2013, NIPS
   RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
   Russakovsky O, 2015, INT J COMPUT VISION, V115, P211, DOI 10.1007/s11263-015-0816-y
   Schroff F, 2015, PROC CVPR IEEE, P815, DOI 10.1109/CVPR.2015.7298682
   Selvaraju RR, 2020, INT J COMPUT VISION, V128, P336, DOI 10.1007/s11263-019-01228-7
   Simonyan K., 2014, ARXIV
   Socher R, 2013, NEURIPS, P935
   Song J, 2018, PROC CVPR IEEE, P1024, DOI 10.1109/CVPR.2018.00113
   Speer R., 2017, PROC AAAI C ARTIF IN, V31
   Sun C, 2019, PROC CVPR IEEE, P273, DOI 10.1109/CVPR.2019.00036
   Tipping ME, 1999, J R STAT SOC B, V61, P611, DOI 10.1111/1467-9868.00196
   Torr P. H. S., 2015, PR MACH LEARN RES
   Verma VK, 2018, PROC CVPR IEEE, P4281, DOI 10.1109/CVPR.2018.00450
   Wah C., 2011, CALTECH UCSD BIRDS 2
   Wan ZY, 2019, ADV NEUR IN, V32
   Wang JB, 2020, PATTERN RECOGN, V98, DOI 10.1016/j.patcog.2019.107075
   Wang XL, 2018, PROC CVPR IEEE, P6857, DOI 10.1109/CVPR.2018.00717
   Wang XL, 2018, PROC CVPR IEEE, P7794, DOI 10.1109/CVPR.2018.00813
   Woo SH, 2018, LECT NOTES COMPUT SC, V11211, P3, DOI 10.1007/978-3-030-01234-2_1
   Xian YQ, 2019, PROC CVPR IEEE, P8248, DOI 10.1109/CVPR.2019.00845
   Xie GS, 2019, PROC CVPR IEEE, P9376, DOI 10.1109/CVPR.2019.00961
   Xu XY, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3996
   Ye M, 2019, PROC CVPR IEEE, P11720, DOI 10.1109/CVPR.2019.01200
   Yunlong Yu, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P14032, DOI 10.1109/CVPR42600.2020.01405
   Zhang L, 2019, P 30 BRIT MACH VIS C
   Zhang XX, 2020, IEEE T MULTIMEDIA, V22, P1692, DOI 10.1109/TMM.2019.2959433
   Zhang Y., 2019, PROC IEEE C COMPUT V, P487
   Zhang ZM, 2015, IEEE I CONF COMP VIS, P4166, DOI 10.1109/ICCV.2015.474
   Zhu YZ, 2019, ADV NEUR IN, V32
NR 73
TC 0
Z9 0
U1 3
U2 3
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1520-9210
EI 1941-0077
J9 IEEE T MULTIMEDIA
JI IEEE Trans. Multimedia
PY 2022
VL 24
BP 2473
EP 2487
DI 10.1109/TMM.2021.3082292
PG 15
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 1D5KQ
UT WOS:000793839600019
OA Green Submitted, Green Accepted
DA 2022-08-23
ER

PT J
AU Zhou, Q
   Peng, W
   Tang, D
AF Zhou, Qing
   Peng, Wei
   Tang, Dai
TI Automatic recommendation of medical departments to outpatients based on
   text analyses and medical knowledge graph
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Automatic recommendation of departments; medical knowledge graph;
   attention mechanism; clinical text classification
ID NAMED ENTITY RECOGNITION; MACHINE
AB In many countries, outpatients generally visit a major hospital without a referral from health professionals due to the shortage of family physicians. Not knowing at which medical specialty department to register, outpatients have to wait in long queues to consult receptionists. We propose to alleviate this situation via a computer system offering an automatic recommendation of departments (ARD) to outpatients, which identifies the appropriate medical department for outpatients according to their chief complaints. Besides, ARD systems can boost the emerging services of online hospital registration and online medical diagnosis, which require that the outpatients know the correct department first. ARD is a typical problem of text classification. Nevertheless, off-the-shelf tools for text processing may not suit ARD, because the chief complaints of outpatients are generally brief and contain much noisy information. To solve this problem, we propose ARD-K, a deep learning framework incorporating external medical knowledge sources. We also propose a dual-attention mechanism to mitigate the interference of noisy words and knowledge entities. The performance of ARD-K is compared with some off-the-shelf techniques on a real-world dataset. The results demonstrate the effectiveness of ARD-K for the automatic recommendation of departments to outpatients.
C1 [Zhou, Qing; Peng, Wei; Tang, Dai] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China.
RP Zhou, Q (通讯作者)，Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China.
EM tzhou@cqu.edu.cn
FU National Natural Science Foundation Project of CQ CSTC
   [cstc2020jcyj-msxmX0554]
FX This research was supported by National Natural Science Foundation
   Project of CQ CSTC (No. cstc2020jcyj-msxmX0554).
CR AbdelRahman S., 2010, IJCSI, V7, P27
   Alanazi S, 2017, NAMED ENTITY RECOGNI
   Alfred R., 2014, MALAY NAMED ENTITY R
   Amini M., 2009, ADV NEURAL INFORM PR, P28, DOI DOI 10.5555/2984093.2984097
   [Anonymous], 2020, TENCENT AI LAB EMBED
   Chen Y.C., 2019, UNITER LEARNING UNIV
   Chieu H.L., 2002, COLING 2002 19 INT C
   Choi H, 2018, NEUROCOMPUTING, V284, P171, DOI 10.1016/j.neucom.2018.01.007
   Chorowski J., 2015, ARXIV PREPRINT ARXIV
   Collobert R, 2011, J MACH LEARN RES, V12, P2493
   Deng YH, 2015, STUD HEALTH TECHNOL, V216, P1038, DOI 10.3233/978-1-61499-564-7-1038
   Dogan RI, 2012, P 2012 WORKSH BIOM N, P91
   Guo QP, 2020, AAAI CONF ARTIF INTE, V34, P7847
   Hassanpour S, 2016, ACAD RADIOL, V23, P84, DOI 10.1016/j.acra.2015.09.014
   Hastie T, 2009, ELEMENTS STAT LEARNI, P9, DOI [10.1007/978-0-387-84858-7_2, DOI 10.1007/978-0-387-84858-7_2]
   Hotho A., 2002, KI, V16, P48
   Huang ZH, 2015, COMPUT INTEL NEUROSC, V2015, DOI 10.1155/2015/685404
   Kaelbling LP, 1996, J ARTIF INTELL RES, V4, P237, DOI 10.1613/jair.301
   Kim Y., 2014, ARXIV14085882, DOI DOI 10.3115/V1/D14-1181
   King DB, 2015, ACS SYM SER, V1214, P1
   Ko Y., 2000, P 18 C COMP LING COL, V1, P453
   Lai SW, 2015, AAAI CONF ARTIF INTE, P2267
   Lauren P, 2018, NEUROCOMPUTING, V277, P129, DOI 10.1016/j.neucom.2017.01.117
   Leaman Robert, 2008, Pac Symp Biocomput, P652
   Leaman R, 2013, BIOINFORMATICS, V29, P2909, DOI 10.1093/bioinformatics/btt474
   Lei JB, 2014, J AM MED INFORM ASSN, V21, P808, DOI 10.1136/amiajnl-2013-002381
   Li J, 2022, IEEE T KNOWL DATA EN, V34, P50, DOI [10.1109/TKDE.2020.2981314, 10.1109/TKDE.2020.3038670]
   Lu JS, 2016, ADV NEUR IN, V29
   Luong MT, 2015, EFFECTIVE APPROACHES
   Marttinen P., 2020, ARXIV PREPRINT ARXIV
   Mujtaba G, 2019, EXPERT SYST APPL, V116, P494, DOI 10.1016/j.eswa.2018.09.034
   Nadeau D, 2007, LINGUIST INVESTIG, V30, P3
   Oliver NM, 2000, IEEE T PATTERN ANAL, V22, P831, DOI 10.1109/34.868684
   Palshikar GK, 2013, BIOINFORMATICS: CONCEPTS, METHODOLOGIES, TOOLS, AND APPLICATIONS, P400, DOI 10.4018/978-1-4666-3604-0.ch022
   Pan SJ, 2010, IEEE T KNOWL DATA EN, V22, P1345, DOI 10.1109/TKDE.2009.191
   Parlak B., 2018, DEV ADV INTELLIGENT, V718, P269
   Settles B., 2009, 1648 U WISC
   Thangaraj M., 2018, Interdisciplinary Journal of Information, Knowledge, and Management, V13, P117, DOI 10.28945/4066
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2000, DOI 10.1145/3308558.3313411
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang WH, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P189, DOI 10.18653/v1/P17-1018
   Wu X, 2018, LECT NOTES COMPUT SC, V11233, P453, DOI 10.1007/978-3-030-02922-7_31
   Xiong CY, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1271, DOI 10.1145/3038912.3052558
   Xu K., 2018, AISC, V639, P355, DOI [10.1007/978-3-319-64861-3_33, DOI 10.1007/978-3-319-64861-3_33, 10.1007/978-3-319-64861, DOI 10.1007/978-3-319-64861]
   Yadav V., 2019, ARXIV PREPRINT ARXIV
   Zhang SD, 2013, J BIOMED INFORM, V46, P1088, DOI 10.1016/j.jbi.2013.08.004
   Zhou P., 2016, ARXIV PREPRINT ARXIV
   Zitouni Imed, 2014, NATURAL LANGUAGE PRO
NR 49
TC 1
Z9 1
U1 8
U2 15
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2021
VL 41
IS 2
BP 3289
EP 3299
DI 10.3233/JIFS-210599
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA US3NM
UT WOS:000697340200053
DA 2022-08-23
ER

PT J
AU Farjana, E
   Kertkeidkachorn, N
   Ichise, R
AF Farjana, Esrat
   Kertkeidkachorn, Natthawut
   Ichise, Ryutaro
TI Competent Triple Identification for Knowledge Graph Completion under the
   Open-World Assumption
SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
LA English
DT Article
DE knowledge extraction; information retrieval; competent triple; knowledge
   graph completion
AB The usefulness and usability of existing knowledge graphs (KGs) are mostly limited because of the incompleteness of knowledge compared to the growing number of facts about the real world. Most existing ontology-based KG completion methods are based on the closed-world assumption, where KGs are fixed. In these methods, entities and relations are defined, and new entity information cannot be easily added. In contrast, in open-world assumptions, entities and relations are not previously defined. Thus there is a vast scope to find new entity information. Despite this, knowledge acquisition under the open-world assumption is challenging because most available knowledge is in a noisy unstructured text format. Nevertheless, Open Information Extraction (OpenIE) systems can extract triples, namely (head text; relation text; tail text), from raw text without any prespecified vocabulary. Such triples contain noisy information that is not essential for KGs. Therefore, to use such triples for the KG completion task, it is necessary to identify competent triples for KGs from the extracted triple set. Here, competent triples are the triples that can contribute to add new information to the existing KGs. In this paper, we propose the Competent Triple Identification (CTID) model for KGs. We also propose two types of feature, namely syntax- and semantic-based features, to identify competent triples from a triple set extracted by a state-of-the-art OpenIE system. We investigate both types of feature and test their effectiveness. It is found that the performance of the proposed features is about 20% better compared to that of the REVERB system in identifying competent triples.
C1 [Farjana, Esrat; Ichise, Ryutaro] Grad Univ Adv Studies, SOKENDAI, Tokyo 1018430, Japan.
   [Kertkeidkachorn, Natthawut] Japan Adv Inst Sci & Technol, Nomi 9231292, Japan.
   [Ichise, Ryutaro] Natl Inst Informat, Tokyo 1018430, Japan.
   [Ichise, Ryutaro] Natl Inst Adv Ind Sci & Technol, Tokyo 1350064, Japan.
RP Farjana, E (通讯作者)，Grad Univ Adv Studies, SOKENDAI, Tokyo 1018430, Japan.
EM esrat_farjana@nii.ac.jp
FU New Energy and Industrial Technology Development Organization (NEDO)
FX This work was partially supported by the New Energy and Industrial
   Technology Development Organization (NEDO).
CR Alani H, 2003, IEEE INTELL SYST, V18, P14, DOI 10.1109/MIS.2003.1179189
   Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   Augenstein Isabelle, 2012, The Semantic Web: Research and Applications. Proceedings 9th Extended Semantic Web Conference (ESWC 2012), P210, DOI 10.1007/978-3-642-30284-8_21
   Banko M, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2670
   Bart R., 2012, P 2012 JOINT C EMP M, P523
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Ebisu T., 2019, P NAACL, P988, DOI [10.18653/v1/N19-1104, DOI 10.18653/V1/N19-1104]
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   Galarraga L.A., 2013, P 22 INT C WORLD WID, P413, DOI DOI 10.1145/2488388.2488425
   Gashteovski K., 2017, MINIE MINIMIZING FAC, DOI DOI 10.18653/V1/D17-1278
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Kertkeidkachorn N., 2017, AAAI WORKSH, P743
   Kriz V, 2014, LECT NOTES ARTIF INT, V8856, P113, DOI 10.1007/978-3-319-13647-9_13
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Luciano DC., 2013, CLAUSIE CLAUSE BASED, P355, DOI DOI 10.1145/2488388.2488420
   Mausam J. Christensen, 2011, P 6 INT C KNOWL CAPT, P113
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Pal H., 2016, P 5 WORKSH AUT KNOWL, P35, DOI DOI 10.18653/V1/W16-1307
   Paulheim H, 2014, INT J SEMANT WEB INF, V10, P63, DOI 10.4018/ijswis.2014040104
   Pennington J., 2014, EMNLP, P1532
   Tanwar M, 2015, 2015 4TH INTERNATIONAL CONFERENCE ON RELIABILITY, INFOCOM TECHNOLOGIES AND OPTIMIZATION (ICRITO) (TRENDS AND FUTURE DIRECTIONS), DOI 10.1109/ICRITO.2015.7359270
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu F, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P118
   Yates A., 2007, P HUM LANG TECHN ANN, P25
   Yih WT, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2, P201
   Zhao LH, 2017, LECT NOTES COMPUT SC, V10675, P164, DOI 10.1007/978-3-319-70682-5_11
NR 30
TC 0
Z9 0
U1 4
U2 4
PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATION ENGINEERS
PI TOKYO
PA KIKAI-SHINKO-KAIKAN BLDG, 3-5-8, SHIBA-KOEN, MINATO-KU, TOKYO, 105-0011,
   JAPAN
SN 1745-1361
J9 IEICE T INF SYST
JI IEICE Trans. Inf. Syst.
PD MAR
PY 2022
VL E105D
IS 3
BP 646
EP 655
DI 10.1587/transinf.2021EDP7148
PG 10
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZK6RL
UT WOS:000763115000022
OA gold
DA 2022-08-23
ER

PT J
AU Lv, K
   Gao, CX
   Si, JK
   Feng, HC
   Cao, WP
AF Lv, Ke
   Gao, Caixia
   Si, Jikai
   Feng, Haichao
   Cao, Wenping
TI Fault coil location of inter-turn short-circuit for direct-drive
   permanent magnet synchronous motor using knowledge graph
SO IET ELECTRIC POWER APPLICATIONS
LA English
DT Article
DE permanent magnet motors; synchronous motors; fault diagnosis; stators;
   fault location; fault coil location; direct-drive permanent magnet
   synchronous motor; inter-turn short-circuit fault; DDPMSM; fault
   location technology; maintenance time; intelligent fault locating
   system; knowledge graph-based diagnostic tool; faulty coil; steady-state
   stator current signals; fault model; multiple branches; employed motor;
   fault indicator; KG system; automatic location tool; operating
   conditions; shorted turns; branch turns
ID DIAGNOSIS; MACHINES; SELECTION; ROTOR; TOOL
AB Inter-turn short-circuit fault (ISF) degrades its reliability and may cause serious catastrophes for direct-drive permanent magnet synchronous motor (DDPMSM). Fault location technology can reduce maintenance time, increase the mean time between failure (MTBF), and then improve the reliability of DDPMSM. Hence, an intelligent fault locating system for DDPMSM is proposed in this paper. This system proposes a knowledge graph (KG) based diagnostic tool for detection and location of the fault coil. First, the fault model of the DDPMSM with multiple branches parallel winding is established, which is used to analyze the fault characteristics of motor. Second, the BDC and BRC are proposed as the fault indicator. The effectiveness and robustness of fault indicator are analyzed. Then, the KG system are designed and established according to the relationship between fault indicator and location of fault coil. Finally, the system is tested by data under different fault and operation conditions. The test results showed that the proposed fault locating system can detect and locate the fault coil in early stage. The minimum ratio of shorted turns to branch turns that can be detected is 0.52%. The minimum ratio of shorted turns to branch turns that can be located is 6.25%.
C1 [Lv, Ke; Gao, Caixia; Feng, Haichao] Henan Polytech Univ, Sch Elect Engn & Automat, Jiaozuo, Henan, Peoples R China.
   [Si, Jikai] Zhengzhou Univ, Coll Elect Engn, Zhengzhou, Henan, Peoples R China.
   [Cao, Wenping] Aston Univ, Sch Engn & Appl Sci, Birmingham B47ET, W Midlands, England.
RP Gao, CX (通讯作者)，Henan Polytech Univ, Sch Elect Engn & Automat, Jiaozuo, Henan, Peoples R China.
EM gcx@hpu.edu.cn
FU Natural Science Foundation of China [51777060]; Key Science and
   Technology National Program of Henan Province [192102210230,
   202102210099]; Key Scientific Research Projects of Higher Education
   Institutions in Henan Province [20A470004]
FX This work was supported in part by the Natural Science Foundation of
   China under grant no. 51777060, in part by the Key Science and
   Technology National Program of Henan Province (grant nos. 192102210230
   and 202102210099), and in part by the Key Scientific Research Projects
   of Higher Education Institutions in Henan Province (grant no.
   20A470004). The authors would like to thank the ShuHua Wang from
   YOKOKAWA robot (Shenzhen) Ltd for manufacturing the prototype.
CR Vinayak BA, 2020, IET ELECTR POWER APP, V14, P82, DOI 10.1049/iet-epa.2019.0273
   Barzegaran M, 2013, IEEE T ENERGY CONVER, V28, P1064, DOI 10.1109/TEC.2013.2281325
   Bouzid MB, 2017, IEEE T IND APPL, V53, P2762, DOI 10.1109/TIA.2017.2661841
   Bouzid MBK, 2008, IEEE T IND ELECTRON, V55, P4277, DOI 10.1109/TIE.2008.2004667
   Bruzzese C, 2014, IEEE T IND ELECTRON, V61, P4206, DOI 10.1109/TIE.2013.2284554
   Ceban A, 2012, IEEE T IND ELECTRON, V59, P2082, DOI 10.1109/TIE.2011.2163285
   Choi G, 2016, IEEE T IND APPL, V52, P2176, DOI 10.1109/TIA.2016.2518992
   Da Y, 2013, IEEE T POWER ELECTR, V28, P4104, DOI 10.1109/TPEL.2012.2227808
   Haroun S, 2018, IET ELECTR POWER APP, V12, P339, DOI 10.1049/iet-epa.2017.0457
   Henao H, 2014, IEEE IND ELECTRON M, V8, P31, DOI 10.1109/MIE.2013.2287651
   Ince T, 2016, IEEE T IND ELECTRON, V63, P7067, DOI 10.1109/TIE.2016.2582729
   Liu XY, 2019, IEEE SENS J, V19, P7884, DOI 10.1109/JSEN.2019.2918018
   Malloy AC, 2015, IEEE T ENERGY CONVER, V30, P323, DOI 10.1109/TEC.2014.2353133
   Maraaba LS, 2019, IEEE ACCESS, V7, P88622, DOI 10.1109/ACCESS.2019.2923812
   Maraaba LS, 2019, IEEE ACCESS, V7, P89014, DOI 10.1109/ACCESS.2019.2923746
   Maraaba LS, 2019, IEEE T ENERGY CONVER, V34, P1213, DOI 10.1109/TEC.2018.2886137
   Mirafzal B, 2006, IEEE T ENERGY CONVER, V21, P871, DOI 10.1109/TEC.2005.853767
   Moon S, 2018, IEEE T ENERGY CONVER, V33, P190, DOI 10.1109/TEC.2017.2726142
   Nerg J, 2014, IEEE T IND ELECTRON, V61, P4286, DOI 10.1109/TIE.2013.2248340
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Qi Y, 2019, IEEE T IND ELECTRON, V66, P7260, DOI 10.1109/TIE.2018.2879281
   Rezaee-Alam F, 2017, IEEE T ENERGY CONVER, V32, P146, DOI 10.1109/TEC.2016.2623566
   Riera-Guasp M, 2015, IEEE T IND ELECTRON, V62, P1746, DOI 10.1109/TIE.2014.2375853
   Tong WM, 2018, IEEE T IND ELECTRON, V65, P9388, DOI 10.1109/TIE.2018.2823698
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Xu S, 2019, IET ELECTR POWER APP, V13, P1553, DOI 10.1049/iet-epa.2019.0087
   Zafarani M, 2018, IEEE J EM SEL TOP P, V6, P2173, DOI 10.1109/JESTPE.2018.2811538
   Zafarani M, 2016, IEEE T IND APPL, V52, P1331, DOI 10.1109/TIA.2015.2487440
   Zarifeh N, 2019, IEEE ACCESS, V7, P108781, DOI 10.1109/ACCESS.2019.2933427
   Zeng C, 2018, IET ELECTR POWER APP, V12, P837, DOI 10.1049/iet-epa.2017.0865
   Zhang B, 2011, IEEE T IND ELECTRON, V58, P2011, DOI 10.1109/TIE.2010.2058072
   Zielichowski M, 2007, IEEE T POWER DELIVER, V22, P813, DOI 10.1109/TPWRD.2007.893358
NR 32
TC 5
Z9 5
U1 12
U2 56
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1751-8660
EI 1751-8679
J9 IET ELECTR POWER APP
JI IET Electr. Power Appl.
PD SEP
PY 2020
VL 14
IS 9
BP 1712
EP 1721
DI 10.1049/iet-epa.2020.0123
PG 10
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA NJ7SR
UT WOS:000566243500023
DA 2022-08-23
ER

PT J
AU Khan, K
   Ahmad, W
   Amin, MN
   Ahmad, A
AF Khan, Kaffayatullah
   Ahmad, Waqas
   Amin, Muhammad Nasir
   Ahmad, Ayaz
TI A Systematic Review of the Research Development on the Application of
   Machine Learning for Concrete
SO MATERIALS
LA English
DT Review
DE machine learning; concrete; prediction; modeling; bibliographic data;
   scientometric analysis
ID HIGH-STRENGTH CONCRETE; COMPRESSIVE STRENGTH; PREDICTION; NETWORKS;
   SCIENCE; DESIGN; TRENDS; IMPACT
AB Research on the applications of new techniques such as machine learning is advancing rapidly. Machine learning methods are being employed to predict the characteristics of various kinds of concrete such as conventional concrete, recycled aggregate concrete, geopolymer concrete, fiber-reinforced concrete, etc. In this study, a scientometric-based review on machine learning applications for concrete was performed in order to evaluate the crucial characteristics of the literature. Typical review studies are limited in their capacity to link divergent portions of the literature systematically and precisely. Knowledge mapping, co-citation, and co-occurrence are among the most challenging aspects of innovative studies. The Scopus database was chosen for searching for and retrieving the data required to achieve the study's aims. During the data analysis, the relevant sources of publications, relevant keywords, productive writers based on publications and citations, top articles based on citations received, and regions actively engaged in research into machine learning applications for concrete were identified. The citation, bibliographic, abstract, keyword, funding, and other data from 1367 relevant documents were retrieved and analyzed using the VOSviewer software tool. The application of machine learning in the construction sector will be advantageous in terms of economy, time-saving, and reduced requirement for effort. This study can aid researchers in building joint endeavors and exchanging innovative ideas and methods, due to the statistical and graphical portrayal of participating authors and countries.
C1 [Khan, Kaffayatullah; Amin, Muhammad Nasir] King Faisal Univ, Coll Engn, Dept Civil & Environm Engn, Al Hasa 31982, Saudi Arabia.
   [Ahmad, Waqas] COMSATS Univ Islamabad, Dept Civil Engn, Abbottabad 22060, Pakistan.
   [Ahmad, Ayaz] Natl Univ Ireland Galway, Coll Sci & Engn, MaREI Ctr, Ryan Inst, Galway H91 HX31, Ireland.
   [Ahmad, Ayaz] Natl Univ Ireland Galway, Coll Sci & Engn, Sch Engn, Galway H91 HX31, Ireland.
RP Khan, K (通讯作者)，King Faisal Univ, Coll Engn, Dept Civil & Environm Engn, Al Hasa 31982, Saudi Arabia.
EM kkhan@kfu.edu.sa; waqasahmad@cuiatd.edu.pk; mgadir@kfu.edu.sa;
   a.ahmad8@nuigalway.ie
RI ; Amin, Muhammad Nasir/D-2089-2014
OI Ahmad, Waqas/0000-0002-1668-7607; /0000-0001-7994-4642; Amin, Muhammad
   Nasir/0000-0001-6524-4389
FU Deanship of Scientific Research, Vice Presidency for Graduate Studies
   and Scientific Research, King Faisal University, Saudi Arabia [GRANT657]
FX This work was supported by the Deanship of Scientific Research, Vice
   Presidency for Graduate Studies and Scientific Research, King Faisal
   University, Saudi Arabia (Project No. GRANT657).
CR Adeli H, 2001, COMPUT-AIDED CIV INF, V16, P126, DOI 10.1111/0885-9507.00219
   Afgan S, 2021, CONSTR BUILD MATER, V278, DOI 10.1016/j.conbuildmat.2021.122344
   Ahmad A, 2022, CASE STUD CONSTR MAT, V16, DOI 10.1016/j.cscm.2021.e00840
   Ahmad A, 2021, BUILDINGS-BASEL, V11, DOI 10.3390/buildings11080324
   Ahmad A, 2021, MATERIALS, V14, DOI 10.3390/ma14092297
   Ahmad A, 2021, MATERIALS, V14, DOI 10.3390/ma14040794
   Ahmad W, 2021, MATERIALS, V14, DOI 10.3390/ma14195762
   Ahmad W, 2021, CASE STUD CONSTR MAT, V15, DOI 10.1016/j.cscm.2021.e00683
   Ahmad W, 2021, MATERIALS, V14, DOI 10.3390/ma14071745
   Ahmed HU, 2022, MATERIALS, V15, DOI 10.3390/ma15051868
   Aldwaik M, 2014, STRUCT MULTIDISCIP O, V50, P899, DOI 10.1007/s00158-014-1148-1
   Alghamdi SJ, 2022, MATERIALS, V15, DOI 10.3390/ma15051950
   Alyousef R, 2021, J CLEAN PROD, V329, DOI 10.1016/j.jclepro.2021.129736
   Amezquita-Sanchez JP, 2016, SCI IRAN, V23, P2417, DOI 10.24200/sci.2016.2301
   Amin MN, 2022, MATERIALS, V15, DOI 10.3390/ma15103478
   Amin MN, 2022, MATERIALS, V15, DOI 10.3390/ma15103431
   Band SS, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12213568
   Band SS, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20195609
   Ben Chaabene W, 2020, CONSTR BUILD MATER, V260, DOI 10.1016/j.conbuildmat.2020.119889
   Bergman EML, 2012, J ACAD LIBR, V38, P370, DOI 10.1016/j.acalib.2012.08.002
   Cakiroglu C, 2022, MATERIALS, V15, DOI 10.3390/ma15082742
   Cao ML, 2020, STRUCT CONCRETE, DOI 10.1002/suco.201900228
   Cao RC, 2022, MATERIALS, V15, DOI 10.3390/ma15072400
   Chadegani A.A., 2013, ASIAN SOC SCI, V9, P18, DOI 10.5539/ass.v9n5p18
   Chahnasir ES, 2018, SMART STRUCT SYST, V22, P413, DOI 10.12989/sss.2018.22.4.413
   Chang QY, 2022, CASE STUD CONSTR MAT, V16, DOI 10.1016/j.cscm.2022.e00907
   Cheng MY, 2014, ENG APPL ARTIF INTEL, V29, P104, DOI 10.1016/j.engappai.2013.11.014
   Chou JS, 2011, J COMPUT CIVIL ENG, V25, P242, DOI 10.1061/(ASCE)CP.1943-5487.0000088
   Darko A, 2019, BUILD ENVIRON, V149, P501, DOI 10.1016/j.buildenv.2018.12.059
   Darko A, 2017, HABITAT INT, V60, P34, DOI 10.1016/j.habitatint.2016.12.007
   Deng FM, 2018, CONSTR BUILD MATER, V175, P562, DOI 10.1016/j.conbuildmat.2018.04.169
   Emadi M, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12142234
   Farooq F, 2021, J CLEAN PROD, V292, DOI 10.1016/j.jclepro.2021.126032
   Farooq F, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10207330
   Ghazvinei PT, 2018, ENG APPL COMP FLUID, V12, P738, DOI 10.1080/19942060.2018.1526119
   Golafshani EM, 2020, CONSTR BUILD MATER, V232, DOI 10.1016/j.conbuildmat.2019.117266
   Huang SM, 2022, INT J ENV RES PUB HE, V19, DOI 10.3390/ijerph19084556
   Iftikhar B, 2022, J CLEAN PROD, V348, DOI 10.1016/j.jclepro.2022.131285
   Javed MF, 2020, CRYSTALS, V10, DOI 10.3390/cryst10090741
   Jin RY, 2018, J CLEAN PROD, V202, P1202, DOI 10.1016/j.jclepro.2018.08.195
   Jordan MI, 2015, SCIENCE, V349, P255, DOI 10.1126/science.aaa8415
   Kaya Keles M, 2017, TEH GLAS, V11, P128
   Khambra G., 2021, MATER TODAY
   Khan K, 2022, MATERIALS, V15, DOI 10.3390/ma15103430
   Khan M, 2022, CASE STUD CONSTR MAT, V16, DOI 10.1016/j.cscm.2022.e00941
   Khan M, 2022, PERIOD POLYTECH-CIV, V66, P344, DOI 10.3311/PPci.18965
   Khan M, 2021, CONSTR BUILD MATER, V300, DOI 10.1016/j.conbuildmat.2021.124046
   Khan MR, 2021, FATIGUE FRACT ENG M, V44, P2135, DOI 10.1111/ffe.13483
   Khan M, 2022, FIRE MATER, V46, P205, DOI 10.1002/fam.2968
   Khan M, 2020, CONSTR BUILD MATER, V244, DOI 10.1016/j.conbuildmat.2020.118382
   Khan M, 2019, CONSTR BUILD MATER, V203, P174, DOI 10.1016/j.conbuildmat.2019.01.103
   Khan M, 2018, CONSTR BUILD MATER, V182, P703, DOI 10.1016/j.conbuildmat.2018.06.150
   Kicinger R, 2005, COMPUT STRUCT, V83, P1943, DOI 10.1016/j.compstruc.2005.03.002
   Kim K, 2022, MATERIALS, V15, DOI 10.3390/ma15031045
   Lei XX, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12152478
   Li GL, 2022, MATERIALS, V15, DOI 10.3390/ma15072664
   Li XM, 2022, J BUILD ENG, V45, DOI 10.1016/j.jobe.2021.103447
   Liao TW, 2011, AUTOMAT CONSTR, V20, P491, DOI 10.1016/j.autcon.2010.12.006
   Libbrecht MW, 2015, NAT REV GENET, V16, P321, DOI 10.1038/nrg3920
   Liu T, 2022, AIN SHAMS ENG J, V13, DOI 10.1016/j.asej.2021.08.006
   Lu PZ, 2012, MATH PROBL ENG, V2012, DOI 10.1155/2012/145974
   Mahdavinejad MS, 2018, DIGIT COMMUN NETW, V4, P161, DOI 10.1016/j.dcan.2017.10.002
   Mardani A, 2015, EXPERT SYST APPL, V42, P4126, DOI 10.1016/j.eswa.2015.01.003
   Markoulli MP, 2017, HUM RESOUR MANAGE R, V27, P367, DOI 10.1016/j.hrmr.2016.10.001
   Marsland S., 2011, MACHINE LEARNING AN, DOI [10.1201/9781420067194, DOI 10.1201/9781420067194]
   Meho LI, 2019, J INFORMETR, V13, P419, DOI 10.1016/j.joi.2019.02.006
   Mesfin WM, 2021, MATERIALS, V14, DOI 10.3390/ma14216311
   Mosavi A, 2021, WATER RESOUR MANAG, V35, P23, DOI 10.1007/s11269-020-02704-3
   Nafees A, 2022, POLYMERS-BASEL, V14, DOI 10.3390/polym14081583
   Nafees A, 2022, POLYMERS-BASEL, V14, DOI 10.3390/polym14010030
   Nafees A, 2021, MATERIALS, V14, DOI 10.3390/ma14247531
   Nasiri S, 2017, ENG FAIL ANAL, V81, P270, DOI 10.1016/j.engfailanal.2017.07.011
   Nunez I, 2021, CONSTR BUILD MATER, V310, DOI 10.1016/j.conbuildmat.2021.125279
   Nunez I, 2020, MATERIALS, V13, DOI 10.3390/ma13194331
   Oraee M, 2017, INT J PROJ MANAG, V35, P1288, DOI 10.1016/j.ijproman.2017.07.001
   Park JY, 2018, RENEW SUST ENERG REV, V82, P2664, DOI 10.1016/j.rser.2017.09.102
   Popov VV, 2022, MATERIALS, V15, DOI 10.3390/ma15062140
   Prasanna P, 2016, IEEE T AUTOM SCI ENG, V13, P591, DOI 10.1109/TASE.2014.2354314
   Qasem SN, 2019, WATER-SUI, V11, DOI 10.3390/w11030582
   Rafiei MH, 2017, STRUCT DES TALL SPEC, V26, DOI 10.1002/tal.1400
   Ramprasad R, 2017, NPJ COMPUT MATER, V3, DOI 10.1038/s41524-017-0056-5
   Sadeghzadeh M, 2020, NANOMATERIALS-BASEL, V10, DOI 10.3390/nano10040697
   Shabani S, 2020, ATMOSPHERE-BASEL, V11, DOI 10.3390/atmos11010066
   Shahin MA, 2016, GEOSCI FRONT, V7, P33, DOI 10.1016/j.gsf.2014.10.002
   Shang MJ, 2022, MATERIALS, V15, DOI 10.3390/ma15020647
   Shen ZJ, 2022, MATERIALS, V15, DOI 10.3390/ma15103523
   Shin HK, 2020, MATERIALS, V13, DOI 10.3390/ma13235549
   Song HW, 2021, CONSTR BUILD MATER, V308, DOI 10.1016/j.conbuildmat.2021.125021
   Su HN, 2010, SCIENTOMETRICS, V85, P65, DOI 10.1007/s11192-010-0259-8
   Sun L, 2021, ENG COMPUT-GERMANY, V37, P1133, DOI 10.1007/s00366-019-00875-1
   Taffese WZ, 2017, AUTOMAT CONSTR, V77, P1, DOI 10.1016/j.autcon.2017.01.016
   Thomas BS, 2021, MATER TODAY SUSTAIN, V15, DOI 10.1016/j.mtsust.2021.100086
   Tomczak K, 2021, MATERIALS, V14, DOI 10.3390/ma14154211
   Torabi M, 2019, ENVIRON PROG SUSTAIN, V38, P66, DOI 10.1002/ep.12934
   Udomsap AD, 2020, J CLEAN PROD, V254, DOI 10.1016/j.jclepro.2020.120073
   Ullah HS, 2022, MATERIALS, V15, DOI 10.3390/ma15093166
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Wang QC, 2022, POLYMERS-BASEL, V14, DOI 10.3390/polym14061074
   Wang W, 2020, MATERIALS, V13, DOI 10.3390/ma13143226
   Wuni IY, 2019, ENERG BUILDINGS, V190, P69, DOI 10.1016/j.enbuild.2019.02.010
   Xiao X, 2019, ENERGIES, V12, DOI 10.3390/en12153011
   Xu YD, 2018, CONSTR BUILD MATER, V171, P291, DOI 10.1016/j.conbuildmat.2018.03.147
   Xu Y, 2021, MATERIALS, V14, DOI 10.3390/ma14227034
   Yang HH, 2022, CASE STUD CONSTR MAT, V16, DOI 10.1016/j.cscm.2021.e00830
   Yang ZH, 2021, MATERIALS, V14, DOI 10.3390/ma14154286
   Yaseen ZM, 2018, ADV ENG SOFTW, V115, P112, DOI 10.1016/j.advengsoft.2017.09.004
   Yu F., 2018, J SCI LIBRARIANSH, V7, P4, DOI [10.7191/jeslib.2018.1123, DOI 10.7191/JESLIB.2018.1123]
   Yuan XZ, 2022, MATERIALS, V15, DOI 10.3390/ma15082823
   Zakka WP, 2021, J CLEAN PROD, V280, DOI 10.1016/j.jclepro.2020.124353
   Zhu YZ, 2022, CRYSTALS, V12, DOI 10.3390/cryst12050569
   Ziolkowski P, 2021, MATERIALS, V14, DOI 10.3390/ma14071661
   Zou Y, 2022, GELS-BASEL, V8, DOI 10.3390/gels8050271
   Zuo J, 2014, RENEW SUST ENERG REV, V30, P271, DOI 10.1016/j.rser.2013.10.021
NR 113
TC 0
Z9 0
U1 6
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1944
J9 MATERIALS
JI Materials
PD JUL
PY 2022
VL 15
IS 13
AR 4512
DI 10.3390/ma15134512
PG 23
WC Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy &
   Metallurgical Engineering; Physics, Applied; Physics, Condensed Matter
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Materials Science; Metallurgy & Metallurgical Engineering;
   Physics
GA 2V0RG
UT WOS:000823559800001
PM 35806636
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Hubsch, C
   Spirig, R
   Kleinknecht-Dolf, M
AF Huebsch, Christine
   Spirig, Rebecca
   Kleinknecht-Dolf, Michael
TI Pflegefachpersonen denken uber eine Definition von durchgefuhrter und
   versaumter Pflege" nach Eine qualitative Studie
SO PFLEGE
LA English
DT Article
DE nursing interventions; missed nursing care; delivery of care; focus
   groups; hospitals
ID NURSING-CARE
AB Nurses reflect upon a definition of performed and missed nursing care" - A qualitative study Abstract. Background: It is known that necessary nursing interventions are repeatedly omitted respectively only carried out partially or at a time that is not appropriate. In the literature, this is referred to as "missed nursing care". In the German-speaking region, there is no critically reflected definition of what is meant by "performed and missed nursing care", using qualitative methods. Therefore, we developed one in a three-phase study. Aim: The aim of phase 2, which is the focus of this article, was to critically reflect on the literature-based definition developed in phase 1 with nurses working in acute care hospitals in German-speaking Switzerland in order to specify it in phase 3. Methods: We conducted two focus group interviews with a total of 17 participants. We provided the participants with the preliminary definition and asked them for their reflections. We analysed the interviews content-analytically with the knowledge mapping technique as well as according to Mayring. Results: The participating nurses described that the contents of the definition were relevant and comprehensible for them. However, they also noticed differences from their understanding and gave indications for refining the definition, especially in relation to terms that include a time-related aspect. Conclusions: By involving nurses in the development of the definition, there is now a practical description of "performed and missed nursing care" that can be useful for quality assurance and development of care.
C1 [Huebsch, Christine] Univ Spital Zurich, Zentrum Klin Pflegewissensch, Ramistr 100, CH-8091 Zurich, Switzerland.
   [Huebsch, Christine; Spirig, Rebecca] Univ Witten Herdecke, Dept Pflegewissensch, Fak Gesundheit, Witten, Germany.
   [Spirig, Rebecca] Univ Basel, Inst Pflegewissensch, Basel, Switzerland.
   [Kleinknecht-Dolf, Michael] Univ Spital Zurich, Direkt Pflege & MTTB, Zurich, Switzerland.
RP Hubsch, C (通讯作者)，Univ Spital Zurich, Zentrum Klin Pflegewissensch, Ramistr 100, CH-8091 Zurich, Switzerland.
EM christine.huebsch@usz.ch
CR Aiken L H, 2001, J Health Hum Serv Adm, V23, P416
   Aiken L. H., 2001, POLICY POLITICS NURS, V2, P255, DOI DOI 10.1177/152715440100200402
   [Anonymous], 1993, Qual Health Care, V2, P117
   Bottega M, 2020, BMC NURS, V19, DOI 10.1186/s12912-020-00486-y
   Creswell JW, 2007, COUNS PSYCHOL, V35, P236, DOI 10.1177/0011000006287390
   DIWAN S, 1995, J APPL GERONTOL, V14, P47, DOI 10.1177/073346489501400104
   Finlayson Marcia, 2004, Occup Ther Health Care, V17, P5, DOI 10.1080/J003v17n03_02
   Hendry C, 2004, J ADV NURS, V47, P427, DOI 10.1111/j.1365-2648.2004.03120.x
   Hubsch C, 2020, J NURS MANAGE, V28, P2048, DOI 10.1111/jonm.13026
   Jones TL, 2015, INT J NURS STUD, V52, P1121, DOI 10.1016/j.ijnurstu.2015.02.012
   Kalisch, 2016, ERRORS OMISSION MISS, V7
   Kalisch BJ, 2009, J ADV NURS, V65, P1509, DOI 10.1111/j.1365-2648.2009.05027.x
   Krueger RA, 2009, FOCUS GROUPS PRACTIC, V4
   Kuckartz U., 2014, MIXED METHODS METHOD
   Lee JL, 1999, HEALTH SERV RES, V34, P1011
   Lucero RJ, 2009, J ADV NURS, V65, P2299, DOI 10.1111/j.1365-2648.2009.05090.x
   Mayring P., 2015, QUALITATIVE INHALTSA, V12
   Papastavrou E, 2012, HLTH SCI J, V6, P185
   Papastavrou E, 2014, NURS ETHICS, V21, P583, DOI 10.1177/0969733013513210
   Pelz C, 2004, FORUM QUALITATIVE SO, V5, DOI [10.17169/fqs-5.2.601, DOI 10.17169/FQS-5.2.601]
   QSR International Pty Ltd, 2018, NVIVO PROQ DAT AN SO
   Rettke H, 2018, NURS RES, V67, P490, DOI 10.1097/NNR.0000000000000307
   Sanmartin C, 2008, INT J TECHNOL ASSESS, V24, P342, DOI 10.1017/S0266462308080458
   Schubert, 2019, PFLEGE GESELLSCHAFT, V24, P293
   Schubert M, 2007, NURS RES, V56, P416, DOI 10.1097/01.NNR.0000299853.52429.62
   Schubert M, 2013, INT J NURS STUD, V50, P230, DOI 10.1016/j.ijnurstu.2012.09.016
   Sochalski J, 2004, MED CARE, V42, P67, DOI 10.1097/01.mlr.0000109127.76128.aa
   Spichiger Eilsabeth, 2006, Pflege, V19, P45, DOI 10.1024/1012-5302.19.1.45
   Spirig Rebecca, 2014, Ger Med Sci, V12, pDoc07, DOI 10.3205/000192
   Vail EF, 1999, INFORM SYST MANAGE, V16, P16, DOI 10.1201/1078/43189.16.4.19990901/31199.3
   Zelenikova R, 2019, J ADV NURS, V75, P2995, DOI 10.1111/jan.14189
NR 31
TC 1
Z9 1
U1 2
U2 2
PU HOGREFE AG-HOGREFE AG SUISSE
PI BERN
PA LANGGASSSTRASSE 76, BERN, SWITZERLAND
SN 1012-5302
EI 1664-283X
J9 PFLEGE
JI Pflege
PD SEP
PY 2021
VL 34
IS 5
BP 263
EP 273
DI 10.1024/1012-5302/a000827
EA AUG 2021
PG 11
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA UW7AU
UT WOS:000690380900001
PM 34435886
DA 2022-08-23
ER

PT J
AU Zhou, CH
   Ye, SH
   Ni, L
   Yao, Y
AF Zhou, Chaohui
   Ye, Shenghui
   Ni, Long
   Yao, Yang
TI A review of heat pump research in China using bibliometric methods
SO JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
LA English
DT Review
ID CIRCUIT OUTDOOR COIL; LIQUID DESICCANT DEHUMIDIFICATION; HOT GAS BYPASS;
   DEFROSTING PERFORMANCE; ENERGY-STORAGE; MELTED FROST; THERMAL
   PERFORMANCE; DYNAMIC PERFORMANCE; ELECTRIC VEHICLES; EMERGING TRENDS
AB The analysis of big data can reveal the macroscopic situation of a certain field from a relatively objective angle and indicate the potential development trends in the future. This study provides a novel approach to review the development of heat pump research from 1994 to 2020 in China using the bibliometric method which belongs to big data analysis. More than 15 000 references were selected, 2716 of which are from the Web of Science core collection and 13 183 of which are from China National Knowledge Infrastructure, including articles and reviews. Their knowledge maps and co-citation analysis were performed. Different major trends were identified for 15 clusters. Results showed that ground source heat pump (GSHP), air source heat pump (ASHP)-on frost, ASHP on heating, and solar-assisted heat pump are the most active research fronts. The studies on the GSHP account for the largest portion, reaching about 21%. Ground heat exchanger and thermal imbalance are the focus points. ASHP is the hottest research direction in recent years, mainly focusing on the technologies of frosting/defrosting and heating. The studies on defrosting experience a transition from the frosting/defrosting process and thermal energy storage for defrosting to frost-free ASHP. Besides, direct-expansion solar-assisted heat pump water heater was becoming more and more active. Finally, industrial heat pump showed great development potential.
C1 [Zhou, Chaohui; Ye, Shenghui; Ni, Long; Yao, Yang] Harbin Inst Technol, Sch Architecture, Harbin 150090, Peoples R China.
   [Zhou, Chaohui; Ye, Shenghui; Ni, Long; Yao, Yang] Minist Ind & Informat Technol, Key Lab Cold Reg Urban & Rural Human Settlement E, Harbin, Peoples R China.
RP Ni, L (通讯作者)，Harbin Inst Technol, Sch Architecture, Harbin 150090, Peoples R China.; Ni, L (通讯作者)，Minist Ind & Informat Technol, Key Lab Cold Reg Urban & Rural Human Settlement E, Harbin, Peoples R China.
EM nilonggn@163.com
OI Ni, Long/0000-0002-4968-6790; Ye, Shenghui/0000-0002-5401-7209
FU National Key R&D Program of China [2021YFE0116100]
FX ACKNOWLEDGMENTS This work is financially supported by the National Key
   R&D Program of China (Grant No. 2021YFE0116100).
CR Ahamed JU, 2011, RENEW SUST ENERG REV, V15, P1593, DOI 10.1016/j.rser.2010.11.039
   Amer M, 2017, RENEW SUST ENERG REV, V73, P53, DOI 10.1016/j.rser.2017.01.120
   Bertsch SS, 2008, INT J REFRIG, V31, P1282, DOI 10.1016/j.ijrefrig.2008.01.006
   Bugge MM, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8070691
   Buker MS, 2016, RENEW SUST ENERG REV, V55, P399, DOI 10.1016/j.rser.2015.10.157
   Byun JS, 2006, INT J REFRIG, V29, P191, DOI 10.1016/j.ijrefrig.2005.06.008
   Byun JS, 2008, INT J REFRIG, V31, P328, DOI 10.1016/j.ijrefrig.2007.05.006
   Chen C., 2017, TEXT MINING VISUALIZ, V2
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen Y., 2015, STUDIES SCI SCI, V33, P242
   China Ground Source Heat Pump Website, 2014, OP STRENGTH CONSTR M
   Choi HJ, 2011, APPL ENERG, V88, P4544, DOI 10.1016/j.apenergy.2011.05.039
   Chow TT, 2010, APPL ENERG, V87, P643, DOI 10.1016/j.apenergy.2009.05.036
   Chua KJ, 2010, APPL ENERG, V87, P3611, DOI 10.1016/j.apenergy.2010.06.014
   Deng WS, 2016, ENERG CONVERS MANAGE, V120, P378, DOI 10.1016/j.enconman.2016.04.102
   Dong JK, 2015, INT J REFRIG, V55, P49, DOI 10.1016/j.ijrefrig.2015.03.018
   Dong JK, 2012, APPL THERM ENG, V37, P380, DOI 10.1016/j.applthermaleng.2011.11.052
   Egghe L, 2006, SCIENTOMETRICS, V69, P131, DOI 10.1007/s11192-006-0144-7
   Esen H, 2017, J EXP THEOR ARTIF IN, V29, P1, DOI 10.1080/0952813X.2015.1056242
   Esen M, 2013, ENERG BUILDINGS, V65, P340, DOI 10.1016/j.enbuild.2013.06.018
   Fan R, 2008, ENERGY, V33, P1671, DOI 10.1016/j.energy.2008.07.017
   Fan R, 2007, ENERGY, V32, P2199, DOI 10.1016/j.energy.2007.05.001
   Florides G, 2007, RENEW ENERG, V32, P2461, DOI 10.1016/j.renene.2006.12.014
   Gao Q, 2009, RENEW SUST ENERG REV, V13, P1383, DOI 10.1016/j.rser.2008.09.012
   Geissdoerfer M, 2017, J CLEAN PROD, V143, P757, DOI 10.1016/j.jclepro.2016.12.048
   Geng Y, 2013, RENEW SUST ENERG REV, V18, P95, DOI 10.1016/j.rser.2012.10.015
   Guo XM, 2008, APPL THERM ENG, V28, P2267, DOI 10.1016/j.applthermaleng.2008.01.007
   Hakkaki-Fard A, 2015, APPL THERM ENG, V78, P695, DOI 10.1016/j.applthermaleng.2014.11.036
   Hamada Y, 2007, ENERG BUILDINGS, V39, P517, DOI 10.1016/j.enbuild.2006.09.006
   Han ZW, 2008, APPL THERM ENG, V28, P1427, DOI 10.1016/j.applthermaleng.2007.09.013
   He Q, 1999, LIBR TRENDS, V48, P133
   Heo J, 2011, INT J REFRIG, V34, P444, DOI 10.1016/j.ijrefrig.2010.10.003
   Hu B, 2015, APPL ENERG, V147, P361, DOI 10.1016/j.apenergy.2015.03.010
   Hu WJ, 2011, APPL THERM ENG, V31, P363, DOI 10.1016/j.applthermaleng.2010.09.024
   Huang D, 2009, APPL ENERG, V86, P1697, DOI 10.1016/j.apenergy.2008.11.023
   Huang WZ, 2016, APPL ENERG, V171, P656, DOI 10.1016/j.apenergy.2016.03.048
   Jia CX, 2006, APPL THERM ENG, V26, P2393, DOI 10.1016/j.applthermaleng.2006.02.016
   Jiang YQ, 2013, INT J REFRIG, V36, P2278, DOI 10.1016/j.ijrefrig.2013.05.016
   Jiang YQ, 2014, ENERG BUILDINGS, V68, P707, DOI 10.1016/j.enbuild.2013.08.055
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   Kong XQ, 2017, INT J REFRIG, V76, P136, DOI 10.1016/j.ijrefrig.2017.01.020
   Kuang YH, 2006, SOL ENERGY, V80, P795, DOI 10.1016/j.solener.2005.06.003
   Kwak K, 2010, APPL THERM ENG, V30, P539, DOI 10.1016/j.applthermaleng.2009.10.016
   Kwon C, 2017, INT J REFRIG, V74, P138, DOI 10.1016/j.ijrefrig.2016.10.004
   Laue H.J., 2006, RENEWABLE ENERGY 9 C
   Li LT, 2014, APPL ENERG, V135, P98, DOI 10.1016/j.apenergy.2014.07.028
   Li M, 2015, APPL ENERG, V151, P178, DOI 10.1016/j.apenergy.2015.04.070
   Liang CH, 2011, ENERG BUILDINGS, V43, P2188, DOI 10.1016/j.enbuild.2011.04.028
   Liao HC, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10010166
   Liu ZJ, 2017, RENEW ENERG, V101, P1131, DOI 10.1016/j.renene.2016.10.006
   Long Z, 2014, APPL ENERG, V133, P101, DOI 10.1016/j.apenergy.2014.07.039
   Ma GY, 2008, ENERG BUILDINGS, V40, P697, DOI 10.1016/j.enbuild.2007.05.003
   Ma YT, 2013, ENERGY, V55, P156, DOI 10.1016/j.energy.2013.03.030
   Man Y, 2008, ENERG BUILDINGS, V40, P2028, DOI 10.1016/j.enbuild.2008.05.010
   Man Y, 2010, APPL ENERG, V87, P2826, DOI 10.1016/j.apenergy.2009.04.044
   Marvuglia A, 2020, RENEW SUST ENERG REV, V124, DOI 10.1016/j.rser.2020.109788
   Meerow S, 2016, LANDSCAPE URBAN PLAN, V147, P38, DOI 10.1016/j.landurbplan.2015.11.011
   Ministry of Environmental Protection of the People's Republic of China, 2016, STRENGTH MEAS AIR PO
   MoE of the PRC, 2017, NAT ED DEV 13 5 YEAR
   Mohanraj M, 2018, RENEW SUST ENERG REV, V83, P124, DOI 10.1016/j.rser.2017.08.086
   Morar M, 2010, COMPUT CHEM ENG, V34, P1171, DOI 10.1016/j.compchemeng.2010.02.038
   Newman MEJ, 2006, P NATL ACAD SCI USA, V103, P8577, DOI 10.1073/pnas.0601602103
   Ni L, 2015, RENEW ENERG, V84, P30, DOI 10.1016/j.renene.2015.06.043
   Niu FX, 2013, APPL THERM ENG, V54, P1, DOI 10.1016/j.applthermaleng.2012.11.022
   Omer AM, 2008, RENEW SUST ENERG REV, V12, P344, DOI 10.1016/j.rser.2006.10.003
   Omerzel DG, 2016, INT J CONTEMP HOSP M, V28, P516, DOI 10.1108/IJCHM-10-2014-0510
   Park JY, 2018, RENEW SUST ENERG REV, V82, P2664, DOI 10.1016/j.rser.2017.09.102
   Pu L, 2015, APPL THERM ENG, V79, P202, DOI 10.1016/j.applthermaleng.2014.12.068
   Qi PC, 2013, APPL THERM ENG, V56, P120, DOI 10.1016/j.applthermaleng.2013.03.045
   Qi ZG, 2014, RENEW SUST ENERG REV, V38, P754, DOI 10.1016/j.rser.2014.07.038
   Qi ZS, 2014, RENEW SUST ENERG REV, V29, P37, DOI 10.1016/j.rser.2013.08.059
   Qin F, 2015, ENERG CONVERS MANAGE, V102, P39, DOI 10.1016/j.enconman.2015.01.024
   Qu ML, 2017, APPL THERM ENG, V121, P728, DOI 10.1016/j.applthermaleng.2017.04.146
   Qu ML, 2012, APPL ENERG, V97, P327, DOI 10.1016/j.apenergy.2011.11.057
   Qu ML, 2012, APPL ENERG, V91, P122, DOI 10.1016/j.apenergy.2011.08.034
   Qu ML, 2010, BUILD ENVIRON, V45, P2354, DOI 10.1016/j.buildenv.2010.04.006
   Qv DH, 2015, RENEW ENERG, V84, P46, DOI 10.1016/j.renene.2015.07.030
   ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7
   Sagia Z, 2012, APPL ENERG, V94, P41, DOI 10.1016/j.apenergy.2012.01.031
   Self SJ, 2013, APPL ENERG, V101, P341, DOI 10.1016/j.apenergy.2012.01.048
   Shenyang Municipal People's Government, 2006, IMPL OP COMPR PROM C
   Song MJ, 2018, APPL ENERG, V211, P1150, DOI 10.1016/j.apenergy.2017.12.022
   Song MJ, 2017, APPL ENERG, V204, P1388, DOI 10.1016/j.apenergy.2017.01.068
   Song MJ, 2017, APPL ENERG, V204, P679, DOI 10.1016/j.apenergy.2017.07.063
   Song MJ, 2016, APPL THERM ENG, V99, P730, DOI 10.1016/j.applthermaleng.2015.12.100
   Song MJ, 2016, APPL ENERG, V165, P371, DOI 10.1016/j.apenergy.2015.12.107
   Song MJ, 2016, APPL ENERG, V164, P36, DOI 10.1016/j.apenergy.2015.11.036
   Song MJ, 2016, APPL THERM ENG, V94, P331, DOI 10.1016/j.applthermaleng.2015.10.082
   Song MJ, 2016, APPL ENERG, V161, P268, DOI 10.1016/j.apenergy.2015.10.026
   Song MJ, 2015, APPL THERM ENG, V91, P975, DOI 10.1016/j.applthermaleng.2015.09.006
   Song MJ, 2014, APPL ENERG, V136, P537, DOI 10.1016/j.apenergy.2014.09.012
   Song MJ, 2014, APPL THERM ENG, V67, P258, DOI 10.1016/j.applthermaleng.2014.03.020
   Su W, 2017, ENERG BUILDINGS, V145, P293, DOI 10.1016/j.enbuild.2017.04.024
   Sumeru K, 2012, RENEW SUST ENERG REV, V16, P4927, DOI 10.1016/j.rser.2012.04.058
   Sun XL, 2015, ENRGY PROCED, V70, P394, DOI 10.1016/j.egypro.2015.02.140
   Sun XL, 2014, APPL THERM ENG, V66, P571, DOI 10.1016/j.applthermaleng.2014.02.060
   The State Council of the People's Republic of China, 2016, DEV PLAN STRAT EM IN
   TUEIA, 2017, COUNTR EN DAT
   Wang BL, 2017, INT J REFRIG, V73, P91, DOI 10.1016/j.ijrefrig.2016.09.017
   Wang DD, 2018, INT J REFRIG, V85, P27, DOI 10.1016/j.ijrefrig.2017.09.009
   Wang EY, 2012, ENERG BUILDINGS, V47, P600, DOI 10.1016/j.enbuild.2011.12.035
   Wang FH, 2015, APPL ENERG, V139, P212, DOI 10.1016/j.apenergy.2014.11.018
   Wang HJ, 2008, ENERG BUILDINGS, V40, P1278, DOI 10.1016/j.enbuild.2007.11.009
   Wang SG, 2013, INT J REFRIG, V36, P701, DOI 10.1016/j.ijrefrig.2012.10.011
   Wang W, 2013, APPL ENERG, V112, P1138, DOI 10.1016/j.apenergy.2012.12.054
   Wang W, 2011, APPL ENERG, V88, P4470, DOI 10.1016/j.apenergy.2011.05.047
   Wang W, 2013, APPL THERM ENG, V50, P177, DOI 10.1016/j.applthermaleng.2012.06.019
   Wang X, 2008, THESIS U MARYLAND
   Wang X, 2010, ENERG BUILDINGS, V42, P2104, DOI 10.1016/j.enbuild.2010.06.022
   Wang ZH, 2017, ENERG CONVERS MANAGE, V153, P603, DOI 10.1016/j.enconman.2017.10.038
   Wang ZH, 2017, APPL THERM ENG, V111, P122, DOI 10.1016/j.applthermaleng.2016.09.076
   Wang ZH, 2014, APPL THERM ENG, V70, P808, DOI 10.1016/j.applthermaleng.2014.05.038
   Willem H, 2017, ENERG BUILDINGS, V143, P191, DOI 10.1016/j.enbuild.2017.02.023
   Wu W, 2014, APPL ENERG, V130, P51, DOI 10.1016/j.apenergy.2014.05.027
   Wu W, 2014, APPL ENERG, V126, P113, DOI 10.1016/j.apenergy.2014.04.006
   Wu W, 2014, ENERG CONVERS MANAGE, V79, P334, DOI 10.1016/j.enconman.2013.11.045
   Wu W, 2013, RENEW ENERG, V59, P39, DOI 10.1016/j.renene.2013.03.020
   Xiao J, 2010, INT J REFRIG, V33, P1006, DOI 10.1016/j.ijrefrig.2010.03.002
   Xiao J, 2009, INT J REFRIG, V32, P1350, DOI 10.1016/j.ijrefrig.2009.02.014
   Xu X, 2011, INT J REFRIG, V34, P402, DOI 10.1016/j.ijrefrig.2010.09.015
   Yan G, 2016, INT J REFRIG, V62, P232, DOI 10.1016/j.ijrefrig.2015.10.024
   Yang H, 2010, APPL ENERG, V87, P16, DOI 10.1016/j.apenergy.2009.04.038
   Yang W, 2010, ENERG POLICY, V38, P323, DOI 10.1016/j.enpol.2009.09.021
   Yeung AWK, 2019, OXID MED CELL LONGEV, V2019, DOI 10.1155/2019/8278454
   Yu YS, 2008, APPL THERM ENG, V28, P1450, DOI 10.1016/j.applthermaleng.2007.09.006
   Yuan YP, 2012, RENEW SUST ENERG REV, V16, P6814, DOI 10.1016/j.rser.2012.07.025
   Zhai XQ, 2011, RENEW SUST ENERG REV, V15, P3133, DOI 10.1016/j.rser.2011.04.024
   Zhang CX, 2014, RENEW SUST ENERG REV, V40, P851, DOI 10.1016/j.rser.2014.08.018
   Zhang D, 2016, SUSTAIN ENERGY TECHN, V18, P127, DOI 10.1016/j.seta.2016.10.008
   Zhang J, 2016, APPL ENERG, V178, P800, DOI 10.1016/j.apenergy.2016.06.049
   Zhang L, 2012, INT J REFRIG, V35, P1327, DOI 10.1016/j.ijrefrig.2012.04.004
   Zhang L, 2012, ENERG CONVERS MANAGE, V57, P107, DOI 10.1016/j.enconman.2011.12.023
   Zhang L, 2010, INT J REFRIG, V33, P116, DOI 10.1016/j.ijrefrig.2009.08.007
   Zhang L, 2018, RENEW SUST ENERG REV, V81, P353, DOI 10.1016/j.rser.2017.08.009
   Zhang QL, 2017, APPL ENERG, V207, P533, DOI 10.1016/j.apenergy.2017.06.083
   Zhao L, 2018, SCI TOTAL ENVIRON, V627, P1253, DOI 10.1016/j.scitotenv.2018.02.006
   Zhiyi W, 2008, APPL ENERG, V85, P1050, DOI 10.1016/j.apenergy.2008.02.020
   Zhou JZ, 2016, APPL ENERG, V178, P484, DOI 10.1016/j.apenergy.2016.06.063
   Zhu N, 2014, APPL THERM ENG, V71, P142, DOI 10.1016/j.applthermaleng.2014.06.040
NR 140
TC 0
Z9 0
U1 26
U2 29
PU AIP Publishing
PI MELVILLE
PA 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA
SN 1941-7012
J9 J RENEW SUSTAIN ENER
JI J. Renew. Sustain. Energy
PD JAN
PY 2022
VL 14
IS 1
AR 012701
DI 10.1063/5.0075868
PG 32
WC Green & Sustainable Science & Technology; Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Energy & Fuels
GA YS7CK
UT WOS:000750830100002
DA 2022-08-23
ER

PT J
AU Koondhar, MA
   Shahbaz, M
   Memon, KA
   Ozturk, I
   Kong, R
AF Koondhar, Mansoor Ahmed
   Shahbaz, Muhammad
   Memon, Kamran Ali
   Ozturk, Ilhan
   Kong, Rong
TI A visualization review analysis of the last two decades for
   environmental Kuznets curve "EKC" based on co-citation analysis theory
   and pathfinder network scaling algorithms
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Review
DE Environmental Kuznets curve (EKC); Bibliometric; Scientometric analysis
   algorithm; Co-citation; Visualization; CiteSpace
ID ECONOMIC-GROWTH; ENERGY-CONSUMPTION; CO2 EMISSIONS; AUTHOR COCITATION;
   HYPOTHESIS; CARBON; QUALITY; CAUSALITY; CHINA; AIR
AB Environmental Kuznets curve (EKC) is a statistical tool to examine the cointegration and causality nexus between economic growth and carbon emissions. The EKC is widely used in energy and environmental economics studies. Although a large number of researchers have analyzed the EKC by applying different statistical models, some review work has been summarized to draw a pictorial view of extending studies in this research field. However, still, the macroscopic overview needs to be considered. Therefore, this study aims to contribute to the literature for finding a new pathway for further research employing, and to facilitate this research, scientometric analysis is carried out by feature in CiteSpace. The dataset was screened and found 2384 records out of 59,225 Web of Science (WoS) references, and the records for the timespan 1999-2019 was used to visualize the knowledge map and outcome of the scientific enterprise. The visualization results reveal the most influencing studies, institutions, authors, countries, keywords, and category cloud, in the research field of EKC. This article reveals that the research on EKC in alignment with green and sustainable technology science requires more attention. Further, this article would help authors and publishers make their decisions for the research of EKC and planning for future perspectives to contribute to academic development and applied methodology.
C1 [Koondhar, Mansoor Ahmed; Kong, Rong] Northwest A&F Univ, Coll Econ & Management, Yangling 712100, Xianyang, Peoples R China.
   [Shahbaz, Muhammad] Beijing Inst Technol, Econ Ctr Energy & Environm Policy Res, Beijing 100081, Peoples R China.
   [Memon, Kamran Ali] Quaid E Awam Univ Engn Sci & Technol, Dept Elect Engn, Nawabshah, Pakistan.
   [Ozturk, Ilhan] Cag Univ, Fac Econ & Adm Sci, Mersin, Turkey.
   [Ozturk, Ilhan] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung, Taiwan.
   [Ozturk, Ilhan] Asia Univ, Dept Finance, 500 Lioufeng Rd, Taichung 41354, Taiwan.
RP Kong, R (通讯作者)，Northwest A&F Univ, Coll Econ & Management, Yangling 712100, Xianyang, Peoples R China.
EM socialist.mansoor@yahoo.com; muhdshahbaz77@gmail.com;
   ali.kamran77@gmail.com; ilhanozturk@cag.edu.tr; kr1996@163.com
RI Ozturk, Ilhan/B-9431-2009; ALI, KAMRAN/AEE-4330-2022; Koondhar, Mansoor
   Ahmed/GOJ-8640-2022
OI Ozturk, Ilhan/0000-0002-6521-0901; ALI, KAMRAN/0000-0003-1353-2586; 
FU College of Economics and Management, Northwest Agriculture and Forestry
   University from the project of National Natural Science Foundation of
   China "NSFC" [71773094]
FX This study was financially supported by the College of Economics and
   Management, Northwest Agriculture and Forestry University from the
   project of National Natural Science Foundation of China "NSFC project
   No. 71773094".
CR Agenor PR, 2008, J MACROECON, V30, P1407, DOI 10.1016/j.jmacro.2008.04.003
   Ahmed K, 2017, RENEW SUST ENERG REV, V70, P1142, DOI 10.1016/j.rser.2016.12.018
   Ahmed MM, 2019, ENERGIES, V12, DOI 10.3390/en12152954
   Ahmed V., 2014, AGRARIAN S J POLITIC, V3, P369, DOI [10.1177/2277976014560944, DOI 10.1177/2277976014560944]
   Ahmed Z, 2019, ENVIRON SCI POLLUT R, V26, P18565, DOI 10.1007/s11356-019-05224-9
   Akbostanci E, 2009, ENERG POLICY, V37, P861, DOI 10.1016/j.enpol.2008.09.088
   Al-Mulali U, 2015, ENERG POLICY, V76, P123, DOI 10.1016/j.enpol.2014.11.019
   Al-mulali U, 2011, ENERGY, V36, P6165, DOI 10.1016/j.energy.2011.07.048
   To AH, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16091636
   Apergis N, 2015, ECOL INDIC, V52, P16, DOI 10.1016/j.ecolind.2014.11.026
   Arouri ME, 2012, ENERG POLICY, V45, P342, DOI 10.1016/j.enpol.2012.02.042
   Aziz N., 2020, ENVIRON SCI POLLUT R, P1, DOI DOI 10.1007/S11356-019-07074-X
   Balsalobre-Lorente D, 2018, ENERG POLICY, V113, P356, DOI 10.1016/j.enpol.2017.10.050
   Beyene Sisay Demissew, 2020, International Journal of Environmental Studies, V77, DOI 10.1080/00207233.2019.1695445
   Blind K, 2012, RES POLICY, V41, P391, DOI 10.1016/j.respol.2011.08.008
   Bouznit M, 2016, ENERG POLICY, V96, P93, DOI 10.1016/j.enpol.2016.05.036
   CALLON M, 1983, SOC SCI INFORM, V22, P191, DOI 10.1177/053901883022002003
   Cao YY, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17051803
   Cederborg J., 2016, IS THERE RELATIONSHI, DOI [10.18034/apjee.v5i1.249, DOI 10.18034/APJEE.V5I1.249]
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Conrad E, 2014, SUSTAINABILITY-BASEL, V6, P6729, DOI 10.3390/su6106729
   Dinda S, 2004, ECOL ECON, V49, P431, DOI 10.1016/j.ecolecon.2004.02.011
   Fang Z, 2020, WORLD ECON, V43, P2622, DOI 10.1111/twec.12717
   Grossman G. M., 1991, ENV IMPACTS N AM FRE
   Halicioglu F, 2009, ENERG POLICY, V37, P1156, DOI 10.1016/j.enpol.2008.11.012
   HASEEB M, 2015, ASIAN J APPL SCI, V8, P27, DOI DOI 10.3923/ajaps.2015.27.36
   Huang LZ, 2018, RENEW SUST ENERG REV, V81, P1906, DOI 10.1016/j.rser.2017.06.001
   Jalil A, 2009, ENERG POLICY, V37, P5167, DOI 10.1016/j.enpol.2009.07.044
   Jeong YK, 2014, J INFORMETR, V8, P197, DOI 10.1016/j.joi.2013.12.001
   Jia Q, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11205596
   Joo YJ, 2015, INT J GREEN ENERGY, V12, P543, DOI 10.1080/15435075.2013.834822
   Kasman A, 2015, ECON MODEL, V44, P97, DOI 10.1016/j.econmod.2014.10.022
   Kleinberg J, 2002, ELECTED PAPERS 8TH A
   Koondhar MA, 2020, ENVIRON SCI POLLUT R, P1
   Koondhar MA, 2018, AGR ECON-CZECH, V64, P265, DOI [10.17221/101/2017-agricecon, 10.17221/101/2017-AGRICECON]
   Kuhn T, 2012, STRUCTURE SCI REVOLU
   Kuznets S, 1955, AM ECON REV, V45, P1
   Leung XY, 2017, INT J HOSP MANAG, V66, P35, DOI 10.1016/j.ijhm.2017.06.012
   Leydesdorff L, 1997, J AM SOC INFORM SCI, V48, P418
   Li C, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16245138
   Liu HJ, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0217319
   Mesagan E. P., 2015, IUP J APPL EC, V14, P61
   Mirza FM, 2017, RENEW SUST ENERG REV, V72, P1233, DOI 10.1016/j.rser.2016.10.081
   Mrabet Z, 2017, RENEW SUST ENERG REV, V70, P1366, DOI 10.1016/j.rser.2016.12.039
   Nasir M, 2011, ENERG POLICY, V39, P1857, DOI 10.1016/j.enpol.2011.01.025
   Nasreen S, 2017, RENEW SUST ENERG REV, V67, P1105, DOI 10.1016/j.rser.2016.09.021
   Nnaji C.E., 2013, INT J ENERGY EC POLI, V3, P262
   Orubu CO, 2011, ENERG POLICY, V39, P4178, DOI 10.1016/j.enpol.2011.04.025
   Ozturk I, 2016, ENVIRON SCI POLLUT R, V23, P1916, DOI 10.1007/s11356-015-5447-x
   Ozturk I, 2015, APPL ENERG, V137, P110, DOI 10.1016/j.apenergy.2014.10.017
   Ozturk I, 2010, RENEW SUST ENERG REV, V14, P3220, DOI 10.1016/j.rser.2010.07.005
   Ozturk I, 2010, ENERG POLICY, V38, P340, DOI 10.1016/j.enpol.2009.09.024
   Panayotou T., 2016, ENV ANTHR, P140
   Pandiella-Dominique A, 2018, 23RD INT C SCI TECHN
   Pearce D., 2013, SUSTAINABLE DEV EC E
   Phuong N.D., 2018, INT J ENERGY EC POLI, V8, P138
   Polasky S, 2019, P NATL ACAD SCI USA, V116, P5233, DOI 10.1073/pnas.1901616116
   Programme UNIDC, 1997, TRENDS ORGAN CRIME, V3, P11, DOI [10.1007/s12117-997-1166-0, DOI 10.1007/S12117-997-1166-0]
   Rauf A, 2018, ENVIRON SCI POLLUT R, V25, P32066, DOI 10.1007/s11356-018-3052-5
   Riti JS, 2017, J CLEAN PROD, V166, P1448, DOI 10.1016/j.jclepro.2017.08.117
   Saboori B, 2012, ENERG POLICY, V51, P184, DOI 10.1016/j.enpol.2012.08.065
   Saini J, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17144942
   Sarkodie SA, 2019, SCI TOTAL ENVIRON, V649, P128, DOI 10.1016/j.scitotenv.2018.08.276
   Shahbaz M, 2019, AUST ECON PAP, V58, P444, DOI 10.1111/1467-8454.12163
   Shahbaz M, 2015, ENERG ECON, V51, P275, DOI 10.1016/j.eneco.2015.06.014
   Shahbaz M, 2014, RENEW SUST ENERG REV, V34, P325, DOI 10.1016/j.rser.2014.03.022
   Shahbaz M, 2013, RENEW SUST ENERG REV, V25, P109, DOI 10.1016/j.rser.2013.04.009
   Shahbaz M, 2012, RENEW SUST ENERG REV, V16, P2947, DOI 10.1016/j.rser.2012.02.015
   Shahri M. H. M., 2015, International Journal of Sport Studies, V5, P475
   Sharif A, 2017, ASIA PAC J TOUR RES, V22, P408, DOI 10.1080/10941665.2016.1273960
   Sheikhnejad Y, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12041293
   Sinha A, 2018, RENEW ENERG, V119, P703, DOI 10.1016/j.renene.2017.12.058
   Sinha A, 2017, ECOL INDIC, V72, P881, DOI 10.1016/j.ecolind.2016.09.018
   Small H, 1980, J DOC
   Smith KR, 2013, BIOFUELS AIR POLLUTI
   Solarin SA, 2017, RENEW SUST ENERG REV, V80, P1578, DOI 10.1016/j.rser.2017.07.028
   Stern DI, 2004, WORLD DEV, V32, P1419, DOI 10.1016/j.worlddev.2004.03.004
   Stern DI, 2014, ENV KUZNETS CURVE PR
   Suiter Amy M, 2019, Mo Med, V116, P461
   Sun JF, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17041277
   Tiba S, 2017, RENEW SUST ENERG REV, V69, P1129, DOI 10.1016/j.rser.2016.09.113
   Trujillo CM, 2018, SCI ADV, V4, DOI 10.1126/sciadv.1701130
   Viglione G, 2020, NATURE
   Wang MX, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16234654
   Wang XY, 2019, J CANCER, V10, P2643, DOI 10.7150/jca.32739
   WANG XY, 2020, ENTROPY-SWITZ, V22, DOI DOI 10.3390/e22010109
   Wang Y, 2016, ENERGY STRATEG REV, V10, P18, DOI 10.1016/j.esr.2016.04.002
   Wei JP, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12030812
   WHITE HD, 1981, J AM SOC INFORM SCI, V32, P163, DOI 10.1002/asi.4630320302
   WHO, 2018, STATE FOOD SEC NUTR
   Wijewickrema M, 2017, LEARN PUBL, V30, P289, DOI 10.1002/leap.1113
   Xue B, 2014, SUSTAINABILITY-BASEL, V6, P1037, DOI 10.3390/su6021037
   Yeh JC, 2017, SUSTAIN ENVIRON RES, V27, P41, DOI 10.1016/j.serj.2016.10.001
   Yuan XL, 2015, HUM ECOL RISK ASSESS, V21, P781, DOI 10.1080/10807039.2014.932204
   [张勤 ZHANG Qin], 2008, [管理工程学报, Journal of Industrial Engineering and Engineering Management], V22, P30
NR 96
TC 42
Z9 42
U1 58
U2 136
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
PD APR
PY 2021
VL 28
IS 13
BP 16690
EP 16706
DI 10.1007/s11356-020-12199-5
EA JAN 2021
PG 17
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA QY6CF
UT WOS:000604562700002
PM 33389469
OA Green Submitted
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Ji, ZH
   Pei, YS
AF Ji, Zehua
   Pei, Yuansheng
TI Bibliographic and visualized analysis of geopolymer research and its
   application in heavy metal immobilization: A review
SO JOURNAL OF ENVIRONMENTAL MANAGEMENT
LA English
DT Review
DE Geopolymer; Citespace; Bibliometric; Scientific knowledge map; Heavy
   metal; Immobilization mechanism
ID ORE PROCESSING RESIDUE; ASH-BASED GEOPOLYMER; FLY-ASH; BIBLIOMETRIC
   ANALYSIS; NATURAL ZEOLITE; SEWAGE-SLUDGE; CEMENT; WASTE; LEAD;
   STABILIZATION/SOLIDIFICATION
AB Geopolymer (GP) is a novel aluminosilicate inorganic polymer, and it possesses excellent characteristics in application of various fields, and its advantages have attracted worldwide attention. Based on the Citespace software, the bibliometric analysis combined with the visualization analysis on GP was summarized on the publications that extracted from Web of Science (WOS) from 1990 to 2017. The analysis results demonstrate that the research on GP develops rapidly in the last years, and the GP have already possessed a degree of application value in several engineering fields. This research shows a multidisciplinary amalgamation tendency in contents and methods. Additionally, the main application in pollution treatment of GP is heavy metal immobilization. The immobilization effects of GP for heavy metal are mainly depended on physical encapsulation, adsorption effect, chemical bonding, substitution for Al3+ and other effects. And these effects are simultaneous action on heavy metal immobilization under different conditions. Furthermore, the majority of GP are based on the fly ash and metakaolin, and the most frequently used leaching method is the Toxicity Characteristic Leaching Procedure (TCLP). Moreover, there are also several problems need to be solved before GP can be widely applied in heavy metal treatment. Overall, GP have possessed a great potential research and application value in the present stage, especially in the aspect of heavy metal immobilization.
C1 [Ji, Zehua; Pei, Yuansheng] Beijing Normal Univ, Sch Environm, Minist Educ, Key Lab Water & Sediment Sci, 19 Xinjiekouwai St, Beijing 100875, Peoples R China.
RP Pei, YS (通讯作者)，Beijing Normal Univ, Sch Environm, Minist Educ, Key Lab Water & Sediment Sci, 19 Xinjiekouwai St, Beijing 100875, Peoples R China.
EM yspei@bnu.edu.cn
RI Pei, Yuansheng/E-8712-2014
FU National Natural Science Foundation of China [51579009]; Major Science
   and Technology Program for Water Pollution Control and Treatment
   [2018ZX07110004]
FX This work was financially supported by the National Natural Science
   Foundation of China (Project no. 51579009) and the Major Science and
   Technology Program for Water Pollution Control and Treatment (Project
   no. 2018ZX07110004).
CR Al-Harahsheh MS, 2015, J ENVIRON CHEM ENG, V3, P1669, DOI 10.1016/j.jece.2015.06.005
   Al-Mashqbeh A, 2018, CERAM INT, V44, P5613, DOI 10.1016/j.ceramint.2017.12.208
   Al-Zboon K, 2011, J HAZARD MATER, V188, P414, DOI 10.1016/j.jhazmat.2011.01.133
   Albitar M, 2017, CONSTR BUILD MATER, V141, P104, DOI 10.1016/j.conbuildmat.2017.03.014
   Andrejkovicova S, 2016, APPL CLAY SCI, V126, P141, DOI 10.1016/j.clay.2016.03.009
   [Anonymous], [No title captured]
   [Anonymous], 1992, 1311 USEPA
   Santa RAAB, 2016, J HAZARD MATER, V318, P145, DOI 10.1016/j.jhazmat.2016.06.059
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Chen QY, 2009, WASTE MANAGE, V29, P390, DOI 10.1016/j.wasman.2008.01.019
   Cheng TW, 2012, APPL CLAY SCI, V56, P90, DOI 10.1016/j.clay.2011.11.027
   China, 2007, 508532007 GB CHIN ST
   Davidovits, 2008, GEOPOLYMER CHEM APPL
   Davidovits J., 1994, J MATER EDUC, V16, P91
   Dermatas D, 2003, ENG GEOL, V70, P377, DOI 10.1016/S0013-7952(03)00105-4
   Duan P, 2016, CERAM INT, V42, P13507, DOI 10.1016/j.ceramint.2016.05.143
   Ei-Eswed BI, 2017, APPL CLAY SCI, V140, P148, DOI 10.1016/j.clay.2017.02.003
   El-Eswed BI, 2015, INT J MINER PROCESS, V137, P34, DOI 10.1016/j.minpro.2015.03.002
   Erdem M, 2011, HYDROMETALLURGY, V105, P270, DOI 10.1016/j.hydromet.2010.10.014
   Pereira CF, 2009, FUEL, V88, P1185, DOI 10.1016/j.fuel.2008.01.021
   Guo B, 2017, J ENVIRON MANAGE, V193, P410, DOI 10.1016/j.jenvman.2017.02.026
   Guo B, 2017, CONSTR BUILD MATER, V134, P123, DOI 10.1016/j.conbuildmat.2016.12.139
   Guo XL, 2017, CONSTR BUILD MATER, V151, P394, DOI 10.1016/j.conbuildmat.2017.05.199
   Guo XL, 2014, CONSTR BUILD MATER, V56, P81, DOI 10.1016/j.conbuildmat.2014.01.062
   Hajimohammadi A, 2018, J CLEAN PROD, V193, P593, DOI 10.1016/j.jclepro.2018.05.086
   Hajimohammadi A, 2008, IND ENG CHEM RES, V47, P9396, DOI 10.1021/ie8006825
   Hu YY, 2015, J HAZARD MATER, V299, P149, DOI 10.1016/j.jhazmat.2015.06.002
   Huang X, 2018, CERAM INT, V44, P3454, DOI 10.1016/j.ceramint.2017.11.148
   Huang X, 2016, CERAM INT, V42, P9538, DOI 10.1016/j.ceramint.2016.03.033
   Huseien GF, 2017, RENEW SUST ENERG REV, V80, P54, DOI 10.1016/j.rser.2017.05.076
   Jin M, 2011, THESIS NANJING U SCI
   Katsioti M, 2008, CEMENT CONCRETE COMP, V30, P1013, DOI 10.1016/j.cemconcomp.2008.03.001
   Lancellotti I, 2010, WASTE MANAGE, V30, P673, DOI 10.1016/j.wasman.2009.09.032
   Lee S, 2016, J HAZARD MATER, V305, P59, DOI 10.1016/j.jhazmat.2015.11.023
   Li WJ, 2018, CERAM INT, V44, P6926, DOI 10.1016/j.ceramint.2018.01.121
   [刘斯凤 Liu Sifeng], 2010, [建筑材料学报, Journal of Building Materials], V13, P665
   Morar M, 2010, COMPUT CHEM ENG, V34, P1171, DOI 10.1016/j.compchemeng.2010.02.038
   Napia C, 2012, WASTE MANAGE, V32, P1459, DOI 10.1016/j.wasman.2012.02.011
   Nath SK, 2017, J THERM ANAL CALORIM, V127, P1953, DOI 10.1007/s10973-016-5823-x
   Nikolic V, 2018, J HAZARD MATER, V350, P98, DOI [10.1016/j.jhazmat2018.02.023, 10.1016/j.jhazmat.2018.02.023]
   Nikolic V, 2014, CERAM INT, V40, P8479, DOI 10.1016/j.ceramint.2014.01.059
   Novais RM, 2016, J HAZARD MATER, V318, P631, DOI 10.1016/j.jhazmat.2016.07.059
   Ouyang W, 2018, SCI TOTAL ENVIRON, V637, P208, DOI 10.1016/j.scitotenv.2018.04.434
   Perez-Villarejo L, 2018, MATER LETT, V229, P6, DOI 10.1016/j.matlet.2018.06.100
   Phair JW, 2001, MINER ENG, V14, P289, DOI 10.1016/S0892-6875(01)00002-4
   Phummiphan I, 2018, J HAZARD MATER, V341, P257, DOI 10.1016/j.jhazmat.2017.07.067
   Rashidian-Dezfouli H, 2018, CONSTR BUILD MATER, V162, P393, DOI 10.1016/j.conbuildmat.2017.09.166
   Rickard WDA, 2011, MAT SCI ENG A-STRUCT, V528, P3390, DOI 10.1016/j.msea.2011.01.005
   ROY DM, 1987, SCIENCE, V235, P651, DOI 10.1126/science.235.4789.651
   Song FY, 2013, CHEMOSPHERE, V92, P344, DOI 10.1016/j.chemosphere.2013.01.022
   Songpiriyakij S, 2011, MATER DESIGN, V32, P3021, DOI 10.1016/j.matdes.2011.01.048
   Steins P, 2012, LANGMUIR, V28, P8502, DOI 10.1021/la300868v
   Steveson M, 2005, J MATER SCI, V40, P2023, DOI 10.1007/s10853-005-1226-2
   Sun T, 2014, J ENVIRON CHEM ENG, V2, P304, DOI 10.1016/j.jece.2013.12.022
   Toniolo N, 2017, CERAM INT, V43, P14545, DOI 10.1016/j.ceramint.2017.07.221
   Tzanakos K, 2014, WASTE MANAGE, V34, P1823, DOI 10.1016/j.wasman.2014.03.021
   Vysvaril M, 2016, PROCEDIA ENGINEER, V151, P162, DOI 10.1016/j.proeng.2016.07.363
   Waijarean N, 2014, CONSTR BUILD MATER, V50, P486, DOI 10.1016/j.conbuildmat.2013.08.047
   Wang MM, 2018, J ENVIRON SCI, V63, P156, DOI 10.1016/j.jes.2017.08.004
   Wang YG, 2018, CONSTR BUILD MATER, V160, P818, DOI 10.1016/j.conbuildmat.2017.12.006
   Ward C., 2003, CHARACTERISTICS AUST
   Wu YG, 2018, CERAM INT, V44, P2578, DOI 10.1016/j.ceramint.2017.11.020
   Xia WY, 2017, CONSTR BUILD MATER, V156, P199, DOI 10.1016/j.conbuildmat.2017.08.149
   Xiang CY, 2017, ECOL ENG, V99, P400, DOI 10.1016/j.ecoleng.2016.11.028
   Xu H, 2002, MINER ENG, V15, P1131, DOI 10.1016/S0892-6875(02)00255-8
   Xu JZ, 2006, MATER LETT, V60, P820, DOI 10.1016/j.matlet.2005.10.019
   [徐子芳 Xu Zifang], 2013, [复合材料学报, Acta Materiae Compositae Sinica], V30, P113
   Yakubu Y, 2018, J ENVIRON MANAGE, V207, P243, DOI 10.1016/j.jenvman.2017.11.042
   Yu DJ, 2018, KNOWL-BASED SYST, V141, P188, DOI 10.1016/j.knosys.2017.11.018
   Yu DJ, 2017, INFORM SCIENCES, V418, P619, DOI 10.1016/j.ins.2017.08.031
   Yu DJ, 2017, RENEW SUST ENERG REV, V74, P1314, DOI 10.1016/j.rser.2016.11.144
   Yun-Ming L, 2016, PROG MATER SCI, V83, P595, DOI 10.1016/j.pmatsci.2016.08.002
   Zeng RC, 2017, ENERG BUILDINGS, V155, P172, DOI 10.1016/j.enbuild.2017.09.025
   Zhang YS, 2007, J HAZARD MATER, V143, P206, DOI 10.1016/j.jhazmat.2006.09.033
   Zhang ZH, 2016, APPL CLAY SCI, V119, P31, DOI 10.1016/j.clay.2015.04.023
   Zhang ZH, 2014, CONSTR BUILD MATER, V56, P113, DOI 10.1016/j.conbuildmat.2014.01.081
   Zhu X.-M., 2005, STUDY FLY ASH GEOPOL
   Zhuang XY, 2016, J CLEAN PROD, V125, P253, DOI 10.1016/j.jclepro.2016.03.019
NR 79
TC 65
Z9 68
U1 43
U2 343
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 0301-4797
EI 1095-8630
J9 J ENVIRON MANAGE
JI J. Environ. Manage.
PD FEB 1
PY 2019
VL 231
BP 256
EP 267
DI 10.1016/j.jenvman.2018.10.041
PG 12
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA HI7MU
UT WOS:000456641100028
PM 30415169
DA 2022-08-23
ER

PT J
AU Yin, MC
   Wang, HS
   Yang, X
   Xu, CQ
   Wang, T
   Yan, YJ
   Fan, ZX
   Ma, JM
   Ye, J
   Mo, W
AF Yin Meng-chen
   Wang Hong-shen
   Yang Xi
   Xu Chong-qing
   Wang Tao
   Yan Yin-jie
   Fan Zhao-xiang
   Ma Jun-ming
   Ye Jie
   Mo Wen
TI A Bibliometric Analysis and Visualization of Current Research Trends in
   Chinese Medicine for Osteosarcoma
SO CHINESE JOURNAL OF INTEGRATIVE MEDICINE
LA English
DT Article
DE osteosarcoma; Chinese medicine; bibliometric analysis; visualization;
   research trends
ID TUMOR-GROWTH; CELLS; APOPTOSIS
AB Background To illustrate the research framework, overall knowledge structure, and development trends of Chinese medicine (CM) treatment for osteosarcoma (OS) by using a bibliometric analysis and newly developed visualization tools. Methods Research datasets were acquired from the Web of Science (WOS) database from January 1, 1980 to September 30, 2019. VOS viewer and Citespace software was used to analyze the data and generate visualization knowledge maps. Annual trends of publications, distribution of institutes, distribution of journals, citation and H-index status, co-authorship status, research hotspots and co-citation status were analyzed. Results A total of 223 publications in the WOS database met the requirement. The number of published articles showed a rise but the citation frequency and the H-index of China were relatively low. The cooperation between the countries, institutes and authors were relatively weak. Most publications were basic researches. Most of the previous researches focused on basic mechanisms of CM in treating OS, and therapy and improvement of dosage form may become a frontier in this research field. Conclusions Compared with other fields, the field of CM treatment for osteosarcome is still in infancy. The distribution of researches is imbalanced and cooperation between countries, institutions and authors remains to be strengthened. Furthermore, basic research occupies an absolute dominant position, and the exploration of the molecular mechanism of CM in preventing and treating OS may become a key point in the future.
C1 [Yin Meng-chen; Wang Hong-shen; Xu Chong-qing; Wang Tao; Yan Yin-jie; Fan Zhao-xiang; Ma Jun-ming; Ye Jie; Mo Wen] Shanghai Univ Tradit Chinese Med, Longhua Hosp, Dept Orthopaed, Shanghai 200032, Peoples R China.
   [Wang Hong-shen] Guangdong Prov Hosp Tradit Chinese Med, Div Spine Surg Ctr, Guangzhou 510120, Peoples R China.
   [Yang Xi] Shanghai Univ Tradit Chinese Med, Longhua Hosp, Canc Inst, Shanghai 200032, Peoples R China.
RP Mo, W (通讯作者)，Shanghai Univ Tradit Chinese Med, Longhua Hosp, Dept Orthopaed, Shanghai 200032, Peoples R China.
EM mw2218@126.com
FU National Science Foundation of China [81704097]; "Sailing Program" of
   the Shanghai Science and Technology Committee [19YF1449600]; "Yumiao
   Project" of Longhua Hospital affiliated to Shanghai University of
   Chinese Medicine [LYTD-60]
FX Supported by the National Science Foundation of China (No. 81704097),
   "Sailing Program" of the Shanghai Science and Technology Committee (No.
   19YF1449600), and the "Yumiao Project" of Longhua Hospital affiliated to
   Shanghai University of Chinese Medicine (No. LYTD-60)
CR Allen L, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0005910
   Boyce R, 2015, TROP MED INT HEALTH, V20, P1507, DOI 10.1111/tmi.12590
   Valderrama-Zurian JC, 2019, INSIGHTS IMAGING, V10, DOI 10.1186/s13244-019-0766-y
   Chang JL, 2019, CHIN J INTEGR MED, V25, P370, DOI 10.1007/s11655-018-2565-6
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Corbiere C, 2003, INT J ONCOL, V22, P899
   Di Genova L, 2019, FRONT PHARMACOL, V10, DOI 10.3389/fphar.2019.00963
   Fajardo-Ortiz D, 2014, J TRANSL MED, V12, DOI 10.1186/s12967-014-0227-9
   Fu YC, 2018, ONCOTARGETS THER, V11, P1553, DOI 10.2147/OTT.S159894
   He BC, 2011, MOL PHARMACOL, V79, P211, DOI 10.1124/mol.110.068668
   Li HY, 2015, CELL DEATH DIS, V6, DOI 10.1038/cddis.2014.543
   Liu YH, 2016, BMJ OPEN, V6, DOI 10.1136/bmjopen-2016-011630
   Mirabello L, 2009, CANCER-AM CANCER SOC, V115, P1531, DOI 10.1002/cncr.24121
   Ottaviani G, 2009, CANCER TREAT RES, V152, P15, DOI [10.1007/978-1-4419-0284-9_2, 10.1007/978-1-4419-0284-9_23]
   Qin JH, 2019, BIOMED PHARMACOTHER, V114, DOI 10.1016/j.biopha.2019.108854
   Saleem A, 2002, J ETHNOPHARMACOL, V81, P327, DOI 10.1016/S0378-8741(02)00099-5
   Sun JS, 2012, MAR POLLUT BULL, V64, P13, DOI 10.1016/j.marpolbul.2011.10.034
   Tamura T, 2018, MOL CLIN ONCOL, V9, P287, DOI 10.3892/mco.2018.1662
   Tan GJS, 2019, PEDIATR BLOOD CANCER, V66, DOI 10.1002/pbc.27462
   Tan T, 2019, ONCOTARGETS THER, V12, P5111, DOI 10.2147/OTT.S204574
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Wang WY, 2018, J CANCER, V9, P4179, DOI 10.7150/jca.27483
   Yan XY, 2016, SUPPORT CARE CANCER, V24, P11, DOI 10.1007/s00520-015-2737-2
   Yao N, 2017, AM J CHINESE MED, V45, P575, DOI [10.1142/S0192415X17500343, 10.1142/s0192415x17500343]
   Zhang QH, 2019, INTEGR CANCER THER, V18, DOI 10.1177/1534735419890917
   Zhang TS, 2015, SPRINGERPLUS, V4, DOI 10.1186/s40064-015-1542-1
NR 26
TC 4
Z9 5
U1 5
U2 27
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1672-0415
EI 1993-0402
J9 CHIN J INTEGR MED
JI Chin. J. Integr. Med.
PD MAY
PY 2022
VL 28
IS 5
BP 445
EP 452
DI 10.1007/s11655-020-3429-4
EA SEP 2020
PG 8
WC Integrative & Complementary Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Integrative & Complementary Medicine
GA 0V8LG
UT WOS:000565464500001
PM 32876857
DA 2022-08-23
ER

PT J
AU Fernandez-Campo, M
   Rodriguez-Morales, B
   Dramstad, WE
   Fjellstad, W
   Diaz-Varela, ER
AF Fernandez-Campo, Martina
   Rodriguez-Morales, Beatriz
   Dramstad, Wenche E.
   Fjellstad, Wendy
   Diaz-Varela, Emilio R.
TI Ecosystem services mapping for detection of bundles, synergies and
   trade-offs: Examples from two Norwegian municipalities
SO ECOSYSTEM SERVICES
LA English
DT Article
DE Ecosystem services mapping; Spatial analysis; GIS multicriteria
   analysis; Norway; Bioenergy resources; Trade-Offs
ID CARBON SEQUESTRATION; FOREST MANAGEMENT; SPATIAL SCALES; BIRCH STANDS;
   BIOMASS; INDICATORS; FRAMEWORK; BIOENERGY; SPRUCE; CLASSIFICATION
AB The main objective of this work was to analyse how increased harvesting for bioenergy production might affect other Ecosystem Services (ES) in two Norwegian municipalities (Ringsaker and Voss). The aim was to identify locations where synergies or conflicts between ES could be expected. The spatial distribution of eight different ES (3 provision, 3 regulation and 2 cultural services) was modelled using information provided by land use spatial databases and additional data sources. Model parameters were set by integrating existing research and expert knowledge. Maps showing the level of provision of ES were analysed using a moving window to analyse scale dependence in the spatial distribution of ES provision. Map algebra was then used to identify areas providing multiple ES, thus defining the most important areas on which to focus the management of both synergies and trade-offs. Finally, specific 'binary bundles' maps, where bioenergy provision was compared with each of the other ES, were developed. The methodology proved its utility to assess the compatibility of bioenergy uses with other services. This straightforward approach is readily replicable in other regions and can be used as a decision support tool for planning and designing provision areas, and to ensure sustainable forest management approaches. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Fernandez-Campo, Martina; Rodriguez-Morales, Beatriz; Diaz-Varela, Emilio R.] Univ Santiago de Compostela, ECOAGRASOC Res Grp, Escola Politen Super, Campus Univ S-N, Lugo 27002, Spain.
   [Dramstad, Wenche E.; Fjellstad, Wendy] NIBIO Norwegian Inst Bioecon Res, POB 115, NO-1431 As, Norway.
RP Diaz-Varela, ER (通讯作者)，Univ Santiago de Compostela, ECOAGRASOC Res Grp, Escola Politen Super, Campus Univ S-N, Lugo 27002, Spain.
EM martinafernandezcampo@gmail.com; beatriz.rodriguez-morales@usc.es;
   wenche.dramstad@nibio.no; wendy.fjellstad@nibio.no; emilio.diaz@usc.es
RI Rodríguez-Morales, Beatriz/S-6894-2019; Diaz-Varela, Emilio/D-2900-2009;
   Fjellstad, Wendy/AAF-6257-2020; Dramstad, Wenche E/G-3259-2019
OI Rodríguez-Morales, Beatriz/0000-0001-9649-8760; Diaz-Varela,
   Emilio/0000-0001-9360-0352; Dramstad, Wenche E/0000-0002-4054-5246;
   Fjellstad, Wendy Jane/0000-0002-2849-9785
FU Norwegian Research Council [233640/E50]
FX This work was financed partially by the Norwegian Research Council
   project 233640/E50 "Securing triple bottom line outcomes from bioenergy
   innovation in rural Norway (TRIBORN)".
CR Alavalapati J. R. R., 2009, SRSGTR178 USDA FOR S, P213
   [Anonymous], 2005, EC HUM WELL BEING SY
   Bagstad KJ, 2013, ECOSYST SERV, V5, pE27, DOI 10.1016/j.ecoser.2013.07.004
   Bagstad KJ, 2013, ECOSYST SERV, V4, P117, DOI 10.1016/j.ecoser.2012.07.012
   Bastian O, 2013, ECOSYST SERV, V4, P15, DOI 10.1016/j.ecoser.2013.04.003
   Batjes NH, 1996, LAND DEGRAD DEV, V7, P353, DOI 10.1002/(SICI)1099-145X(199612)7:4<353::AID-LDR239>3.0.CO;2-N
   Beetz S, 2013, BIOGEOSCIENCES, V10, P1067, DOI 10.5194/bg-10-1067-2013
   Bene S, 2007, ARCH TIERZUCHT, V50, P363, DOI 10.5194/aab-50-363-2007
   Bennett EM, 2009, ECOL LETT, V12, P1394, DOI 10.1111/j.1461-0248.2009.01387.x
   Bjordal I., 2006, AR5 KLASSIFIKASJONSS
   Bouget C, 2012, CAN J FOREST RES, V42, P1421, DOI [10.1139/X2012-078, 10.1139/x2012-078]
   Bureau of Land Management (BLM), 2015, VIS RES MAN VRM
   Burkhard B., 2014, LANDSC ONLINE, V34, P1, DOI [10.3097/LO.201434, DOI 10.3097/LO.201434]
   Burkhard B, 2012, ECOL INDIC, V21, P17, DOI 10.1016/j.ecolind.2011.06.019
   Butarbutar T, 2016, CARBON BAL MANAGE, V11, DOI 10.1186/s13021-016-0046-9
   Calder I., 2007, Unasylva (English ed.), V58, P3
   Castano-Santamaria J, 2013, INT FOREST REV, V15, P273, DOI 10.1505/146554813807700083
   Cerasino L, 2014, BIOMASS BIOENERG, V70, P553, DOI 10.1016/j.biombioe.2014.08.006
   COLE JJ, 1994, SCIENCE, V265, P1568, DOI 10.1126/science.265.5178.1568
   Cole JJ, 2001, MAR FRESHWATER RES, V52, P101, DOI 10.1071/MF00084
   Common International Classification for Ecosystem Services (CICES), 2016, CICES VERS 4 3
   Costanza R, 2008, BIOL CONSERV, V141, P350, DOI 10.1016/j.biocon.2007.12.020
   County governor of Hedmark, 2009, AGR FOR HEDM COUNT N
   Crossman ND, 2013, ECOSYST SERV, V4, P4, DOI 10.1016/j.ecoser.2013.02.001
   Crouzat E, 2015, J APPL ECOL, V52, P1145, DOI 10.1111/1365-2664.12502
   de Groot RS, 2010, ECOL COMPLEX, V7, P260, DOI 10.1016/j.ecocom.2009.10.006
   de Groot RS, 2002, ECOL ECON, V41, P393, DOI 10.1016/S0921-8009(02)00089-7
   Dean WE, 1998, GEOLOGY, V26, P535, DOI 10.1130/0091-7613(1998)026<0535:MASOCB>2.3.CO;2
   Delgado Calvo-Flores G., 1987, ECOLOGIA, V1, P5
   Derner JD, 2007, J SOIL WATER CONSERV, V62, P77
   Diaz-Varela E. R., 2009, ACT C INT CONJ 22 IN, P229
   Diaz-Varela ER, 2011, TRANSPORT RES D-TR E, V16, P281, DOI 10.1016/j.trd.2011.01.002
   Directorate for Cultural Heritage, 2015, ASK RIKS OFF DAT FRE
   Divdiakova J., 2014, INHERENT PROPERTIES
   EASTMAN JR, 1995, PHOTOGRAMM ENG REM S, V61, P539
   Eid T., 2010, ESTIMATION AVAILABIL
   Eigenbrod F, 2010, J APPL ECOL, V47, P377, DOI 10.1111/j.1365-2664.2010.01777.x
   Rafael DVE, 2009, FOREST ECOL MANAG, V258, P2490, DOI 10.1016/j.foreco.2009.09.005
   Ene E., 2002, FRAGSTATS V3 SPATIAL
   Eriksson LO, 2011, FOREST POLICY ECON, V13, P284, DOI 10.1016/j.forpol.2011.02.002
   European Commission, 2014, 2014080 EUR COMM
   Fisher B, 2009, ECOL ECON, V68, P643, DOI 10.1016/j.ecolecon.2008.09.014
   Floistad I. S., 2008, FOREST VEGETATION MA
   Follett RF, 2001, SOIL TILL RES, V61, P77, DOI 10.1016/S0167-1987(01)00180-5
   Fortin MJ, 2005, SPATIAL ANALYSIS: A GUIDE FOR ECOLOGISTS
   Frank S, 2015, FORESTS, V6, P533, DOI 10.3390/f6030533
   Frank S, 2012, ECOL INDIC, V21, P30, DOI 10.1016/j.ecolind.2011.04.027
   Furst C, 2013, J ENVIRON MANAGE, V127, pS1, DOI 10.1016/j.jenvman.2012.12.015
   Gaucherel C, 2007, LANDSCAPE URBAN PLAN, V82, P95, DOI 10.1016/j.landurbplan.2007.01.022
   GORHAM E, 1991, ECOL APPL, V1, P182, DOI 10.2307/1941811
   Gret-Regamey A, 2015, ECOSYST SERV, V13, P16, DOI 10.1016/j.ecoser.2014.10.008
   Gret-Regamey A, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0112601
   Groot R. de, 2006, Valuing wetlands: guidance for valuing the benefits derived from wetland ecosystem services
   Gundersen P, 2010, AMBIO, V39, P555, DOI 10.1007/s13280-010-0073-9
   Gundersen V, 2016, SCAND J FOREST RES, V31, P323, DOI 10.1080/02827581.2015.1099725
   Gurmesa GA, 2013, FOREST ECOL MANAG, V309, P47, DOI 10.1016/j.foreco.2013.02.015
   Haines-Young Roy, 2009, METHODOLOGIES DEFINI
   Hansson K, 2013, FOREST ECOL MANAG, V309, P28, DOI 10.1016/j.foreco.2013.05.029
   Hayek UW, 2016, ECOL INDIC, V61, P90, DOI 10.1016/j.ecolind.2015.03.035
   Hayha T, 2015, ECOSYST SERV, V14, P12, DOI 10.1016/j.ecoser.2015.03.001
   Hein L, 2006, ECOL ECON, V57, P209, DOI 10.1016/j.ecolecon.2005.04.005
   HINDAR K, 1982, CAN J FISH AQUAT SCI, V39, P1030, DOI 10.1139/f82-138
   Hjeljord O, 2014, J FORESTRY RES, V25, P941, DOI 10.1007/s11676-014-0487-5
   Hoffmann T, 2009, GEOMORPHOLOGY, V108, P127, DOI 10.1016/j.geomorph.2007.11.015
   Howe C, 2014, GLOBAL ENVIRON CHANG, V28, P263, DOI 10.1016/j.gloenvcha.2014.07.005
   Jacobs S, 2015, ECOL MODEL, V295, P21, DOI 10.1016/j.ecolmodel.2014.08.024
   Jandl R., 2007, CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, V2, DOI 10.1079/PAVSNNR20072017
   Jandl R, 2007, GEODERMA, V137, P253, DOI 10.1016/j.geoderma.2006.09.003
   Janssens IA, 2003, SCIENCE, V300, P1538, DOI 10.1126/science.1083592
   Jenssen T, 2014, RENEW ENERG, V61, P74, DOI 10.1016/j.renene.2012.08.014
   Jose Martinez-Harms Maria, 2012, International Journal of Biodiversity Science Ecosystem Services & Management, V8, P17, DOI 10.1080/21513732.2012.663792
   Kandziora M, 2013, ECOSYST SERV, V4, P47, DOI 10.1016/j.ecoser.2013.04.001
   Kareiva P, 2011, NATURAL CAPITAL: THEORY & PRACTICE OF MAPPING ECOSYSTEM SERVICES, P1, DOI 10.1093/acprof:oso/9780199588992.001.0001
   Kartverket, 2016, DTM 10 10 MET GRID
   Klein TM, 2015, ECOSYST SERV, V13, P173, DOI 10.1016/j.ecoser.2015.02.006
   Kreye MM, 2014, FORESTS, V5, P862, DOI 10.3390/f5050862
   Lal R, 2002, ENVIRON POLLUT, V116, P353, DOI 10.1016/S0269-7491(01)00211-1
   Lattimore B, 2009, BIOMASS BIOENERG, V33, P1321, DOI 10.1016/j.biombioe.2009.06.005
   Lavelle P, 2006, EUR J SOIL BIOL, V42, pS3, DOI 10.1016/j.ejsobi.2006.10.002
   Lee H, 2016, ECOL INDIC, V66, P340, DOI 10.1016/j.ecolind.2016.02.004
   Lien G., 2003, P NJF SEM NORW AGR E, V345, P137
   Luyssaert S, 2008, NATURE, V455, P213, DOI 10.1038/nature07276
   Maes J, 2016, ROUTLEDGE HDB ECOSYS, P188, DOI [DOI 10.2139/SSM.2160714, DOI 10.4324/9781315775302-17]
   Maes J., 2011, EUROPEAN ASSESSMENT
   Maes J, 2012, ECOSYST SERV, V1, P31, DOI 10.1016/j.ecoser.2012.06.004
   Malczewski J., 2015, MULTICRITERIA DECISI
   Malczewski J, 2006, INT J GEOGR INF SCI, V20, P703, DOI 10.1080/13658810600661508
   Marie H., 2011, CEPA ACTION PLAN WET
   Martin-Ortega J, 2014, ECOSYST SERV, V9, P34, DOI 10.1016/j.ecoser.2014.06.007
   Martinussen I., 2008, POTENTIAL EUROPEAN W
   Miller WW, 2010, FORESTS, V1, P131, DOI 10.3390/f1030131
   Mitasova H., 2001, TERRAIN MODELLING SO
   Nelson E, 2009, FRONT ECOL ENVIRON, V7, P4, DOI 10.1890/080023
   NIBIO, 2015, AR5 LAND RES MAP
   Nikolaisen L., 2002, QUALITY CHARACTERIST
   Nordic Council of Ministers, 2014, CLIM CHANG PRIM IND
   Norwegian Environment Agency (Miljodirektoratet), 2014, AC RAIN
   Norwegian Millennium Ecosystem Assessment, 2002, NORW MILL EC ASS PIL
   Norwegian Ministry of Climate and Environment, 2014, NORW 6 NAT COMM
   OECD, 2012, COMP CIT POL COMP AS
   Oliveira A., 2013, DEV TOPOGRAPHIC FACT, DOI [10.5772/54439, DOI 10.5772/54439]
   Olsson BA, 2012, FOREST ECOL MANAG, V269, P197, DOI 10.1016/j.foreco.2011.12.031
   Palomo I, 2013, ECOSYST SERV, V4, P104, DOI 10.1016/j.ecoser.2012.09.001
   Pelkonen P, 2014, WHAT SCI CAN TELL US
   Peman J., 1998, REPOBLACIONES FOREST, P400
   Potschin MB, 2011, PROG PHYS GEOG, V35, P575, DOI 10.1177/0309133311423172
   Raudsepp-Hearne C, 2010, P NATL ACAD SCI USA, V107, P5242, DOI 10.1073/pnas.0907284107
   Raudsepp-Hearne C, 2016, ECOL SOC, V21, DOI 10.5751/ES-08605-210316
   Reed MS, 2014, ECOSYST SERV, V9, P1, DOI 10.1016/j.ecoser.2014.04.007
   Renard K.G., 1997, PREDICTING SOIL EROS, V703
   Roces-Diaz JV, 2015, ECOL INDIC, V50, P24, DOI 10.1016/j.ecolind.2014.10.027
   Roces-Diaz JV, 2014, ECOL INDIC, V36, P495, DOI 10.1016/j.ecolind.2013.09.010
   Rodland K., 2009, C REG AFF LANDSC NOR
   Rodriguez-Morales B, 2013, ACCIDENT ANAL PREV, V60, P121, DOI 10.1016/j.aap.2013.07.032
   Schulze ED, 2000, SCIENCE, V289, P2058, DOI 10.1126/science.289.5487.2058
   Schulze ED, 2012, GCB BIOENERGY, V4, P611, DOI 10.1111/j.1757-1707.2012.01169.x
   Sedell J., 2000, WATER FOREST SERVICE
   Serna-Chavez HM, 2014, ECOL INDIC, V39, P24, DOI 10.1016/j.ecolind.2013.11.024
   Seyfang G, 2013, ENERG POLICY, V61, P977, DOI 10.1016/j.enpol.2013.06.030
   Sikkema R., 2014, THESIS, P274
   Skoropanov S. G., 1968, RECLAMATION CULTIVAT
   Skroppa T., 2012, STATE FOREST GENETIC
   Slepetiene A, 2010, ZEMDIRBYSTE, V97, P25
   Soil Survey Division Staff, 1993, USDA HDB, V18
   Spangenberg JH, 2014, ECOL ECON, V104, P22, DOI 10.1016/j.ecolecon.2014.04.025
   Statistisk sentralbyra (SSB), 2012, AGR AR REG CROP TIM
   Statistisk sentralbyra (SSB), 2014, COMM ROUNDW REM 1000
   Statistisk sentralbyra (SSB), 2016, POP POP CHANG Q1 201
   Statistisk sentralbyra (SSB), 2013, COMM REM IND ROUNDW
   Statistisk sentralbyra (SSB), 2014, JORDBR OG MILJ
   Statistisk sentralbyra (SSB), 2002, BEF RUT
   Syrbe RU, 2012, ECOL INDIC, V21, P80, DOI 10.1016/j.ecolind.2012.02.013
   Szabo F, 1999, LIVEST PROD SCI, V61, P253, DOI 10.1016/S0301-6226(99)00074-3
   Tognetti S, 2009, ESTUDIO FAO MONTES, V155
   Trepel M, 2010, ECOL COMPLEX, V7, P320, DOI 10.1016/j.ecocom.2010.02.006
   Vesterdal L, 2013, FOREST ECOL MANAG, V309, P4, DOI 10.1016/j.foreco.2013.01.017
   Wallace KJ, 2007, BIOL CONSERV, V139, P235, DOI 10.1016/j.biocon.2007.07.015
   Wei YW, 2013, PLOS ONE, V8, DOI [10.1371/journal.pone.0072201, 10.1371/journal.pone.0060044]
   Wohl E, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms2274
   Worrall F, 2003, SCI TOTAL ENVIRON, V312, P133, DOI 10.1016/S0048-9697(03)00226-2
NR 140
TC 16
Z9 18
U1 1
U2 51
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2212-0416
J9 ECOSYST SERV
JI Ecosyst. Serv.
PD DEC
PY 2017
VL 28
SI SI
BP 283
EP 297
DI 10.1016/j.ecoser.2017.08.005
PN C
PG 15
WC Ecology; Environmental Sciences; Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA FO7VY
UT WOS:000417087100004
DA 2022-08-23
ER

PT J
AU Bi, S
   Ali, Z
   Wang, M
   Wu, TX
   Qi, GL
AF Bi, Sheng
   Ali, Zafar
   Wang, Meng
   Wu, Tianxing
   Qi, Guilin
TI Learning heterogeneous graph embedding for Chinese legal document
   similarity
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Legal document similarity; Legal-domain knowledge graph; Representation
   learning; Heterogeneous content; Semantic alignment
AB Measuring the similarity between legal documents to find prior documents from a massive collection that are similar to a current document is an essential component in legal assistant systems. This type of system can automatically link related legal documents to ensure that the same situations are treated identically in judicial practice. Most existing methodologies propose text-and citation-based methods to calculate the similarity between legal documents. However, those methods have difficulty capturing the semantics of many legal entities and giving more accurate similarity. The main reason is the lack of legal domain knowledge and citation relations between legal documents. We introduce practical, generic heterogeneous graph representation learning based on a legal heterogeneous knowledge graph to address these challenges. Specifically, we construct a heterogeneous knowledge graph containing legal entities and documents and develop a graph-based embedding model called L-HetGRL. A legal entity can simply be simply a legal-related encyclopedia entry that contains legal domain knowledge utilized to enhance document representation. L-HetGRL incorporates learning legal document information and external legal domain knowledge in a unified manner by jointly considering heterogeneous content. In addition, we designed a legal case-aware semantic alignment module that effectively combines legal entities and their semantics in documents, thus improving the representation of entities. We conducted comprehensive experiments, including similar case matching and charge prediction, to evaluate the performance of our L-HetGRL on two real-world datasets. As a result, the experimental evaluations demonstrate that L-HetGRL outperforms other competitive baselines. In addition, we present a series of suggestions for document representation in the legal domain, which provide valuable guidelines for follow-up studies. (C) 2022 Published by Elsevier B.V.
C1 [Bi, Sheng; Ali, Zafar; Wang, Meng; Wu, Tianxing; Qi, Guilin] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China.
   [Bi, Sheng] Southeast Univ, Judicial Big Data Res Ctr, Sch Law, Nanjing, Jiangsu, Peoples R China.
RP Qi, GL (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China.
EM bisheng@seu.edu.cn; zafarali@seu.edu.cn; meng.wang@seu.edu.cn;
   tianxingwu@seu.edu.cn; gqi@seu.edu.cn
RI ; Ali, Zafar/D-7320-2017
OI Bi, Sheng/0000-0001-8292-2110; Ali, Zafar/0000-0002-6404-645X
FU Natural Science Foundation of China [U21A20488, 62006040]; National
   Social Science Foundation of China [21AFX013]; Fundamental Research
   Funds for the Central Universities, China [2242021k10011]; Project for
   Doctor of Entrepreneurship and Innovation in Jiangsu Province, China
   [JSSCBS20210126]
FX This work was supported by the Natural Science Foundation of China
   [U21A20488, 62006040] ; the National Social Science Foundation of China
   [21AFX013] ; the Fundamental Research Funds for the Central
   Universities, China [2242021k10011] ; the Project for the Doctor of
   Entrepreneurship and Innovation in Jiangsu Province, China
   [JSSCBS20210126] .
CR Alshahrani M, 2017, BIOINFORMATICS, V33, P2723, DOI 10.1093/bioinformatics/btx275
   Ba L.J., 2015, INT C LEARN REPR
   Baldi P, 2012, P ICML WORKSH UNS LE, P37
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Brown P. F., 1992, Computational Linguistics, V18, P467
   Cai TT, 2022, IEEE T KNOWL DATA EN, V34, P1993, DOI 10.1109/TKDE.2020.3003047
   Chang SY, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P119, DOI 10.1145/2783258.2783296
   Chen T, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P295, DOI 10.1145/3018661.3018735
   Cohen W., 2003, KDD WORKSH DAT CLEAN, V3, P73
   Cui P, 2019, IEEE T KNOWL DATA EN, V31, P833, DOI 10.1109/TKDE.2018.2849727
   Devlin J, 2018, P 2019 C N AM CHAPT
   Dong YX, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4861
   Dong YX, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P135, DOI 10.1145/3097983.3098036
   Gao HC, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3364
   Gupta V, 2020, AAAI CONF ARTIF INTE, V34, P7863
   Hamilton WL, 2017, ADV NEUR IN, V30
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hong ZL, 2020, IEEE IJCNN
   Huang P.-Y., 2019, ARXIV PREPRINT ARXIV
   Huang PS, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P2333
   Jeh G., 2002, P 8 ACM SIGKDD INT C, P538, DOI DOI 10.1145/775047.775126
   KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103
   Kingma Diederik P, 2014, ICLR 2014 INT C LEAR
   Kipf T. N., 2016, ARXIV161107308, P1
   Kumar S, 2011, P 4 ANN ACM BANG C, P1
   Kumar S., 2011, COMPUTE, P17
   Kusner MJ, 2015, PR MACH LEARN RES, V37, P957
   Leszczynski L, 2020, INT J SEMIOTIC LAW, V33, P231, DOI 10.1007/s11196-019-09674-9
   Liu QB, 2021, KNOWL-BASED SYST, V227, DOI 10.1016/j.knosys.2021.107186
   Liu RL, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9235176
   Liu X., THE OPEN, V2, P43
   Liu Y, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P5070
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Minocha A, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P1085, DOI 10.1145/2740908.2744717
   Neculoiu P, 2016, P 1 WORKSH REPR LEAR, P148, DOI [10.18653/v1/w16-1617, DOI 10.18653/V1/W161617]
   Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711
   Pennington J., 2014, EMNLP, P1532
   Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
   Le Q, 2014, PR MACH LEARN RES, V32, P1188
   SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0
   Sedgwick P, 2012, BRIT MED J, V344, DOI 10.1136/bmj.e4483
   Shao YQ, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3501
   Shen S., 2020, COLING, P100
   Shen Yelong, 2014, P 23 ACM INT C C INF, DOI DOI 10.1145/2661829.2661935
   Sheng Bi, 2019, Knowledge Science, Engineering and Management. 12th International Conference, KSEM 2019. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 11775), P628, DOI 10.1007/978-3-030-29551-6_56
   Shi C., 2015, P 24 ACM INT C INF K, P453
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Song XY, 2022, KNOWL-BASED SYST, V241, DOI 10.1016/j.knosys.2022.108274
   Song XY, 2021, INFORM SCIENCES, V580, P510, DOI 10.1016/j.ins.2021.08.100
   Steinbach M, 2000, IN TEXT MIN WORKSH K
   Sun Y., 2012, SYNTH LECT DATA MIN, V3, P1, DOI DOI 10.2200/S00433ED1V01Y201207DMK005
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Wang M, 2019, IEEE DATA MINING, P1360, DOI 10.1109/ICDM.2019.00174
   Wang M, 2018, LECT NOTES COMPUT SC, V11136, P513, DOI 10.1007/978-3-030-00671-6_30
   Wang Y., 2021, ABS210313868 CORR
   Wang Z., 2021, WWW, P3349
   Wu L., 2018, ARXIV E PRINTS
   Wu Y, 2021, KNOWL-BASED SYST, V220, DOI 10.1016/j.knosys.2021.106884
   Xiao C., 2019, ABS191108962 CORR
   Xiya Cheng, 2020, Natural Language Processing and Chinese Computing. 9th CCF International Conference, NLPCC 2020. Proceedings. Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science (LNAI 12430), P667, DOI 10.1007/978-3-030-60450-9_53
   Xu C, 2021, IEEE T CYBERNETICS, DOI 10.1109/TCYB.2021.3062830
   Xu LC, 2017, WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P859, DOI 10.1145/3041021.3054268
   Xue GT, 2022, NEUROCOMPUTING, V472, P212, DOI 10.1016/j.neucom.2021.03.138
   Yang C, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P2111
   Yang S., 2021, ABS210407295 CORR
   Yin H, 2021, WORLD WIDE WEB, V24, P1027, DOI 10.1007/s11280-020-00850-7
   Yokoi Sho, 2020, P 2020 C EMPIRICAL M, P2944
   Zhang CX, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P793, DOI 10.1145/3292500.3330961
   Zhang YZ, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P399, DOI 10.1145/3178876.3186106
   Zhao K, 2021, KNOWL-BASED SYST, V219, DOI 10.1016/j.knosys.2021.106888
NR 71
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD AUG 17
PY 2022
VL 250
AR 109046
DI 10.1016/j.knosys.2022.109046
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1W5KI
UT WOS:000806811900014
DA 2022-08-23
ER

PT J
AU Ding, LH
   Sun, B
   Shi, P
AF Ding Lian-Hong
   Sun Bin
   Shi Peng
TI Empirical study of knowledge network based on complex network theory
SO ACTA PHYSICA SINICA
LA Chinese
DT Article
DE microsoft concept graph; complex network; subnet extraction; approximate
   average path
ID INFLUENTIAL SPREADERS; EVOLUTION
AB Knowledge graph is a hot topic in artificial intelligence area and has been widely adopted in intelligent search and question-and-answer system. Knowledge graph can be regarded as a complex network system and analyzed by complex network theory, which studies the interaction or relationship between various factors and basic characteristics of complex system. Its characteristics and their physical meanings are very helpful in understanding the nature of the knowledge graph. Concept graph is a large-scaled knowledge graph published by Microsoft. In this paper, we construct a huge complex network according to Microsoft's concept graph. Its complex network characteristics, such as degree distribution, average shortest distance, clustering coefficient and degree correlation, are calculated and analyzed. The concept graph is not a connected network and its scale is very large; an approach is proposed to extract its largest connected subnet. The method has obvious advantages in both time complexity and space complexity. In this paper, we also present a method of calculating the approximate average shortest path of the largest connected subnet. The method estimates the maximum and minimum value of the shortest distance between nodes according to the distance between the central node and the network layer that the node belongs to and the distance between different layers. In order to calculate the clustering coefficient, different methods are introduced for nodes with different degree values and Map/Reduce idea is adopted to reduce the time cost. The experimental results show that the largest subnet of the concept graph is an ultra-small world network with the characteristics of scale-free. The average shortest path length decreases towards 4 with the network size increasing, which can be easily explained by the diamond-shaped network structure. The concept graph is a disassortative network where low degree nodes tend to connect to high degree nodes. The subConcepts account for 99.5% of nodes in the innermost k-core after k-shell decomposition. It shows that the subConcepts play an important role in the connectivity of network. The absence of subConcept affects the complexness of concept graph most, the concept next, and the instance least. The 82% instance nodes and 40% concept nodes of the concept graph each have a degree value of 1. It is believed that compared with the concept words, the instance words do not lead to the ambiguity in the understanding of natural language, caused by polysemy.
C1 [Ding Lian-Hong; Sun Bin] Beijing Wuxi Univ, Sch Informat, Beijing 101149, Peoples R China.
   [Shi Peng] Univ Sci & Technol Beijing, Natl Ctr Mat Serv Safety, Beijing 100083, Peoples R China.
RP Shi, P (通讯作者)，Univ Sci & Technol Beijing, Natl Ctr Mat Serv Safety, Beijing 100083, Peoples R China.
EM shipengustb@sina.com
FU National Key R&D Program of China [2017YFB0203703]; Key Program of the
   National Natural Science Foundation of China [71831001]; Science and
   Technology Plan General Program of Beijing Municipal Education
   Commission, China [KM201910037186]; Social Science Grant of Beijing
   Municipal Education Commission, China [SM201610037001]; Research Team
   Collaborative Fund of Beijing Wuzi University, China [2017GG05]
FX Project supported by the National Key R&D Program of China (Grant No.
   2017YFB0203703), the Key Program of the National Natural Science
   Foundation of China (Grant No. 71831001), the Science and Technology
   Plan General Program of Beijing Municipal Education Commission, China
   (Grant No. KM201910037186), the Social Science Grant of Beijing
   Municipal Education Commission, China (Grant No. SM201610037001), and
   the Research Team Collaborative Fund of Beijing Wuzi University, China
   (Grant No. 2017GG05).
CR Aiello W., 2000, Proceedings of the Thirty Second Annual ACM Symposium on Theory of Computing, P171, DOI 10.1145/335305.335326
   Aiello W, 2001, ANN IEEE SYMP FOUND, P510, DOI 10.1109/SFCS.2001.959927
   Albert R, 1999, NATURE, V401, P130, DOI 10.1038/43601
   Albert R, 2000, NATURE, V406, P378, DOI 10.1038/35019019
   Almog A, 2015, NEW J PHYS, V17, DOI 10.1088/1367-2630/17/1/013009
   An HZ, 2014, ENERGY, V74, P254, DOI 10.1016/j.energy.2014.06.095
   Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509
   Broder A, 2000, COMPUT NETW, V33, P309, DOI 10.1016/S1389-1286(00)00083-9
   Colombo A, 2017, IEEE T AUTOMAT CONTR, V62, P4933, DOI 10.1109/TAC.2017.2676464
   Fiedor P, 2015, ACTA PHYS POL A, V127, P863, DOI 10.12693/APhysPolA.127.863
   Hagberg A. A., 2008, P 7 PYTH SCI C PAS, P11
   Han DD, 2017, ACTA PHYS SIN-CH ED, V66, DOI 10.7498/aps.66.248901
   Holme P, 2002, PHYS REV E, V65, DOI 10.1103/PhysRevE.65.056109
   Hu YH, 2009, PHYSICA A, V388, P2061, DOI 10.1016/j.physa.2008.12.016
   Jiang J, 2016, CHINESE PHYS LETT, V33, DOI 10.1088/0256-307X/33/10/108901
   Kitsak M, 2010, NAT PHYS, V6, P888, DOI [10.1038/nphys1746, 10.1038/NPHYS1746]
   Kong JT, 2018, ACTA PHYS SIN-CH ED, V67, DOI 10.7498/aps.67.20172295
   Li Q, 2014, PHYSICA A, V404, P47, DOI 10.1016/j.physa.2014.02.041
   [刘志宏 Liu Zhihong], 2014, [计算机研究与发展, Journal of Computer Research and Development], V51, P2788
   Newman MEJ, 2003, PHYS REV E, V67, DOI 10.1103/PhysRevE.67.026126
   Niu RW, 2016, CHINESE PHYS LETT, V33, DOI 10.1088/0256-307X/33/6/068901
   Pien KC, 2015, TRANSPORTMETRICA A, V11, P772, DOI 10.1080/23249935.2015.1087233
   Qiu L, 2016, ACTA PHYS SIN-CH ED, V65, DOI 10.7498/aps.65.198901
   Rattigan M. J., 2006, P 12 ACM SIGKDD INT, P357
   Ruan YR, 2017, ACTA PHYS SIN-CH ED, V66, DOI 10.7498/aps.66.038902
   Ruan YR, 2016, CHINESE PHYS LETT, V33, DOI 10.1088/0256-307X/33/2/028901
   Sun YF, 2018, ACTA PHYS SIN-CH ED, V67, DOI 10.7498/aps.67.20172490
   Tang Jin-Tao, 2011, Journal of Software, V22, P2279, DOI 10.3724/SP.J.1001.2011.03924
   Wan X, 2015, ACCIDENT ANAL PREV, V82, P90, DOI 10.1016/j.aap.2015.05.015
   Wang GJ, 2018, COMPUT ECON, V51, P607, DOI 10.1007/s10614-016-9627-7
   Wang Z Y, 2015, ACM INT C INF KNOWL, P653
   Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918
   Xing X, 2017, ACTA PHYS SIN-CH ED, V66, DOI 10.7498/aps.66.230501
   Ye K H, 2018, RESOURCES DEV MARKET, V34, P59
NR 34
TC 0
Z9 0
U1 12
U2 33
PU CHINESE PHYSICAL SOC
PI BEIJING
PA P O BOX 603, BEIJING 100080, PEOPLES R CHINA
SN 1000-3290
J9 ACTA PHYS SIN-CH ED
JI Acta Phys. Sin.
PD JUN 20
PY 2019
VL 68
IS 12
AR 128902
DI 10.7498/aps.68.20190106
PG 15
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA IE6SH
UT WOS:000472505500034
OA gold
DA 2022-08-23
ER

PT J
AU Qiu, ZP
   Hu, YF
   Wu, X
AF Qiu, Zhaopeng
   Hu, Yunfan
   Wu, Xian
TI Graph Neural News Recommendation with User Existing and Potential
   Interest Modeling
SO ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
LA English
DT Article
DE Recommendation; graph neural networks; knowledge graph
AB Personalized news recommendations can alleviate the information overload problem. To enable personalized recommendation, one critical step is to learn a comprehensive user representation to model her/his interests. Many existing works learn user representations from the historical clicked news articles, which reflect their existing interests. However, these approaches ignore users' potential interests and pay less attention to news that may interest the users in the future. To address this problem, we propose a novel Graph neural news Recommendation model with user Existing and Potential interest modeling, named GREP. Different from existing works, GREP introduces three modules to jointly model users' existing and potential interests: (1) Existing Interest Encoding module mines user historical clicked news and applies the multi-head selfattention mechanism to capture the relatedness among the news; (2) Potential Interest Encoding module leverages the graph neural network to explore the user potential interests on the knowledge graph; and (3) Bidirectional Interaction module dynamically builds a news-entity bipartite graph to further enrich two interest representations. Finally, GREP combines the existing and potential interest representations to represent the user and leverages a prediction layer to estimate the clicking probability of the candidate news. Experiments on two real-world large-scale datasets demonstrate the state-of-the-art performance of GREP.
C1 [Qiu, Zhaopeng; Wu, Xian] Tencent, 8 Xibei Wang East Rd, Beijing 100080, Peoples R China.
   [Hu, Yunfan] Santa Clara Univ, Camino Real, Santa Clara, CA 95053 USA.
RP Wu, X (通讯作者)，Tencent, 8 Xibei Wang East Rd, Beijing 100080, Peoples R China.
EM zhaopengqiu@tencent.com; freddy@pku.edu.cn; azonhu@tencent.com
OI Qiu, Zhaopeng/0000-0001-8622-2402; Hu, Yunfan/0000-0001-8060-248X
FU National Key R&D Program of China [2018YFC0117000]
FX This work was supported by National Key R&D Program of China, No.
   2018YFC0117000.
CR An MX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P336
   [Anonymous], 2016, STAT-US
   Bansal Trapit, 2015, P 9 ACM C REC SYST, DOI DOI 10.1145/2792838.2800186
   Cheng H.T., 2016, P 1 WORKSHOP DEEP LE, P7, DOI DOI 10.1145/2988450.2988454
   Ge SY, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P2863, DOI 10.1145/3366423.3380050
   Gulla JA, 2017, 2017 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2017), P1042, DOI 10.1145/3106426.3109436
   Guo HF, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1725
   Ho YC, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P253, DOI 10.1145/2556195.2566589
   Hu LM, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P4255
   Hu LM, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2019.102142
   IJNTEMA W, 2010, P 2010 EDBT ICDT WOR, P1
   Kipf T.N, 2016, INT C LEARN REPR ICL
   Li L., 2010, P 19 INT C WORLD WID, P661, DOI [DOI 10.1145/1772690.1772758, 10.1145/1772690.1772758]
   Li Q., 2018, ARXIV180107606
   Lian JX, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3805
   Lin Yuanguo, 2021, ABS210910665 CORR
   Liu DY, 2020, RECSYS 2020: 14TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, P200, DOI 10.1145/3383313.3412237
   Morales G. D. F., 2012, P 5 ACM INT C WEB SE, P153, DOI DOI 10.1145/2124295.2124315
   Okura S, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1933, DOI 10.1145/3097983.3098108
   Pennington J., 2014, EMNLP, P1532
   Qi Tao, 2021, P 59 ANN M ASS COMPU, V1, P5446, DOI [10.18653/v1/2021. acl-long.423, DOI 10.18653/V1/2021.ACL-LONG.423]
   Qiu Z, 2020, COLING, P2096
   Qiu ZP, 2021, AAAI CONF ARTIF INTE, V35, P4320
   Qiu ZP, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P139, DOI 10.1145/3357384.3358013
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Shi Yudong, 2020, ARXIV PREPRINT ARXIV
   Wang C., 2011, P 17 ACM SIGKDD INT, P448, DOI DOI 10.1145/2020408.2020480
   Wang HY, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P836
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wu CH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3863
   Wu CH, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2576, DOI 10.1145/3292500.3330665
   Wu Chuhan, 2019, EMNLP IJCNLP, P6390
   Wu FZ, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3597
   Wu ZH, 2021, IEEE T NEUR NET LEAR, V32, P4, DOI 10.1109/TNNLS.2020.2978386
   Zhang H, 2019, IEEE DATA MINING, P1456, DOI 10.1109/ICDM.2019.00190
   Zhang XY, 2021, WEB CONFERENCE 2021: COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2021), P412, DOI 10.1145/3442442.3452329
   Zheng GJ, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P167, DOI 10.1145/3178876.3185994
   Zolaktaf Z, 2018, PROC INT CONF DATA, P149, DOI 10.1109/ICDE.2018.00023
NR 38
TC 0
Z9 0
U1 3
U2 3
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1556-4681
EI 1556-472X
J9 ACM T KNOWL DISCOV D
JI ACM Trans. Knowl. Discov. Data
PD OCT
PY 2022
VL 16
IS 5
AR 96
DI 10.1145/3511708
PG 17
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1P6WJ
UT WOS:000802146500016
DA 2022-08-23
ER

PT J
AU Nagy, S
   Hayde, M
   Panzenbock, B
   Adlassnig, KP
   Pollak, A
AF Nagy, S
   Hayde, M
   Panzenbock, B
   Adlassnig, KP
   Pollak, A
TI Diagnosis of toxoplasmosis during pregnancy: computer-assisted
   interpretation of serological test results
SO WIENER KLINISCHE WOCHENSCHRIFT
LA German
DT Article
DE computer-assisted decision making; computer-assisted drug-therapy;
   expert system; serology; toxoplasmosis
ID CONGENITAL TOXOPLASMOSIS
AB Primary infection with Toxoplasma gondii during pregnancy can result in fetal infection with serious sequelae for the unborn if not treated properly. Early diagnosis enables drug therapy and significantly reduces the risk of fetal disease. A systematic serological screening procedure was established in Austria in 1975 to detect primary toxoplasma infection as early as possible during pregnancy. Since the screening program is based solely on observation and interpretation of serological data, the question arises whether a knowledge-based system for automatic interpretation can achieve a sufficient interpretative accuracy for introduction to routine work.
   For this reason the system TOXOPERI-I was developed. The system is aimed at facilitating routine laboratory work, as well as assuring quality by setting standards for therapy. The required knowledge base was designed as a knowledge graph, each state representing a certain interpretation. One or more available serological test results cause the knowledge graph to change its current state. if all available test results are processed, the final state reached corresponds to the respective current interpretation for the patient.
   A retrospective analysis of 1000 pregnant women yielded a total diagnostic sensitivity and specificity of over 99% in comparison with the clinician's diagnosis which was used as the Gold Standard.
C1 UNIV VIENNA, INST MED COMP WISSENSCH, VIENNA, AUSTRIA.
   UNIV VIENNA, KLIN KINDER & JUGENDHEILKUNDE, ABT NEONATOL ANGEBORENE STORUNGEN & INTENS MED, VIENNA, AUSTRIA.
CR ADLASSNIG KP, 1993, 1 FACHT GI FG FEB 4
   ASPOCK H, 1992, SCAND J INFECT DIS, P32
   COUVREUR J, 1984, ANN PEDIATR-PARIS, V31, P815
   DAFFOS F, 1988, NEW ENGL J MED, V318, P271, DOI 10.1056/NEJM198802043180502
   Desmonts G, 1979, PERINATAL MED
   FLAMM H, 1975, OSTERR ARZTEZTG, V30, P15
   KNERER B, 1995, WIEN KLIN WOCHENSCHR, V107, P137
   KOPPE JG, 1974, EUR J OBSTET GYN R B, V4, P101, DOI 10.1016/0028-2243(74)90029-X
   NAGY S, 1995, 40 JAHR DTSCH GES ME
   NAGY S, 1993, MES91993 I MED COMP
   POLLAK A, 1995, WIEN KLIN WOCHENSCHR, V107, P111
   REMINGTON JS, 1990, INFECT DIS FETUS UNB
   THALHAMMER O, 1983, ZBL GYNAKOL, V105, P1086
NR 13
TC 4
Z9 4
U1 0
U2 1
PU SPRINGER WIEN
PI WIEN
PA SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA
SN 0043-5325
EI 1613-7671
J9 WIEN KLIN WOCHENSCHR
JI Wien. Klin. Wochen.
PD SEP 5
PY 1997
VL 109
IS 16
BP 641
EP 646
PG 6
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA XV047
UT WOS:A1997XV04700004
PM 9412085
DA 2022-08-23
ER

PT J
AU Zhao, Y
   Li, ZQ
   Deng, W
   Xie, RB
   Li, Q
AF Zhao, Yu
   Li, Zhiquan
   Deng, Wei
   Xie, Ruobing
   Li, Qing
TI Learning entity type structured embeddings with trustworthiness on noisy
   knowledge graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Entity type; Noise detection; Trustworthiness
AB Knowledge graphs (KGs) automatic construction generally involves fine-grained entity typing, i.e., assigning the types to given entities as (entity, entity type). Since the non-negligible inaccuracy of entity typing systems and lack of sufficient human supervision, KGs inevitably face entity type noises. However, most conventional entity type embedding models unreasonably assume that all entity type instances in existing KGs are completely correct, which ignore noises and could lead to potential errors for down-stream tasks. To address this issue, we propose TrustE to build trustworthiness-aware entity type structured embeddings, which takes possible entity type noises into consideration for learning better representations. Specifically, since entities and entity types are completely distinct objects, we encode them in separate entity space and entity type space with a structural projecting matrix, and learn entity type embeddings with tuple trustworthiness. To make the trustworthiness more universal, we only utilize the internal structural knowledge in existing KGs and build two tuple trustworthiness considering the local tuple and global triple information respectively, which correspondingly makes it more challenging due to the limited knowledge. We evaluate our models on three tasks: entity type noise detection, entity type prediction and classification. Experimental results on real-world datasets (FB15kET and YAGO43kET) show that our models outperform all baselines on all tasks, which verify the capability of TrustE in learning better entity type structural embeddings on noisy KGs. The source code and data of this paper can be obtained from https://github.com/QuanSWUFE/TrustE (C) 2020 Elsevier B.V. All rights reserved.
C1 [Zhao, Yu; Li, Zhiquan; Li, Qing] Southwestern Univ Finance & Econ, Fintech Innovat Ctr, Sch Econ Informat Engn, Chengdu, Peoples R China.
   [Zhao, Yu] Southwestern Univ Finance & Econ, Financial Intelligence & Financial Engn Key Lab S, Chengdu, Peoples R China.
   [Deng, Wei] Southwestern Univ Finance & Econ, Ctr Stat Res, Chengdu, Peoples R China.
   [Xie, Ruobing] Tencent, WeChat Search Applicat Dept, Search Prod Ctr, Shenzhen, Peoples R China.
RP Deng, W (通讯作者)，Southwestern Univ Finance & Econ, Ctr Stat Res, Chengdu, Peoples R China.
EM dengwei@swufe.edu.cn
OI Deng, Wei/0000-0001-7487-8519
FU National Key Research and Development Program of China [2016YFB1000905];
   National Natural Science Foundation of China [61906159]; Sichuan Science
   and Technology Program, China [2018JY0607]; Fundamental Research Funds
   for the Central Universities, China [JBK2003008]; Fintech Innovation
   Center, China; Financial Intelligence and Financial Engineering Key
   Laboratory of Sichuan Province, China; National Nature Science
   Foundation of China [61936001]
FX This work was supported by the National Key Research and Development
   Program of China (No. 2016YFB1000905), the National Natural Science
   Foundation of China under Grant No. 61906159; the Sichuan Science and
   Technology Program, China under Grant No. 2018JY0607; the Fundamental
   Research Funds for the Central Universities, China under Grant No.
   JBK2003008; Fintech Innovation Center, China; the Financial Intelligence
   and Financial Engineering Key Laboratory of Sichuan Province, China, the
   State Key Program of National Nature Science Foundation of China (No.
   61936001).
CR Balasubramaniam T, 2019, P WWW
   Barati M, 2019, J WEB SEMANT, V59, DOI 10.1016/j.websem.2019.100533
   Barbosa D, 2018, P NAACL
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2011, AAAI
   Bose A.J., 2019, ARXIV PREPRINT ARXIV
   Demsar J, 2006, J MACH LEARN RES, V7, P1
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Elsahar H., 2018, P C N AM CHAPTER ASS, P218, DOI DOI 10.18653/V1/N18-1020
   Gupta Nitish, 2017, P C EMP METH NAT LAN
   Heindorf S, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P327, DOI 10.1145/2983323.2983740
   Jain P., 2018, P ACL
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Jia SB, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2865, DOI 10.1145/3308558.3313586
   Jiang SP, 2012, IEEE DATA MINING, P912, DOI 10.1109/ICDM.2012.156
   Kotnis B., 2019, AUTOMATED KNOWLEDGE
   Li Q, 2019, KNOWL-BASED SYST, V176, P122, DOI 10.1016/j.knosys.2019.03.025
   Liang JQ, 2017, AAAI CONF ARTIF INTE, P1178
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lv X, 2018, EMNLP, P1971
   Ma Y., 2014, CCIS, V480, P29, DOI DOI 10.1007/978-3-662-45495-4
   Makni B, 2019, SEMANT WEB, V10, P823, DOI 10.3233/SW-190363
   Melo A., 2017, P ACM, P22
   Moon C, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2215, DOI 10.1145/3132847.3133095
   Nathani D., 2019, P ACL
   Neelakantan A., 2015, ARXIV PREPRINT ARXIV
   Neil D., 2017, P NEURIPS
   Nemenyi P., 1963, DISTRIBUTION FREE MU
   Nguyen D.Q., 2019, P NAACL HLT 2019, P2180, DOI DOI 10.18653/V1/N19-1226
   Nguyen D. Q., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Paulheim H, 2014, INT J SEMANT WEB INF, V10, P63, DOI 10.4018/ijswis.2014040104
   Paulheim H, 2013, LECT NOTES COMPUT SC, V8218, P510, DOI 10.1007/978-3-642-41335-3_32
   Pujara J., 2017, P 2017 C EMP METH NA, P1751, DOI DOI 10.18653/V1/D17-1184
   Ren X., 2016, P EMNLP
   Ren X, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1825, DOI 10.1145/2939672.2939822
   Samatova N., 2017, P PACIS
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun Y., 2019, P KDD
   Tempelmeier N, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1297, DOI 10.1145/3178876.3186028
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao H, 2017, AAAI CONF ARTIF INTE, P3104
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xie RB, 2018, AAAI CONF ARTIF INTE, P4954
   Zaveri A, 2016, SEMANT WEB, V7, P63, DOI 10.3233/SW-150175
   Zhang R., 2018, P AAAI
   Zhou Ben, 2018, P 2018 C EMP METH NA, P2065
NR 55
TC 1
Z9 1
U1 1
U2 13
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD MAR 5
PY 2021
VL 215
AR 106630
DI 10.1016/j.knosys.2020.106630
EA JAN 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QK5XY
UT WOS:000620459300006
DA 2022-08-23
ER

PT J
AU Han, X
   Zhang, CH
   Sun, TT
   Ji, Y
   Hu, Z
AF Han, Xiao
   Zhang, Chunhong
   Sun, Tingting
   Ji, Yang
   Hu, Zheng
TI A Triple-Branch Neural Network for Knowledge Graph Embedding
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph; neural networks; representation learning
AB The distributed representation of knowledge graphs (KGs), which embeds the structured graphs into low-dimensional embedding spaces, is widely used to facilitate various applications of AI, such as information retrieval and question answering. The primary elements of KGs, the entities viewed as nodes and the relations regarded as links between entities, naturally make up the local embedding context for each other, which is called the multi-restriction property of KGs. However, this property is not fully explored by previous models, where either only part of the multi-restriction is captured, or the capability of the embedding model is limited to a fixed function without clear interpretation. To address this issue, we propose TBNN, a triple-branch neural network to learn the embeddings of KGs. In particular, the embedding of any element of a KG is determined by its multi-restriction via an interaction layer followed by parallel branched layers. Thus, the entities and relations can be treated equivalently in spite of their seeming differences in the original KG. We define the loss function of TBNN based on the confidence score of the three elements of each triple. In addition, we propose using the log-sum-exp pairwise loss to smooth the hinge loss, which results in better performance. Empirically, we evaluate our model on the tasks of link prediction and triple classification with the subsets of WordNet and Freebase. Experiment results show that our model performs better than the baselines, especially providing stable performance for relations with different mapping properties.
C1 [Han, Xiao; Zhang, Chunhong; Sun, Tingting; Ji, Yang] Beijing Univ Posts & Telecommun, Minist Educ, Key Lab Universal Wireless Commun, Beijing, Peoples R China.
   [Hu, Zheng] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China.
RP Han, X (通讯作者)，Beijing Univ Posts & Telecommun, Minist Educ, Key Lab Universal Wireless Commun, Beijing, Peoples R China.
EM hanxiao1007@bupt.edu.cn
FU National Natural Science Foundation of China [61602048, 61601046,
   61520106007]; BUPT-SICE Excellent Graduate Students Innovation Fund
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61602048, Grant 61601046, and Grant
   61520106007 and in part by the BUPT-SICE Excellent Graduate Students
   Innovation Fund in 2016.
CR [Anonymous], PROBABILISTIC REASON
   Bergstra J., 2010, P 9 PYTH SCI C JUN 2, V4, P1
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2012, AISTATS, V351, P423
   Bordes A, 2011, AAAI, V6, P6
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Das R., 2016, CHAINS REASONING ENT
   Dettmers T, 2017, CONVOLUTIONAL 2D KNO
   Ebisu T, 2017, TORUSE KNOWLEDGE GRA
   Fan M, 2017, PATTERN RECOGN LETT, V93, P31, DOI 10.1016/j.patrec.2016.09.005
   Glorot X., 2010, J MACH LEARN RES, V9, P249
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Han X, 2018, LECT NOTES ARTIF INT, V10938, P247, DOI 10.1007/978-3-319-93037-4_20
   Hermjakob U., 2000, P 6 WORLD MULT SYST
   Huang PS, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P2333
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kingma D. P., 2017, ARXIV
   Lin Y, 2015, MODELING RELATION PA
   Lin YC, 2015, ADV SOC SCI EDUC HUM, V39, P2181
   Ma SH, 2017, LECT NOTES ARTIF INT, V10534, P717, DOI 10.1007/978-3-319-71249-9_43
   Mikolov T., 2013, ADV NEURAL INFORM PR
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nickel M., 2016, P AAAI, V2, P2
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Perozzi B., 2014, P 20 ACM SIGKDD INT, P701, DOI DOI 10.1145/2623330.2623732
   Ravishankar S., 2017, REVISITING SIMPLE NE
   Rinaldi F, 2003, LECT NOTES ARTIF INT, V2773, P785
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang DX, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1225, DOI 10.1145/2939672.2939753
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wical K., 2000, U.S. Patent, Patent No. [6038560A, 6038560]
   Wical K., 1999, U.S. Patent, Patent No. [5940821A, 5940821]
   Xiao H, 2017, AAAI CONF ARTIF INTE, P3104
   Xie R, 2016, IMAGE EMBODIED KNOWL
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Yang B., 2014, EMBEDDING ENTITIES R
   Zhang CH, 2017, TSINGHUA SCI TECHNOL, V22, P185
   Zhang K, 2017, IEEE SYS MAN CYBERN, P918, DOI 10.1109/SMC.2017.8122727
NR 45
TC 2
Z9 4
U1 4
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2018
VL 6
BP 76606
EP 76615
DI 10.1109/ACCESS.2018.2884012
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HF8JQ
UT WOS:000454488200001
OA gold
DA 2022-08-23
ER

PT J
AU Huang, K
AF Huang, Kai
TI Global trends of researches on bone metastasis A bibliometric and
   visualization study
SO MEDICINE
LA English
DT Review
DE bibliometric analysis; bone metastasis; research trends; visualization
ID LUNG-CANCER; CHEMOTHERAPY; TRIAL
AB Background: Bone metastasis (BM) has become an important health problem. In recent years, studies on BM are growing rapidly, but there were no bibliometric studies regarding BM. This study aimed to illustrate the overall knowledge structure and development trends of BM. Methods: Research datasets were acquired from the Web of Science database. The time span was defined as "1980-2020". VOS viewer and Citespace software was provided to analyze the data and generate visualization knowledge maps. Annual trends of publications, distribution, H-index status, co-authorship status, and research hotspots were analyzed. Results: Six hundred eighty-two publications met the requirement. USA published most papers (264, 38.7%), and both total citations (13,997) and H-index (57) of USA ranked first. The most productive institution on BM is Amgen Inc. (43). Supportive Care in Cancer (24) published the most papers on BM. "Safety", "skeletal related event", "open label", "management", "health", and "prognosis" are the research hotspots in the recent years. Conclusion: In this study, we conduct a systematic and comprehensive analysis on the research in BM field. The publication number was rising in recent years stably. USA contributed mostly not only in quality, but also in quantity. Amgen Inc. published the largest number of articles, Supportive Care in Cancer was the most productive journal related to BM. "Safety", "skeletal related event", "open label", "management", "health", and "prognosis" are the research hotspots in recent years. We believe this study can not only show the global research overview in past 40 years but also point the research trend of BM in the future.
C1 [Huang, Kai] Second Peoples Hosp Changshu, Dept Spinal Orthopaed, Changshu 215500, Jiangsu, Peoples R China.
RP Huang, K (通讯作者)，Second Peoples Hosp Changshu, Dept Spinal Orthopaed, Changshu 215500, Jiangsu, Peoples R China.
EM 39097432@qq.com
CR Borgman CL, 2002, ANNU REV INFORM SCI, V36, P3
   Burdett S, 2008, J CLIN ONCOL, V26, P4617, DOI 10.1200/JCO.2008.17.7162
   Clark PE, 2003, CLIN ORTHOP RELAT R, pS148, DOI 10.1097/01.blo.0000093840.72468.e1
   Coleman RE, 1997, CANCER, V80, P1588, DOI 10.1002/(SICI)1097-0142(19971015)80:8+<1588::AID-CNCR9>3.0.CO;2-G
   Delea T, 2004, ONCOLOGY-BASEL, V67, P390, DOI 10.1159/000082923
   Fajardo-Ortiz D, 2014, J TRANSL MED, V12, DOI 10.1186/s12967-014-0227-9
   Fisch MJ, 2003, J CLIN ONCOL, V21, P1937, DOI 10.1200/JCO.2003.08.025
   Hernandez RK, 2018, BMC CANCER, V18, DOI 10.1186/s12885-017-3922-0
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   HOLLAND JC, 1991, J CLIN ONCOL, V9, P1004, DOI 10.1200/JCO.1991.9.6.1004
   Jacobs WCH, 2013, EUR SPINE J, V22, P1936, DOI 10.1007/s00586-013-2823-4
   Leefmann J, 2016, FRONT HUM NEUROSCI, V10, DOI 10.3389/fnhum.2016.00336
   Lin GX, 2020, EUR SPINE J, V29, P153, DOI 10.1007/s00586-019-06171-2
   Liu YH, 2016, BMJ OPEN, V6, DOI 10.1136/bmjopen-2016-011630
   Mishra S, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0195773
   Ogden TL, 2008, ANN OCCUP HYG, V52, P73, DOI 10.1093/annhyg/men002
   Plana NM, 2017, NEW ENGL J MED, V376, P2489, DOI 10.1056/NEJMc1616626
   Roodman GD, 2004, NEW ENGL J MED, V350, P1655, DOI 10.1056/NEJMra030831
   Sandler A, 2006, NEW ENGL J MED, V355, P2542, DOI 10.1056/NEJMoa061884
   Scagliotti GV, 2008, J CLIN ONCOL, V26, P3543, DOI 10.1200/JCO.2007.15.0375
   Shotton D, 2013, NATURE, V502, P295, DOI 10.1038/502295a
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Walker J, 2013, LUNG CANCER, V79, P46, DOI 10.1016/j.lungcan.2012.09.014
   Wurtz M., 2017, UGESKR LAEGER, V179
   Zabora J, 2001, PSYCHO-ONCOL, V10, P19, DOI 10.1002/1099-1611(200101/02)10:1<19::AID-PON501>3.0.CO;2-6
NR 25
TC 0
Z9 0
U1 8
U2 9
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 0025-7974
EI 1536-5964
J9 MEDICINE
JI Medicine (Baltimore)
PD FEB 11
PY 2022
VL 101
IS 6
AR e28761
DI 10.1097/MD.0000000000028761
PG 10
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA YX3GY
UT WOS:000753996800005
PM 35147101
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wang, ZY
   Ma, DB
   Pang, R
   Xie, F
   Zhang, JX
   Sun, DQ
AF Wang, Ziyi
   Ma, Debin
   Pang, Ru
   Xie, Fan
   Zhang, Jingxiang
   Sun, Dongqi
TI Research Progress and Development Trend of Social Media Big Data (SMBD):
   Knowledge Mapping Analysis Based on CiteSpace
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE social media big data; visualization; CiteSpace; research hotspots;
   research trends; bibliometric analysis
ID RISE
AB Social Media Big Data (SMBD) is widely used to serve the economic and social development of human beings. However, as a young research and practice field, the understanding of SMBD in academia is not enough and needs to be supplemented. This paper took Web of Science (WoS) core collection as the data source, and used traditional statistical methods and CiteSpace software to carry out the scientometrics analysis of SMBD, which showed the research status, hotspots and trends in this field. The results showed that: (1) More and more attention has been paid to SMBD research in academia, and the number of journals published has been increased in recent years, mainly in subjects such as Computer Science Engineering and Telecommunications. The results were published primarily in IEEE Access Sustainability and Future Generation Computer Systems the International Journal of eScience and so on; (2) In terms of contributions, China, the United States, the United Kingdom and other countries (regions) have published the most papers in SMBD, high-yield institutions also mainly from these countries (regions). There were already some excellent teams in the field, such as the Wanggen Wan team at Shanghai University and Haoran Xie team from City University of Hong Kong; (3) we studied the hotspots of SMBD in recent years, and realized the summary of the frontier of SMBD based on the keywords and co-citation literature, including the deep excavation and construction of social media technology, the reflection and concerns about the rapid development of social media, and the role of SMBD in solving human social development problems. These studies could provide values and references for SMBD researchers to understand the research status, hotspots and trends in this field.
C1 [Wang, Ziyi; Zhang, Jingxiang] Nanjing Univ, Sch Architecture & Urban Planning, Nanjing 210093, Peoples R China.
   [Ma, Debin; Pang, Ru] Jiangsu Normal Univ, Sch Geog Geomat & Planning, Xuzhou 221116, Jiangsu, Peoples R China.
   [Xie, Fan] Beijing Univ Technol, Fac Urban Construct, Beijing 100124, Peoples R China.
   [Sun, Dongqi] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China.
RP Sun, DQ (通讯作者)，Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China.
EM wangziyi1011@smail.nju.edu.cn; 2020190069@jsnu.edu.cn;
   2020190073@jsnu.edu.cn; xiefan@emails.bjut.edu.cn;
   Jingxiangzhang@nju.edu.cn; sundq@igsnrr.ac.cn
OI wang, ziyi/0000-0001-9055-6798
FU National Natural Science Foundation of China [41971162]
FX This research was funded by National Natural Science Foundation of China
   (NO. 41971162).
CR Ahmed E, 2017, COMPUT NETW, V129, P459, DOI 10.1016/j.comnet.2017.06.013
   Alipour M, 2020, J CIV STRUCT HEALTH, V10, P313, DOI 10.1007/s13349-020-00386-4
   Amalina F, 2020, IEEE ACCESS, V8, P3629, DOI 10.1109/ACCESS.2019.2923270
   Bello-Orgaz G, 2016, INFORM FUSION, V28, P45, DOI 10.1016/j.inffus.2015.08.005
   Bharati P, 2019, INFORM SYST FRONT, V21, P1357, DOI 10.1007/s10796-018-9839-6
   Bollen J, 2011, J COMPUT SCI-NETH, V2, P1, DOI 10.1016/j.jocs.2010.12.007
   Boshmaf Y, 2013, COMPUT NETW, V57, P556, DOI 10.1016/j.comnet.2012.06.006
   Boyd D, 2012, INFORM COMMUN SOC, V15, P662, DOI 10.1080/1369118X.2012.678878
   Bragazzi NL, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0185263
   Bragazzi NL, 2017, CLIN RHEUMATOL, V36, P1911, DOI 10.1007/s10067-016-3513-5
   Cai Y, 2014, NEURAL NETWORKS, V58, P98, DOI 10.1016/j.neunet.2014.05.017
   Cao Q., 2014, P 2014 ACM SIGSAC C
   Cappella JN, 2017, HUM COMMUN RES, V43, P545, DOI 10.1111/hcre.12114
   Chen CLP, 2014, INFORM SCIENCES, V275, P314, DOI 10.1016/j.ins.2014.01.015
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen M, 2014, MOBILE NETW APPL, V19, P171, DOI 10.1007/s11036-013-0489-0
   de Albuquerque JP, 2015, INT J GEOGR INF SCI, V29, P667, DOI 10.1080/13658816.2014.996567
   Deepamala N., 2018, HDB STAT, V38, P429
   Ebrahimpour Z, 2019, ISPRS INT J GEO-INF, V8, DOI 10.3390/ijgi8100440
   Eichstaedt JC, 2015, PSYCHOL SCI, V26, P159, DOI 10.1177/0956797614557867
   Ferrara E, 2016, COMMUN ACM, V59, P96, DOI 10.1145/2818717
   Friedenthal S., 2008, PRACT GUIDE SYSML, V39, P270
   Gandomi A, 2015, INT J INFORM MANAGE, V35, P137, DOI 10.1016/j.ijinfomgt.2014.10.007
   Ghani NA, 2019, COMPUT HUM BEHAV, V101, P417, DOI 10.1016/j.chb.2018.08.039
   Ginsberg J, 2009, NATURE, V457, P1012, DOI 10.1038/nature07634
   Grover V., 2020, J ASSOC INF SYST, V21, P9
   Han XH, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17082788
   Hashem IAT, 2015, INFORM SYST, V47, P98, DOI 10.1016/j.is.2014.07.006
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Houston JB, 2015, DISASTERS, V39, P1, DOI 10.1111/disa.12092
   Huang L, 2020, J CLEAN PROD, V252, DOI 10.1016/j.jclepro.2019.119908
   Jiang DD, 2017, IEEE ACCESS, V5, P2373, DOI 10.1109/ACCESS.2016.2607218
   Kankanamge N, 2020, INT J DISAST RISK RE, V42, DOI 10.1016/j.ijdrr.2019.101360
   Karahalios K., 2009, P 27 INT C HUM FACT
   Karmegam D, 2020, INT J HEALTH GEOGR, V19, DOI 10.1186/s12942-020-00214-4
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   Kryvasheyeu Y, 2016, SCI ADV, V2, DOI 10.1126/sciadv.1500779
   Lazer D, 2014, SCIENCE, V343, P1203, DOI 10.1126/science.1248506
   Liu Q, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9061028
   Liu ZG, 2015, SCIENTOMETRICS, V103, P135, DOI 10.1007/s11192-014-1517-y
   Jimenez-Marquez JL, 2019, INT J INFORM MANAGE, V44, P1, DOI 10.1016/j.ijinfomgt.2018.09.003
   Magdy A, 2020, VLDB J, V29, P177, DOI 10.1007/s00778-019-00569-6
   Manovich L., TRENDING PROMISES CH
   Marengo D, 2020, ADDICT BEHAV, V102, DOI 10.1016/j.addbeh.2019.106150
   Mgudlwa S, 2018, S AFR J INFORM MANAG, V20, DOI 10.4102/sajim.v20i1.894
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   O'Doherty KC, 2016, BMC MED ETHICS, V17, DOI 10.1186/s12910-016-0137-x
   Rodriguez-Espindola O, 2020, INT J PROD RES, V58, P4610, DOI 10.1080/00207543.2020.1761565
   Schmidhuber J, 2015, NEURAL NETWORKS, V61, P85, DOI 10.1016/j.neunet.2014.09.003
   Seo EJ, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12041691
   Si HY, 2019, J CLEAN PROD, V213, P415, DOI 10.1016/j.jclepro.2018.12.157
   Sivarajah U, 2020, IND MARKET MANAG, V86, DOI 10.1016/j.indmarman.2019.04.005
   Smith M., 2020, ANAL SOCIAL MEDIA NE, P161, DOI DOI 10.1016/B978-0-12-817756-3.00011-X
   Tang GY, 2013, 2013 9TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS), P409, DOI 10.1109/CIS.2013.93
   Tsan-Ming C., 2020, TRANSPORT RES B-LOG, V135, P101860
   Ullah H, 2019, ISPRS INT GEO-INF, V8, DOI 10.3390/ijgi8110506
   Vargas-Quesada B, 2010, INFORM VISUAL, V9, P288, DOI 10.1057/ivs.2009.33
   Wang HW, 2020, TRANSPORT RES C-EMER, V115, DOI 10.1016/j.trc.2020.102619
   Wang X.L., 2016, N HORTIC, V10, P198
   Xie HR, 2014, NEURAL NETWORKS, V58, P111, DOI 10.1016/j.neunet.2014.05.009
   Yang CC, 2013, INTELL SYST SER, P51, DOI 10.1016/B978-0-12-404702-0.00003-3
   Ye N, 2020, J CLEAN PROD, V272, DOI 10.1016/j.jclepro.2020.122679
   Zhang D, 2019, IEEE T BIG DATA, V5, P195, DOI 10.1109/TBDATA.2018.2824812
   Zhang Y, 2020, IEEE ACCESS, V8, P125702, DOI 10.1109/ACCESS.2020.3004933
NR 64
TC 5
Z9 5
U1 13
U2 62
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2220-9964
J9 ISPRS INT J GEO-INF
JI ISPRS Int. J. Geo-Inf.
PD NOV
PY 2020
VL 9
IS 11
AR 632
DI 10.3390/ijgi9110632
PG 16
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Physical Geography; Remote Sensing
GA OW9RP
UT WOS:000593215800001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Mustaphi, CJC
   Capitani, C
   Boles, O
   Kariuki, R
   Newman, R
   Munishi, L
   Marchant, R
   Lane, P
AF Mustaphi, Colin J. Courtney
   Capitani, Claudia
   Boles, Oliver
   Kariuki, Rebecca
   Newman, Rebecca
   Munishi, Linus
   Marchant, Rob
   Lane, Paul
TI Integrating evidence of land use and land cover change for land
   management policy formulation along the Kenya-Tanzania borderlands
SO ANTHROPOCENE
LA English
DT Review
DE Landscape; Multidisciplinary; Science-policy interface; Serengeti;
   Socio-ecological systems; Policy support
ID LAKE VICTORIA BASIN; AFRICAN WILD DOGS; CLIMATE-CHANGE; VEGETATION
   DYNAMICS; NORTHERN TANZANIA; RIFT-VALLEY; BIODIVERSITY CONSERVATION;
   SUSTAINABLE DEVELOPMENT; ENVIRONMENTAL-ISSUES; KILIMANJARO REGION
AB This paper presents an overview of the scientific evidence providing insights into long term ecosystem and social dynamics across the northern Tanzania and southern Kenya borderlands. The data sources covered a range from palaeoenvironmental records and archaeological information to remote sensing and social science studies that examined human-environmental interactions and land use land cover changes (LULCC) in the region. This knowledge map of published LULCC research contributes to current debates about the drivers and dynamics of LULCC. The review aims to facilitate both multidisciplinary LULCC research and evidence-based policy analyses to improve familiarity and engagement between LULCC knowledge producers and end-users and to motivate research integration for land management policy formulation. Improving familiarity among researchers and non-academic stakeholders through the collation and synthesis of the scientific literature is among the challenges hindering policy formulation and land management decision-making by various stakeholders along the Kenya-Tanzania borderlands. Knowledge syntheses are necessary; yet, do not fully bridge the gap between knowledge and policy action. Cooperation across the science-policy interface is fundamental for the co-production of research questions by academics, policy makers and diverse stakeholders aimed at supporting land management decision making. For improved co-development and co-benefitting outcomes, the LULCC scientific community needs to mobilise knowledge for a broader audience and to advance co-development of relevant and meaningful LULCC products. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Mustaphi, Colin J. Courtney] Univ Basel, Dept Environm Sci, Geoecol, CH-4056 Basel, Switzerland.
   [Mustaphi, Colin J. Courtney; Capitani, Claudia; Kariuki, Rebecca; Newman, Rebecca; Marchant, Rob] Univ York, York Inst Trop Ecosyst, Dept Environm & Geog, York YO10 5NG, N Yorkshire, England.
   [Boles, Oliver] Univ Penn, Dept Anthropol, Philadelphia, PA 19104 USA.
   [Kariuki, Rebecca; Munishi, Linus] Nelson Mandela African Inst Sci & Technol, Sch Life Sci & Bioengn, POB 447, Arusha, Tanzania.
   [Lane, Paul] Univ Cambridge, Dept Archaeol, Downing St, Cambridge CB2 3DZ, England.
   [Lane, Paul] Uppsala Univ, Dept Archaeol & Ancient Hist, POB 256, S-75105 Uppsala, Sweden.
   [Lane, Paul] Univ Witwatersrand, Sch Geog Archaeol & Environm Sci, ZA-2000 Johannesburg, South Africa.
RP Mustaphi, CJC (通讯作者)，Univ Basel, Dept Environm Sci, Geoecol, CH-4056 Basel, Switzerland.
EM colin.courtney-mustaphi@unibas.ch
RI Lane, Paul/AAK-1995-2020
OI Lane, Paul/0000-0002-9936-1310; Courtney Mustaphi,
   Colin/0000-0002-4439-2590
FU 'Adaptation and Resilience to Climate Change (ARCC)' project under the
   Sustainability and Resilience - tackling climate and environmental
   changes programme through Swedish Research Council (Vetenskapsradet),
   Sida; 'Adaptation and Resilience to Climate Change (ARCC)' project under
   the Sustainability and Resilience - tackling climate and environmental
   changes programme through Swedish Research Council (Vetenskapsradet),
   Formas [2016-06355]; European Commission Marie Sklodowska-Curie Actions
   Initial Training Network grant [606879]; Leverhulme Trust [IN-2014-022];
   UK Economic and Social Research Council (ESRC) White Rose Doctoral
   Training Partnership (WRDTP)
FX We thank special issue editor Lindsey Gillson and anonymous reviewers
   for their constructive criticism that improved the manuscript and to
   researchers who contributed unpublished metadata (Table SM2).
   Conceptualisation of the paper emerged at a Past Global Changes (PAGES)
   supported workshop at the British Institute in Eastern Africa, Nairobi,
   Kenya, November 2015, as a contribution to PAGES LandCover6k
   (www.pastglobalchanges.org/ini/wg/landcover6k/intro) and Global
   Paleofire Working Groups (GPWG2)
   (http://pastglobalchanges.org/ini/wg/gpwg2/intro). CCM and RK were
   supported through the 'Adaptation and Resilience to Climate Change
   (ARCC)' project awarded to PL, RM and LM under the Sustainability and
   Resilience - tackling climate and environmental changes programme funded
   through Swedish Research Council (Vetenskapsradet), Sida and Formas
   (2016-06355). Several ideations emerged from the 'Resilience in East
   African Landscapes' (REAL) project funded by a European Commission Marie
   Sklodowska-Curie Actions Initial Training Network grant to PL and RM
   (FP7-PEOPLE-2013-ITN project number 606879) involving CCM, RK, RM and
   PL; and CCM's participation in the Leverhulme Trust funded 'Uncovering
   the variable roles of fire in savannah ecosystems' project
   (IN-2014-022). The UK Economic and Social Research Council (ESRC) White
   Rose Doctoral Training Partnership (WRDTP) supported RN. The British
   Institute in Eastern Africa is acknowledged for their continued support.
CR Abrams L., 2016, ORAL HIST THEORY
   Adams WM, 2004, SCIENCE, V306, P1146, DOI 10.1126/science.1097920
   Alcamo J, 2011, ENVIRON MODELL SOFTW, V26, P1017, DOI 10.1016/j.envsoft.2011.03.002
   Aleman JC, 2018, NAT ECOL EVOL, V2, P26, DOI 10.1038/s41559-017-0406-1
   Andama M., 2012, J ENV SCI ENG B, V1, P1054
   Andela N, 2019, EARTH SYST SCI DATA, V11, P529, DOI 10.5194/essd-11-529-2019
   Anderson D, 2008, J EAST AFR STUD, V2, P328, DOI 10.1080/17531050802095536
   Anderson David, 1989, GERMAN STUDIES REV, P1
   Anderson TM, 2010, ECOLOGY, V91, P1519, DOI 10.1890/09-0739.1
   Andreae MO, 2004, SCIENCE, V303, P1337, DOI 10.1126/science.1092779
   [Anonymous], 1985, ETHNOS, V49, P186
   [Anonymous], 1979, SERENGETI DYNAMICS E
   Anyah R. O., 2006, Monthly Weather Review, V134, P3588, DOI 10.1175/MWR3266.1
   Archibald S, 2008, J WILDLIFE MANAGE, V72, P492, DOI 10.2193/2007-045
   Archibald S, 2016, PHILOS T R SOC B, V371, DOI 10.1098/rstb.2015.0309
   Arhem K, 1985, PASTORAL MAN GARDEN
   Arlin C., 2011, BECOMING WILDERNESS
   Armstrong CG, 2019, J ETHNOBIOL, V39, P14, DOI 10.2993/0278-0771-39.1.14
   Armstrong CG, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0171883
   Ashley GM, 2011, GEOARCHAEOLOGY, V26, P809, DOI 10.1002/gea.20374
   Ashley GM, 2002, WETLANDS, V22, P686, DOI 10.1672/0277-5212(2002)022[0686:ABWAPW]2.0.CO;2
   Bachofer F, 2014, REMOTE SENS-BASEL, V6, P2195, DOI 10.3390/rs6032195
   Barnett J, 2016, NAT CLIM CHANGE, V6, P976, DOI 10.1038/nclimate3140
   Baumann O., 1894, DURCH MASSAILAND NIL
   Baynard C.W., 2013, ADV REMOTE SENS, V2, P228, DOI [DOI 10.4236/ARS.2013.23025, 10.4236/ars.2013.23025]
   Beale CM, 2018, ECOL LETT, V21, P557, DOI 10.1111/ele.12921
   Beinart W, 2009, ENVIRON HIST-US, V14, P429, DOI 10.1093/envhis/14.3.429
   Berger AR, 2017, ATL GEOL, V53, P115, DOI 10.4138/atlgeol.2017.005
   Bhaduri B., 2002, GEOINFORMATICS, V5, P34
   Birks H. John B., 2012, International Journal of Biodiversity Science Ecosystem Services & Management, V8, P292, DOI 10.1080/21513732.2012.701667
   Bithell M, 2009, ENVIRON MODELL SOFTW, V24, P173, DOI 10.1016/j.envsoft.2008.06.016
   Bluwstein J, 2018, J POLIT ECOL, V25, P144, DOI 10.2458/v25i1.22865
   Boles OJC, 2019, HUM ECOL, V47, P419, DOI 10.1007/s10745-019-0072-9
   Bollig M, 2014, Z ETHNOL, V139, P253
   Bond WJ, 2005, TRENDS ECOL EVOL, V20, P387, DOI 10.1016/j.tree.2005.04.025
   Bond WJ, 2005, NEW PHYTOL, V165, P525, DOI 10.1111/j.1469-8137.2004.01252.x
   Boone RB, 2011, ECOL SOC, V16
   Boone RB, 2002, AFR J ECOL, V40, P138, DOI 10.1046/j.1365-2028.2002.00357.x
   Booth C, 2009, FUTURES, V41, P87, DOI 10.1016/j.futures.2008.07.037
   Borjeson L, 2009, AFR J ECOL, V47, P185, DOI 10.1111/j.1365-2028.2008.01068.x
   Borjeson L, 2008, INT J AFR HIST STUD, V41, P523
   Bower J.R., 1976, AZANIA, V11, P176
   Bower J. R. F., 1973, AZANIA, V8, P71, DOI DOI 10.1080/00672707309511573
   Bower J.R.F., 1986, AZANIA, V11, P129, DOI DOI 10.1080/00672708609511374
   Boyd I, 2013, NATURE, V501, P159, DOI 10.1038/501159a
   Bulte EH, 2008, ENVIRON DEV ECON, V13, P245, DOI 10.1017/S1355770X08004348
   Butz RJ, 2009, INT J WILDLAND FIRE, V18, P442, DOI 10.1071/WF07067
   Cai WJ, 2013, NAT GEOSCI, V6, P999, DOI [10.1038/ngeo2009, 10.1038/NGEO2009]
   Callaway E, 2015, NATURE, V523, P115, DOI 10.1038/523115a
   Capitani C, 2019, ENVIRON CONSERV, V46, P17, DOI 10.1017/S0376892918000255
   Capitani C, 2019, SUSTAIN SCI, V14, P191, DOI 10.1007/s11625-018-0622-x
   Capitani C, 2016, ECOL SOC, V21, DOI 10.5751/ES-08565-210304
   Charnley S, 2005, HUM ORGAN, V64, P75, DOI 10.17730/humo.64.1.u8fer0aap3ceg4a1
   Christin ZL, 2016, FOR ECOSYST, V3, DOI 10.1186/s40663-016-0062-y
   Chritz KL, 2019, QUATERNARY SCI REV, V204, P119, DOI 10.1016/j.quascirev.2018.11.029
   Chritz KL, 2015, P NATL ACAD SCI USA, V112, P3674, DOI 10.1073/pnas.1423953112
   Chuhila M., 2016, INT J SOC SCI HUM RE, V4, P528
   Chuhila M.J., 2016, THESIS
   Collett D., 1987, P129
   Conte Christopher Allan, 2004, HIGHLAND SANCTUARY E
   Cook J, 2016, ENVIRON RES LETT, V11, DOI 10.1088/1748-9326/11/4/048002
   Cramer W, 2001, GLOBAL CHANGE BIOL, V7, P357, DOI 10.1046/j.1365-2486.2001.00383.x
   Crowther A, 2018, QUATERN INT, V489, P101, DOI 10.1016/j.quaint.2017.01.014
   Davidson DJ, 2010, SOC NATUR RESOUR, V23, P1135, DOI 10.1080/08941921003652940
   Davis A. E., 2010, THESIS
   Davis A, 2019, ORYX, V53, P491, DOI 10.1017/S0030605317000898
   Dawson JB, 2008, GEOL SOC MEM, V33, P1, DOI 10.1144/M33.1
   Dawson J. B., 1995, IAVCEI P VOLCANOLOGY, V4, P47
   de Bont C, 2019, WORLD DEV, V116, P15, DOI 10.1016/j.worlddev.2018.11.018
   de Bont C, 2018, WATER ALTERN, V11, P893
   Deocampo DM, 2002, QUATERNARY RES, V57, P271, DOI 10.1006/qres.2001.2317
   Detsch F, 2016, REMOTE SENS ENVIRON, V178, P70, DOI 10.1016/j.rse.2016.03.007
   Donaldson JE, 2018, J APPL ECOL, V55, P225, DOI 10.1111/1365-2664.12956
   Ekblom A, 2019, LAND-BASEL, V8, DOI 10.3390/land8010005
   Ellis E., 2007, ENCY EARTH, V1, P1
   Ellis E, 2016, NATURE, V540, P192, DOI 10.1038/540192a
   Enfors EI, 2008, ECOL SOC, V13
   Estes AB, 2012, BIOL CONSERV, V147, P255, DOI 10.1016/j.biocon.2012.01.010
   Farler JP., 1882, P ROYAL GEOGRAPHICAL, V4, P730, DOI DOI 10.2307/1800695
   Fazey I, 2018, ENERGY RES SOC SCI, V40, P54, DOI 10.1016/j.erss.2017.11.026
   Fer I, 2017, BIOGEOSCIENCES, V14, P4355, DOI 10.5194/bg-14-4355-2017
   Fer I, 2016, PALAEOGEOGR PALAEOCL, V444, P144, DOI 10.1016/j.palaeo.2015.12.001
   Ferrier S, 2016, SECRETARIAT INTERGOV
   Fick SE, 2017, INT J CLIMATOL, V37, P4302, DOI 10.1002/joc.5086
   FINCH J, 2017, HOLOCENE, V27, P796, DOI DOI 10.1177/0959683616675937
   Fokou G, 2016, REV SCI TECH OIE, V35, P533, DOI 10.20506/rst.35.2.2540
   Foley JA, 2005, SCIENCE, V309, P570, DOI 10.1126/science.1111772
   FOLEY R., 1981, BAR INT SERIES, V3, P97
   Ford J, 1971, ROLE TRYPANOSOMIASIS
   Fox D.N., 2017, THESIS VIRGINIA POLY
   Frieler K, 2017, GEOSCI MODEL DEV, V10, P4321, DOI 10.5194/gmd-10-4321-2017
   Fritz S, 2018, 4 AUSTR CIT SCI C FE, P1
   Funk C.C., 2014, QUASIGLOBAL PRECIPIT, V832
   Funk C, 2008, P NATL ACAD SCI USA, V105, P11081, DOI 10.1073/pnas.0708196105
   Galvin KA, 2018, ECOL SOC, V23, DOI 10.5751/ES-10217-230339
   Boone Randall B., 2008, P341, DOI 10.1007/978-1-4020-4906-4_11
   Gardner B, 2017, SOC SEMIOT, V27, P348, DOI 10.1080/10350330.2017.1301799
   GASCOYNE SC, 1993, J WILDLIFE DIS, V29, P396, DOI 10.7589/0090-3558-29.3.396
   GIERYN TF, 1983, AM SOCIOL REV, V48, P781, DOI 10.2307/2095325
   Gifford-Gonzalez D, 2017, QUATERN INT, V436, P283, DOI 10.1016/j.quaint.2015.10.054
   Gilbert J, 2016, COMMUNITY DEV J, V51, P350, DOI 10.1093/cdj/bsv025
   Gillson L, 2006, AFR J ECOL, V44, P458, DOI 10.1111/j.1365-2028.2006.00662.x
   Gillson L, 2014, TRENDS ECOL EVOL, V29, P317, DOI 10.1016/j.tree.2014.03.010
   Githumbi EN, 2017, THESIS
   Githumbi EN, 2018, AMBIO, V47, P682, DOI 10.1007/s13280-018-1014-2
   Githumbi EN, 2018, FRONT EARTH SC-SWITZ, V5, DOI 10.3389/feart.2017.00113
   Goldewijk KK, 2017, EARTH SYST SCI DATA, V9, P927, DOI 10.5194/essd-9-927-2017
   Goldewijk KK, 2010, HOLOCENE, V20, P565, DOI 10.1177/0959683609356587
   Goldman M. J., 2011, Conservation and Society, V9, P65, DOI 10.4103/0972-4923.79194
   Goldman MJ, 2014, J POLIT ECOL, V21, P408, DOI 10.2458/v21i1.21143
   Goldman MJ, 2013, GLOBAL ENVIRON CHANG, V23, P588, DOI 10.1016/j.gloenvcha.2013.02.010
   Gorddard R, 2016, ENVIRON SCI POLICY, V57, P60, DOI 10.1016/j.envsci.2015.12.004
   Green KE, 2015, FOREST POLICY ECON, V60, P27, DOI 10.1016/j.forpol.2014.11.012
   GROVE AT, 1977, PHILOS T ROY SOC B, V278, P457, DOI 10.1098/rstb.1977.0055
   Grzimek B., 1960, SERENGETI SHALL NOT
   Hahn R.W, 2001, PRIMER ENV POLICY DE
   Hakansson NT, 2007, GEOGR ANN B, V89B, P233, DOI 10.1111/j.1468-0467.2007.00251.x
   Hamilton A.C, 1982, ENV HIST E AFRICA ST
   Hamilton Alan C, 2016, Plant Divers, V38, P23, DOI 10.1016/j.pld.2016.04.001
   Hamilton Carolyn, 2002, REFIGURING ARCH
   Hansen MC, 2013, SCIENCE, V342, P850, DOI [10.1126/science.1244693, 10.1126/science.1215904]
   Hantson S, 2016, BIOGEOSCIENCES, V13, P3359, DOI 10.5194/bg-13-3359-2016
   Harding A, 2016, LARGE SCALE LAND ACQ, P1
   Haxeltine A, 1996, GLOBAL BIOGEOCHEM CY, V10, P693, DOI 10.1029/96GB02344
   HAY RL, 1989, J VOLCANOL GEOTH RES, V37, P77, DOI 10.1016/0377-0273(89)90114-5
   Hempson G. P., 2018, Ecography, V41, P889, DOI 10.1111/ecog.03109
   Higgins L, 2017, THESIS
   Higgins L, 2019, LANDSCAPE RES, V44, P35, DOI 10.1080/01426397.2017.1416599
   Higgins SI, 2012, NATURE, V488, P209, DOI 10.1038/nature11238
   Hissen N, 2017, J ENVIRON DEV, V26, P186, DOI 10.1177/1070496517696149
   Hodge J.M, 2011, RES INT STUDIES GLOB, V12, P239
   Homewood K, 2001, P NATL ACAD SCI USA, V98, P12544, DOI 10.1073/pnas.221053998
   HOMEWOOD K, 1995, AFRICA, V65, P331, DOI 10.2307/1161050
   Homewood K, 2009, STUD HUM ECOL ADAPT, V5, P1, DOI 10.1007/978-0-387-87492-0_1
   Howe C.A., 2019, ORYX
   Hughes RE, 2018, LAND-BASEL, V7, DOI 10.3390/land7010009
   ICSU & ISSC, 2015, REV SUST DEV GOALS S
   INGOLD T, 1993, WORLD ARCHAEOL, V25, P152, DOI 10.1080/00438243.1993.9980235
   Jackson ST, 2007, FRONT ECOL ENVIRON, V5, P455, DOI 10.1890/1540-9295(2007)5[455:LFFTPH]2.0.CO;2
   John B., 2017, P 10 TAWIRI SCI C 2, P183
   Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2
   Kaltenborn BP, 2008, J NAT CONSERV, V16, P96, DOI 10.1016/j.jnc.2008.02.001
   Kamau P.N., 2013, THESIS
   Kamau PN, 2014, LANDSCAPE URBAN PLAN, V124, P76, DOI 10.1016/j.landurbplan.2014.01.010
   Kaplan JO, 2011, HOLOCENE, V21, P775, DOI 10.1177/0959683610386983
   Kariuki R., 2018, ROUTLEDGE HDB AFRICA
   Kariuki R, 2018, LAND-BASEL, V7, DOI 10.3390/land7020047
   Kaufman B., 2018, GEOGRAPHICAL B, V59, P5
   Kay AU, 2019, J WORLD PREHIST, V32, P179, DOI 10.1007/s10963-019-09131-2
   Kay AU, 2015, ANTHROPOCENE, V9, P14, DOI 10.1016/j.ancene.2015.05.001
   Kaye-Zwiebel E, 2014, ECOL SOC, V19, DOI 10.5751/ES-06753-190317
   Keller J, 2010, B VOLCANOL, V72, P893, DOI 10.1007/s00445-010-0371-x
   Kelly S, 2014, TLS-TIMES LIT SUPPL, P19
   KENDALL RL, 1969, ECOL MONOGR, V39, P121, DOI 10.2307/1950740
   Kideghesho Jafari R., 2005, International Journal of Biodiversity Science & Management, V1, P150
   Kilungu H, 2019, TOUR PLAN DEV, V16, P235, DOI 10.1080/21568316.2019.1569121
   Kitchin R, 2013, T I BRIT GEOGR, V38, P480, DOI 10.1111/j.1475-5661.2012.00540.x
   KNOWLES JN, 1989, AFRICA, V59, P433, DOI 10.2307/1159941
   Kock RA, 2006, PREV VET MED, V75, P63, DOI 10.1016/j.prevetmed.2006.01.016
   Kok MTJ, 2017, SUSTAIN SCI, V12, P177, DOI 10.1007/s11625-016-0354-8
   Koning N, 2005, LAND USE POLICY, V22, P3, DOI 10.1016/j.landusepol.2003.08.003
   Kopnina H., 2013, ENV ANTHR FUTURE DIR
   KRISHNAMURTHY RV, 1985, NATURE, V317, P160, DOI 10.1038/317160a0
   Kruger Fred, 2015, Transactions of the Royal Society of South Africa, V70, P117, DOI 10.1080/0035919X.2015.1029275
   Kusimba C.M., 2005, AFRICAN ARCHAEOLOGY, P392
   Kwok R, 2018, NATURE, V556, P137, DOI 10.1038/d41586-018-03924-9
   Lamprey RH, 2004, J BIOGEOGR, V31, P997, DOI 10.1111/j.1365-2699.2004.01062.x
   Lamprey Richard, 1990, EARLY PASTORALISTS S, P16
   Lane P., 2004, AZANIA J BRIT I E AF, V39, P243, DOI [DOI 10.1080/00672700409480402, 10.1080/00672700409480402]
   Lane P.J., 2013, OXFORD HDB AFRICAN A, P581
   Lane PJ, 2016, ARCHAEOLOGY OF ENTANGLEMENT, P127
   Lane PJ, 2015, HERITAGE KEYWORDS: RHETORIC AND REDESCRIPTION IN CULTURAL HERITAGE, P259, DOI 10.5876/9781607323846.c016
   Lane PJ, 2015, J FIELD ARCHAEOL, V40, P485, DOI 10.1179/2042458215Y.0000000022
   Lang C, 2017, QUATERNARY RES, V88, P382, DOI 10.1017/qua.2017.54
   Lariu P., 2015, THESIS
   Last WM, 2001, TRACKING ENV CHANGE, V2
   Leal W, 2017, CLIMATIC CHANGE, V145, P71, DOI 10.1007/s10584-017-2087-9
   Lekan T, 2011, GER HIST, V29, P224, DOI 10.1093/gerhis/ghr040
   Lesorogol CK, 2016, INT J COMMONS, V10, P747, DOI 10.18352/ijc.656
   Lesur J., 2011, PEOPLE ANIMALS HOLOC, P43
   Lopez-Carr D, 2014, POPUL ENVIRON, V35, P323, DOI 10.1007/s11111-014-0209-0
   Loveland TR, 2004, GEOPHYS MONOGR SER, V153, P231, DOI 10.1029/153GM18
   Lovschal M, 2017, SCI REP-UK, V7, DOI 10.1038/srep41450
   Mabulla A., 2005, AZANIA, V40, P19
   Mabulla AZP, 2007, AFR ARCHAEOL REV, V24, P15, DOI 10.1007/s10437-007-9013-x
   Magliocca NR, 2018, GLOBAL ENVIRON CHANG, V50, P1, DOI 10.1016/j.gloenvcha.2018.03.003
   Maingi John K., 2006, Journal of East African Natural History, V95, P181, DOI 10.2982/0012-8317(2006)95[181:GRITSF]2.0.CO;2
   Mapedza E, 2010, J HIST GEOGR, V36, P356, DOI 10.1016/j.jhg.2010.05.010
   Marchant R, 2018, EARTH-SCI REV, V178, P322, DOI 10.1016/j.earscirev.2017.12.010
   Marchant R, 2014, J ARCHAEOL SCI, V51, P12, DOI 10.1016/j.jas.2013.07.005
   Marlon JR, 2016, BIOGEOSCIENCES, V13, P3225, DOI 10.5194/bg-13-3225-2016
   Marres N., 2013, J CULT ECON-UK, V6, P313, DOI [10.1080/17530350.2013.772070, DOI 10.1080/17530350.2013.772070]
   Marshall F, 2002, J WORLD PREHIST, V16, P99, DOI 10.1023/A:1019954903395
   MARSHALL F, 1990, AM ANTHROPOL, V92, P873, DOI 10.1525/aa.1990.92.4.02a00020
   Masao FT, 2015, AFR ARCHAEOL REV, V32, P137, DOI 10.1007/s10437-014-9170-7
   Masenga E.H., 2017, P 10 TAWIRI SCI C 2, P67
   Masenga EH, 2016, AFR J ECOL, V54, P95, DOI 10.1111/aje.12244
   MCNAUGHTON SJ, 1985, ECOL MONOGR, V55, P259, DOI 10.2307/1942578
   Millard AR, 2014, RADIOCARBON, V56, P555, DOI 10.2458/56.17455
   Miller BW, 2014, HUM ECOL, V42, P753, DOI 10.1007/s10745-014-9683-3
   MITCHELL N, 2006, P ISPRS TC7 MID TERM
   Mitchell Nicholas O., 2011, THESIS
   Mmbaga NE, 2017, J LAND USE SCI, V12, P168, DOI [10.1080/1747423x.2017.1313324, 10.1080/1747423X.2017.1313324]
   Mongi H., 2010, African Journal of Environmental Science and Technology, V4, P371
   Morgan A., 2012, HOLOCENE CLIMATE ENV
   Morrison K. D., 2018, Past Global Changes Magazine, V26, P8, DOI 10.22498/pages.26.1.8
   Morton C., 2015, AFRICAN PHOTOGRAPHIC
   Msoffe F.U, 2010, THESIS
   Muchiru AN, 2009, J ARID ENVIRON, V73, P322, DOI 10.1016/j.jaridenv.2008.09.018
   Munishi LK, 2015, INT J CLIM CHANG STR, V7, P17, DOI 10.1108/IJCCSM-07-2013-0094
   Munzert S., 2014, AUTOMATED DATA COLLE
   Muriuki G, 2011, LANDSCAPE URBAN PLAN, V99, P154, DOI 10.1016/j.landurbplan.2010.10.002
   Mustaphi Colin Courtney, 2016, Open Quaternary, V2, P3, DOI 10.5334/oq.22
   Mustaphi CJC, 2019, QUAT GEOCHRONOL, V52, P77, DOI 10.1016/j.quageo.2019.04.003
   Mustaphi CJC, 2016, AFR INVERTEBR, V57, P39, DOI 10.3897/afrinvertebr.57.8191
   Mwangi HM, 2018, FORESTS, V9, DOI 10.3390/f9010008
   Mwaura F, 2016, INT J AGR FOR FISH, V4, P17
   Nakintu J., 2016, J ENVIRON SCI ENG, V5, P626
   Neumann R., 2002, IMPOSING WILDERNESS
   Neumann R. P., 1992, Land Degradation & Rehabilitation, V3, P85, DOI 10.1002/ldr.3400030203
   Neumann R.P., 1994, THESIS
   Neumann R. P., 1995, ECUMENE, V2, P149, DOI DOI 10.1177/147447409500200203
   Neumann R.P., 2003, POLITICAL ECOLOGY IN, P240
   Niboye EP., 2010, OPEN GEOGRAPHY J, V3, P15
   Nique W., 2010, DATABASE MARKETING C, V17, P6, DOI DOI 10.1057/DBM.2009.32
   Niu Feng, 2012, VLDS, V12, P25
   Njeumi F, 2012, REV SCI TECH OIE, V31, P729
   Norton-Griffiths M, 1979, SERENGETI DYNAMICS E, P310
   O'Brien K, 2012, PROG HUM GEOG, V36, P667, DOI 10.1177/0309132511425767
   Oberg H, 2012, J PALEOLIMNOL, V48, P401, DOI 10.1007/s10933-012-9615-4
   Ogutu JO, 2009, J ZOOL, V278, P1, DOI 10.1111/j.1469-7998.2008.00536.x
   Okello Moses Makonjio, 2005, Human Dimensions of Wildlife, V10, P19, DOI 10.1080/10871200590904851
   Okumu W, 2010, J EAST AFR STUD, V4, P279, DOI 10.1080/17531055.2010.487338
   ONJALA I, 2003, AZANIA, V38, P99
   Orozco-Quintero A, 2018, J POLIT ECOL, V25, P40, DOI 10.2458/v25i1.22007
   Oteros-Rozas E, 2015, ECOL SOC, V20, DOI 10.5751/ES-07985-200432
   Pachauri R.K., 2014, CLIMATE CHANGE 2014
   Pallaver Karin, 2014, HIST AFR, V41, P307
   Patrut A, 2010, RADIOCARBON, V52, P717
   Pecl GT, 2017, SCIENCE, V355, DOI 10.1126/science.aai9214
   Peters SE, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0113523
   Pfeifer M, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0039337
   Pickles J, 2004, HIST SPACES CARTOGRA
   Platts PJ, 2015, AFR J ECOL, V53, P103, DOI 10.1111/aje.12180
   Platts PJ, 2010, DIVERS DISTRIB, V16, P628, DOI 10.1111/j.1472-4642.2010.00668.x
   PLOWRIGHT W, 1967, J HYG-CAMB, V65, P343, DOI 10.1017/S0022172400045861
   Pohl C, 1998, INT J REMOTE SENS, V19, P823, DOI 10.1080/014311698215748
   Poole AK, 2018, ENVIRON VALUE, V27, P55, DOI 10.3197/096327118X15144698637522
   PPTCH (the Project on Promoting Tanzania's Cultural Heritage), 2019, TANZ CULT HER MAP
   Prendergast ME, 2013, AZANIA, V48, P498, DOI 10.1080/0067270X.2013.841927
   Prendergast M. E., 2009, DOC ARCHAEOBIOL, V7, P203
   Prendergast ME, 2019, SCIENCE, V365, P44, DOI 10.1126/science.aaw6275
   Prendergast ME, 2019, ARCHAEOL ANTHROP SCI, V11, P3047, DOI 10.1007/s12520-018-0737-0
   Prendergast ME, 2014, J AFR ARCHAEOL, V12, P89, DOI 10.3213/2191-5784-10245
   PRENTICE IC, 1992, J BIOGEOGR, V19, P117, DOI 10.2307/2845499
   Prestele R, 2016, GLOBAL CHANGE BIOL, V22, P3967, DOI 10.1111/gcb.13337
   Probert JR, 2019, GLOBAL CHANGE BIOL, V25, P3406, DOI 10.1111/gcb.14711
   Rabin SS, 2017, GEOSCI MODEL DEV, V10, P1175, DOI 10.5194/gmd-10-1175-2017
   Rass N., 2006, PPLPI Working Paper - Pro-Poor Livestock Policy Initiative, FAO
   Reed J, 2017, LAND USE POLICY, V63, P481, DOI 10.1016/j.landusepol.2017.02.021
   Reed J, 2016, GLOBAL CHANGE BIOL, V22, P2540, DOI 10.1111/gcb.13284
   Reid RS, 2016, P NATL ACAD SCI USA, V113, P4579, DOI 10.1073/pnas.0900313106
   Reid RS, 2012, SAVANNAS OF OUR BIRTH: PEOPLE, WILDLIFE, AND CHANGE IN EAST AFRICA, P1
   Rempel R, 1998, REV CAN ETUD DEV, V19, P529
   Richter R.E., 1994, AFRICA SPECTRUM, V29, P265
   Robertshaw, 1991, AZANIA, V26, P63, DOI [10.1080/00672709109511425, DOI 10.1080/00672709109511425]
   Rockstrom J, 2009, NATURE, V461, P472, DOI 10.1038/461472a
   Roeder P, 2013, PHILOS T R SOC B, V368, DOI 10.1098/rstb.2012.0139
   Rucina SM, 2010, HOLOCENE, V20, P667, DOI 10.1177/0959683609358910
   Rull V., 2010, OPEN J ECOL, V3, P1, DOI [10.2174/1874213001003020001, DOI 10.2174/1874213001003020001]
   Ryner M, 2008, J PALEOLIMNOL, V40, P583, DOI 10.1007/s10933-007-9184-0
   Saggerson E.P., 1963, 58 GEOL SURV KEN
   Salerno J, 2016, ORYX, V50, P702, DOI 10.1017/S0030605315000393
   Sardar Z, 2010, FUTURES, V42, P177, DOI 10.1016/j.futures.2009.11.001
   Savo V, 2016, NAT CLIM CHANGE, V6, P462, DOI [10.1038/nclimate2958, 10.1038/NCLIMATE2958]
   Scheiter S, 2009, GLOBAL CHANGE BIOL, V15, P2224, DOI 10.1111/j.1365-2486.2008.01838.x
   Schindler J, 2016, AGR SYST, V148, P38, DOI 10.1016/j.agsy.2016.07.002
   Schreier M., 2014, QUALITATIVE CONTENT, P170
   Schuler L, 2014, HOLOCENE, V24, P702, DOI 10.1177/0959683614526939
   Schuler L, 2012, QUATERNARY SCI REV, V39, P1, DOI 10.1016/j.quascirev.2012.02.003
   Schuette P, 2016, J APPL ECOL, V53, P1066, DOI 10.1111/1365-2664.12610
   Seddon AWR, 2014, J ECOL, V102, P256, DOI 10.1111/1365-2745.12195
   Seitsonen O, 2006, AZANIA, V41, P41
   Seitsonen O., 2012, NYAME AKUMA, V77, P11
   Seki HA, 2018, AFR J ECOL, V56, P518, DOI 10.1111/aje.12488
   Shanguhyia M, 2014, AFRIC HISTOR MODERN, P191
   Shetler J.B., 2003, AFRICAN SOURCES AFRI, V4
   Shetler Jan Bender, 2007, IMAGING SERENGETI HI
   Shilling A., 2013, THESIS
   Shoemaker A., 2018, THESIS
   Shukla S, 2014, HYDROL EARTH SYST SC, V18, P3907, DOI 10.5194/hess-18-3907-2014
   Sinclair A.R.E., 2008, P7
   Sinclair A. R. E., 1995, P3
   Sinclair A.R.E., 2015, SERENGETI 4 SUSTAINI, P11
   Sinclair ARE, 1979, SERENGETI DYNAMICS E
   Sinclair ARE., 2012, SERENGETI STORY LIFE
   SINCLAIR ARE, 1979, SERENGETI DYNAMICS E
   Sitch S, 2003, GLOBAL CHANGE BIOL, V9, P161, DOI 10.1046/j.1365-2486.2003.00569.x
   Skoglund P, 2017, CELL, V171, P59, DOI 10.1016/j.cell.2017.08.049
   Smith B, 2001, GLOBAL ECOL BIOGEOGR, V10, P621, DOI 10.1046/j.1466-822X.2001.00256.x
   Smith GE, 1907, GEOGR J, V29, P249, DOI 10.2307/1776709
   Smol J. P., 2001, DEV PALEOENVIRONMENT
   Smucker TA, 2015, GEOFORUM, V59, P39, DOI 10.1016/j.geoforum.2014.11.018
   Snider R., 2012, THESIS
   Stager JC, 2005, PALAEOGEOGR PALAEOCL, V218, P347, DOI 10.1016/j.palaeo.2004.12.025
   Stager JC, 2003, QUATERNARY RES, V59, P172, DOI 10.1016/S0033-5894(03)00008-5
   Stahl Ann B, 2001, MAKING HIST BANDA AN
   Stewart K., 1993, AFR ARCHAEOL REV, V11, P21, DOI [DOI 10.1007/BF01118141, 10.1007/BF01118141]
   STOLER A.L., 2010, ALONG ARCH GRAIN EPI
   Stump D., 2006, AZANIA J BR I E AFR, V41, P69
   Stump D., 2009, BAR INT SERIES, P107
   Stump D, 2010, WORLD DEV, V38, P1251, DOI 10.1016/j.worlddev.2010.02.007
   Sunseri T., 2009, WIELDING AX STATE FO
   Sunseri T, 2013, J HIST GEOGR, V39, P29, DOI 10.1016/j.jhg.2012.08.017
   Swanson A, 2016, CONSERV BIOL, V30, P520, DOI 10.1111/cobi.12695
   Swanson A, 2015, SCI DATA, V2, DOI 10.1038/sdata.2015.26
   Swetnam RD, 2011, J ENVIRON MANAGE, V92, P563, DOI 10.1016/j.jenvman.2010.09.007
   TALBOT MR, 1989, PALAEOGEOGR PALAEOCL, V70, P121, DOI 10.1016/0031-0182(89)90084-9
   TAYLOR WP, 1967, EPIDEMIOL INFECT, V65, P537
   Thornton PK, 2015, NAT CLIM CHANGE, V5, P830, DOI [10.1038/NCLIMATE2754, 10.1038/nclimate2754]
   Thornton PK, 2006, AGR SYST, V87, P331, DOI 10.1016/j.agsy.2005.03.001
   Tschakert P, 2016, CURR OPIN ENV SUST, V20, P21, DOI 10.1016/j.cosust.2016.04.003
   Turner K., 1978, SERENGETI HOME
   Turner M., 1988, MY SERENGETI YEARS M
   United Nations, 2015, 2030 AG SUST DEV
   URT (United Republic of Tanzania), 1976, ATL TANZ
   van de Bildt MWG, 2002, EMERG INFECT DIS, V8, P211
   van Grunderbeek M.-C., 1992, AZANIA, V27, P53, DOI DOI 10.1080/00672709209511431
   Veldhuis MP, 2019, ECOL LETT, V22, P1536, DOI 10.1111/ele.13350
   Veldhuis MP, 2019, SCIENCE, V363, P1424, DOI 10.1126/science.aav0564
   Verschuren D, 2002, P ROY SOC B-BIOL SCI, V269, P289, DOI 10.1098/rspb.2001.1850
   Verschuren D, 2000, ECOLOGY, V81, P164, DOI 10.1890/0012-9658(2000)081[0164:EODSAS]2.0.CO;2
   Vervoort J. M., 2013, 63 CCAFS
   Wakefield T., 1882, P ROYAL GEOGRAPHICAL, V4, P742
   Wakefield T., 1870, J ROYAL GEOGRAPHICAL, V11, P303
   WALLER RD, 1990, J AFR HIST, V31, P81, DOI 10.1017/S0021853700024798
   Warner K, 2010, NAT HAZARDS, V55, P689, DOI 10.1007/s11069-009-9419-7
   Watson JEM, 2014, NATURE, V515, P67, DOI 10.1038/nature13947
   Webb R. H., 2010, REPEAT PHOTOGRAPHY M
   White C.A., 2012, RESTOR ECOL, V20, P288
   Widgren M, 2018, PLANTS PEOPLE AFRICA, P303
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Willcock S, 2016, GLOBAL CHANGE BIOL, V22, P2787, DOI 10.1111/gcb.13218
   Williams L.A.J., 1972, NAIROBI GEOLOGICAL S, V90
   Willis KJ, 2007, PHILOS T R SOC B, V362, P175, DOI 10.1098/rstb.2006.1977
   Willis KJ, 2018, SCIENCE, V359, P988, DOI 10.1126/science.aar5439
   Winker DM, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL030135
   Witt ABR, 2017, KOEDOE, V59, DOI 10.4102/koedoe.v59i1.1426
   Woodhouse E, 2018, ECOL SOC, V23, DOI 10.5751/ES-09986-230143
   WYANT JG, 1992, AFR J ECOL, V30, P176, DOI 10.1111/j.1365-2028.1992.tb00491.x
   Zech M, 2011, QUATERN INT, V243, P327, DOI 10.1016/j.quaint.2011.05.020
   Zhang C, 2013, P 2013 ACM SIGMOD IN, P993
NR 352
TC 7
Z9 7
U1 6
U2 34
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 2213-3054
J9 ANTHROPOCENE
JI Anthropocene
PD DEC
PY 2019
VL 28
AR 100228
DI 10.1016/j.ancene.2019.100228
PG 21
WC Environmental Sciences; Geography, Physical; Geosciences,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Physical Geography; Geology
GA JS4UL
UT WOS:000500301900005
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Luo, JL
   Leng, SJ
   Bai, YH
AF Luo, Jianli
   Leng, Shujuan
   Bai, Yanhu
TI Food Supply Chain Safety Research Trends From 1997 to 2020: A
   Bibliometric Analysis
SO FRONTIERS IN PUBLIC HEALTH
LA English
DT Article
DE food supply chain; food safety; bibliometrics; pandemic; traceability
ID RISK-FACTORS; MANAGEMENT; IMPACTS; MODEL; SECURITY; COVID-19; PURCHASE;
   CANCER; FIELD
AB Background: The COVID-19 pandemic has exposed the fragility of the global food supply chain, strengthened consumers' awareness of the traceability system throughout the supply chain, and gradually changed consumers' consumption concepts and consumption patterns. Therefore, the aim of this study was to analyse the relevant literature on food safety in the food supply chain, examine its current status, hot spots, and development trends, and provide some suggestions for academics and relevant government departments in food supply chain safety research.
   Methods: We collected the literature on the food safety research of the food supply chain from the Scopus database, used BibExcel to count the subject categories, published journals, geographical distributions, research institutions, authors, and keywords in the literature, and used Pajek software to analyse the keywords in the literature, perform co-occurrence analysis, draw related knowledge maps, and perform cluster analysis on primary keywords. Finally, to study the development trend, we used CorTexT software to illustrate the theme evolution path map in this research field.
   Results: The keyword visualization network revealed the following key research topics: (1) food safety at the consumer end of the food supply chain, (2) food safety management in the food supply chain, (3) risk management of food safety in the food safety chain, and (4) food safety at the production end of the food supply chain.
   Conclusions: After comprehensive discussion and analysis, we concluded that food supply chain management may be a hot topic in the future, especially in traceability management combined with the blockchain. It is necessary to explore in-depth how the blockchain can affect the food supply chain to provide a theoretical basis for managing the latter.
C1 [Luo, Jianli; Leng, Shujuan; Bai, Yanhu] Wenzhou Univ, Sch Business, Dept Finance, Wenzhou, Peoples R China.
RP Bai, YH (通讯作者)，Wenzhou Univ, Sch Business, Dept Finance, Wenzhou, Peoples R China.
EM baiyanhu2008@126.com
FU Zhejiang Provincial Philosophy and Social Science Planning Foundation of
   China [21NDJC318YBM]
FX The authors acknowledge the financial support of the Zhejiang Provincial
   Philosophy and Social Science Planning Foundation of China (No.
   21NDJC318YBM).
CR Ali Herrera A, 2009, ACIMED, V205, P76
   Ariyawardana A, 2017, FOOD CONTROL, V73, P193, DOI 10.1016/j.foodcont.2016.08.006
   Auler DP, 2017, INT FOOD AGRIBUS MAN, V20, P99, DOI [10.22434/IFAMR2016.0003, 10.22434/ifamr2016.0003]
   Azadbakht L, 2005, AM J CLIN NUTR, V82, P523, DOI 10.1093/ajcn/82.3.523
   Azmi FR, 2020, INT J SUPPLY CHAIN M, V94, P36
   Camacho J, 2012, MEASURING ORG INFORM, P62
   Chaturvedi A, 2014, HEALTH TECHNOL-GER, V4, P159, DOI 10.1007/s12553-014-0086-7
   Cornelis MC, 2006, JAMA-J AM MED ASSOC, V295, P1135, DOI 10.1001/jama.295.10.1135
   Dani S, 2010, INT J LOGIST-RES APP, V13, P395, DOI 10.1080/13675567.2010.518564
   Dong X., 2015, J FOOD SCI TECHNOLOG, V7, P94, DOI [10.19026/ajfst.7.1273, DOI 10.19026/AJFST.7.1273]
   Faisal M. N., 2016, Journal of Foodservice Business Research, V19, P171, DOI 10.1080/15378020.2016.1159894
   Farooq U, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8090839
   Friedrichsen CN, 2019, GEODERMA, V343, P166, DOI 10.1016/j.geoderma.2019.02.037
   Gebeyehu S, 2019, DEV PRACT, V29, P854, DOI 10.1080/09614524.2019.1641181
   George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021
   Giovannucci E, 2002, JNCI-J NATL CANCER I, V94, P391, DOI 10.1093/jnci/94.5.391
   Gu HY, 2020, J INTEGR AGR, V19, P2866, DOI 10.1016/S2095-3119(20)63429-3
   Heldens W, 2020, GEOSCI MODEL DEV, V13, P5833, DOI 10.5194/gmd-13-5833-2020
   Hew JJ, 2020, SUPPLY CHAIN MANAG, V25, P863, DOI 10.1108/SCM-01-2020-0044
   Horton P, 2019, FRONT SUSTAIN FOOD S, V3, DOI 10.3389/fsufs.2019.00079
   Hsu SY, 2016, BRIT FOOD J, V118, P200, DOI 10.1108/BFJ-11-2014-0376
   Hu FB, 1999, AM J CLIN NUTR, V70, P1001
   Iftekhar A, 2020, J FOOD QUALITY, V2020, DOI 10.1155/2020/5385207
   Kamariah Ismail, 2012, Research Journal of Applied Sciences, Engineering and Technology, V4, P466
   Khan S, 2022, J ISLAMIC MARK, V13, P45, DOI 10.1108/JIMA-10-2018-0206
   Kim R, 2003, J INT FOOD AGRIBUS M, V15, P153, DOI 10.1300/J047v15n01_08
   Klein AZ, 2014, J GLOB INF MANAG, V22, P14, DOI 10.4018/jgim.2014010102
   Ko YC, 1997, INT J EPIDEMIOL, V26, P24, DOI 10.1093/ije/26.1.24
   Kummu M, 2012, SCI TOTAL ENVIRON, V438, P477, DOI 10.1016/j.scitotenv.2012.08.092
   Lagerkvist CJ, 2018, FOOD CONTROL, V93, P265, DOI 10.1016/j.foodcont.2018.06.019
   Li JP, 2012, OUTLOOK AGR, V41, P57, DOI 10.5367/oa.2012.0076
   Lin H, 2020, REV ARGENT CLIN PSIC, V292, P1282, DOI [10.24205/03276716.2020.370, DOI 10.24205/03276716.2020.370]
   Liu B, 2005, CHIN J CLIN REHAB, V922, P220
   Liu XJ, 2019, INT J LOGIST MANAG, V30, P1078, DOI 10.1108/IJLM-02-2018-0039
   Liu Y, 2014, ADV J FOOD SCI TECHN, V612, P1318, DOI [10.19026/ajfst.6.203, DOI 10.19026/AJFST.6.203]
   MacMahon A., 2015, J ENV STUDIES SCI, V5, P378, DOI DOI 10.1007/S13412-015-0278-0
   Malm O, 1998, ENVIRON RES, V77, P73, DOI 10.1006/enrs.1998.3828
   Manzini R, 2013, J FOOD ENG, V115, P251, DOI 10.1016/j.jfoodeng.2012.10.026
   Mete R, 2019, BMC PUBLIC HEALTH, V19, DOI 10.1186/s12889-019-8064-7
   Opsomer G, 2000, THERIOGENOLOGY, V53, P841, DOI 10.1016/S0093-691X(00)00234-X
   Ouden M. den, 1996, Agribusiness (New York), V12, P277, DOI 10.1002/(SICI)1520-6297(199605/06)12:3&lt;277::AID-AGR7&gt;3.0.CO;2-Y
   Ovca A, 2009, FOOD CONTROL, V20, P167, DOI 10.1016/j.foodcont.2008.03.010
   Pal A, 2020, ACM T SENSOR NETWORK, V16, DOI 10.1145/3360726
   Pandey Deepak, 2019, Organic Agriculture, V9, P357, DOI 10.1007/s13165-018-0240-z
   Prashar D, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12083497
   Raab V, 2013, WIT TRANS ECOL ENVIR, V170, P147, DOI 10.2495/FENV130141
   Redmond EC, 2005, BRIT FOOD J, V107, P467, DOI 10.1108/00070700510606882
   Reif C, 2015, OUTLOOK AGR, V44, P11, DOI 10.5367/oa.2015.0193
   Rejeb A, 2020, LOGFORUM, V16, P475, DOI 10.17270/J.LOG.2020.502
   Rogerson M, 2020, SUPPLY CHAIN MANAG, V25, P601, DOI 10.1108/SCM-08-2019-0300
   Roth AV, 2008, J SUPPLY CHAIN MANAG, V44, P22, DOI 10.1111/j.1745-493X.2008.00043.x
   Saetta Stefano A., 2015, International Journal of Integrated Supply Management, V9, P307
   Saglietto L, 2015, INT J TRANSP ECON, V42, P461
   Segovia-Villarreal M, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11102862
   Shin MH, 2002, JNCI-J NATL CANCER I, V94, P1301, DOI 10.1093/jnci/94.17.1301
   Song C, 2018, INT T OPER RES, V25, P1855, DOI 10.1111/itor.12269
   Stringer MF, 2007, FOOD CONTROL, V18, P755, DOI 10.1016/j.foodcont.2006.01.008
   Sun SN, 2019, J CLEAN PROD, V217, P658, DOI 10.1016/j.jclepro.2019.01.296
   Tandon A, 2020, APPETITE, V154, DOI 10.1016/j.appet.2020.104786
   Teasley R, 2016, HEALTH SYST, V5, P149, DOI 10.1057/hs.2015.16
   Tenenhaus-Aziza F, 2014, RISK ANAL, V34, P56, DOI 10.1111/risa.12074
   Thapa G, 2020, J AGRIC APPL ECON, V52, P503, DOI 10.1017/aae.2020.14
   Theng Lieong Sook, 2017, Malaysian Applied Biology, V46, P131
   Vorotnikov I, 2017, ECON ANN-XXI, V164, P94, DOI 10.21003/ea.V164-21
   Wahyuni H, 2019, J IND ENG MANAG-JIEM, V12, P373, DOI 10.3926/jiem.2803
   Xiao Z, 2015, ADV J FOOD SCI TECHN, V710, P789, DOI [10.19026/ajfst.7.1739, DOI 10.19026/AJFST.7.1739]
   Xiao Z, 2015, ADV J FOOD SCI TECHN, V810, P751, DOI [10.19026/ajfst.8.1602, DOI 10.19026/AJFST.8.1602]
   Xu W, 2020, COMPUT COMMUN, V151, P320, DOI 10.1016/j.comcom.2019.12.033
   Yeung R, 2010, BRIT FOOD J, V112, P306, DOI 10.1108/00070701011029174
   Zahry NR, 2019, J RISK RES, V22, P16, DOI 10.1080/13669877.2017.1351470
   Zhou YL, 2019, LIBR HI TECH, V37, P233, DOI 10.1108/LHT-08-2018-0101
   Zhu Z, 2012, INT C INF ENG APPL I
NR 72
TC 0
Z9 0
U1 15
U2 15
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2296-2565
J9 FRONT PUBLIC HEALTH
JI Front. Public Health
PD FEB 3
PY 2022
VL 9
AR 742980
DI 10.3389/fpubh.2021.742980
PG 15
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA 0E1RG
UT WOS:000776461400001
PM 35186862
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Bawden, KR
   Williams, ED
   Babbitt, CW
AF Bawden, Kim R.
   Williams, Eric D.
   Babbitt, Callie W.
TI Mapping product knowledge to life cycle inventory bounds: a case study
   of steel manufacturing
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Article
DE Life cycle assessment; Uncertainty; Product knowledge mapping; Steel
   manufacturing; Bill of attributes
ID ENERGY USE; UNCERTAINTY; BUILDINGS; LCA
AB This study develops and demonstrates a bounding methodology to quantify uncertainty in life cycle inventory (LCI) results arising from lack of detailed information on constituent materials. The method starts with the observation that the LCI of a material can change significantly with different attributes such as country of origin and recycled content, information often not specified in available bill-of materials data. This lack of detailed information can be mapped to numerical bounds for LCI results. We demonstrate this idea via a case study of the contribution of steel manufacturing to the cumulative energy demand (CED) and life cycle global warming potential (GWP) of residential buildings. If steel type, recycled content and country of origin are all unknown, life cycle CO2-equivalent emissions of steel can vary from .7 to 5.9 kg CO(2)eq/kg. When used in compiling an LCI of a building, this wide range leads to overlapping results in a comparison of life cycle GWP impact between steel- and concrete-framed buildings. That is, without knowledge of the particulars of steel used, life cycle assessment (LCA) cannot distinguish between the two building types. In contrast, with knowledge that the steel is low or un-alloyed, produced in the U.S., and has greater than 60% recycled content, uncertainty bounds are reduced to .8-1.4 kg CO(2)eq/kg steel. With this range, the net impact of concrete-framed buildings is unambiguously larger than steel-framed residences. While demonstrated here for steel manufacturing, this bounding approach is broadly applicable in LCA. (C) 2015 Elsevier Ltd. All rights reserved.
C1 [Bawden, Kim R.] Rochester Inst Technol, New York State Pollut Prevent Inst, 111 Lomb Mem Dr,Sustainabil Hall, Rochester, NY 14623 USA.
   [Williams, Eric D.; Babbitt, Callie W.] Rochester Inst Technol, Golisano Inst Sustainabil, 190 Lomb Mem Dr,Sustainabil Hall, Rochester, NY 14623 USA.
RP Williams, ED (通讯作者)，Rochester Inst Technol, Golisano Inst Sustainabil, 190 Lomb Mem Dr,Sustainabil Hall, Rochester, NY 14623 USA.
EM exwgis@rit.edu
OI Williams, Eric/0000-0002-6352-2928
FU Civil Infrastructure Systems program at the National Science Foundation
   (CMMI) [1031690]
FX This study was supported by the Civil Infrastructure Systems program at
   the National Science Foundation (CMMI grant # 1031690). The authors
   would like to thank Xianzheng Gong for his correspondences with regard
   to life cycle data used from his previously published article (Gong et
   al., 2012), Jules Chiavaroli for information on building design, and the
   reviewers for very helpful comments.
CR Adalberth K, 1997, BUILD ENVIRON, V32, P321, DOI 10.1016/S0360-1323(96)00069-8
   American Iron and Steel Institute (AISI), 2012, GLOSSARY
   Bjorklund AE, 2002, INT J LIFE CYCLE ASS, V7, P64, DOI 10.1065/lca2001.12/071
   Blengini GA, 2010, ENERG BUILDINGS, V42, P869, DOI 10.1016/j.enbuild.2009.12.009
   BUCHANAN AH, 1994, ENERG BUILDINGS, V20, P205, DOI 10.1016/0378-7788(94)90024-8
   Chevalier J.-L., 1996, INT J LIFE CYCLE ASS, V1, P90, DOI [10.1007/BF02978652, DOI 10.1007/BF02978652]
   Ciroth A, 2016, INT J LIFE CYCLE ASS, V21, P1338, DOI 10.1007/s11367-013-0670-5
   Classen M., 2009, 10 EC, P89
   Cole RJ, 1996, BUILD ENVIRON, V31, P307, DOI 10.1016/0360-1323(96)00017-0
   Crawford G. L., 2013, LCI DATA STELL PRODU
   Deng LQ, 2011, J CLEAN PROD, V19, P1198, DOI 10.1016/j.jclepro.2011.03.004
   Dones R., 2007, 5 EC, P164
   Eckelman MJ, 2010, RESOUR CONSERV RECY, V54, P256, DOI 10.1016/j.resconrec.2009.08.008
   ecoinvent Centre, 2007, EC REP, V1-25
   Fenton M. D., 2005, MINERAL COMMODITY PR
   Finnveden G, 2009, J ENVIRON MANAGE, V91, P1, DOI 10.1016/j.jenvman.2009.06.018
   Frischknecht R., 2007, 1 EC, P42
   Goedkoop M., 2010, INTRO LCA SIMAPRO, V7, P34
   Gong XZ, 2012, J IND ECOL, V16, P576, DOI 10.1111/j.1530-9290.2011.00415.x
   Hammond G.P., 2008, P I CIVIL ENG-ENERGY, V161, P87, DOI DOI 10.1680/ENER.2008.161.2.87
   Heijungs R, 1996, J CLEAN PROD, V4, P159, DOI DOI 10.1016/S0959-6526(96)00042-X
   Heijungs R, 2004, COMPLEXITY INTEGRATE, V1, P332
   Huijbregts M., 1998, INT J LIFE CYCLE ASS, V3, P273, DOI [10.1007/BF02979835, DOI 10.1007/BF02979835]
   Huijbregts MAJ, 2003, ENVIRON SCI TECHNOL, V37, P2600, DOI 10.1021/es020971+
   IPCC, 2007, 4 ASSESSMENT REPORT
   Junnila S, 2006, J INFRASTRUCT SYST, V12, P10, DOI 10.1061/(ASCE)1076-0342(2006)12:1(10)
   Kahhat R., 2011, BILL ATTRIBUTES BOA
   Kasulaitis BV, 2015, RESOUR CONSERV RECY, V100, P1, DOI 10.1016/j.resconrec.2015.03.014
   Keoleian G A, 2000, J IND ECOL, V4, P135, DOI DOI 10.1162/108819800569726
   Lloyd SM, 2007, J IND ECOL, V11, P161, DOI 10.1162/jiec.2007.1136
   Markus Engineering Services, 2002, CRADL TO GAT LIF CYC
   McMillan CA, 2009, ENVIRON SCI TECHNOL, V43, P1571, DOI 10.1021/es800815w
   NREL, 2013, US LIF CYCL INV DAT
   Nucor Corporation, 2013, REC CONT NUC STEEL P
   Olivetti E, 2013, ENVIRON SCI TECHNOL, V47, P5208, DOI 10.1021/es3042934
   PRe Consultants, 2012, SIMAPRO 7 3 3
   Puettmann ME, 2010, WOOD FIBER SCI, V42, P15
   Ramesh T, 2010, ENERG BUILDINGS, V42, P1592, DOI 10.1016/j.enbuild.2010.05.007
   Scheuer C, 2003, ENERG BUILDINGS, V35, P1049, DOI 10.1016/S0378-7788(03)00066-5
   Sharma A, 2011, RENEW SUST ENERG REV, V15, P871, DOI 10.1016/j.rser.2010.09.008
   Sonnemann GW, 2003, J CLEAN PROD, V11, P279, DOI 10.1016/S0959-6526(02)00028-8
   U.S. EIAb Department of Energy, 2013, CARB DIOX EM FACT CO
   U.S. EIAc U.S. Department of Energy, 2013, INT EN STAT CO2 CONS
   U.S. EIAd U.S. Department of Energy, 2013, RES  EN CONS SURV RE
   United Nations Environment Programme (UNEP), 2013, SUST BUILD CLIM IN W
   Weidema B, 1998, INT J LIFE CYCLE ASS, V3, P237, DOI 10.1007/BF02977574
   Weidema BP, 1996, J CLEAN PROD, V4, P167, DOI DOI 10.1016/S0959-6526(96)00043-1
   Williams E, 2004, ENVIRON SCI TECHNOL, V38, P6166, DOI 10.1021/es035152j
   Williams E., 2011, THERMODYNAMICS DESTR, P190
   Williams E.J.., 2012, P 2012 WINTER SIMULA, P1
   Williams ED, 2002, ENVIRON SCI TECHNOL, V36, P5504, DOI 10.1021/es025643o
   Williams ED, 2009, J IND ECOL, V13, P928, DOI 10.1111/j.1530-9290.2009.00170.x
   World Energy Council (WEC), 2010, EN RES COAL
   Xing S, 2008, ENERG BUILDINGS, V40, P1188, DOI 10.1016/j.enbuild.2007.10.016
   Bribian IZ, 2011, BUILD ENVIRON, V46, P1133, DOI 10.1016/j.buildenv.2010.12.002
NR 55
TC 10
Z9 10
U1 0
U2 26
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0959-6526
EI 1879-1786
J9 J CLEAN PROD
JI J. Clean Prod.
PD FEB 1
PY 2016
VL 113
BP 557
EP 564
DI 10.1016/j.jclepro.2015.10.014
PG 8
WC Green & Sustainable Science & Technology; Engineering, Environmental;
   Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics; Engineering; Environmental Sciences
   & Ecology
GA DE9XM
UT WOS:000370993200054
OA Bronze
DA 2022-08-23
ER

PT J
AU Burkhard, RA
   Meier, M
AF Burkhard, RA
   Meier, M
TI Tube map visualization: Evaluation of a novel knowledge visualization
   application for the transfer of knowledge in long-term projects
SO JOURNAL OF UNIVERSAL COMPUTER SCIENCE
LA English
DT Article; Proceedings Paper
CT 4th International Conference on Knowledge Management
CY JUN 30-JUL 02, 2004
CL Graz, AUSTRIA
DE knowledge visualization; information visualization; visual metaphor;
   storytelling; knowledge communication; project management
ID DIAGRAMS; SPACE
AB This article introduces two theoretical concepts for the emerging field Knowledge Visualization and discusses a new visualization application that was used to communicate a long-term project to various stakeholders in an organization. First, we introduce a theoretical framework and a model for Knowledge Visualization. The framework and the model identify and relate the key-aspects for successful Knowledge Visualization applications. Next, we present an evaluation of an implemented Knowledge Visualization application: The Tube Map Visualization. A quality development process had to be established in an education centre for health care professions. Traditional project plans, flyers, and mails did not manage to get the attention, did present overview and detail insufficiently, and did not motivate the employees for actions. Because Visual Metaphors are effective for Knowledge Communication we developed a customized Knowledge Map based on the tube system metaphor. The Tube Map Visualization illustrates the whole project, where each tube line represents a target group and each station a milestone. The visualization was printed as a poster and displayed at prominent locations in the organization. The findings of an evaluation indicate that the Tube Map Visualization is a powerful metaphor to communicate a complex project to different target groups and to build up a mutual story. The employees considered it useful because it provides overview and detailed information in one image and because it initiates discussion. The Tube Map Visualization is therefore helpful to complement traditional project plans of ( 1) long-term projects where ( 2) different stakeholders are involved. The theoretical framework, the model, and the findings have implications for researchers in the fields of Knowledge Management, Knowledge Visualization, Information Visualization, and Communication Sciences.
C1 Univ St Gallan, St Gallen, Switzerland.
RP Burkhard, RA (通讯作者)，Univ St Gallan, St Gallen, Switzerland.
EM remo.burkhard@unisg.ch; mm@vasp.ch
OI Burkhard, Remo/0000-0002-4398-8026
CR Alavi M, 2001, MIS QUART, V25, P107, DOI 10.2307/3250961
   BAUER MI, 1993, PSYCHOL SCI, V4, P372, DOI 10.1111/j.1467-9280.1993.tb00584.x
   Bertin J., 1974, GRAPHISCHE SEMIOLOGI
   BRUNER JS, 1957, PSYCHOL REV, V64, P123, DOI 10.1037/h0043805
   Burkhard R., 2004, 8 INT C INF VIS IV04
   CHEN C, 1999, INFORMATION VISUALIS
   ELLIS WD, 1938, SOURCE BOOK GESTALT
   FARAH MJ, 2000, COGNITIVE NEUROSCIEN
   Garland, 1979, INFORMATION DESIGN J, V1, P15
   GLENBERG AM, 1992, J MEM LANG, V31, P129, DOI 10.1016/0749-596X(92)90008-L
   GREGORY RL, 1980, PHILOS T ROY SOC B, V290, P181, DOI 10.1098/rstb.1980.0090
   Gregory RL, 1998, EYE BRAIN PSYCHOL SE
   Horn RE, 1998, VISUAL LANGUAGE GLOB
   Jakobson R., 1960, STYLE LANG, P350
   Johnson B., 1991, Proceedings Visualization '91 (Cat. No.91CH3046-0), P284, DOI 10.1109/VISUAL.1991.175815
   Koffka K., 2013, PRINCIPLES GESTALT P
   Kosslyn S. M., 1980, IMAGE MIND
   LARKIN JH, 1987, COGNITIVE SCI, V11, P65, DOI 10.1016/S0364-0213(87)80026-5
   Lasswell H.D., 1948, COMMUNICATION IDEAS, P37
   Loebbert M., 2003, STORYMANAGEMENT NARR
   MILLER GA, 1956, PSYCHOL REV, V63, P81, DOI 10.1037/0033-295X.101.2.343
   Munzner T, 1998, IEEE COMPUT GRAPH, V18, P18, DOI 10.1109/38.689657
   NEWCOMB TM, 1953, PSYCHOL REV, V60, P393, DOI 10.1037/h0063098
   NONAKA I, 1991, HARVARD BUS REV, V69, P96
   Novick LR, 2001, PSYCHOL LEARN MOTIV, V40, P279
   ROBERTSON GG, 1991, P SIGCHI C HUM FACT, P189, DOI DOI 10.1145/108844.108883
   Rock I., 1983, LOGIC PERCEPTION
   Shannon CE, 1949, MATH THEORY COMMUNIC, P117
   Shepard R., 1982, MENTAL IMAGES THEIR
   SHNEIDERMAN B, 1992, ACM T GRAPHIC, V11, P92, DOI 10.1145/102377.115768
   Shneiderman B, 1996, IEEE SYMPOSIUM ON VISUAL LANGUAGES, PROCEEDINGS, P336, DOI 10.1109/VL.1996.545307
   Shneiderman B., 1999, READINGS INFORM VISU, P686
   Tufte E., 1997, VISUAL EXPLANATIONS
   Ungerleider L., 1982, ANAL VISUAL BEHAV, P549
   WARE C, 2000, INFORMATION VISUALIZ
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NR 48
TC 25
Z9 25
U1 0
U2 18
PU GRAZ UNIV TECHNOLGOY, INST INFORMATION SYSTEMS COMPUTER MEDIA-IICM
PI GRAZ
PA INFFELDGASSE 16C, GRAZ, A-8010, AUSTRIA
SN 0948-695X
J9 J UNIVERS COMPUT SCI
JI J. Univers. Comput. Sci.
PY 2005
VL 11
IS 4
BP 473
EP 494
PG 22
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA 958HH
UT WOS:000231434800004
DA 2022-08-23
ER

PT J
AU Zhao, Y
   Hou, JY
   Yu, ZJ
   Zhang, Y
   Li, Q
AF Zhao, Yu
   Hou, Jiayue
   Yu, Zongjian
   Zhang, Yun
   Li, Qing
TI Confidence-Aware Embedding for Knowledge Graph Entity Typing
SO COMPLEXITY
LA English
DT Article
AB Knowledge graphs (KGs) entity typing aims to predict the potential types to an entity, that is, (entity, entity type = ?). Recently, several embedding models are proposed for KG entity types prediction according to the existing typing information of the (entity, entity type) tuples in KGs. However, most of them unreasonably assume that all existing entity typing instances in KGs are completely correct, which ignore the nonnegligible entity type noises and may lead to potential errors for the downstream tasks. To address this problem, we propose ConfE, a novel confidence-aware embedding approach for modeling the (entity, entity type) tuples, which takes tuple confidence into consideration for learning better embeddings. Specifically, we learn the embeddings of entities and entity types in separate entity space and entity type space since they are different objects in KGs. We utilize an asymmetric matrix to specify the interaction of their embeddings and incorporate the tuple confidence as well. To make the tuple confidence more universal, we consider only the internal structural information in existing KGs. We evaluate our model on two tasks, including entity type noise detection and entity type prediction. The extensive experimental results in two public benchmark datasets (i.e., FB15kET and YAGO43kET) demonstrate that our proposed model outperforms all baselines on all tasks, which verify the effectiveness of ConfE in learning better embeddings on noisy KGs. The source code and data of this work can be obtained from .
C1 [Zhao, Yu; Hou, Jiayue; Yu, Zongjian; Zhang, Yun; Li, Qing] Southwestern Univ Finance & Econ, Sch Comp Sci, Fintech Innovat Ctr, Chengdu, Peoples R China.
RP Zhang, Y (通讯作者)，Southwestern Univ Finance & Econ, Sch Comp Sci, Fintech Innovat Ctr, Chengdu, Peoples R China.
EM zhaoyu@swufe.edu.cn; jiayuehou@smail.swufe.edu.cn; yuzj@swufe.edu.cn;
   zhangyun@swufe.edu.cn; liq_t@swufe.edu.cn
OI Zhao, Yu/0000-0002-8454-0025
FU National Natural Science Foundation of China [61906159]; Sichuan Science
   and Technology Program [2018JY0607]; Fundamental Research Funds for the
   Central Universities [JBK2003008]; Fintech Innovation Center; Financial
   Intelligence and Financial Engineering Key Laboratory of Sichuan
   Province
FX This work was supported by the National Natural Science Foundation of
   China under Grant no. 61906159, the Sichuan Science and Technology
   Program under Grant no. 2018JY0607, the Fundamental Research Funds for
   the Central Universities under Grant no. JBK2003008, and Fintech
   Innovation Center, and Financial Intelligence and Financial Engineering
   Key Laboratory of Sichuan Province.
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2011, P JOINT EUROPEAN C M, P809
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2011, AAAI
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Elsahar H., 2018, P C N AM CHAPTER ASS, P218, DOI DOI 10.18653/V1/N18-1020
   Gupta Nitish, 2017, P C EMP METH NAT LAN
   Heindorf S, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P327, DOI 10.1145/2983323.2983740
   Jain P., 2018, P ACL
   Jiang SP, 2012, IEEE DATA MINING, P912, DOI 10.1109/ICDM.2012.156
   Liang JQ, 2017, AAAI CONF ARTIF INTE, P1178
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Ma Y., 2014, CCIS, V480, P29, DOI DOI 10.1007/978-3-662-45495-4
   Melo A., 2017, P ACM, P22
   Moon C, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2215, DOI 10.1145/3132847.3133095
   Nathani D., 2019, P ACL
   Neelakantan A, 2015, INFERRING MISSING EN
   Neil D., 2017, P NEURIPS
   Nguyen D.Q., 2019, P NAACL HLT 2019, P2180, DOI DOI 10.18653/V1/N19-1226
   Nguyen D. Q., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Paulheim H, 2014, INT J SEMANT WEB INF, V10, P63, DOI 10.4018/ijswis.2014040104
   Paulheim H, 2013, LECT NOTES COMPUT SC, V8218, P510, DOI 10.1007/978-3-642-41335-3_32
   Pujara J., 2017, P 2017 C EMP METH NA, P1751, DOI DOI 10.18653/V1/D17-1184
   Ren X, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1825, DOI 10.1145/2939672.2939822
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Tempelmeier N, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1297, DOI 10.1145/3178876.3186028
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao H, 2017, AAAI CONF ARTIF INTE, P3104
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xie RB, 2018, AAAI CONF ARTIF INTE, P4954
   Zaveri A, 2016, SEMANT WEB, V7, P63, DOI 10.3233/SW-150175
   Zhang R, 2018, PROCEDDINGS AAAI
   Zhao Y, 2021, KNOWL-BASED SYST, V215, DOI 10.1016/j.knosys.2020.106630
   Zhao Y, 2019, ENTROPY-SWITZ, V21, DOI 10.3390/e21111083
   Zhao Yu., 2020, P ACL
   Zhou Ben, 2018, P 2018 C EMP METH NA, P2065
NR 42
TC 1
Z9 1
U1 5
U2 8
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1076-2787
EI 1099-0526
J9 COMPLEXITY
JI Complexity
PD APR 16
PY 2021
VL 2021
AR 3473849
DI 10.1155/2021/3473849
PG 8
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA SY4IB
UT WOS:000665852500005
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, BY
   Tang, C
   Yao, YL
   Chen, XH
   Zhou, C
   Wei, ZT
   Xing, FY
   Chen, L
   Cai, X
   Zhang, ZY
   Sun, SY
   Liu, Q
AF Zhang, Biyu
   Tang, Chen
   Yao, Yanli
   Chen, Xiaohan
   Zhou, Chi
   Wei, Zhiting
   Xing, Feiyang
   Chen, Lan
   Cai, Xiang
   Zhang, Zhiyuan
   Sun, Shuyang
   Liu, Qi
TI The tumor therapy landscape of synthetic lethality
SO NATURE COMMUNICATIONS
LA English
DT Article
AB Synthetic lethality is emerging as an important cancer therapeutic paradigm, while the comprehensive selective treatment opportunities for various tumors have not yet been explored. We develop the Synthetic Lethality Knowledge Graph (SLKG), presenting the tumor therapy landscape of synthetic lethality (SL) and synthetic dosage lethality (SDL). SLKG integrates the large-scale entity of different tumors, drugs and drug targets by exploring a comprehensive set of SL and SDL pairs. The overall therapy landscape is prioritized to identify the best repurposable drug candidates and drug combinations with literature supports, in vitro pharmacologic evidence or clinical trial records. Finally, cladribine, an FDA-approved multiple sclerosis treatment drug, is selected and identified as a repurposable drug for treating melanoma with CDKN2A mutation by in vitro validation, serving as a demonstrating SLKG utility example for novel tumor therapy discovery. Collectively, SLKG forms the computational basis to uncover cancer-specific susceptibilities and therapy strategies based on the principle of synthetic lethality. Various methods have been proposed to identify synthetic lethality interactions, but selective treatment opportunities for various tumors have not yet been explored. Here, the authors develop the Synthetic Lethality Knowledge Graph webserver (SLKG, http://www.slkg.net) to explore the comprehensive tumor therapy landscape and uncover cancer-specific susceptibilities based on the principle of synthetic lethality.
C1 [Zhang, Biyu; Tang, Chen; Chen, Xiaohan; Zhou, Chi; Wei, Zhiting; Xing, Feiyang; Liu, Qi] Tongji Univ, Translat Med Ctr Stem Cell Therapy, Shanghai, Peoples R China.
   [Zhang, Biyu; Tang, Chen; Chen, Xiaohan; Zhou, Chi; Wei, Zhiting; Xing, Feiyang; Liu, Qi] Tongji Univ, Inst Regenerat Med, Shanghai East Hosp, Bioinformat Dept,Sch Life Sci & Technol, Shanghai, Peoples R China.
   [Yao, Yanli; Chen, Lan; Zhang, Zhiyuan; Sun, Shuyang] Shanghai Jiao Tong Univ, Coll Stomatol, Dept Oral & Maxillofacial Head Neck Oncol, Shanghai Peoples Hosp 9,Sch Med, Shanghai, Peoples R China.
   [Cai, Xiang] Alibaba Cloud, Hangzhou, Peoples R China.
RP Liu, Q (通讯作者)，Tongji Univ, Translat Med Ctr Stem Cell Therapy, Shanghai, Peoples R China.; Liu, Q (通讯作者)，Tongji Univ, Inst Regenerat Med, Shanghai East Hosp, Bioinformat Dept,Sch Life Sci & Technol, Shanghai, Peoples R China.; Sun, SY (通讯作者)，Shanghai Jiao Tong Univ, Coll Stomatol, Dept Oral & Maxillofacial Head Neck Oncol, Shanghai Peoples Hosp 9,Sch Med, Shanghai, Peoples R China.
EM shuyangs@shsmu.edu.cn; qiliu@tongji.edu.cn
OI Zhou, Chi/0000-0002-2417-6663; xiaohan, Chen/0000-0003-0619-6019
FU National Key Research and Development Program of China [2017YFC0908500,
   2016YFC1303205]; National Natural Science Foundation of China [31970638,
   61572361]; Shanghai Natural Science Foundation Program [17ZR1449400];
   Shanghai Artificial Intelligence Technology Standard Project
   [19DZ2200900]; Fundamental Research Funds for the Central Universities
FX This work was supported by the National Key Research and Development
   Program of China (Grant No. 2017YFC0908500, No. 2016YFC1303205),
   National Natural Science Foundation of China (Grant No. 31970638,
   61572361), Shanghai Natural Science Foundation Program (Grant No.
   17ZR1449400), Shanghai Artificial Intelligence Technology Standard
   Project (Grant No. 19DZ2200900) and Fundamental Research Funds for the
   Central Universities.
CR Basu A, 2013, CELL, V154, P1151, DOI 10.1016/j.cell.2013.08.003
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Dwane L, 2021, NUCLEIC ACIDS RES, V49, pD1365, DOI 10.1093/nar/gkaa882
   Forbes SA, 2017, NUCLEIC ACIDS RES, V45, pD777, DOI 10.1093/nar/gkw1121
   Guo J, 2016, NUCLEIC ACIDS RES, V44, pD1011, DOI 10.1093/nar/gkv1108
   Hoadley KA, 2018, CELL, V173, P291, DOI 10.1016/j.cell.2018.03.022
   Kolde R, 2012, BIOINFORMATICS, V28, P573, DOI 10.1093/bioinformatics/btr709
   Law V, 2014, NUCLEIC ACIDS RES, V42, pD1091, DOI 10.1093/nar/gkt1068
   Liu YN, 2017, J GENET GENOMICS, V44, P119, DOI 10.1016/j.jgg.2016.12.004
   McDonald ER, 2017, CELL, V170, P577, DOI 10.1016/j.cell.2017.07.005
   Pearre DC, 2018, THER CLIN RISK MANAG, V14, P2189, DOI 10.2147/TCRM.S149248
   Pineiro-Yanez E, 2018, GENOME MED, V10, DOI 10.1186/s13073-018-0546-1
   Pinero J, 2017, NUCLEIC ACIDS RES, V45, pD833, DOI 10.1093/nar/gkw943
   Ryan CJ, 2014, CANCER CELL, V26, P306, DOI 10.1016/j.ccr.2014.08.008
   Sajesh Babu V, 2013, Cancers (Basel), V5, P739, DOI 10.3390/cancers5030739
   Sanseau P, 2012, NAT BIOTECHNOL, V30, P317, DOI 10.1038/nbt.2151
   Singhi AD, 2019, GASTROENTEROLOGY, V156, P2242, DOI 10.1053/j.gastro.2019.02.037
   Sui HY, 2015, DRUG DES DEV THER, V9, P3183, DOI 10.2147/DDDT.S82035
   Sullivan-Reed K, 2018, CELL REP, V23, P3127, DOI 10.1016/j.celrep.2018.05.034
   Tang ZF, 2017, NUCLEIC ACIDS RES, V45, pW98, DOI 10.1093/nar/gkx247
   Tewari KS, 2015, CLIN CANCER RES, V21, P3829, DOI 10.1158/1078-0432.CCR-15-0088
   Tsherniak A, 2017, CELL, V170, P564, DOI 10.1016/j.cell.2017.06.010
   Turk AA, 2018, CANCER-AM CANCER SOC, V124, P2498, DOI 10.1002/cncr.31307
   Wagner AH, 2016, NUCLEIC ACIDS RES, V44, pD1036, DOI 10.1093/nar/gkv1165
   Walsh N, 2013, INVEST NEW DRUG, V31, P558, DOI 10.1007/s10637-012-9891-x
   Wang RP, 2019, BRIEF BIOINFORM, V20, P1295, DOI 10.1093/bib/bbx172
   Wang ZY, 2013, NAT BIOTECHNOL, V31, P1080, DOI 10.1038/nbt.2758
   Yang WJ, 2013, NUCLEIC ACIDS RES, V41, pD955, DOI 10.1093/nar/gks1111
   Zagidullin B, 2019, NUCLEIC ACIDS RES, V47, pW43, DOI 10.1093/nar/gkz337
   Zhao M, 2016, NUCLEIC ACIDS RES, V44, pD1023, DOI 10.1093/nar/gkv1268
   Zhu F, 2012, NUCLEIC ACIDS RES, V40, pD1128, DOI 10.1093/nar/gkr797
NR 31
TC 7
Z9 7
U1 9
U2 19
PU NATURE RESEARCH
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 2041-1723
J9 NAT COMMUN
JI Nat. Commun.
PD FEB 24
PY 2021
VL 12
IS 1
AR 1275
DI 10.1038/s41467-021-21544-2
PG 11
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA QT5EH
UT WOS:000626610200018
PM 33627666
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Bakal, G
   Talari, P
   Kakani, EV
   Kavuluru, R
AF Bakal, Gokhan
   Talari, Preetham
   Kakani, Elijah, V
   Kavuluru, Ramakanth
TI Exploiting semantic patterns over biomedical knowledge graphs for
   predicting treatment and causative relations
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Information extraction; Relation prediction; Semantic graph patterns
ID DISCOVERY; KERNEL
AB Background: Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach.
   Objective: To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs.
   Methods: We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80-20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios.
   Results: Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset.
   Conclusions: We employed semantic graph patterns connecting pairs of candidate biomedical entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction.
C1 [Kavuluru, Ramakanth] Univ Kentucky, Dept Internal Med, Div Biomed Informat, Lexington, KY 40506 USA.
   [Bakal, Gokhan; Kavuluru, Ramakanth] Univ Kentucky, Dept Comp Sci, Lexington, KY 40506 USA.
   [Talari, Preetham; Kakani, Elijah, V] Univ Kentucky, Dept Internal Med, Div Hosp Med, Lexington, KY 40506 USA.
RP Kavuluru, R (通讯作者)，Univ Kentucky, Dept Internal Med, Div Biomed Informat, Lexington, KY 40506 USA.
EM mgokhanbakal@uky.edu; preetham.talari@uky.edu; elijah.kakani@oky.edu;
   ramakanth.kavuluru@uky.edu
RI Bakal, Mehmet Gokhan/AAT-2797-2020; Bakal, Mehmet G/E-6835-2018
OI Bakal, Mehmet Gokhan/0000-0003-2897-3894; Kakani,
   Elijah/0000-0002-3727-7375
FU U.S. National Library of Medicine through NIH grant [R21LM012274]; U.S.
   National Center for Advancing Translational Sciences [UL1TR001998];
   Ministry of National Education, Republic of Turkey; NATIONAL CANCER
   INSTITUTE [P30CA177558] Funding Source: NIH RePORTER; NATIONAL CENTER
   FOR ADVANCING TRANSLATIONAL SCIENCES [UL1TR001998] Funding Source: NIH
   RePORTER; NATIONAL LIBRARY OF MEDICINE [R21LM012274] Funding Source: NIH
   RePORTER
FX We thank reviewers for their constructive criticism that helped improved
   the quality of this manuscript. We are grateful for the support of the
   U.S. National Library of Medicine through NIH grant R21LM012274 and also
   thankful for partial support offered by the U.S. National Center for
   Advancing Translational Sciences via grant UL1TR001998. The content is
   solely the responsibility of the authors and does not necessarily
   represent the official views of the NIH. We also acknowledge the
   Ministry of National Education, Republic of Turkey, for providing
   financial support to Gokhan Bakal with full scholarship for his doctoral
   studies.
CR Andronis C, 2011, BRIEF BIOINFORM, V12, P357, DOI 10.1093/bib/bbr005
   [Anonymous], 2011, J MACH LEARN RES
   Bakal Gokhan, 2015, Min Intell Knowl Explor (2015), V9468, P586, DOI 10.1007/978-3-319-26832-3_55
   Ben Abacha Asma, 2011, J Biomed Semantics, V2 Suppl 5, pS4, DOI 10.1186/2041-1480-2-S5-S4
   Breiman L., 1984, CLASSIFICATION REGRE, V1st ed., DOI 10.1201/9781315139470
   Brown AS, 2017, SCI DATA, V4, DOI 10.1038/sdata.2017.29
   Cameron D, 2015, J BIOMED INFORM, V54, P141, DOI 10.1016/j.jbi.2015.01.014
   Cameron D, 2013, J BIOMED INFORM, V46, P238, DOI 10.1016/j.jbi.2012.09.004
   Cohen T, 2012, J BIOMED INFORM, V45, P1049, DOI 10.1016/j.jbi.2012.07.003
   Cohen T, 2010, J BIOMED INFORM, V43, P240, DOI 10.1016/j.jbi.2009.09.003
   Cohen Trevor, 2014, SYST PHARM, V3, pe140
   Craven Mark, 1999, P 7 INT C INT SYST M, P78
   Fundel K, 2007, BIOINFORMATICS, V23, P365, DOI 10.1093/bioinformatics/btl616
   Hristovski Dimitar, 2006, AMIA ANN S
   Kavuluru R., 2012, INT HLTH INF S, P275
   Kilicoglu H, 2012, BIOINFORMATICS, V28, P3158, DOI 10.1093/bioinformatics/bts591
   Kim S, 2015, J BIOMED INFORM, V55, P23, DOI 10.1016/j.jbi.2015.03.002
   Kleinbaum Daniel G., 2010, LOGISTIC REGRESSION
   Li J, 2016, BRIEF BIOINFORM, V17, P2, DOI 10.1093/bib/bbv020
   Liu Ying, 2012, AMIA Annu Symp Proc, V2012, P587
   Lu ZY, 2011, DATABASE-OXFORD, DOI 10.1093/database/baq036
   Luo Yuan, 2017, BRIEF BIOINFORM, V18, P160
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   National Library of Medicine, 2016, SEM MEDLINE DAT
   National Library of Medicine, 2009, UN MED LANG SYST REF
   National Library of Medicine, 2003, CURR HIER UMLS SEM T
   National Library of Medicine, 2013, SEMREP NLMS SEM PRED
   National Library of Medicine, 2003, CURR HIER UMLS PRED
   Riedel S, 2010, LECT NOTES ARTIF INT, V6323, P148, DOI 10.1007/978-3-642-15939-8_10
   Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003
   Ritter A., 2013, T ASSOC COMPUT LING, V1, P367, DOI DOI 10.1162/tacl_a_00234
   Segura-Bedmar I, 2011, J BIOMED INFORM, V44, P789, DOI 10.1016/j.jbi.2011.04.005
   Surdeanu M., 2012, P 2012 JOINT C EMP M, P455
   Tran T, 2017, J BIOMED INFORM, V75, pS138, DOI 10.1016/j.jbi.2017.06.010
   Ursu O, 2017, NUCLEIC ACIDS RES, V45, pD932, DOI 10.1093/nar/gkw993
   Uzuner O, 2011, J AM MED INFORM ASSN, V18, P552, DOI 10.1136/amiajnl-2011-000203
   Wallace Byron C., 2011, 2011 IEEE 11 INT C, P754
   Weeber M, 2001, J AM SOC INF SCI TEC, V52, P548, DOI 10.1002/asi.1104.abs
   Xu Hua, 2014, J AM MED INFORM ASS
   Xu R., 2009, P WORKSH CURR TRENDS, P63
   Xu W., 2013, P 51 ANN M ASS COMP, P665
   Zhang R, 2014, CANCER INFORM, V13, P103, DOI 10.4137/CIN.S13889
   Zhang R, 2014, J BIOMED INFORM, V49, P134, DOI 10.1016/j.jbi.2014.01.004
NR 43
TC 19
Z9 21
U1 3
U2 40
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD JUN
PY 2018
VL 82
BP 189
EP 199
DI 10.1016/j.jbi.2018.05.003
PG 11
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA GU1VF
UT WOS:000445054600016
PM 29763706
OA Green Accepted, Bronze
DA 2022-08-23
ER

PT J
AU Chen, W
   Xu, ZY
   Zheng, XY
   Yu, QY
   Luo, YL
AF Chen, Wen
   Xu, Zhiyun
   Zheng, Xiaoyao
   Yu, Qingying
   Luo, Yonglong
TI Research on Sentiment Classification of Online Travel Review Text
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE user generated content; sentiment analysis; classification; keyword
   extraction; text representation; sampling; machine learning; TripAdvisor
ID SOCIAL MEDIA ANALYTICS; BIG DATA; TOURISM; HOSPITALITY; INTELLIGENCE;
   ONTOLOGY; MACHINE; AIRBNB
AB In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists' sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research.
C1 [Chen, Wen; Xu, Zhiyun; Luo, Yonglong] Anhui Normal Univ, Sch Geog & Tourism, Wuhu 241003, Peoples R China.
   [Chen, Wen] Tongling Coll, Sch Math & Comp Sci, Tongling 244000, Peoples R China.
   [Zheng, Xiaoyao; Yu, Qingying; Luo, Yonglong] Anhui Normal Univ, Anhui Prov Key Lab Network & Informat Secur, Wuhu 241003, Peoples R China.
RP Luo, YL (通讯作者)，Anhui Normal Univ, Sch Geog & Tourism, Wuhu 241003, Peoples R China.; Luo, YL (通讯作者)，Anhui Normal Univ, Anhui Prov Key Lab Network & Informat Secur, Wuhu 241003, Peoples R China.
EM wchen@ahnu.edu.cn; zhyxu@mail.ahnu.edu.cn; zxiaoyao@ahnu.edu.cn;
   ahnuyuq@ahnu.edu.cn; ylluo@ustc.edu.cn
OI Luo, Yonglong/0000-0003-4987-0376
FU National Natural Science Foundation of China [61972439, 61672039,
   61702010, 61772034]
FX This research was funded by the National Natural Science Foundation of
   China under Grants 61972439, 61672039, 61702010, 61772034.
CR Adhi M.S., 2019, J RESTI, V3, P141, DOI [10.29207/resti.v3i2.901, DOI 10.29207/RESTI.V3I2.901]
   Afzaal M, 2019, EXPERT SYST, V36, DOI 10.1111/exsy.12371
   Akhtar N, 2017, PROCEDIA COMPUT SCI, V115, P563, DOI 10.1016/j.procs.2017.09.115
   Alaei AR, 2019, J TRAVEL RES, V58, P175, DOI 10.1177/0047287517747753
   Ali F, 2020, INFORM FUSION, V63, P208, DOI 10.1016/j.inffus.2020.06.008
   Ali F, 2017, TRANSPORT RES C-EMER, V77, P33, DOI 10.1016/j.trc.2017.01.014
   Ali F, 2016, APPL SOFT COMPUT, V47, P235, DOI 10.1016/j.asoc.2016.06.003
   Alowaidi S, 2017, INT J ADV COMPUT SC, V8, P256
   An HW, 2019, J AMB INTEL HUM COMP, DOI 10.1007/s12652-019-01521-w
   [Anonymous], 2016, PROMINENT FEATURE EX
   [Anonymous], 2004, P EMNLP
   Asgarian E, 2018, COGN COMPUT, V10, P117, DOI 10.1007/s12559-017-9513-1
   Asghar MZ, 2018, EXPERT SYST, V35, DOI 10.1111/exsy.12233
   Balli S, 2019, IET SOFTW, V13, P295, DOI 10.1049/iet-sen.2018.5046
   Bansal B, 2019, APPL INTELL, V49, P137, DOI 10.1007/s10489-018-1299-7
   Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
   Campos R, 2020, INFORM SCIENCES, V509, P257, DOI 10.1016/j.ins.2019.09.013
   Chawla NV, 2004, ACM SIGKDD EXPLORATI, V6, P6, DOI 10.1145/3262579
   Chen F, 2019, NEUROCOMPUTING, V368, P51, DOI 10.1016/j.neucom.2019.08.054
   Cheng WC, 2015, Proceedings of the 11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, P133, DOI 10.4108/eai.19-8-2015.2260108
   Chuang SH, 2020, IND MARKET MANAG, V84, P202, DOI 10.1016/j.indmarman.2019.06.012
   Corrado G. S, 2013, NIPS 13, V3111, P3119
   Dong L., 2017, P 15 C EUR CHAPT ASS, V1, P623, DOI DOI 10.18653/V1/E17-1059
   Dong YR, 2020, IEEE ACCESS, V8, P30548, DOI 10.1109/ACCESS.2019.2954985
   Fang B, 2016, TOURISM MANAGE, V52, P498, DOI 10.1016/j.tourman.2015.07.018
   Gagliardi I, 2020, MULTIMODAL TECHNOLOG, V4, DOI 10.3390/mti4020030
   Garcia-Pablos A, 2016, INF TECHNOL TOUR, V16, P45, DOI 10.1007/s40558-015-0047-7
   Ghiassi M, 2018, EXPERT SYST APPL, V106, P197, DOI 10.1016/j.eswa.2018.04.006
   Gunther WA, 2017, J STRATEGIC INF SYST, V26, P191, DOI 10.1016/j.jsis.2017.07.003
   Guo Y, 2017, TOURISM MANAGE, V59, P467, DOI 10.1016/j.tourman.2016.09.009
   Hinterstoisser S., 2018, P EUR C COMP VIS ECC
   Hopken W, 2017, TOURISM VERGE, P129, DOI 10.1007/978-3-319-44263-1_8
   Hu QH, 2010, IEEE T SYST MAN CY B, V40, P137, DOI 10.1109/TSMCB.2009.2024166
   Kauffmann E, 2020, IND MARKET MANAG, V90, P523, DOI 10.1016/j.indmarman.2019.08.003
   Kim K, 2017, TECHNOL FORECAST SOC, V123, P362, DOI 10.1016/j.techfore.2017.01.001
   Kim K, 2018, EXPERT SYST APPL, V109, P49, DOI 10.1016/j.eswa.2018.05.023
   Kirilenko AP, 2018, J TRAVEL RES, V57, P1012, DOI 10.1177/0047287517729757
   Ko C.-H., 2018, OPEN ACCESS LIB J, V05, P1, DOI DOI 10.4236/OALIB.1104877
   Kumar A, 2019, MULTIMED TOOLS APPL, V78, P29529, DOI 10.1007/s11042-019-7278-0
   Lawani A, 2019, REG SCI URBAN ECON, V75, P22, DOI 10.1016/j.regsciurbeco.2018.11.003
   Lecca M, 2017, IEEE T IMAGE PROCESS, V26, P2767, DOI 10.1109/TIP.2017.2686652
   Li JJ, 2018, TOURISM MANAGE, V68, P301, DOI 10.1016/j.tourman.2018.03.009
   Li X, 2017, TOURISM MANAGE, V59, P57, DOI 10.1016/j.tourman.2016.07.005
   Liu B., 2007, WEB DATA MINING
   Luo Y, 2019, INT J HOSP MANAG, V80, P144, DOI 10.1016/j.ijhm.2019.02.008
   Ma E, 2018, INT J CONTEMP HOSP M, V30, P3287, DOI 10.1108/IJCHM-10-2017-0704
   Manning C., 1999, FDN STAT NATURAL LAN
   Mariani M, 2018, INT J CONTEMP HOSP M, V30, P3514, DOI 10.1108/IJCHM-07-2017-0461
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Mirzaalian F, 2019, J HOSP TOUR TECHNOL, V10, P764, DOI 10.1108/JHTT-08-2018-0078
   Murni, 2019, 2019 2ND INTERNATIONAL CONFERENCE OF COMPUTER AND INFORMATICS ENGINEERING (IC2IE 2019), P232
   Nasar Z, 2019, INFORM PROCESS MANAG, V56, DOI 10.1016/j.ipm.2019.102088
   Neidhardt Julia, 2016, INFORM COMMUNICATION, P593
   Pan D., 2019, ARXIV190702046
   Paramonov I, 2016, COMM COM INF SC, V649, P129, DOI 10.1007/978-3-319-45880-9_11
   Parlar T, 2018, HUM-CENT COMPUT INFO, V8, DOI 10.1186/s13673-018-0135-8
   Pu XJ, 2019, MULTIMEDIA SYST, V25, P21, DOI 10.1007/s00530-017-0550-0
   Rosso P., 2019, P 2019 IEEE INT C BI
   Schmunk Sergej, 2013, INFORM COMMUNICATION, V2014, P253, DOI DOI 10.1007/978-3-319-03973-2_19
   Shouzhong T, 2016, J CHINA U POSTS TELE, V23, P40, DOI DOI 10.1016/S1005-8885(16)60056-0
   Sindhu I, 2019, IEEE ACCESS, V7, P108729, DOI 10.1109/ACCESS.2019.2928872
   Sivarajah U, 2017, J BUS RES, V70, P263, DOI 10.1016/j.jbusres.2016.08.001
   Stepchenkova S, 2018, TOUR HOSP EVENT MAN, P129, DOI 10.1007/978-3-319-78553-0_9
   Valdivia A, 2019, NEUROCOMPUTING, V353, P3, DOI 10.1016/j.neucom.2018.09.096
   Valdivia A, 2017, IEEE INTELL SYST, V32, P72, DOI 10.1109/MIS.2017.3121555
   Wang J, 2017, P ANN INT IEEE EMBS, P291, DOI 10.1109/EMBC.2017.8036819
   Wu W, 2012, ACM SIGMOD, DOI DOI 10.1145/2213836.2213891
   Xiang Z, 2017, TOURISM MANAGE, V58, P51, DOI 10.1016/j.tourman.2016.10.001
   Zainuddin N, 2018, APPL INTELL, V48, P1218, DOI 10.1007/s10489-017-1098-6
   Zhang W, 2019, CLUSTER COMPUT, V22, P12619, DOI 10.1007/s10586-017-1693-7
NR 70
TC 7
Z9 7
U1 32
U2 80
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD AUG
PY 2020
VL 10
IS 15
AR 5275
DI 10.3390/app10155275
PG 21
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Chemistry; Engineering; Materials Science; Physics
GA MZ4SR
UT WOS:000559115000001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Shin, S
   Lee, KH
AF Shin, Sangjin
   Lee, Kyong-Ho
TI Processing knowledge graph-based complex questions through question
   decomposition and recomposition
SO INFORMATION SCIENCES
LA English
DT Article
DE Question answering; Complex questions; Decomposition; Constraints
AB Nowadays, question answering (QA) over complex questions is the most spotlighted research topic in diverse communities. Different from existing QA approaches for simple questions that require a single relation in a knowledge graph (KG) to find an answer set, QA for complex questions requires to process multiple KG relations. Complex questions often include some representations for constraints (ordinal, temporal, etc.) that restrict an answer set for the given question. Despite lots of efforts to process such questions, there are still limitations in processing constraints, multiple relations, and variables. To solve the issues, we propose a novel QA method that first decomposes an input question and then generates a correct query graph with fully complete semantics. In order to fill with lossy semantics caused by the decomposition task, the proposed method employs Bi-GRU based model. The model integrates individual compositional semantics of sub-query graphs matched to decomposed sub-questions. Experimental results show the best performance compared to the state-of-art on complex questions. (C) 2020 Elsevier Inc. All rights reserved.
C1 [Shin, Sangjin; Lee, Kyong-Ho] Yonsei Univ, Dept Comp Sci, 50 Yonsei Ro, Seoul 03722, South Korea.
RP Lee, KH (通讯作者)，Yonsei Univ, Dept Comp Sci, 50 Yonsei Ro, Seoul 03722, South Korea.
EM khlee89@yonsei.ac.kr
OI Shin, Sangjin/0000-0003-3687-7587; LEE, KYONG-HO/0000-0002-1581-917X
FU Graduate School of YONSEI University Research Scholarship; National
   Research Foundation of Korea (NRF) - Korea government (MSIP; Ministry of
   Science, ICT & Future Planning) [NRF-2019R1A2B5B01070555]
FX This research was supported by the Graduate School of YONSEI University
   Research Scholarship Grants in 2019 and the National Research Foundation
   of Korea (NRF) grant funded by the Korea government(MSIP; Ministry of
   Science, ICT & Future Planning) (No. NRF-2019R1A2B5B01070555).
CR Abujabal A, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1053, DOI 10.1145/3178876.3186004
   Abujabal A, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1191, DOI 10.1145/3038912.3052583
   Bao J, 2016, COLING
   Bao JW, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P967
   Bast H., 2015, P 24 ACM INT C INF K, P1431, DOI [10.1145/2806416.2806472, DOI 10.1145/2806416.2806472]
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Breiman L., 2001, RANDOM FORESTS, V45, P5, DOI 10.1023/A:1010933404324
   Du L, 2018, INFORM SCIENCES, V467, P342, DOI 10.1016/j.ins.2018.07.043
   Fader A, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P1156, DOI 10.1145/2623330.2623677
   Fader Anthony, 2013, ACL
   Feng Y., 2016, P COLING 2016 26 INT, P2397
   Finkel J.R., 2005, P 43 ANN M ASS COMP, P363, DOI DOI 10.3115/1219840.1219885
   Hearst M.A., 1992, 14 INT C COMP LING C, P539, DOI 10.3115/992133.992154
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Iyyer M., 2014, P 2014 C EMP METH NA, P633, DOI DOI 10.3115/V1/D14-1070
   Iyyer M, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1821, DOI 10.18653/v1/P17-1167
   Kolomiyets O, 2011, INFORM SCIENCES, V181, P5412, DOI 10.1016/j.ins.2011.07.047
   Li X, 2014, 2014 IEEE WORKSHOP ON ELECTRONICS, COMPUTER AND APPLICATIONS, P740, DOI 10.1109/IWECA.2014.6845728
   Lin Z., 2017, ARXIV170303130
   Lukovnikov D, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1211, DOI 10.1145/3038912.3052675
   Luo K., 2018, P 2018 C EMP METH NA, P2185
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Pennington J., 2014, EMNLP, P1532
   Rodrigo A, 2015, IEEE T KNOWL DATA EN, V27, P1157, DOI 10.1109/TKDE.2014.2373363
   Savenkov D, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P235, DOI 10.1145/2911451.2911536
   Spitkovsky VI, 2012, LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P3168
   Unger C., 2012, P 21 INT C WORLD WID, P639, DOI DOI 10.1145/2187836.2187923
   Wang XJ, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION PROBLEM-SOLVING (ICCP), P130, DOI 10.1109/ICCPS.2015.7454108
   Xu K, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2326
   Yang Y, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P504
   Yih WT, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2, P201
   Yih WT, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P1321
   Yih WT, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P643
   Yin W, 2016, ARXIV160603391
   Zhang K, 2016, PROCEEDINGS OF THE NINTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'16), P533, DOI 10.1145/2835776.2835786
   Zheng WG, 2019, INFORM SCIENCES, V481, P141, DOI 10.1016/j.ins.2018.12.032
NR 37
TC 9
Z9 9
U1 5
U2 30
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD JUN
PY 2020
VL 523
BP 234
EP 244
DI 10.1016/j.ins.2020.02.065
PG 11
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LE8YZ
UT WOS:000527016100015
DA 2022-08-23
ER

PT J
AU Morton, K
   Wang, P
   Bizon, C
   Cox, S
   Balhoff, J
   Kebede, Y
   Fecho, K
   Tropsha, A
AF Morton, Kenneth
   Wang, Patrick
   Bizon, Chris
   Cox, Steven
   Balhoff, James
   Kebede, Yaphet
   Fecho, Karamarie
   Tropsha, Alexander
TI ROBOKOP: an abstraction layer and user interface for knowledge graphs to
   support question answering
SO BIOINFORMATICS
LA English
DT Article
AB A Summary: Knowledge graphs (KGs) are quickly becoming a common-place tool for storing relationships between entities from which higher-level reasoning can be conducted. KGs are typically stored in a graph-database format, and graph-database queries can be used to answer questions of interest that have been posed by users such as biomedical researchers. For simple queries, the inclusion of direct connections in the KG and the storage and analysis of query results are straightforward; however, for complex queries, these capabilities become exponentially more challenging with each increase in complexity of the query. For instance, one relatively complex query can yield a KG with hundreds of thousands of query results. Thus, the ability to efficiently query, store, rank and explore sub-graphs of a complex KG represents a major challenge to any effort designed to exploit the use of KGs for applications in biomedical research and other domains. We present Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways as an abstraction layer and user interface to more easily query KGs and store, rank and explore query results.
C1 [Morton, Kenneth; Wang, Patrick] CoVar Appl Technol, Durham, NC 27701 USA.
   [Bizon, Chris; Cox, Steven; Balhoff, James; Kebede, Yaphet; Fecho, Karamarie] Univ N Carolina, Renaissance Comp Inst, Chapel Hill, NC 27599 USA.
   [Tropsha, Alexander] Univ N Carolina, Sch Pharm, Chapel Hill, NC 27599 USA.
RP Morton, K (通讯作者)，CoVar Appl Technol, Durham, NC 27701 USA.; Fecho, K (通讯作者)，Univ N Carolina, Renaissance Comp Inst, Chapel Hill, NC 27599 USA.
EM kenny@covar.org; kfecho@copperlineprofessionalsolutions.com
OI Fecho, Karamarie/0000-0002-6704-9306; Cox, Steven/0000-0002-8561-4508;
   Kebede, Yaphet/0000-0002-5046-0246; Wang, Patrick/0000-0002-6693-6106;
   Morton, Kenneth/0000-0002-9352-8669
FU National Center for Advancing Translational Sciences, National
   Institutes of Health [OT3TR002020, OT2R002514]
FX This work was supported by the National Center for Advancing
   Translational Sciences, National Institutes of Health [grant numbers
   OT3TR002020, OT2R002514].
CR [Anonymous], 2017, INT J DATA SCI ANAL, DOI DOI 10.1007/S41060-017-0046-1
   Fang Y, 2016, PROC INT CONF DATA, P277, DOI 10.1109/ICDE.2016.7498247
   Fecho K, 2019, CTS-CLIN TRANSL SCI, V12, P86, DOI 10.1111/cts.12591
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Huang ZP, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1595, DOI 10.1145/2939672.2939815
   KLEIN DJ, 1993, J MATH CHEM, V12, P81, DOI 10.1007/BF01164627
   Liekens AML, 2011, GENOME BIOL, V12, DOI 10.1186/gb-2011-12-6-r57
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   The Biomedical Data Translator Consortium, 2019, CTS-CLIN TRANSL SCI, V12, P91
   Zhao H, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P635, DOI 10.1145/3097983.3098063
NR 10
TC 12
Z9 12
U1 1
U2 8
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD DEC 15
PY 2019
VL 35
IS 24
BP 5382
EP 5384
DI 10.1093/bioinformatics/btz604
PG 3
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA KF6PH
UT WOS:000509361200060
PM 31410449
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Korn, D
   Bobrowski, T
   Li, M
   Kebede, Y
   Wang, P
   Owen, P
   Vaidya, G
   Muratov, E
   Chirkova, R
   Bizon, C
   Tropsha, A
AF Korn, Daniel
   Bobrowski, Tesia
   Li, Michael
   Kebede, Yaphet
   Wang, Patrick
   Owen, Phillips
   Vaidya, Gaurav
   Muratov, Eugene
   Chirkova, Rada
   Bizon, Chris
   Tropsha, Alexander
TI COVID-KOP: integrating emerging COVID-19 data with the ROBOKOP database
SO BIOINFORMATICS
LA English
DT Article
AB In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP) biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to generate new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19 by establishing respective confirmatory pathways of drug action.
C1 [Korn, Daniel; Li, Michael] Univ N Carolina, Dept Comp Sci, Chapel Hill, NC 27515 USA.
   [Bobrowski, Tesia; Muratov, Eugene; Tropsha, Alexander] Univ N Carolina, Div Chem Biol & Med Chem, UNC Eshelman Sch Pharm, Chapel Hill, NC 27515 USA.
   [Kebede, Yaphet; Owen, Phillips; Vaidya, Gaurav; Bizon, Chris] Univ N Carolina, Renaissance Comp Inst, Chapel Hill, NC 27599 USA.
   [Wang, Patrick] CoVar Appl Technol, Durham, NC 27701 USA.
   [Chirkova, Rada] North Carolina State Univ, Dept Comp Sci, Raleigh, NC 27606 USA.
RP Tropsha, A (通讯作者)，Univ N Carolina, Div Chem Biol & Med Chem, UNC Eshelman Sch Pharm, Chapel Hill, NC 27515 USA.; Bizon, C (通讯作者)，Univ N Carolina, Renaissance Comp Inst, Chapel Hill, NC 27599 USA.
EM bizon@renci.org; alex_tropsha@unc.edu
RI Muratov, Eugene/C-4454-2014
OI Muratov, Eugene/0000-0003-4616-7036; Korn, Daniel/0000-0002-1780-9872
FU National Center for Advancing Translational Sciences, National
   Institutes of Health [OT2R002514]; National Institutes of Health
   [1U01CA207160]
FX This work was supported by the National Center for Advancing
   Translational Sciences, National Institutes of Health [OT2R002514] and
   National Institutes of Health [1U01CA207160].
CR Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Barchetta Ilaria, 2020, Diabetes Res Clin Pract, V163, P108165, DOI 10.1016/j.diabres.2020.108165
   Bizon C, 2019, J CHEM INF MODEL, V59, P4968, DOI 10.1021/acs.jcim.9b00683
   Fang L, 2020, LANCET RESP MED, V8, pE21, DOI 10.1016/S2213-2600(20)30116-8
   Federhen S, 2012, NUCLEIC ACIDS RES, V40, pD136, DOI 10.1093/nar/gkr1178
   Morton K, 2019, BIOINFORMATICS, V35, P5382, DOI 10.1093/bioinformatics/btz604
   Nakhleh A, 2020, DIABETES METAB SYND, V14, P509, DOI 10.1016/j.dsx.2020.04.040
   Neill DR, 2012, PLOS PATHOG, V8, DOI 10.1371/journal.ppat.1002660
   Pal R, 2020, DIABETES RES CLIN PR, V162, DOI 10.1016/j.diabres.2020.108088
   Schett G, 2020, NAT REV IMMUNOL, V20, P271, DOI 10.1038/s41577-020-0312-7
   Scott LJ, 2011, DRUGS, V71, P611, DOI 10.2165/11207400-000000000-00000
   Wang XY, 2015, J HEPATOL, V62, pS809, DOI 10.1016/S0168-8278(15)31402-1
   Zhang LH, 2015, CARDIOVASC DRUG THER, V29, P243, DOI 10.1007/s10557-015-6592-7
NR 13
TC 5
Z9 5
U1 2
U2 10
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD FEB 15
PY 2021
VL 37
IS 4
BP 586
EP 587
DI 10.1093/bioinformatics/btaa718
PG 2
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA SE5CO
UT WOS:000652090200023
PM 33175089
OA Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Luo, WB
   Deng, ZP
   Zhong, SH
   Deng, MJ
AF Luo, Wenbing
   Deng, Zhongping
   Zhong, Shihu
   Deng, Mingjun
TI Trends, Issues and Future Directions of Urban Health Impact Assessment
   Research: A Systematic Review and Bibliometric Analysis
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Review
DE urban; health impact assessment; bibliometric analysis; CiteSpace;
   knowledge mapping
ID POLYCYCLIC AROMATIC-HYDROCARBONS; RISK-ASSESSMENT; STREET DUST;
   HEAVY-METALS; SPATIAL-DISTRIBUTION; INDUSTRIAL-CITY;
   RESPIRATORY-DISEASES; HOSPITAL ADMISSIONS; PHYSICAL-ACTIVITY; KHORASAN
   RAZAVI
AB Health impact assessment (HIA) has been regarded as an important means and tool for urban planning to promote public health and further promote the integration of health concept. This paper aimed to help scientifically to understand the current situation of urban HIA research, analyze its discipline co-occurrence, publication characteristics, partnership, influence, keyword co-occurrence, co-citation, and structural variation. Based on the ISI Web database, this paper used a bibliometric method to analyze 2215 articles related to urban HIA published from 2012 to 2021. We found that the main research directions in the field were Environmental Sciences and Public Environmental Occupational Health; China contributed most articles, the Tehran University of Medical Sciences was the most influential institution, Science of the Total Environment was the most influential journal, Yousefi M was the most influential author. The main hotspots include health risk assessment, source appointment, contamination, exposure, particulate matter, heavy metals and urban soils in 2012-2021; road dust, source apposition, polycyclic aromatic hydrocarbons, air pollution, urban topsoil and the north China plain were always hot research topics in 2012-2021, drinking water and water quality became research topics of great concern in 2017-2021. There were 25 articles with strong transformation potential during 2020-2021, but most papers carried out research on the health risk assessment of toxic elements in soil and dust. Finally, we also discussed the limitations of this paper and the direction of bibliometric analysis of urban HIA in the future.
C1 [Luo, Wenbing; Deng, Zhongping] Hunan Univ Sci & Technol, Sch Business, Xiangtan 411201, Peoples R China.
   [Luo, Wenbing] Hunan Univ Technol & Business, Sch Accounting, Changsha 410205, Peoples R China.
   [Zhong, Shihu] Shanghai Natl Accounting Inst, Shanghai 201702, Peoples R China.
   [Deng, Mingjun] Hunan Univ Sci & Technol, Big Data & Intelligent Decis Res Ctr, Xiangtan 411201, Peoples R China.
RP Zhong, SH (通讯作者)，Shanghai Natl Accounting Inst, Shanghai 201702, Peoples R China.
EM wbluo@hnust.edu.cn; 21021502041@mail.hnust.edu.cn;
   zhongshihu@163.sufe.edu.cn; mjdeng@hnust.edu.cn
OI Zhong, Shihu/0000-0001-6100-1750
FU National Social Science Foundation of China [20BGL201]; Hunan Provincial
   Natural Science Foundation of China [2021JJ30281]
FX This study was supported by the National Social Science Foundation of
   China (20BGL201) and Hunan Provincial Natural Science Foundation of
   China (2021JJ30281).
CR Abbasnejad B, 2019, INT J ENVIRON HEAL R, V29, P668, DOI 10.1080/09603123.2019.1566523
   Ahamad A, 2021, ENVIRON RES, V202, DOI 10.1016/j.envres.2021.111685
   Akbari H, 2018, DATA BRIEF, V20, P463, DOI 10.1016/j.dib.2018.08.031
   Aleixandre-Benavent R, 2017, J CLEAN PROD, V147, P406, DOI 10.1016/j.jclepro.2017.01.112
   Baccini M, 2013, EPIDEMIOL PREV, V37, P252
   Bacigalupe A, 2010, J EPIDEMIOL COMMUN H, V64, DOI 10.1136/jech.2009.091256
   Badeenezhad A, 2021, INT J ENVIRON AN CH, DOI 10.1080/03067319.2021.1903455
   Boldo E, 2006, EUR J EPIDEMIOL, V21, P449, DOI 10.1007/s10654-006-9014-0
   Bulejko P, 2016, ENVIRON SCI POLLUT R, V23, P20462, DOI 10.1007/s11356-016-7172-5
   Cai LM, 2019, ENVIRON SCI POLLUT R, V26, P19282, DOI 10.1007/s11356-019-05329-1
   Arranz MC, 2014, BMJ OPEN, V4, DOI 10.1136/bmjopen-2014-005999
   Cave B, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18041392
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen CM, 2012, J AM SOC INF SCI TEC, V63, P431, DOI 10.1002/asi.21694
   Chen W, 2020, ENVIRON SCI POLLUT R, V27, P23503, DOI 10.1007/s11356-020-08760-x
   Chen XJ, 2019, ENVIRON SCI POLLUT R, V26, P11387, DOI 10.1007/s11356-019-04550-2
   Claudio F, 2018, IMPACT ASSESS PROJ A, V36, P105, DOI 10.1080/14615517.2017.1364025
   Ding YZ, 2020, ENVIRON SCI POLLUT R, V27, P16439, DOI 10.1007/s11356-020-08132-5
   Duan XC, 2020, ECOL INDIC, V112, DOI 10.1016/j.ecolind.2020.106085
   Ellegaard O, 2015, SCIENTOMETRICS, V105, P1809, DOI 10.1007/s11192-015-1645-z
   Faisal M, 2021, ATMOSPHERE-BASEL, V12, DOI 10.3390/atmos12091213
   Fatima B., 2016, CAN RESPIR J, V2016
   Forsyth A, 2010, J PLAN LIT, V24, P231, DOI 10.1177/0885412209358047
   Fu QL, 2015, HUM ECOL RISK ASSESS, V21, P117, DOI 10.1080/10807039.2014.884398
   Gamache S, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17207556
   Gamarra AR, 2021, ATMOSPHERE-BASEL, V12, DOI 10.3390/atmos12010032
   Gao P, 2019, ENVIRON POLLUT, V255, DOI 10.1016/j.envpol.2019.113296
   Geravandi S, 2017, ENVIRON SCI POLLUT R, V24, P18152, DOI 10.1007/s11356-017-9270-4
   Gerike R, 2016, BMJ OPEN, V6, DOI 10.1136/bmjopen-2015-009924
   Giallouros G, 2020, ENVIRON INT, V140, DOI 10.1016/j.envint.2020.105679
   Gibson JM, 2015, HEALTH PLACE, V35, P79, DOI 10.1016/j.healthplace.2015.07.005
   Green L, 2020, IMPACT ASSESS PROJ A, V38, P214, DOI 10.1080/14615517.2019.1678968
   Gu Y, 2020, ENVIRON POLLUT, V265, DOI 10.1016/j.envpol.2020.115023
   Guo QY, 2021, ATMOSPHERE-BASEL, V12, DOI 10.3390/atmos12060667
   Han LF, 2016, ENVIRON SCI POLLUT R, V23, P3247, DOI 10.1007/s11356-015-5535-y
   Han XF, 2016, ENVIRON EARTH SCI, V75, DOI 10.1007/s12665-016-5316-z
   Harris P, 2010, PLAN PRACT RES, V25, P531, DOI 10.1080/02697459.2010.522851
   Heidari M, 2021, CHEMOSPHERE, V273, DOI 10.1016/j.chemosphere.2021.129656
   Hood WW, 2001, SCIENTOMETRICS, V52, P291, DOI 10.1023/A:1017919924342
   Hoseini M, 2016, ENVIRON SCI POLLUT R, V23, P1820, DOI 10.1007/s11356-015-5355-0
   Hoshiko M, 2012, PUBLIC HEALTH, V126, P168, DOI 10.1016/j.puhe.2011.10.005
   Islam MS, 2015, ECOTOX ENVIRON SAFE, V122, P462, DOI 10.1016/j.ecoenv.2015.09.022
   Iungman T, 2021, ENVIRON RES, V196, DOI 10.1016/j.envres.2021.110988
   Izhar S, 2016, CHEMOSPHERE, V146, P582, DOI 10.1016/j.chemosphere.2015.12.039
   Izquierdo R, 2020, ENVIRON RES, V183, DOI [10.1016/j.envres.2019.109021, 10.1016/j.envres.2020.109021]
   Jan R, 2018, ENVIRON GEOCHEM HLTH, V40, P255, DOI 10.1007/s10653-016-9900-7
   Jiang HM, 2020, AEROSOL AIR QUAL RES, V20, P2859, DOI 10.4209/aaqr.2020.02.0068
   Jiang YF, 2018, ENVIRON GEOCHEM HLTH, V40, P2007, DOI 10.1007/s10653-017-0028-1
   Jiang YF, 2014, ECOTOX ENVIRON SAFE, V106, P11, DOI 10.1016/j.ecoenv.2014.04.031
   Jinjin Wang, 2021, Arabian Journal of Geosciences, V14, DOI 10.1007/s12517-021-06556-y
   Keshavarzi B, 2015, ATMOS ENVIRON, V119, P1, DOI 10.1016/j.atmosenv.2015.08.001
   Keuken M, 2011, ATMOS ENVIRON, V45, P5294, DOI 10.1016/j.atmosenv.2011.06.058
   Khaniabadi YO, 2017, ENVIRON SCI POLLUT R, V24, P16860, DOI 10.1007/s11356-017-9298-5
   Kheirbek I, 2013, AIR QUAL ATMOS HLTH, V6, P473, DOI 10.1007/s11869-012-0185-4
   Khomenko S, 2021, LANCET PLANET HEALTH, V5, pE121, DOI 10.1016/S2542-5196(20)30272-2
   Lai HK, 2013, BMC PUBLIC HEALTH, V13, DOI 10.1186/1471-2458-13-360
   Tuyen LH, 2014, ENVIRON POLLUT, V194, P272, DOI 10.1016/j.envpol.2014.07.029
   Lehtomaki H, 2020, ATMOSPHERE-BASEL, V11, DOI 10.3390/atmos11050467
   Li CQ, 2020, J ENVIRON SCI, V93, P1, DOI 10.1016/j.jes.2019.11.006
   Li P, 2021, LAND-BASEL, V10, DOI 10.3390/land10101016
   Li YD, 2020, CHEMOSPHERE, V250, DOI 10.1016/j.chemosphere.2020.126283
   Liang M, 2019, CHEMOSPHERE, V234, P875, DOI 10.1016/j.chemosphere.2019.06.119
   Liu WJ, 2019, ECOL ECON, V156, P293, DOI 10.1016/j.ecolecon.2018.09.001
   Long LP, 2021, ENVIRON GEOCHEM HLTH, V43, P2945, DOI 10.1007/s10653-020-00757-2
   Lu XW, 2014, ECOTOX ENVIRON SAFE, V106, P154, DOI 10.1016/j.ecoenv.2014.04.022
   Luo HP, 2020, J ENVIRON MANAGE, V269, DOI 10.1016/j.jenvman.2020.110791
   Luo M, 2021, ATMOSPHERE-BASEL, V12, DOI 10.3390/atmos12030323
   Ma XM, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16050842
   Mansfield TJ, 2015, BIOMED RES INT, V2015, DOI 10.1155/2015/812325
   Mirzabeygi M, 2018, DATA BRIEF, V18, P40, DOI 10.1016/j.dib.2018.02.069
   Mondal Shilpi, 2021, Environ Geochem Health, V43, P2081, DOI 10.1007/s10653-020-00785-y
   Morelli X, 2016, ENVIRON RES, V147, P59, DOI 10.1016/j.envres.2016.01.030
   Moryani HT, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17197124
   Najmeddin A, 2019, ENVIRON GEOCHEM HLTH, V41, P1267, DOI 10.1007/s10653-018-0209-6
   Najmeddin A, 2018, J GEOCHEM EXPLOR, V190, P154, DOI 10.1016/j.gexplo.2018.03.004
   Othman M, 2020, ENVIRON SCI POLLUT R, V27, P11227, DOI 10.1007/s11356-020-07633-7
   Pereira CAR, 2017, ENVIRON IMPACT ASSES, V65, P175, DOI 10.1016/j.eiar.2016.09.005
   Qasemi M, 2019, DESALIN WATER TREAT, V170, P176, DOI 10.5004/dwt.2019.24691
   Qishlaqi A, 2019, J ENVIRON HEALTH SCI, V17, P1029, DOI 10.1007/s40201-019-00417-3
   Radfard M, 2019, HUM ECOL RISK ASSESS, V25, P949, DOI 10.1080/10807039.2018.1458210
   Roue-Le Gall A, 2017, GLOB HEALTH PROMOT, V24, P25, DOI 10.1177/1757975916675577
   Sacks JD, 2018, ENVIRON MODELL SOFTW, V104, P118, DOI 10.1016/j.envsoft.2018.02.009
   Sah D, 2019, ENVIRON GEOCHEM HLTH, V41, P1445, DOI 10.1007/s10653-018-0223-8
   Shabanda IS, 2019, ENVIRON SCI POLLUT R, V26, P37193, DOI 10.1007/s11356-019-06718-2
   Shahab A, 2020, ENVIRON POLLUT, V266, DOI 10.1016/j.envpol.2020.115419
   Shen RR, 2019, SCI TOTAL ENVIRON, V655, P581, DOI 10.1016/j.scitotenv.2018.11.220
   Shi YL, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11133716
   Simos J, 2006, SOZ PRAVENTIV MED, V51, P133, DOI 10.1007/s00038-006-0030-3
   Sohrabi S, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17041166
   Sun T, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17134841
   Tang JX, 2020, POL J ENVIRON STUD, V29, P1379, DOI 10.15244/pjoes/108517
   Thondoo M, 2020, ENVIRON INT, V144, DOI 10.1016/j.envint.2020.106027
   Tohid L, 2019, ATMOS POLLUT RES, V10, P556, DOI 10.1016/j.apr.2018.10.007
   Uddin S, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0123537
   Varol S, 2016, J WATER HEALTH, V14, P471, DOI 10.2166/wh.2015.187
   Wang HZ, 2021, CATENA, V196, DOI 10.1016/j.catena.2020.104930
   Wang X, 2020, ARCH ENVIRON CON TOX, V78, P392, DOI 10.1007/s00244-019-00704-y
   Winkler MS, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17092988
   Xia Q, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12052121
   Xiong KR, 2018, EPIDEMIOL PREV, V42, P49, DOI 10.19191/EP18.5-6.S1.P049.087
   Xiong Y, 2020, SCI TOTAL ENVIRON, V706, DOI 10.1016/j.scitotenv.2019.135970
   Yi YT, 2020, SCIENTOMETRICS, V123, P1051, DOI 10.1007/s11192-020-03416-6
   Yoo Weon-Seob, 2007, J Prev Med Public Health, V40, P439
   Yu BB, 2014, ENVIRON SCI POLLUT R, V21, P2817, DOI 10.1007/s11356-013-2190-z
   Yue HF, 2015, ENVIRON SCI TECHNOL, V49, P14484, DOI 10.1021/es506280c
   Zapata-Diomedi B, 2016, HEALTH PLACE, V42, P19, DOI 10.1016/j.healthplace.2016.08.010
   Zhang HX, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11209615
   Zhang Y, 2019, AEROSOL AIR QUAL RES, V19, P2281, DOI 10.4209/aaqr.2018.12.0462
   Zhang Y, 2019, AEROSOL AIR QUAL RES, V19, P871, DOI 10.4209/aaqr.2018.09.0353
   Zhao D., 2015, ANAL VISUALIZATION C, DOI [10.2200/S00624ED1V01Y201501ICR039, DOI 10.2200/S00624ED1V01Y201501ICR039]
   Zhong P, 2020, AIR QUAL ATMOS HLTH, V13, P871, DOI 10.1007/s11869-020-00846-2
   Zhou QH, 2015, ENVIRON GEOCHEM HLTH, V37, P207, DOI 10.1007/s10653-014-9640-5
   Zhou Y, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17093082
NR 113
TC 0
Z9 0
U1 16
U2 16
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD MAY
PY 2022
VL 19
IS 10
AR 5957
DI 10.3390/ijerph19105957
PG 23
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA 1R4HB
UT WOS:000803330700001
PM 35627492
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU He, X
   Miao, QF
   Yi, Y
   Hu, WJ
   Wu, M
   Wu, YP
   Zhang, Q
AF He, Xiao
   Miao, Qinfang
   Yi, Yi
   Hu, Weijie
   Wu, Min
   Wu, Yiping
   Zhang, Qi
TI Analysis of the regulation of metastasis-associated lung adenocarcinoma
   transcript 1 on the biological behavior of breast cancer
SO TRANSLATIONAL CANCER RESEARCH
LA English
DT Article
DE Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1); breast
   cancer; CiteSpace; meta-analysis; survival rate
ID NONCODING RNA MALAT1; POOR-PROGNOSIS; DOWN-REGULATION; UP-REGULATION;
   EXPRESSION
AB Background: Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a class of long non-coding RNA (lncRNA) that has been proved to be closely related to many cancers.
   Methods: The relevant research on MALAT1 in cancers published in recent years were collected and integrated. CiteSpace was employed to draw a knowledge map of MALAT1 in breast cancer, to evaluate the research front-burner issues. Then, multiple microarray data sets were searched from online data for meta-analysis to evaluate the relationship between MALAT1 and breast cancer survival rate.
   Results: The results showed that MALAT1 had been widely studied in the proliferation and differentiation of cancer cells, and MALAT1 might regulate breast cancer through the PI3K/AKT/mTOR signaling pathway. In addition, high expression of MALAT1 had a significant correlation with relapse-free survival (HR: 1.51, 95% CI: 0.79-2.29), while there was no significant correlation between MALAT1 expression and overall survival (HR: 1.09, 95% CI: 0.63-1.72).
   Conclusions: The expression level of lncRNA MALAT1 was closely related to cancer progression and prognosis of cancer patients. Moreover, the expression of MALAT1 was strongly associated with the survival rate of recurrence-free breast cancer. However, since the present study only adopted the existing literature for visual analysis, subsequent experiments are needed to be done to verify this conclusion. It is hoped that this work could provide a theoretical foundation for promoting the clinical adoption of MALAT1 in the prediction, diagnosis, and treatment of breast cancer, and point out a new direction for the in-depth exploration of the function of MALAT1.
C1 [He, Xiao; Yi, Yi; Hu, Weijie; Wu, Min; Wu, Yiping; Zhang, Qi] Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Plast Surg, Wuhan, Peoples R China.
   [Miao, Qinfang] China Three Gorges Univ, Med Coll, Hubei Key Lab Tumor Microenvironm & Immunotherapy, Yichang, Peoples R China.
RP Wu, YP; Zhang, Q (通讯作者)，1095 Jiefang Ave, Wuhan, Peoples R China.
EM tongjiplastic@163.com; zhangqi06172@163.com
RI Hu, Weijie/ABE-6704-2021
FU China Guanghua Science and Technology Foundation [2019JZXM001]; Wuhan
   Science and Technology Bureau [2020020601012241]
FX This work was supported by the China Guanghua Science and Technology
   Foundation (grant number 2019JZXM001) and the Wuhan Science and
   Technology Bureau (grant number 2020020601012241).
CR Arun G, 2016, GENE DEV, V30, P34, DOI 10.1101/gad.270959.115
   Assefa AT, 2018, GENOME BIOL, V19, DOI 10.1186/s13059-018-1466-5
   Dai WC, 2018, BIOMED PHARMACOTHER, V99, P191, DOI 10.1016/j.biopha.2017.12.105
   Egger SS, 2006, DRUG AGING, V23, P823, DOI 10.2165/00002512-200623100-00005
   Fan Y, 2014, CLIN CANCER RES, V20, P1531, DOI 10.1158/1078-0432.CCR-13-1455
   Gupta RK, 2010, NATURE, V464, P619, DOI 10.1038/nature08816
   Gutschner T, 2013, J MOL MED, V91, P791, DOI 10.1007/s00109-013-1028-y
   Gutschner T, 2013, CANCER RES, V73, P1180, DOI 10.1158/0008-5472.CAN-12-2850
   Hirata H, 2015, CANCER RES, V75, P1322, DOI 10.1158/0008-5472.CAN-14-2931
   Jathar S, 2017, ADV EXP MED BIOL, V1008, P283, DOI 10.1007/978-981-10-5203-3_10
   Ji P, 2003, ONCOGENE, V22, P8031, DOI 10.1038/sj.onc.1206928
   Jin Y, 2017, EUR REV MED PHARMACO, V21, P3176
   Kim J, 2018, NAT GENET, V50, P1705, DOI 10.1038/s41588-018-0252-3
   Kwong JC, 2018, NEW ENGL J MED, V378, P345, DOI 10.1056/NEJMoa1702090
   Lei L, 2017, MOL MED REP, V15, P3977, DOI 10.3892/mmr.2017.6532
   Lin QB, 2018, ONCOL REP, V39, P2644, DOI 10.3892/or.2018.6384
   Liu GF, 2013, SCIENTOMETRICS, V94, P1037, DOI 10.1007/s11192-012-0782-x
   Peterson AE, 2014, J BEHAV HEALTH SER R, V41, P337, DOI 10.1007/s11414-013-9347-x
   Ponting CP, 2009, CELL, V136, P629, DOI 10.1016/j.cell.2009.02.006
   Prowell TM, 2019, J PATHOL, V247, P612
   Schmidt LH, 2011, J THORAC ONCOL, V6, P1984, DOI 10.1097/JTO.0b013e3182307eac
   Shu J, 2018, NEOPLASMA, V65, P664, DOI 10.4149/neo_2018_170713N480
   Stone JK, 2019, J BIOL CHEM, V294, P11213, DOI 10.1074/jbc.RA118.006889
   Sun ZX, 2020, CELL MOL BIOL, V66, P72, DOI 10.14715/cmb/2020.66.3.11
   Tripathi V, 2010, MOL CELL, V39, P925, DOI 10.1016/j.molcel.2010.08.011
   Xiao YW, 2019, FEBS OPEN BIO, V9, P1212, DOI 10.1002/2211-5463.12649
   Xu SP, 2015, INT J CLIN EXP PATHO, V8, P4881
   Yang H, 2018, ONCOL REP, V40, P261, DOI 10.3892/or.2018.6412
   Yang LQ, 2011, CELL, V147, P773, DOI 10.1016/j.cell.2011.08.054
   Zhang HM, 2015, TUMOR BIOL, V36, P2947, DOI 10.1007/s13277-014-2925-6
   Zheng HT, 2014, INT J CLIN EXP PATHO, V7, P3174
   Zheng LH, 2019, BIOSCIENCE REP, V39, DOI 10.1042/BSR20181284
NR 32
TC 0
Z9 0
U1 1
U2 1
PU AME PUBL CO
PI SHATIN
PA FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG
   00000, PEOPLES R CHINA
SN 2218-676X
EI 2219-6803
J9 TRANSL CANCER RES
JI Transl. Cancer Res.
PD APR
PY 2021
VL 10
IS 4
BP 1609
EP 1619
DI 10.21037/tcr-20-3221
PG 11
WC Oncology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Oncology
GA RV8HZ
UT WOS:000646069200001
PM 35116487
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Jiang, YF
   Hou, LY
   Shi, TM
   Gui, QC
AF Jiang, Yunfang
   Hou, Luyao
   Shi, Tiemao
   Gui, Qinchang
TI A Review of Urban Planning Research for Climate Change
SO SUSTAINABILITY
LA English
DT Review
DE urban planning; climate change; knowledge map; CiteSpace; collaborative
   planning; city level
ID EMERGING RESEARCH AGENDAS; CHANGE ADAPTATION; LAND-USE; LOCAL CLIMATE;
   HEAT-ISLAND; GREEN INFRASTRUCTURE; ECOSYSTEM SERVICES; THERMAL
   BIOCLIMATE; METROPOLITAN-AREA; SPATIAL-PATTERN
AB This paper identified the research focus and development tendency of urban planning and climate change research from 1990 to 2016 using CiteSpace, which is based on the Web of Science database. Through cluster analysis and a document sorting method, the research direction of city planning and climate change were mainly divided into four academic groupings, 15 clusters with homogenous themes representing the current research focus direction at the sub-level. The detailed study on the framework presented three mainstream developing directions: (1) The index assessment and spatial simulation on the impact of urban spatial systems for climate change have become important methods to identify and improve the adaptability of urban space. (2) Adaptive governance as a bottom-up strategy giving priority to institutional adaptation policy and collaborative polices for responding to climate change has become the hot direction in recent years. (3) The policies of urban public health-related urban equity, vulnerability, and environmental sustainability were addressed especially during the period from 2007 to 2009. Dynamic evolution trends of the research field were discussed: (1) The total numbers of papers in this field increased distinctly between 2005 and 2008, research focus shifted from single-dimension to multi-dimension comprehensive studies, and the humanism tendency was obvious. (2) After 2010, research on multi-level governance and spatial adaptation strategies became the key issues, and a bottom-up level adaptation policies were addressed. Finally, the critical influence of the important literature and the forefront issues of the research field were put forward.
C1 [Jiang, Yunfang; Hou, Luyao] East China Normal Univ, Sch Urban & Reg Sci, Ctr Modern Chinese City Studies, Shanghai 200062, Peoples R China.
   [Jiang, Yunfang] Shanghai Inst Ecochongming, Res Ctr Eco Civilizat, Shanghai 200062, Peoples R China.
   [Shi, Tiemao] Shenyang Jianzhu Univ, Inst Ecol Urbanizat & Green Bldg, Shenyang 110168, Liaoning, Peoples R China.
   [Gui, Qinchang] East China Normal Univ, Inst Innovat & Strateg Studies, Shanghai 200062, Peoples R China.
RP Jiang, YF (通讯作者)，East China Normal Univ, Sch Urban & Reg Sci, Ctr Modern Chinese City Studies, Shanghai 200062, Peoples R China.; Jiang, YF (通讯作者)，Shanghai Inst Ecochongming, Res Ctr Eco Civilizat, Shanghai 200062, Peoples R China.; Shi, TM (通讯作者)，Shenyang Jianzhu Univ, Inst Ecol Urbanizat & Green Bldg, Shenyang 110168, Liaoning, Peoples R China.
EM yfjiang@re.ecnu.edu.cn; houluyaov5@163.com; tiemaos@sjzu.edu.cn;
   52173902007@stu.ecnu.edu.cn
FU National Natural Science Foundation of China [51108182, 51578344]; Key
   base Project of Humanities and Social Sciences from Ministry of
   Education in China [16JJD790012]; Shanghai Pujiang Program [12PJC031];
   project "Research on the innovation environment of Shanghai" from
   Institute for Innovation and Strategic Studies of ECNU
   [41300-120212-10006/002/]; Thinktank project for Eco-civilization
   studies from Shanghai Institute of Eco-Chongming
FX This research is supported by the National Natural Science Foundation of
   China project (Grant Nos. 51108182 and 51578344), the Key base Project
   of Humanities and Social Sciences from Ministry of Education in China
   (16JJD790012), and the Shanghai Pujiang Program (12PJC031). Support is
   also given by the project "Research on the innovation environment of
   Shanghai" (41300-120212-10006/002/) from Institute for Innovation and
   Strategic Studies of ECNU, and the Thinktank project for
   Eco-civilization studies from Shanghai Institute of Eco-Chongming. The
   authors would like to thank the editor and the anonymous reviewers for
   their helpful comments.
CR Abreu-Harbich LV, 2014, URBAN ECOSYST, V17, P489, DOI 10.1007/s11252-013-0339-7
   Adger WN, 2006, GLOBAL ENVIRON CHANG, V16, P268, DOI 10.1016/j.gloenvcha.2006.02.006
   Alexander PJ, 2016, LANDSCAPE URBAN PLAN, V152, P72, DOI 10.1016/j.landurbplan.2016.02.006
   Amorim JH, 2013, SCI TOTAL ENVIRON, V461, P541, DOI 10.1016/j.scitotenv.2013.05.031
   Arnfield AJ, 2003, INT J CLIMATOL, V23, P1, DOI 10.1002/joc.859
   AUER AH, 1978, J APPL METEOROL, V17, P636, DOI 10.1175/1520-0450(1978)017<0636:COLUAC>2.0.CO;2
   Betsill M. M., 2001, LOCAL ENV INT J JUST, V6, P393, DOI [10.1080/13549830120091699, DOI 10.1080/13549830120091699]
   Blair RB, 1996, ECOL APPL, V6, P506, DOI 10.2307/2269387
   Blanco H, 2009, PROG PLANN, V72, P195, DOI 10.1016/j.progress.2009.09.001
   Blanco H, 2009, PROG PLANN, V71, P153, DOI 10.1016/j.progress.2009.03.001
   Bolund P, 1999, ECOL ECON, V29, P293, DOI 10.1016/S0921-8009(99)00013-0
   Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106
   Bowler DE, 2010, LANDSCAPE URBAN PLAN, V97, P147, DOI 10.1016/j.landurbplan.2010.05.006
   Brazel A, 2000, CLIMATE RES, V15, P123, DOI 10.3354/cr015123
   Bulkeley H, 2005, ENVIRON POLIT, V14, P42, DOI 10.1080/0964401042000310178
   Bulkeley H, 2006, URBAN STUD, V43, P2237, DOI 10.1080/00420980600936491
   Burley JG, 2012, REG ENVIRON CHANGE, V12, P581, DOI 10.1007/s10113-011-0271-4
   Campbell S, 1996, J AM PLANN ASSOC, V62, P296, DOI 10.1080/01944369608975696
   Campbell-Lendrum D, 2007, J URBAN HEALTH, V84, pI109, DOI 10.1007/s11524-007-9170-x
   Cavan G, 2014, ECOL INDIC, V42, P43, DOI 10.1016/j.ecolind.2014.01.025
   Chen C., 2014, THE CITESPACE MANUAL
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   [陈悦 Chen Yue], 2005, [科学学研究, Studies in Science of Science], V23, P149
   Cobo MJ, 2011, J AM SOC INF SCI TEC, V62, P1382, DOI 10.1002/asi.21525
   Cui LL, 2012, URBAN CLIM, V2, P1, DOI 10.1016/j.uclim.2012.10.008
   Debbage N, 2015, COMPUT ENVIRON URBAN, V54, P181, DOI 10.1016/j.compenvurbsys.2015.08.002
   Demuzere M, 2014, J ENVIRON MANAGE, V146, P107, DOI 10.1016/j.jenvman.2014.07.025
   Diem JE, 2003, PROF GEOGR, V55, P343
   Dixon PG, 2003, J APPL METEOROL, V42, P1273, DOI 10.1175/1520-0450(2003)042<1273:PACOAU>2.0.CO;2
   Dugord PA, 2014, COMPUT ENVIRON URBAN, V48, P86, DOI 10.1016/j.compenvurbsys.2014.07.005
   Emadodin I, 2016, URBAN CLIM, V17, P230, DOI 10.1016/j.uclim.2016.08.008
   Fitter AH, 2002, SCIENCE, V296, P1689, DOI 10.1126/science.1071617
   Folke C, 2005, ANNU REV ENV RESOUR, V30, P441, DOI 10.1146/annurev.energy.30.050504.144511
   Fu Y, 2017, CITIES, V60, P113, DOI 10.1016/j.cities.2016.08.003
   Funfgeld H, 2015, CURR OPIN ENV SUST, V12, P67, DOI 10.1016/j.cosust.2014.10.011
   Geneletti D, 2016, LAND USE POLICY, V50, P38, DOI 10.1016/j.landusepol.2015.09.003
   Gill S.E., 2007, BUILT ENV, V33, P115, DOI [DOI 10.2148/BENV.33.1.115, 10.2148/benv.33.1.115]
   Gober P, 2010, J AM PLANN ASSOC, V76, P109, DOI 10.1080/01944360903433113
   Gore CD, 2010, REV POLICY RES, V27, P27, DOI 10.1111/j.1541-1338.2009.00425.x
   Grimm NB, 2008, SCIENCE, V319, P756, DOI 10.1126/science.1150195
   Gurney KR, 2012, ENVIRON SCI TECHNOL, V46, P12194, DOI 10.1021/es3011282
   Gwilliam J, 2006, P I CIVIL ENG-MUNIC, V159, P245, DOI 10.1680/muen.2006.159.4.245
   Hamin EM, 2014, J AM PLANN ASSOC, V80, P110, DOI 10.1080/01944363.2014.949590
   Haunschild R, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0160393
   He CY, 2015, SCI TOTAL ENVIRON, V532, P48, DOI 10.1016/j.scitotenv.2015.05.103
   Horton RM, 2011, J APPL METEOROL CLIM, V50, P2247, DOI 10.1175/2011JAMC2521.1
   House-Peters LA, 2011, LANDSCAPE URBAN PLAN, V103, P139, DOI 10.1016/j.landurbplan.2011.07.005
   Hung HC, 2016, LAND USE POLICY, V50, P48, DOI 10.1016/j.landusepol.2015.08.029
   Jackson R, 2008, ANNU REV PUBL HEALTH, V29, P57, DOI 10.1146/annurev.publhealth.29.020907.090755
   Jamei E, 2015, SUSTAIN CITIES SOC, V14, P280, DOI 10.1016/j.scs.2014.10.001
   Jamei E, 2016, RENEW SUST ENERG REV, V54, P1002, DOI 10.1016/j.rser.2015.10.104
   Kamalski J, 2012, CITIES, V29, pS3, DOI 10.1016/j.cities.2012.06.012
   Ketterer C, 2014, LANDSCAPE URBAN PLAN, V122, P78, DOI 10.1016/j.landurbplan.2013.11.003
   Kim H, 2016, SCI TOTAL ENVIRON, V571, P355, DOI 10.1016/j.scitotenv.2016.05.046
   Kinney PL, 2008, ENVIRON SCI POLICY, V11, P87, DOI 10.1016/j.envsci.2007.08.001
   Kjellstrom T, 2007, J URBAN HEALTH, V84, pI86, DOI 10.1007/s11524-007-9171-9
   Kong FH, 2014, URBAN FOR URBAN GREE, V13, P846, DOI 10.1016/j.ufug.2014.09.009
   Kong FH, 2014, LANDSCAPE URBAN PLAN, V128, P35, DOI 10.1016/j.landurbplan.2014.04.018
   Kotzee I, 2016, ECOL INDIC, V60, P45, DOI 10.1016/j.ecolind.2015.06.018
   Kumar P, 2016, LAND USE POLICY, V58, P514, DOI 10.1016/j.landusepol.2016.08.018
   Lafortezza R, 2013, IFOREST, V6, P102, DOI 10.3832/ifor0723-006
   Lehmann I, 2014, ECOL INDIC, V42, P58, DOI 10.1016/j.ecolind.2014.02.036
   Leydesdorff L, 2010, J AM SOC INF SCI TEC, V61, P1622, DOI 10.1002/asi.21347
   Lindley SJ, 2006, J RISK RES, V9, P543, DOI 10.1080/13669870600798020
   Lindseth G, 2004, LOCAL ENVIRON, V9, P325, DOI [10.1080/1354983042000246252, DOI 10.1080/1354983042000246252]
   Liu Z, 2012, ENERGY, V37, P245, DOI 10.1016/j.energy.2011.11.040
   Luber G, 2008, AM J PREV MED, V35, P429, DOI 10.1016/j.amepre.2008.08.021
   Maimaitiyiming M, 2014, ISPRS J PHOTOGRAMM, V89, P59, DOI 10.1016/j.isprsjprs.2013.12.010
   McEvoy D, 2006, P I CIVIL ENG-MUNIC, V159, P185, DOI 10.1680/muen.2006.159.4.185
   McGranahan G, 2007, ENVIRON URBAN, V19, P17, DOI 10.1177/0956247807076960
   McKinney ML, 2002, BIOSCIENCE, V52, P883, DOI 10.1641/0006-3568(2002)052[0883:UBAC]2.0.CO;2
   Meerow S, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8070701
   Milan BF, 2015, CURR OPIN ENV SUST, V14, P221, DOI 10.1016/j.cosust.2015.08.002
   Muller A, 2011, NAT HAZARD EARTH SYS, V11, P2107, DOI 10.5194/nhess-11-2107-2011
   Muller N, 2014, THEOR APPL CLIMATOL, V115, P243, DOI 10.1007/s00704-013-0890-4
   Musacchio LR, 2009, LANDSCAPE ECOL, V24, P993, DOI 10.1007/s10980-009-9396-y
   Ng MK, 2012, CITIES, V29, P88, DOI 10.1016/j.cities.2011.08.001
   O'Neill MS, 2009, MATURITAS, V64, P98, DOI 10.1016/j.maturitas.2009.08.005
   O'Neill MS, 2009, J OCCUP ENVIRON MED, V51, P13, DOI 10.1097/JOM.0b013e318173e122
   Ortega-Alvarez R, 2009, LANDSCAPE URBAN PLAN, V90, P189, DOI 10.1016/j.landurbplan.2008.11.003
   Pacala S, 2004, SCIENCE, V305, P968, DOI 10.1126/science.1100103
   PARRY M, 1966, GEOGR J, V132, P84, DOI 10.2307/1793062
   Patz J, 2008, ANNU REV PUBL HEALTH, V29, P27, DOI 10.1146/annurev.publhealth.29.020907.090750
   Pickett STA, 2004, LANDSCAPE URBAN PLAN, V69, P369, DOI 10.1016/j.landurbplan.2003.10.035
   Picketts IM, 2014, J ENVIRON PLANN MAN, V57, P984, DOI 10.1080/09640568.2013.776951
   Picketts IM, 2012, ENVIRON SCI POLICY, V17, P82, DOI 10.1016/j.envsci.2011.12.011
   Poyar KA, 2010, WEATHER CLIM SOC, V2, P237, DOI 10.1175/2010WCAS1047.1
   Rafael S, 2016, SCI TOTAL ENVIRON, V566, P1500, DOI 10.1016/j.scitotenv.2016.06.037
   Reckien D, 2014, CLIMATIC CHANGE, V122, P331, DOI 10.1007/s10584-013-0989-8
   Reyer C, 2012, REG ENVIRON CHANGE, V12, P523, DOI 10.1007/s10113-011-0269-y
   Algeciras JAR, 2016, INT J BIOMETEOROL, V60, P1261, DOI 10.1007/s00484-015-1121-8
   Algeciras JAR, 2016, INT J BIOMETEOROL, V60, P1151, DOI 10.1007/s00484-015-1109-4
   Rosenfeld D, 2000, SCIENCE, V287, P1793, DOI 10.1126/science.287.5459.1793
   Roy M, 2009, HABITAT INT, V33, P276, DOI 10.1016/j.habitatint.2008.10.022
   Sampson NR, 2013, GLOBAL ENVIRON CHANG, V23, P475, DOI 10.1016/j.gloenvcha.2012.12.011
   Santamouris M, 2015, ENERG BUILDINGS, V91, P43, DOI 10.1016/j.enbuild.2015.01.027
   Santamouris M, 2014, SOL ENERGY, V103, P682, DOI 10.1016/j.solener.2012.07.003
   Seager R, 2007, SCIENCE, V316, P1181, DOI 10.1126/science.1139601
   Semenza JC, 1999, AM J PREV MED, V16, P269, DOI 10.1016/S0749-3797(99)00025-2
   Setaih K, 2014, J INF TECHNOL CONSTR, V19, P248
   SMALL H, 1985, J INFORM SCI, V11, P147, DOI 10.1177/016555158501100402
   Smit B, 2006, GLOBAL ENVIRON CHANG, V16, P282, DOI 10.1016/j.gloenvcha.2006.03.008
   Suarez M, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8080774
   Taha H, 1997, ENERG BUILDINGS, V25, P99, DOI 10.1016/S0378-7788(96)00999-1
   Tzoulas K, 2007, LANDSCAPE URBAN PLAN, V81, P167, DOI 10.1016/j.landurbplan.2007.02.001
   Viegas CV, 2013, CITIES, V35, P1, DOI 10.1016/j.cities.2013.05.006
   Voogt JA, 2003, REMOTE SENS ENVIRON, V86, P370, DOI 10.1016/S0034-4257(03)00079-8
   Walker BJA, 2015, URBAN STUD, V52, P2250, DOI 10.1177/0042098014544759
   Wamsler C, 2016, CLIMATIC CHANGE, V137, P71, DOI 10.1007/s10584-016-1660-y
   Wamsler C, 2015, ECOL SOC, V20, DOI 10.5751/ES-07489-200230
   Watson V, 2009, PROG PLANN, V72, P151, DOI 10.1016/j.progress.2009.06.002
   Weber S, 2015, APPL GEOGR, V63, P231, DOI 10.1016/j.apgeog.2015.07.006
   Wheeler S, 2008, J AM PLANN ASSOC, V74, P481, DOI 10.1080/01944360802377973
   Whitford V, 2001, LANDSCAPE URBAN PLAN, V57, P91, DOI 10.1016/S0169-2046(01)00192-X
   Wilby RL, 2012, PROG PHYS GEOG, V36, P348, DOI 10.1177/0309133312438908
   Wilhelmi OV, 2010, ENVIRON RES LETT, V5, DOI 10.1088/1748-9326/5/1/014021
   Younger M, 2008, AM J PREV MED, V35, P517, DOI 10.1016/j.amepre.2008.08.017
   Yu DJ, 2015, SCIENTOMETRICS, V105, P115, DOI 10.1007/s11192-015-1695-2
   Yu M, 2012, ATMOS ENVIRON, V60, P544, DOI 10.1016/j.atmosenv.2012.06.075
   Zolch T, 2016, URBAN FOR URBAN GREE, V20, P305, DOI 10.1016/j.ufug.2016.09.011
   [No title captured]
   [No title captured]
NR 124
TC 22
Z9 22
U1 22
U2 127
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD DEC
PY 2017
VL 9
IS 12
AR 2224
DI 10.3390/su9122224
PG 21
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA FR7FA
UT WOS:000419231500073
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, L
   Yang, XR
   Li, SJ
   Liao, TY
   Pan, G
AF Zhang, Li
   Yang, Xiaoran
   Li, Shijian
   Liao, Tianyi
   Pan, Gang
TI Answering medical questions in Chinese using automatically mined
   knowledge and deep neural networks: an end-to-end solution
SO BMC BIOINFORMATICS
LA English
DT Article
DE knowledge graph; Question-answering system; Text mining; Bootstrapping;
   Deep learning; Memory neural network; Natural language processing
ID HEALTH INFORMATION
AB Background Medical information has rapidly increased on the internet and has become one of the main targets of search engine use. However, medical information on the internet is subject to the problems of quality and accessibility, so ordinary users are unable to obtain answers to their medical questions conveniently. As a solution, researchers build medical question answering (QA) systems. However, research on medical QA in the Chinese language lags behind work on English-based systems. This lag is mainly due to the difficulty of constructing a high-quality knowledge base and the underutilization of medical corpora in the Chinese language. Results This study developed an end-to-end solution to implement a medical QA system for the Chinese language with low cost and time. First, we created a high-quality medical knowledge graph from hospital data (electronic health/medical records) in a nearly automatic manner that trained a supervised model based on data labeled using bootstrapping techniques. Then, we designed a QA system based on a memory-based neural network and attention mechanism. Finally, we trained the system to generate answers from the knowledge base and a QA corpus on the internet. Conclusions Bootstrapping and deep neural network techniques can construct a knowledge graph from electronic health/medical records with satisfactory precision and coverage. Our proposed context bridge mechanisms perform training with a variety of language features. Our QA system can achieve state-of-the-art quality in answering medical questions with constrained topics. As we evaluated, complex Chinese language processing techniques, such as segmentation and parsing, were not necessary for practice and complex architectures were not necessary to build the QA system. Lastly, we created an application using our method for internet QA usage.
C1 [Zhang, Li] Zhejiang Univ, Adv Technol Res Inst, Hangzhou, Peoples R China.
   [Zhang, Li; Yang, Xiaoran; Li, Shijian; Liao, Tianyi; Pan, Gang] Zhejiang Univ, Dept Comp Sci, Hangzhou, Peoples R China.
RP Li, SJ (通讯作者)，Zhejiang Univ, Dept Comp Sci, Hangzhou, Peoples R China.
EM shijianli@zju.edu.cn
FU Outstanding Youth Foundation of China [61925603]
FX Supported by Outstanding Youth Foundation of China (No. 61925603).
CR Abacha AB, 2017, TREC
   Amante DJ, 2015, J MED INTERNET RES, V17, DOI 10.2196/jmir.4126
   Aronson AR, 2001, J AM MED INFORM ASSN, P17
   Athenikos SJ, 2010, COMPUT METH PROG BIO, V99, P1, DOI 10.1016/j.cmpb.2009.10.003
   Aziz HA, 2016, LAB MED, V47, pE38, DOI 10.1093/labmed/lmw038
   Bastian M., 2009, P INT AAAI C WEB SOC, P361, DOI 10.13140/2.1.1341.1520
   Ben Abacha A, 2015, INFORM PROCESS MANAG, V51, P570, DOI 10.1016/j.ipm.2015.04.006
   Benigeri M, 2003, HEALTH PROMOT INT, V18, P381, DOI 10.1093/heapro/dag409
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Berland GK, 2001, JAMA-J AM MED ASSOC, V285, P2612, DOI 10.1001/jama.285.20.2612
   Bordes A, 2014, P 2014 C EMP METH NA, P615, DOI DOI 10.3115/V1/D14-1067
   Bordes Antoine, 2015, ARXIV150602075
   Bostock M, 2011, IEEE T VIS COMPUT GR, V17, P2301, DOI 10.1109/TVCG.2011.185
   [奥德玛 Byambasuren Odmaa], 2019, [中文信息学报, Journal of Chinese Information Processing], V33, P1
   Chen Y., 2019, P 2019 C N AM CHAPTE, V1, P2913
   China Association for Science and Technology Baidu China Science Research Institute, 2016, CHIN INT SCI POP NEE
   Diefenbach D, 2018, KNOWL INF SYST, V55, P529, DOI 10.1007/s10115-017-1100-y
   Ernst P, 2014, PROC INT CONF DATA, P1254, DOI 10.1109/ICDE.2014.6816754
   Ferrucci D, 2010, AI MAG, V31, P59, DOI 10.1609/aimag.v31i3.2303
   Fu B., 2020, ARXIV200713069
   Goethals B., 2003, SURVEY FREQUENT PATT, V19, P840
   Grainger T., 2014, SOLR IN ACTION
   Guo JL, 2019, J BIOMED INFORM, V94, DOI 10.1016/j.jbi.2019.103177
   He JQ, 2019, BMC MED INFORM DECIS, V19, DOI 10.1186/s12911-019-0761-8
   He SZ, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P199, DOI 10.18653/v1/P17-1019
   Hoffner K, 2017, SEMANT WEB, V8, P895, DOI 10.3233/SW-160247
   Hoffner K., 2016, SEMANT WEB, V9, P1, DOI DOI 10.3233/SW-160249
   Hongxia Liu, 2017, Smart Health. International Conference, ICSH 2017. Proceedings: LNCS 10347, P286, DOI 10.1007/978-3-319-67964-8_29
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Huang ZH, 2015, COMPUT INTEL NEUROSC, V2015, DOI 10.1155/2015/685404
   Ji Z, 2012, P 21 ACM INT C INF K, P2471, DOI DOI 10.1145/2396761.2398669
   Lee Minsuk, 2006, AMIA Annu Symp Proc, P469
   Li P, 1996, PROCEEDINGS OF THE EIGHTEENTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P228
   Liang C, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P23, DOI 10.18653/v1/P17-1003
   Liu Z, 2017, LECT NOTES COMPUT SC, V10219, P44, DOI 10.1007/978-3-319-59858-1_5
   Ndapandula N., 2011, P 4 ACM INT C WEB SE, P227
   Qiu YQ, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P474, DOI 10.1145/3336191.3371812
   Le Q, 2014, PR MACH LEARN RES, V32, P1188
   Sharma S., 2015, INT J ENG TECH RES, V3, P131
   Sharma Yashvardhan, 2018, Procedia Computer Science, V132, P785, DOI 10.1016/j.procs.2018.05.090
   Shi LX, 2017, BIOMED RES INT, V2017, DOI 10.1155/2017/2858423
   Sukhbaatar S, 2015, ADV NEUR IN, V28
   Sun A, 2010, P1194
   Sun H., 2019, P 2019 C EMP METH NA, P2380
   Tan M, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P464, DOI 10.18653/v1/p16-1044
   Wang CY, 2015, 2015 13TH IEEE INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOPS (ICDEW), P59, DOI 10.1109/ICDEW.2015.7129545
   Xu K., 2018, P C EMP METH NAT LAN, P918
   Xu M., 2016, ARXIV161100801
   Xu Z, 2016, ARXIV160505110, P3
   Yanhong L., 2017, INT J BIOMED SCI ENG, V5, P29, DOI [10.11648/j.ijbse.20170503.13, DOI 10.11648/J.IJBSE.20170503.13]
   Yao XC, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P956
   Yih WT, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P1321
   Zeng YH, 2019, PROC CVPR IEEE, P1486, DOI 10.1109/CVPR.2019.00158
   Zhang L, 2012, UBICOMP'12: PROCEEDINGS OF THE 2012 ACM INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING, P627
   Zhang Yuanzhe, 2016, ABS160600979 CORR
   Zheng SC, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1227, DOI 10.18653/v1/P17-1113
NR 56
TC 0
Z9 0
U1 5
U2 5
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD APR 15
PY 2022
VL 23
IS 1
AR 136
DI 10.1186/s12859-022-04658-2
PG 32
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
GA 0N3XZ
UT WOS:000782776000005
PM 35428175
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Zhang, ZY
   Zhang, L
   Yang, DQ
   Yang, L
AF Zhang, Zhenyu
   Zhang, Lei
   Yang, Dingqi
   Yang, Liu
TI KRAN: Knowledge Refining Attention Network for Recommendation
SO ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
LA English
DT Article
DE Knowledge graph; refine; attention mechanism; data sparsity; item cold
   start
ID SYSTEMS
AB Recommender algorithms combining knowledge graph and graph convolutional network are becoming more and more popular recently. Specifically, attributes describing the items to be recommended are often used as additional information. These attributes along with items are highly interconnected, intrinsically forming a Knowledge Graph (KG). These algorithms use KGs as an auxiliary data source to alleviate the negative impact of data sparsity. However, these graph convolutional network based algorithms do not distinguish the importance of different neighbors of entities in the KG, and according to Pareto's principle, the important neighbors only account for a small proportion. These traditional algorithms can not fully mine the useful information in the KG.
   To fully release the power of KGs for building recommender systems, we propose in this article KRAN, a Knowledge Refining Attention Network, which can subtly capture the characteristics of the KG and thus boost recommendation performance. We first introduce a traditional attention mechanism into the KG processing, making the knowledge extraction more targeted, and then propose a refining mechanism to improve the traditional attention mechanism to extract the knowledge in the KG more effectively. More precisely, KRAN is designed to use our proposed knowledge-refining attention mechanism to aggregate and obtain the representations of the entities (both attributes and items) in the KG. Our knowledge-refining attention mechanism first measures the relevance between an entity and it's neighbors in the KG by attention coefficients, and then further refines the attention coefficients using a "richer-get-richer" principle, in order to focus on highly relevant neighbors while eliminating less relevant neighbors for noise reduction. In addition, for the item cold start problem, we propose KRAN-CD, a variant of KRAN, which further incorporates pretrained KG embeddings to handle cold start items. Experiments show that KRAN and KRAN-CD consistently outperform state-of-the-art baselines across different settings.
C1 [Zhang, Zhenyu; Zhang, Lei; Yang, Liu] Tianjin Univ, Coll Intelligence & Comp, 135 Yaguan Rd, Tianjin 300350, Peoples R China.
   [Zhang, Zhenyu] Beijing Dahua Elect Instrument LLC, 135 Yaguan Rd, Tianjin 300350, Peoples R China.
   [Yang, Dingqi] Univ Macau, State Key Lab Internet Things Smart City, Macau, Peoples R China.
   [Yang, Dingqi] Univ Macau, Dept Comp & Informat Sci, Macau, Peoples R China.
RP Yang, L (通讯作者)，Tianjin Univ, Coll Intelligence & Comp, 135 Yaguan Rd, Tianjin 300350, Peoples R China.
EM zhen.yu.zhang@foxmail.com; lzhang@tju.edu.cn; dingqiyang@um.edu.mo;
   yangliuyl@tju.edu.cn
RI zhang, zhenyu/AHE-1078-2022
FU NSFC [61772364, 62076179, 61732011]; Tianjin Science and Technology Plan
   Project [19ZXZNGX00050]; Open Research Fund of the Public Security
   Behavioral Science Laboratory, People's Public Security University of
   China [2020SYS10]; University of Macau [SRG2021-00002-IOTSC]; FDCT Macau
   SAR [SKL-IOTSC-2021-2023]
FX This work is supported by NSFC under Grant 61772364, Grant 62076179, and
   Grant 61732011, Tianjin Science and Technology Plan Project under Grant
   19ZXZNGX00050, funded by Open Research Fund of the Public Security
   Behavioral Science Laboratory, People's Public Security University of
   China under Grant 2020SYS10, University of Macau under Grant
   SRG2021-00002-IOTSC and FDCT Macau SAR under Grant SKL-IOTSC-2021-2023.
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
   Blanco-Fernandez Y, 2011, INFORM SCIENCES, V181, P4823, DOI 10.1016/j.ins.2011.06.016
   Bojnordi E., 2012, 2012 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), P191, DOI 10.1109/AISP.2012.6313742
   Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
   Cantador I, 2010, P 4 ACM C REC SYST, P237, DOI DOI 10.1145/1864708.1864756
   Chiang WL, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P257, DOI 10.1145/3292500.3330925
   Cho YH, 2004, EXPERT SYST APPL, V26, P233, DOI 10.1016/S0957-4174(03)00138-6
   Corciolani M, 2014, MANAGE DECIS, V52, P755, DOI 10.1108/MD-03-2012-0241
   Costa H, 2013, INT C PRACT APPL AG, P178, DOI DOI 10.1007/978-3-642-38061-7_18
   [付宇新 Fu Yuxin], 2015, [计算机工程与应用, Computer Engineering and Application], V51, P120
   GOLUB GH, 1970, NUMER MATH, V14, P403, DOI 10.1007/BF02163027
   Hamilton Will, 2017, NEURIPS
   Harper FM, 2016, ACM T INTERACT INTEL, V5, DOI 10.1145/2827872
   Haupt J., 2009, MUSIC REFERENCE SERV, V12, P23
   He Weihong, 2006, Wuhan University Journal of Natural Sciences, V11, P1091, DOI 10.1007/BF02829216
   Juan YC, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P43, DOI 10.1145/2959100.2959134
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944
   L Si, 2004, P 13 ACM INT C INF K, P156, DOI DOI 10.1145/1031171.1031201
   Lecue F, 2010, IEEE INT C SEMANT CO, P200, DOI 10.1109/ICSC.2010.37
   LeCun Y, 1998, LECT NOTES COMPUT SC, V1524, P9, DOI 10.1007/3-540-49430-8_2
   Li GH, 2019, IEEE I CONF COMP VIS, P9266, DOI 10.1109/ICCV.2019.00936
   Lukovnikov D, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1211, DOI 10.1145/3038912.3052675
   Oramas Sergio, 2017, ACM Transactions on Intelligent Systems and Technology, V8, DOI 10.1145/2926718
   Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
   Rendle Steffen, 2010, Proceedings 2010 10th IEEE International Conference on Data Mining (ICDM 2010), P995, DOI 10.1109/ICDM.2010.127
   Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
   Tarus JK, 2018, ARTIF INTELL REV, V50, P21, DOI 10.1007/s10462-017-9539-5
   Tkalcic M, 2013, IEEE T MULTIMEDIA, V15, P391, DOI 10.1109/TMM.2012.2229970
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang Guoxia, 2012, Computer Engineering and Applications, V48, P66, DOI 10.3778/j.issn.1002-8331.2012.07.018
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang HW, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P592, DOI 10.1145/3159652.3159666
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Xiangnan He, 2020, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, P639, DOI 10.1145/3397271.3401063
   Ying R, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P974, DOI 10.1145/3219819.3219890
   Yu X, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P283, DOI 10.1145/2556195.2556259
   Zhang L, 2019, INT C PAR DISTRIB SY, P69, DOI 10.1109/ICPADS47876.2019.00018
NR 40
TC 0
Z9 0
U1 10
U2 14
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 1556-4681
EI 1556-472X
J9 ACM T KNOWL DISCOV D
JI ACM Trans. Knowl. Discov. Data
PD APR
PY 2021
VL 16
IS 2
AR 39
DI 10.1145/3470783
PG 20
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UQ6XB
UT WOS:000696204600020
DA 2022-08-23
ER

PT J
AU Xu, ZZ
   Xu, YC
   Cheung, F
   Cheng, M
   Lung, D
   Law, YW
   Chiang, B
   Zhang, QP
   Yip, PSF
AF Xu, Zhongzhi
   Xu, Yucan
   Cheung, Florence
   Cheng, Mabel
   Lung, Daniel
   Law, Yik Wa
   Chiang, Byron
   Zhang, Qingpeng
   Yip, Paul S. F.
TI Detecting suicide risk using knowledge-aware natural language processing
   and counseling service data
SO SOCIAL SCIENCE & MEDICINE
LA English
DT Article
DE Online counseling services; Suicide prevention; Natural language
   processing; Knowledge graph; Artificial intelligence
ID IMPACT
AB Rationale: Detecting users at risk of suicide in text-based counseling services is essential to ensure that at-risk individuals are flagged and prioritized. Objective: The objective of this study is to develop a domain knowledge-aware risk assessment (KARA) model to improve our ability of suicide detection in online counseling systems. Methods: We obtained the largest known de-identified dataset from an emotional support system established in Hong Kong, comprising 5682 Cantonese conversations between help-seekers and counselors. Of those, 682 conversations disclosed crisis intentions of suicide. We constructed a suicide-knowledge graph, representing suicide-related domain knowledge as a computer-processible graph. Such knowledge graph was embedded into a deep learning model to improve its ability to identify help-seekers in crisis. As the baseline, a standard NLP model was applied to the same task. 80% of the study samples were randomly sampled to train model parameters. The remaining 20% were used for model validation. Evaluation metrics including precision, recall, and c-statistic were reported. Results: Both KARA and the baseline achieved high precision (0.984 and 0.951, shown in Table 2) and high recall (0.942 and 0.947) towards non-crisis cases. For crisis cases, however, KARA model achieved a much higher recall than the baseline (0.870 vs 0.791). The c-statistics of KARA and the baseline were 0.815 and 0.760, respectively. Conclusion: KARA significantly outperformed standard NLP models, demonstrating good translational value and clinical relevance.
C1 [Xu, Zhongzhi; Zhang, Qingpeng] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China.
   [Xu, Zhongzhi; Xu, Yucan; Cheung, Florence; Cheng, Mabel; Lung, Daniel; Law, Yik Wa; Chiang, Byron; Yip, Paul S. F.] Univ Hong Kong, Hong Kong Jockey Club Ctr Suicide Res & Prevent, Hong Kong, Peoples R China.
RP Zhang, QP (通讯作者)，City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China.; Yip, PSF (通讯作者)，Univ Hong Kong, Hong Kong Jockey Club Ctr Suicide Res & Prevent, Hong Kong, Peoples R China.
EM qingpeng.zhang@cityu.edu.hk; sfpyip@hku.hk
RI Zhang, Qingpeng/D-4682-2011
OI Zhang, Qingpeng/0000-0002-6819-0686; Xu, Yucan/0000-0003-0247-0404;
   Cheng, Nga Sum/0000-0002-6335-5517
FU National Natural Science Foundation of China (NSFC) [71972164,
   71672163]; Health and Medical Research Fund Grant (HMRF) [16171991];
   Hong Kong Jockey Club Charity Trusts; Theme-Based Research Scheme of the
   Research Grants Council of Hong Kong [T32102/14N]
FX This study is funded in part by the National Natural Science Foun-dation
   of China (NSFC) Grant Nos. 71972164 and 71672163, in part by the Health
   and Medical Research Fund Grant (HMRF) No. 16171991, in part by the Hong
   Kong Jockey Club Charity Trusts and in part by The Theme-Based Research
   Scheme of the Research Grants Council of Hong Kong Grant No. T32102/14N.
CR Alao AO, 1999, AM J PSYCHIAT, V156, P1836
   Bantilan N, 2021, PSYCHOTHER RES, V31, P302, DOI 10.1080/10503307.2020.1781952
   Chan GH, 2020, CHILD YOUTH SERV REV, V114, DOI 10.1016/j.childyouth.2020.105042
   Cheng QJ, 2015, INT J ENV RES PUB HE, V12, P11506, DOI 10.3390/ijerph120911506
   Fahey RA, 2020, SOC SCI MED, V253, DOI 10.1016/j.socscimed.2020.112960
   Gilmore AK, 2019, J TELEMED TELECARE, V25, P59, DOI 10.1177/1357633X17735559
   Gkotsis G, 2016, P 3 WORKSH COMP LING, P95
   Hanley T., 2009, COUNS PSYCHOL, V24, P4, DOI DOI 10.1080/14733140600857535.
   Hayes AF, 2007, COMMUN METHODS MEAS, V1, P77, DOI 10.1080/19312450709336664
   Jashinsky J, 2014, CRISIS, V35, P51, DOI 10.1027/0227-5910/a000234
   Kelly CM, 2008, BMC PSYCHIATRY, V8, DOI 10.1186/1471-244X-8-17
   Kiesler S., 1997, CULT INTERNET, P87
   Kirtley OJ, 2020, SOC SCI MED, V262, DOI 10.1016/j.socscimed.2019.112691
   Kraus R., 2004, ONLINE COUNSELING HD, P123
   Krysinska KE, 2007, OMEGA-J DEATH DYING, V55, P237, DOI 10.2190/OM.55.3.e
   Lutter M, 2020, SOC SCI MED, V246, DOI 10.1016/j.socscimed.2019.112755
   Mallen M.J., 2005, COUNSEL PSYCHOL
   McClatchey K, 2019, ARCH SUICIDE RES, V23, P411, DOI 10.1080/13811118.2018.1480983
   McVeigh MJ, 2020, CHILD AUST, V45, P266, DOI 10.1017/cha.2020.59
   Naghavi M, 2019, LANCET, V364, pl94, DOI 10.1136/bmj.l94
   Pavalanathan U, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P315, DOI 10.1145/2740908.2743049
   Scherr S, 2020, SOC SCI COMPUT REV, V38, P673, DOI 10.1177/0894439319836389
   Scherr S, 2021, CURR PSYCHOL, V40, P957, DOI 10.1007/s12144-018-0020-7
   Scherr S, 2016, COMPUT HUM BEHAV, V61, P80, DOI 10.1016/j.chb.2016.03.009
   Sharma AP, 2018, POLYM TEST, V65, P301, DOI 10.1016/j.polymertesting.2017.12.001
   Wang H., 2019, ACM T INFORM SYST
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   World Health Organization, 2019, WORLD
   Xu ZY, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2020.3047826
   Xu ZZ, 2020, J AFFECT DISORDERS, V277, P402, DOI 10.1016/j.jad.2020.08.044
   Xu ZZ, 2019, IEEE SYS MAN CYBERN, P814, DOI 10.1109/SMC.2019.8914644
   Xu ZZ, 2019, INT J MED INFORM, V132, DOI 10.1016/j.ijmedinf.2019.103973
   Yip P, 2020, LANCET REG HEALTH-W, V4, DOI 10.1016/j.lanwpc.2020.100047
   Yip PSF, 2020, CRISIS, V41, P153, DOI 10.1027/0227-5910/a000710
NR 35
TC 1
Z9 1
U1 5
U2 7
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0277-9536
EI 1873-5347
J9 SOC SCI MED
JI Soc. Sci. Med.
PD AUG
PY 2021
VL 283
AR 114176
DI 10.1016/j.socscimed.2021.114176
EA JUN 2021
PG 8
WC Public, Environmental & Occupational Health; Social Sciences, Biomedical
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health; Biomedical Social Sciences
GA TU6VV
UT WOS:000681172900010
PM 34214846
DA 2022-08-23
ER

PT J
AU Xu, J
   Wang, JB
   Wang, XX
   Lv, P
AF Xu, Jia
   Wang, Junbin
   Wang, Xiaoxin
   Lv, Pin
TI iTest: A novel online testing system based on the WeChat platform
SO COMPUTER APPLICATIONS IN ENGINEERING EDUCATION
LA English
DT Article
DE online testing; personalized exercise recommendation; testing behavior
   data; WeChat public platform; weighted knowledge graph
AB Timely feedback is important for both teachers and students in teaching and learning practice. Teachers rely on feedback from students to gain a better understanding of their learning effects. Students also need feedback to identify gaps in their knowledge. Conducting in-class tests is considered an effective way to obtain such feedback. However, traditional testing methods are inflexible and inefficient. In this paper, we introduce iTest, a novel online testing system developed on the popular WeChat platform. With the help of iTest, the teacher organizes an in-class test conveniently, and the students take the test with their smartphones regardless of which operating systems the smartphones use. When the student answers questions in a test, abundant behavior data for the student are collected. According to the analysis of the behavior data and the testing results, the teacher gains a deeper insight into the learning effect for each student. A weighted knowledge graph is also constructed for each student by virtue of the prerequisite relationships among the knowledge points and the behavior data. On the basis of the weighted knowledge graph, iTest recommends personalized exercises in an appropriate order to each student after the test, which helps to improve the learning effect. Teaching practice in five courses verifies that iTest is effective and efficient in promoting teaching and learning. Both teachers and students have high praise for iTest.
C1 [Xu, Jia; Wang, Junbin; Wang, Xiaoxin; Lv, Pin] Guangxi Univ, Dept Comp Sci & Technol, Sch Comp Elect & Informat, Nanning 530004, Peoples R China.
RP Lv, P (通讯作者)，Guangxi Univ, Dept Comp Sci & Technol, Sch Comp Elect & Informat, Nanning 530004, Peoples R China.
EM lvpin@gxu.edu.cn
CR Bordes Antoine, 2016, ARXIV160507683
   Bridgeman S, 2000, SIGCSE BULL, V32, P139
   Dellos R., 2015, INT J INSTRUCT TECHN, V12, P49
   Ito M, 2015, STUD HEALTH TECHNOL, V216, P1095, DOI 10.3233/978-1-61499-564-7-1095
   Lv Pin, 2018, P ACM TUR CEL C CHIN, P53
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Pang Y., 2018, COMPUT APPL ENG EDUC, V26, P1
   Popovic O, 2016, COMPUT APPL ENG EDUC, V24, P412, DOI 10.1002/cae.21719
   Riedel S, 2010, LECT NOTES ARTIF INT, V6323, P148, DOI 10.1007/978-3-642-15939-8_10
   Shapira B., 2014, ED DATA MINING 2014, P68
   SHEN GX, 2015, INT C TEACH ASS LEAR, P1
   Slisko J., 2013, EUROPEAN J PHYS ED, V4, P17
   Suknovic, 2013, RAPIDMINER DATA MINI, P145
   Wang ST, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P317, DOI 10.1145/2983323.2983725
NR 14
TC 3
Z9 3
U1 1
U2 22
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1061-3773
EI 1099-0542
J9 COMPUT APPL ENG EDUC
JI Comput. Appl. Eng. Educ.
PD JUL
PY 2019
VL 27
IS 4
BP 885
EP 893
DI 10.1002/cae.22122
PG 9
WC Computer Science, Interdisciplinary Applications; Education, Scientific
   Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Education & Educational Research; Engineering
GA II8XR
UT WOS:000475477500010
DA 2022-08-23
ER

PT J
AU Chen, Y
   Wu, JJ
   Lin, JY
   Liu, R
   Zhang, H
   Ye, ZW
AF Chen, Yong
   Wu, Junjie
   Lin, Jianying
   Liu, Rui
   Zhang, Hui
   Ye, Zhiwen
TI Affinity Regularized Non-Negative Matrix Factorization for Lifelong
   Topic Modeling
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Data models; Semantics; Task analysis; Graphics processing units; Big
   Data; Convergence; Maintenance engineering; Lifelong topic model (LTM);
   non-negative matrix factorization (NMF); semantic affinity; knowledge
   graph
AB Lifelong topic model (LTM), an emerging paradigm for never-ending topic learning, aims to yield higher-quality topics as time passes through knowledge accumulated from the past yet learned for the future. In this paper, we propose a novel lifelong topic model based on non-negative matrix factorization (NMF), called Affinity Regularized NMF for LTM (NMF-LTM), which to our best knowledge is distinctive from the popular LDA-based LTMs. NMF-LTM achieves lifelong learning by introducing word-word graph Laplacian as semantic affinity regularization. Other priors such as sparsity, diversity, and between-class affinity are incorporated as well for better performance, and a theoretical guarantee is provided for the algorithmic convergence to a local minimum. Extensive experiments on various public corpora demonstrate the effectiveness of NMF-LTM, particularly its human-like behaviors in two carefully designed learning tasks and the ability in topic modeling of big data. A further exploration of semantic relatedness in knowledge graphs and a case study on a large-scale real-world corpus exhibit the strength of NMF-LTM in discovering high-quality topics in an efficient and robust way.
C1 [Chen, Yong; Lin, Jianying; Liu, Rui; Zhang, Hui; Ye, Zhiwen] Beihang Univ, Dept Comp Sci & Engn, Beijing 100191, Peoples R China.
   [Wu, Junjie] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Sch Econ & Management, Beijing Key Lab Emergency Support Simulat Technol, Beijing 100191, Peoples R China.
RP Wu, JJ (通讯作者)，Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Sch Econ & Management, Beijing Key Lab Emergency Support Simulat Technol, Beijing 100191, Peoples R China.
EM chenyong@nlsde.buaa.edu.cn; wujj@buaa.edu.cn; jianying.lin@buaa.edu.cn;
   lr@buaa.edu.cn; hzhang@nlsde.buaa.edu.cn; yezhiwen@buaa.edu.cn
FU National Natural Science Foundation of China (NSFC) [71725002, 71531001,
   U1636210, 71471009, 71490723]; CSC (China Scholarship Council); National
   Key Research and Development Program of China [2017YFB1400200]
FX Junjie Wu was partially supported by the National Natural Science
   Foundation of China (NSFC) (71725002, 71531001, U1636210, 71471009,
   71490723). Yong Chen was funded by CSC (China Scholarship Council) as a
   visiting Ph.D. student in Birkbeck and UCL from January 2018 to January
   2019. This work was also sponsored in part by the National Key Research
   and Development Program of China under grant No. 2017YFB1400200. The
   authors also thank the Network Information Center in Beihang University
   for providing highperformance servers.
CR Andrzejewski David, 2009, Proc Int Conf Mach Learn, V382, P25
   Belkin M, 2002, ADV NEUR IN, V14, P585
   Bhadury A, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P381, DOI 10.1145/2872427.2883046
   Blei D.M., 2006, ACM INT C P SERIES, P113
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Carlson Andrew, 2010, P 3 ACM INT C WEB SE, P101, DOI 10.1145/ 1718487.1718501
   CARON F, 2007, 23 C UNC ART INT UAI, P33
   Chen X., 2013, THESIS, P1
   Chen Y, 2015, IEEE DATA MINING, P61, DOI 10.1109/ICDM.2015.96
   Chen Y, 2013, IEEE INT C COMPUT, P1075, DOI 10.1109/CSE.2013.156
   Chen Z., 2015, 8 INT C MOD TRANSF, P133, DOI DOI 10.1007/978-3-319-21155-8_11
   Chen Z., 2016, LIFELONG MACHINE LEA, P1
   Chen ZW, 2014, PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON ADVANCED ICT, (ICAICTE 2014), P307
   Chen ZY, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (IJCNLP), VOL 2, P750
   Chen ZY, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P347
   Chen ZY, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P209, DOI 10.1145/2505515.2505519
   Conroy Bryan, 2012, JMLR Workshop Conf Proc, V22, P246
   Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50, DOI 10.1145/312624.312649
   Khan M. Taimoor, 2016, COMPLEX ADAPTIVE SYS, V4, P1
   Le Q.V., 2014, COMPUT RES REPOS COR, P1, DOI [DOI 10.1145/2740908.2742760, 10.1145/2740908.2742760]
   Lee DD, 1999, NATURE, V401, P788, DOI 10.1038/44565
   Lee DD, 2001, ADV NEUR IN, V13, P556
   Li L., 2015, P AAAI C ART INT, P1
   Li Z, 2010, PATTERN RECOGN LETT, V31, P905, DOI 10.1016/j.patrec.2009.12.023
   Liu B, 2017, FRONT COMPUT SCI-CHI, V11, P359, DOI 10.1007/s11704-016-6903-6
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Mimno D., 2011, P 2011 C EMP METH NA, P262, DOI DOI 10.5555/2145432.2145462
   Mitchell T, 2015, AAAI CONF ARTIF INTE, P2302
   Phan X.H., 2008, P 17 INT C WORLD WID, P91, DOI DOI 10.1145/1367497.1367510
   Saha  A., 2012, P 5 ACM INT C WEB SE, P693, DOI DOI 10.1145/2124295.2124376
   Shalev-Shwartz S, 2012, FOUND TRENDS MACH LE, V4, P107, DOI 10.1561/2200000018
   Silver Daniel L., 2011, Artificial General Intelligence. Proceedings 4th International Conference, AGI 2011, P370, DOI 10.1007/978-3-642-22887-2_45
   Silver D. L., 2013, LIFELONG MACHINE LEA, V5-13, P49
   Sorkin GB, 2008, LECT NOTES COMPUT SC, V5171, P571
   Tekin C., 2015, FDN TRENDS NETW, V8, P281, DOI [10.1561/1300000050, DOI 10.1561/1300000050]
   THRUN S, 1995, ROBOT AUTON SYST, V15, P25, DOI 10.1016/0921-8890(95)00004-Y
   Vaca C, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P527, DOI 10.1145/2566486.2568041
   Wang C., 2008, P 24 C UNC ART INT, P579
   Wang C, 2015, INTERNATIONAL CONFERENCE ON INFORMATICS, CONTROL AND AUTOMATION (ICA 2015), P106
   Wang S, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P167, DOI 10.1145/2872427.2883086
   Xu ZB, 2012, IEEE T NEUR NET LEAR, V23, P1013, DOI 10.1109/TNNLS.2012.2197412
   Zhou J, 2013, IEEE GLOB COMM CONF, P1657, DOI 10.1109/GLOCOM.2013.6831311
NR 42
TC 3
Z9 3
U1 1
U2 10
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD JUL 1
PY 2020
VL 32
IS 7
BP 1249
EP 1262
DI 10.1109/TKDE.2019.2904687
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA MC0RT
UT WOS:000543006000002
DA 2022-08-23
ER

PT J
AU Agarwal, P
   Sinha, BB
AF Agarwal, Pratik
   Sinha, Bam Bahadur
TI Scalable resource description framework clustering: A distributed
   approach for analyzing knowledge graphs using minHash locality sensitive
   hashing
SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
LA English
DT Article
DE Big data; clustering; GraphFrames; linked data; locality sensitive
   hashing; semantic web; Silhouette
AB Web is becoming rich in data. Some of the sources from where these data are originating includes Blogs, YouTube, Twitter, Emails, E-commerce, Banking, sensors, and the Internet of Things. But these data are structured in a very poor fashion. The content of the web is becoming heterogeneous in nature both in terms of compendium and structure. It can be said that these data are human-readable data but the main motive is to draw inferences from these data which is only possible if it can be made machine-accessible. Clustering is considered an important task to organize these data and draw meaningful inferences from these data. In this paper, a clustering approach is proposed that can be applied to knowledge graphs and the possibility of applying Locality Sensitive Hashing is explored. Given the size of linked data, it is observed that this approach can be effective and scalable in comparison to other clustering approaches such as Hierarchical clustering, K-Means clustering, and K-Medoid clustering in discovering different communities that are defined by the link structure of the graph. The experimental results on different types of Linked Data sources justify the efficacy of the proposed model in terms of scalability and efficiency.
C1 [Agarwal, Pratik] Umlaut Co, Crowd Engn, Aachen, North Rhine Wes, Germany.
   [Sinha, Bam Bahadur] Indian Inst Informat Technol Ranchi, Comp Sci & Engn, Ranchi 834010, Jharkhand, India.
RP Sinha, BB (通讯作者)，Indian Inst Informat Technol Ranchi, Comp Sci & Engn, Ranchi 834010, Jharkhand, India.
EM bahadurbam@iiitranchi.ac.in
RI SINHA, BAM BAHADUR/AAE-5953-2021
OI SINHA, BAM BAHADUR/0000-0002-7284-9850
CR Algergawy A, 2011, LECT NOTES COMPUT SC, V6909, P415, DOI 10.1007/978-3-642-23737-9_30
   Alqaisi R, 2020, IEEE ACCESS, V8, P228206, DOI 10.1109/ACCESS.2020.3046494
   Alu? G., 2015, ARXIV PREPRINT ARXIV
   Aydar M, 2019, KNOWL INF SYST, V58, P275, DOI 10.1007/s10115-018-1199-5
   Christodoulou K., 2015, T LARGE SCALE DATA K, V8990, DOI [10.1007/978-3-662-46562-2_1, DOI 10.1007/978]
   Dhanalakshmi R., 2019, SYSTEM, V78, P411
   Eddamiri S, 2019, PROCEDIA COMPUT SCI, V148, P208, DOI 10.1016/j.procs.2019.01.038
   Fanizzi N, 2007, LECT NOTES ARTIF INT, V4694, P653
   Giannini S, 2013, LECT NOTES BUS INF P, V160, P220
   Hendler J., 2020, SEMANTIC WEB WORKING
   Hu J, 2021, TSINGHUA SCI TECHNOL, V26, P185, DOI 10.26599/TST.2019.9010078
   Jabeen Hajira, 2018, Knowledge Engineering and Knowledge Management. 21st International Conference, EKAW 2018. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 11313), P534, DOI 10.1007/978-3-030-03667-6_34
   Ma YZ, 2020, IEEE ACCESS, V8, P121665, DOI 10.1109/ACCESS.2020.3007028
   Miller E., 1998, D-Lib Magazine, DOI 10.1002/bult.105
   Rao TR, 2021, J SUPERCOMPUT, V77, P5984, DOI 10.1007/s11227-020-03488-4
   Rattigan M. J., 2007, P 24 INT C MACH LEAR, V277, P783, DOI DOI 10.1145/1273496.1273595
   Ruiz-Calleja A, 2021, J WEB SEMANT, V70, DOI 10.1016/j.websem.2021.100657
   Sinaga KP, 2020, IEEE ACCESS, V8, P80716, DOI 10.1109/ACCESS.2020.2988796
   Sinha Bam Bahadur, 2018, 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE), P273, DOI 10.1109/ICACCE.2018.8441755
   Sinha BB, 2022, J KING SAUD UNIV-COM, V34, P1047, DOI 10.1016/j.jksuci.2019.06.008
   Sinha BB, 2019, SOFT COMPUT, V23, P12169, DOI 10.1007/s00500-019-04143-8
   Tian F, 2014, AAAI CONF ARTIF INTE, P1293
   Xiong A, 2021, TSINGHUA SCI TECHNOL, V26, P886, DOI 10.26599/TST.2020.9010051
   Xu Q, 2020, EXPERT SYST APPL, V151, DOI 10.1016/j.eswa.2020.113367
   Xueting Liao, 2021, Big Data Mining and Analytics, V4, P242, DOI 10.26599/BDMA.2021.9020010
   Zhonghao Xue, 2021, Big Data Mining and Analytics, V4, P183, DOI 10.26599/BDMA.2021.9020001
NR 26
TC 0
Z9 0
U1 0
U2 0
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-0626
EI 1532-0634
J9 CONCURR COMP-PRACT E
JI Concurr. Comput.-Pract. Exp.
PD JUL 10
PY 2022
VL 34
IS 15
AR e6966
DI 10.1002/cpe.6966
EA MAR 2022
PG 17
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2C1CA
UT WOS:000773368100001
DA 2022-08-23
ER

PT J
AU Ait-Mlouk, A
   Jiang, LL
AF Ait-Mlouk, Addi
   Jiang, Lili
TI KBot: A Knowledge Graph Based ChatBot for Natural Language Understanding
   Over Linked Data
SO IEEE ACCESS
LA English
DT Article
DE Knowledge based systems; Linked data; Natural languages; Task analysis;
   Engines; Semantic Web; Machine learning; Linked data; Chatbot; SPARQL;
   intent classification; natural language understanding; myPersonality
   dataset
AB With the rapid progress of the semantic web, a huge amount of structured data has become available on the web in the form of knowledge bases (KBs). Making these data accessible and useful for end-users is one of the main objectives of chatbots over linked data. Building a chatbot over linked data raises different challenges, including user queries understanding, multiple knowledge base support, and multilingual aspect. To address these challenges, we first design and develop an architecture to provide an interactive user interface. Secondly, we propose a machine learning approach based on intent classification and natural language understanding to understand user intents and generate SPARQL queries. We especially process a new social network dataset (i.e., myPersonality) and add it to the existing knowledge bases to extend the chatbot capabilities by understanding analytical queries. The system can be extended with a new domain on-demand, flexible, multiple knowledge base, multilingual, and allows intuitive creation and execution of different tasks for an extensive range of topics. Furthermore, evaluation and application cases in the chatbot are provided to show how it facilitates interactive semantic data towards different real application scenarios and showcase the proposed approach for a knowledge graph and data-driven chatbot.
C1 [Ait-Mlouk, Addi; Jiang, Lili] Umea Univ, Dept Comp Sci, S-90187 Umea, Sweden.
RP Jiang, LL (通讯作者)，Umea Univ, Dept Comp Sci, S-90187 Umea, Sweden.
EM lili.jiang@umu.se
OI Addi, Ait-Mlouk/0000-0003-0385-9390
FU Umea University, Sweden
FX This work was supported by Umea University, Sweden, on federated
   database research.
CR AbuShawar B., 2015, COMPUTACION SISTEMAS, V19
   Al-Zubaide H., 2011, 2011 Fourth International Symposium on Innovation in Information & Communication Technology (ISIICT), P7, DOI 10.1109/ISIICT.2011.6149594
   Ali DA, 2016, P COLING 2016 26 INT, P208, DOI DOI 10.18653/V1/2020.COLING-DEMOS
   Athreya RG, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P143, DOI 10.1145/3184558.3186964
   Bell T, 2002, CONTRIBUTIONS OF SURFACE ENGINEERING TO MODERN MANUFACTURING AND REMANUFACTURING, P3
   Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, P144, DOI 10.1145/130385.130401
   Colby K. M., 1999, HUMAN COMPUTER CONVE, P9, DOI [10.1007/978-1-4757-5687-6_3, DOI 10.1007/978-1-4757-5687-6_3]
   Comendador Benilda Eleonor V., 2015, Journal of Automation and Control Engineering, V3, P137, DOI 10.12720/joace.3.2.137-140
   Cui L, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017): SYSTEM DEMONSTRATIONS, P97, DOI 10.18653/v1/P17-4017
   Divya S, 2018, J WEB DEV WEB DESIGN, V3, P7, DOI 10.1007/978-981-15-6707-0_57]
   Heller B., 2005, P WORLD C MULT HYP T
   Hettige B., 2016, P 26 INT C COMP LING
   Hsu P., 2017, P 2017 CHI C EXT ABS, P74
   Jia JY, 2009, KNOWL-BASED SYST, V22, P249, DOI 10.1016/j.knosys.2008.09.001
   Kazi H., 2012, INT J COMPUTER APPL, V55, P1
   Keyner S, 2019, LECT NOTES COMPUT SC, V11762, P111, DOI 10.1007/978-3-030-32327-1_22
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Neumaier S, 2017, IN SY AP IN WE HC, V10577, P132, DOI 10.1007/978-3-319-70407-4_25
   Oh KJ, 2017, IEEE INT CONF MOB DA, P371, DOI 10.1109/MDM.2017.64
   Qiu MH, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2, P498, DOI 10.18653/v1/P17-2079
   Segura C., 2018, P 9 INT WORKSH SPOK, P319
   Turing A. M., 1950, MIND, P433, DOI [10.1093/mind/LIX.236.433, 10.1093/MIND/LIX.236.433]
   Vaira L, 2018, P 22 INT DAT ENG APP, DOI [10.1145/3216122.3216173, DOI 10.1145/3216122.3216173]
   Vu XS, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3595, DOI 10.1145/3308558.3314138
   Vu Xuan-Son, 2019, P 20 INT C COMP LING, P1
   WEIZENBAUM J, 1966, COMMUN ACM, V9, P36, DOI 10.1145/365153.365168
   Xu AB, 2017, PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), P3506, DOI 10.1145/3025453.3025496
   Zhao R, 2018, 18TH ACM INTERNATIONAL CONFERENCE ON INTELLIGENT VIRTUAL AGENTS (IVA'18), P239, DOI 10.1145/3267851.3267880
NR 28
TC 15
Z9 15
U1 6
U2 19
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 149220
EP 149230
DI 10.1109/ACCESS.2020.3016142
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA ND8BI
UT WOS:000562126500001
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Du, ZL
   Lin, DS
   Li, HF
   Li, Y
   Chen, HA
   Dou, WQ
   Qin, L
   An, Y
AF Du, Zhaolin
   Lin, Dasong
   Li, Haifeng
   Li, Yang
   Chen, Hongan
   Dou, Weiqiang
   Qin, Li
   An, Yi
TI Bibliometric Analysis of the Influencing Factors, Derivation, and
   Application of Heavy Metal Thresholds in Soil
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Review
DE soil; crop; heavy metal; threshold; risk assessment
ID ECOLOGICAL RISK-ASSESSMENT; HEALTH-RISK; TOXICITY THRESHOLDS;
   AGRICULTURAL SOIL; TRACE-ELEMENTS; ARABLE SOILS; CADMIUM; CONTAMINATION;
   POLLUTION; QUALITY
AB The study of threshold levels of heavy metals in soil is essential for the assessment and management of soil environmental quality. This study reviewed the influencing factors, the derivation, and application aspects of heavy metals' threshold values comprehensively by a combination of bibliometric analysis and scientific knowledge mapping. A total of 1106 related studies were comprehensively extracted from the Web of Science database during the period from 2001 to 2020. The results showed that the publication output has been growing strongly. An analysis on the subject, journal, country, and institution was carried out to demonstrate the development and evolution of this research branch during the two decades. According to high-frequency keywords analysis, external factors (e.g., soil physicochemical properties) and internal factors (e.g., crop genotype) can affect heavy metal threshold values in the soil-crop system. The current methods mainly include the Point model (e.g., evaluation factor method), the Probability model (e.g., species sensitivity distribution method), and the Empirical model (e.g., ecological environment effect method). A threshold study can be applicable to the risk assessment for soil heavy metal contamination in order to determinate the soil pollution degree and its spatial and temporal distribution characteristics. Moreover, challenges and prospects of the study of heavy metal threshold values are proposed, indicating that research should focus on the relationships between human health risks and the established threshold values of heavy metals in the soil, long-term field trials and bioavailability of heavy metals for the derivation of the thresholds, and the establishment of more scientific and rational soil environmental benchmarks.
C1 [Du, Zhaolin; Lin, Dasong; Li, Yang; Chen, Hongan; Qin, Li; An, Yi] Minist Agr & Rural Affairs, Agroenvironm Protect Inst, Tianjin 300191, Peoples R China.
   [Li, Haifeng] Beijing Municipal Key Lab Agr Environm Monitoring, Beijing 100097, Peoples R China.
   [Dou, Weiqiang] China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China.
RP Qin, L; An, Y (通讯作者)，Minist Agr & Rural Affairs, Agroenvironm Protect Inst, Tianjin 300191, Peoples R China.
EM carlcarl1988@163.com; lindasong608@126.com; haifli@126.com;
   liy0128@163.com; 18702229081@163.com; dwq18513262537@126.com;
   ql-tj@163.com; simon8601@126.com
OI Qin, Li/0000-0002-4914-0165
FU Open Project Foundation in Beijing Research Center for Agricultural
   Standards and Testing [ZBZX202104]; National Natural Science Foundation
   of China [42107081]
FX This research was funded by the Open Project Foundation in Beijing
   Research Center for Agricultural Standards and Testing (ZBZX202104) and
   the National Natural Science Foundation of China (No. 42107081).
CR Antoniadis V, 2019, ENVIRON INT, V124, P79, DOI 10.1016/j.envint.2018.12.053
   Aschner M, 2021, ENVIRON RES, V194, DOI 10.1016/j.envres.2020.110675
   Bezak N, 2021, ENVIRON RES, V197, DOI 10.1016/j.envres.2021.111087
   Bigalke M, 2017, ENVIRON POLLUT, V221, P85, DOI 10.1016/j.envpol.2016.11.035
   Birch GF, 2017, SCI TOTAL ENVIRON, V580, P813, DOI 10.1016/j.scitotenv.2016.12.028
   Bolan NS, 2004, CRIT REV ENV SCI TEC, V34, P291, DOI 10.1080/10643380490434128
   Cao HC, 2009, STOCH ENV RES RISK A, V23, P57, DOI 10.1007/s00477-007-0195-1
   Chen CM, 2014, EXPERT OPIN BIOL TH, V14, P1295, DOI 10.1517/14712598.2014.920813
   Ding CF, 2018, SCI TOTAL ENVIRON, V619, P700, DOI 10.1016/j.scitotenv.2017.11.137
   Ding CF, 2014, ECOTOX ENVIRON SAFE, V108, P179, DOI 10.1016/j.ecoenv.2014.07.006
   Doabi SA, 2018, ECOTOX ENVIRON SAFE, V163, P153, DOI 10.1016/j.ecoenv.2018.07.057
   Du ZL, 2021, CHEMOSPHERE, V268, DOI 10.1016/j.chemosphere.2020.128833
   Galhardi JA, 2020, CHEMOSPHERE, V261, DOI 10.1016/j.chemosphere.2020.127679
   Gillet S, 2002, EUR J SOIL SCI, V53, P529, DOI 10.1046/j.1365-2389.2002.00479.x
   Grzebisz W, 2001, POL J ENVIRON STUD, V10, P269
   Guala SD, 2010, ECOL MODEL, V221, P1148, DOI 10.1016/j.ecolmodel.2010.01.003
   Hani A, 2011, ENVIRON MONIT ASSESS, V176, P677, DOI 10.1007/s10661-010-1612-3
   Harmanescu M, 2011, CHEM CENT J, V5, DOI 10.1186/1752-153X-5-64
   He K, 2019, SCI TOTAL ENVIRON, V670, P236, DOI 10.1016/j.scitotenv.2019.03.184
   Hu BF, 2020, ENVIRON POLLUT, V266, DOI 10.1016/j.envpol.2020.114961
   Hu BF, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15040710
   Hu WY, 2017, CHEMOSPHERE, V167, P82, DOI 10.1016/j.chemosphere.2016.09.122
   Huang JJ, 2021, ENVIRON IMPACT ASSES, V91, DOI 10.1016/j.eiar.2021.106637
   Ibezim-Ezeani MU, 2020, INT J ENVIRON AN CH, DOI 10.1080/03067319.2020.1832482
   Irem S, 2019, CHEMOSPHERE, V225, P104, DOI 10.1016/j.chemosphere.2019.02.202
   Ispas G, 2018, CARPATH J EARTH ENV, V13, P505, DOI 10.26471/cjees/2018/013/043
   Jain P, 2005, WASTE MANAGE, V25, P25, DOI 10.1016/j.wasman.2004.08.009
   Jia ZM, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-21569-6
   Keller A, 2003, EUR J AGRON, V20, P181, DOI 10.1016/S1161-0301(03)00075-3
   Kumar V, 2020, INT J SEDIMENT RES, V35, P516, DOI 10.1016/j.ijsrc.2020.03.012
   Li CX, 2017, ENVIRON SCI POLLUT R, V24, P24733, DOI 10.1007/s11356-017-0440-1
   Li K, 2019, SCI TOTAL ENVIRON, V692, P1022, DOI 10.1016/j.scitotenv.2019.07.091
   Li PY, 2014, ENVIRON MONIT ASSESS, V186, P1385, DOI 10.1007/s10661-013-3461-3
   Li X, 2020, ENVIRON SCI POLLUT R, V27, P14322, DOI 10.1007/s11356-020-08176-7
   Li X, 2017, ENVIRON SCI POLLUT R, V24, P17103, DOI 10.1007/s11356-017-9294-9
   Li Y, 2008, J ENVIRON SCI-CHINA, V20, P607, DOI 10.1016/S1001-0742(08)62101-4
   Liu H, 2009, ENVIRON MONIT ASSESS, V152, P25, DOI 10.1007/s10661-008-0293-7
   Liu HY, 2005, SCI TOTAL ENVIRON, V339, P153, DOI 10.1016/j.scitotenv.2004.07.030
   Liu XM, 2006, ENVIRON POLLUT, V141, P257, DOI 10.1016/j.envpol.2005.08.048
   Naz A, 2020, ARAB J GEOSCI, V13, DOI 10.1007/s12517-020-06216-7
   Negahban S, 2021, ENVIRON RES, V192, DOI 10.1016/j.envres.2020.110305
   Oorts K, 2006, ENVIRON TOXICOL CHEM, V25, P836, DOI 10.1897/04-672R.1
   Ordonez A, 2011, J ENVIRON MONITOR, V13, P128, DOI 10.1039/c0em00132e
   Ouyang W, 2018, SCI TOTAL ENVIRON, V637, P208, DOI 10.1016/j.scitotenv.2018.04.434
   Ozturk E, 2020, ARAB J GEOSCI, V13, DOI 10.1007/s12517-020-06200-1
   Paschke MW, 2002, ENVIRON TOXICOL CHEM, V21, P2692, DOI 10.1897/1551-5028(2002)021<2692:CTTFIR>2.0.CO;2
   Pedrot M, 2008, J COLLOID INTERF SCI, V325, P187, DOI 10.1016/j.jcis.2008.05.019
   Puthusseri RM, 2021, J ENVIRON MANAGE, V298, DOI 10.1016/j.jenvman.2021.113492
   Qu JH, 2022, J HAZARD MATER, V423, DOI 10.1016/j.jhazmat.2021.127043
   Qu JH, 2021, J HAZARD MATER, V403, DOI 10.1016/j.jhazmat.2020.123607
   Qu JH, 2021, SCI TOTAL ENVIRON, V752, DOI 10.1016/j.scitotenv.2020.141854
   Queiroz HM, 2018, SCI TOTAL ENVIRON, V637, P498, DOI 10.1016/j.scitotenv.2018.04.370
   Reimann C, 2018, APPL GEOCHEM, V88, P302, DOI 10.1016/j.apgeochem.2017.01.021
   Relic D, 2010, ENVIRON MONIT ASSESS, V171, P229, DOI 10.1007/s10661-009-1274-1
   Rivera-Hernandez JR, 2016, ARCH ENVIRON CON TOX, V70, P348, DOI 10.1007/s00244-015-0253-5
   Rutigliano FA, 2019, SCI TOTAL ENVIRON, V691, P195, DOI 10.1016/j.scitotenv.2019.07.037
   Shi YL, 2009, PROG CHEM, V21, P369
   Smolders E, 2009, ENVIRON TOXICOL CHEM, V28, P1633, DOI 10.1897/08-592.1
   Song WE, 2015, J INTEGR AGR, V14, P1845, DOI 10.1016/S2095-3119(14)60926-6
   Sun FF, 2013, J SCI FOOD AGR, V93, P1967, DOI 10.1002/jsfa.6000
   Taati A, 2020, ENVIRON RES, V187, DOI 10.1016/j.envres.2020.109577
   Toth G, 2016, SCI TOTAL ENVIRON, V565, P1054, DOI 10.1016/j.scitotenv.2016.05.115
   Dung TTT, 2013, REV ENVIRON SCI BIO, V12, P335, DOI 10.1007/s11157-013-9315-1
   Varol M, 2021, ENVIRON RES, V202, DOI 10.1016/j.envres.2021.111806
   Vilavert L, 2015, BIOL TRACE ELEM RES, V163, P48, DOI 10.1007/s12011-014-0140-3
   Wang ZY, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15061101
   Wheeler JR, 2002, MAR POLLUT BULL, V45, P192, DOI 10.1016/S0025-326X(01)00327-7
   Xiao R, 2019, ENVIRON POLLUT, V248, P349, DOI 10.1016/j.envpol.2019.02.045
   Yotova G, 2018, SCI TOTAL ENVIRON, V643, P1297, DOI 10.1016/j.scitotenv.2018.06.237
   Zabel TF, 1999, J CHART INST WATER E, V13, P436
   Zhang HW, 2021, ENVIRON RES, V202, DOI 10.1016/j.envres.2021.111702
   Zhang S, 2016, SCI TOTAL ENVIRON, V569, P1595, DOI 10.1016/j.scitotenv.2016.07.007
NR 72
TC 0
Z9 0
U1 13
U2 13
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD JUN
PY 2022
VL 19
IS 11
AR 6561
DI 10.3390/ijerph19116561
PG 12
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA 1Z1RY
UT WOS:000808611300001
PM 35682145
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Kao, XX
   Wang, WS
   Kao, QY
   Zhang, JM
AF Kao, Xiaoxuan
   Wang, Wensheng
   Kao, Qingyun
   Zhang, Jianmin
TI Visualization of energy-environment-economy system research
   characteristics and hotspots evolution trends based on CiteSpace
SO ENVIRONMENTAL RESEARCH COMMUNICATIONS
LA English
DT Article
DE energy-environment-economy; 3E system; scientific knowledge map; visual
   analysis
ID EMISSION TRADING SCHEME; WORLDWIDE RESEARCH; CARBON EMISSIONS; GROWTH
   EVIDENCE; CLIMATE-CHANGE; CHINA; EFFICIENCY; INDUSTRY; IMPACT; POLICY
AB With the recovery of the world economy, the increase in energy demand, global environmental pollution, climate change, and many other contradictory issues, after China proposed the Double-Carbon goal, research on the Energy-Environment-Economy system has attracted much attention. Through the visual analysis of Energy-Environment-Economy research literature, this paper provides scholars committed to this field with research characteristics and hotspots evolution trends in this field, and provides a direction guidance and practical method reference for subsequent research on Energy-Environment-Economy system-related issues. This study selected the literature from the Energy-Environment-Economy system field of Web of Science and CNKI database for nearly 20 years, uses CiteSpace to conduct research bibliometrics and visual analysis, and explores the main characteristics, research hotspots, and evolution trends of Energy-Environment-Economy system in Chinese and English. The research found that: (1)there are many research results on Energy-Environment-Economy systems, and the research interest in the field of Energy-Environment-Economy systems continues to rise; (2)the number of documents and research institutions in China ranks first in the world, and the influence of research results needs to be improved; (3) the hot topics of research topics in each period are bright, research hotspots evolve from single subsystems to systemic issues; (4) the attention to environment and climate change has increased significantly, and the evolutionary characteristics of research hotspots are in line with the development trend in reality.
C1 [Kao, Xiaoxuan; Wang, Wensheng; Zhang, Jianmin] China Univ Min & Technol Beijing, Sch Management, Beijing 100083, Peoples R China.
   [Kao, Qingyun] Northeast Forestry Univ, Coll Econ & Management, Harbin 150040, Heilongjiang, Peoples R China.
RP Wang, WS (通讯作者)，China Univ Min & Technol Beijing, Sch Management, Beijing 100083, Peoples R China.
EM kao_xiaoxuan@student.cumtb.edu.cn; wws@cumtb.edu.cn;
   1259498426@nefu.edu.cn; 17865613218@163.com
OI Kao, Xiaoxuan/0000-0003-2133-9129
FU National Social Science Foundation of China [16BJY054]
FX This work was supported by the National Social Science Foundation of
   China (Grant No. 16BJY054).
CR Archambault E, 2009, PRO INT CONF SCI INF, V1, P80
   Badeeb RA, 2020, RESOUR POLICY, V68, DOI 10.1016/j.resourpol.2020.101694
   Bell J., 2021 RESPONSE CLIMAT
   Cang DB., 2019, J CENTRAL U FINANCE, V03, P85, DOI [10.19681/j.cnki.jcufe.2019.03.007, DOI 10.19681/J.CNKI.JCUFE.2019.03.007]
   Cang DB., 2020, J APPL STAT MANAGEME, V39, P1, DOI [10.13860/j.cnki.sltj.20190807-002, DOI 10.13860/J.CNKI.SLTJ.20190807-002]
   Cang DB, 2017, J CENTRAL U FINANCE, V10, P78, DOI 1000-1549 ( 2017) 10-78-07
   Cang DB, 2017, J IND TECHNICAL EC, V36, P27, DOI [10.3969/j.issn.1004-910X.2017.12.004, DOI 10.3969/J.ISSN.1004-910X.2017.12.004]
   [仓定帮 Cang Dingbang], 2020, [中国管理科学, Chinese Journal of Management Science], V28, P146
   Chang H, 2018, J QINGDAO U NATURAL, V31, P133, DOI [10.3969/j.issn.10061037.2018.02.25, DOI 10.3969/J.ISSN.10061037.2018.02.25]
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen JD, 2020, J CLEAN PROD, V276, DOI 10.1016/j.jclepro.2020.123848
   Chen Y., 2019, J XIAN JIAOTONG U SO, V1, P73, DOI [10.15896/j.xjtuskxb.201901009, DOI 10.15896/J.XJTUSKXB.201901009]
   Deng YY., 2006, GANSU SOCIAL SCI, V03, P209, DOI [10.15891/j.cnki.cn62-1093/c.2006.03.053, DOI 10.15891/J.CNKI.CN62-1093/C.2006.03.053]
   Du ZL, 2019, RESOUR CONSERV RECY, V143, P17, DOI 10.1016/j.resconrec.2018.12.007
   Fan FY, 2013, ECOLOGICAL EC, V12, P42
   Fischer EM, 2021, NAT CLIM CHANGE, V11, P689, DOI 10.1038/s41558-021-01092-9
   Guo ZY., 2014, STAT DECISION, V24, P61, DOI [10.13546/j.cnki.tjyjc.2014.24.017, DOI 10.13546/J.CNKI.TJYJC.2014.24.017]
   Hafeez M, 2020, CARBON MANAG, V11, P139, DOI 10.1080/17583004.2020.1721974
   Hafeez M, 2019, ENVIRON SCI POLLUT R, V26, P25026, DOI 10.1007/s11356-019-05757-z
   Hafeez M, 2019, ENERGIES, V12, DOI 10.3390/en12071358
   Hsiang S, 2017, SCIENCE, V356, P1362, DOI 10.1126/science.aal4369
   Jiang Y, 2019, J U ELECT SCI TECHNO, V21, P92, DOI [10.14071/j.1008-8105(2018)-3012, DOI 10.14071/J.1008-8105(2018)-3012]
   Jiang Y., 2016, SOFT SCI, V30, P43, DOI [10.13956/j.ss.1001-8409.2016.06.10, DOI 10.13956/J.SS.1001-8409.2016.06.10]
   Jiang Y., 2017, COMMERCIAL RES, V08, P158, DOI [10.13902/j.cnki.syyj.2017.08.019, DOI 10.13902/J.CNKI.SYYJ.2017.08.019]
   Leal PH, 2021, ENVIRON DEV SUSTAIN, V23, P7412, DOI 10.1007/s10668-020-00923-7
   Li J., 2016, CITESPACE SCI TEXT M
   Li JL, 2017, APPL ENERG, V200, P347, DOI 10.1016/j.apenergy.2017.05.087
   Li W, 2017, MITIG ADAPT STRAT GL, V22, P973, DOI 10.1007/s11027-016-9710-3
   Li W, 2017, ENERGY, V119, P800, DOI 10.1016/j.energy.2016.11.059
   Lin BQ, 2019, ENERG ECON, V83, P264, DOI 10.1016/j.eneco.2019.07.010
   Lin BQ, 2019, ENERG ECON, V84, DOI 10.1016/j.eneco.2019.104496
   Lin BQ, 2019, ENERG ECON, V78, P301, DOI 10.1016/j.eneco.2018.11.030
   Lin BQ, 2019, SCI TOTAL ENVIRON, V667, P658, DOI 10.1016/j.scitotenv.2019.02.405
   Lin BQ, 2019, SCI TOTAL ENVIRON, V654, P525, DOI 10.1016/j.scitotenv.2018.11.106
   Lin BQ, 2018, ENERGY, V159, P558, DOI 10.1016/j.energy.2018.06.167
   Lin BQ, 2018, J CLEAN PROD, V197, P25, DOI 10.1016/j.jclepro.2018.06.094
   Lin BQ, 2018, J CLEAN PROD, V188, P312, DOI 10.1016/j.jclepro.2018.03.293
   Lin BQ, 2018, ENERGY, V149, P190, DOI 10.1016/j.energy.2018.02.039
   Lin BQ, 2017, J CLEAN PROD, V153, P331, DOI 10.1016/j.jclepro.2017.01.064
   Lin BQ, 2011, ENERG ECON, V33, P273, DOI 10.1016/j.eneco.2010.07.005
   Lin BQ, 2016, EC ANAL CHINA PETROL, V07, P5
   Liu HX, 2016, ENERG BUILDINGS, V129, P150, DOI 10.1016/j.enbuild.2016.07.058
   Liu J, 2016, CHINESE J POPULATION, V03, P23
   Lu J., 2020, J XIAN JIAOTONG U, V40, P104, DOI [10.15896/j.xjtuskxb.202002012, DOI 10.15896/J.XJTUSKXB.202002012]
   Lu J., 2019, J CHINA UNIV GEOSCI, V19, P128, DOI [10.16493/j.cnki.42-1627/c.2019.06.012, DOI 10.16493/J.CNKI.42-1627/C.2019.06.012]
   Lu J., 2020, J FINANCE EC, V46, P109, DOI [10.16538/j.cnki.jfe.2020.04.008, DOI 10.16538/J.CNKI.JFE.2020.04.008]
   Lu J., 2016, ECON GEOGR, V36, P42, DOI [10.15957/j.cnki.jjdl.2016.09.006, DOI 10.15957/J.CNKI.JJDL.2016.09.006]
   [逯进 Lu JIN], 2019, [资源科学, Resources Science], V41, P2107
   [逯进 Lu Jin], 2017, [中国人口·资源与环境, China Population Resources and Environment], V27, P60
   Ruiz-Real JL, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15122699
   Origin Energy Ltd, 2021, PATHWAYS NET ZERO WW
   Peng SJ., 2017, J XIAMEN U ARTS SOCI, V01, P1
   Peng SJ., 2020, J XIAMEN U ARTS SOCI, V04, P85
   Peng YL, 2019, ENVIRON SCI POLLUT R, V26, P28919, DOI 10.1007/s11356-019-06071-4
   Shahbaz M, 2020, ENERGY, V207, DOI 10.1016/j.energy.2020.118162
   Song ML, 2019, PROCESS SAF ENVIRON, V121, P281, DOI 10.1016/j.psep.2018.10.023
   Song ML, 2014, ECOL INDIC, V36, P59, DOI 10.1016/j.ecolind.2013.07.007
   Song ML, 2013, J CLEAN PROD, V42, P42, DOI 10.1016/j.jclepro.2012.11.010
   Su J., 2013, ECON GEOGR, V33, P19, DOI [DOI 10.15957/J.CNKI.JJDL.2013.09.004, 10.15957/j.cnki.jjdl.2013.09.004]
   [孙玉阳 Sun Yuyang], 2019, [干旱区资源与环境, Journal of Arid Land Resources and Environment], V33, P135
   Uribe-Toril J, 2019, ENERGIES, V12, DOI 10.3390/en12061120
   Wang L, 2020, J ENVIRON MANAGE, V268, DOI 10.1016/j.jenvman.2020.110712
   Wang SH, 2018, INT J PROD RES, V56, P2963, DOI 10.1080/00207543.2018.1454613
   Wang SY., 2016, J CAPITAL U EC BUSIN, V18, P26, DOI [10.13504/j.cnki.issn1008-2700.2016.01.003, DOI 10.13504/J.CNKI.ISSN1008-2700.2016.01.003]
   Wang WS., 2019, J COMMERCIAL EC, V18, P112
   Wesseh PK, 2018, ENERG POLICY, V118, P1, DOI 10.1016/j.enpol.2018.03.031
   Wesseh PK, 2016, APPL ENERG, V167, P34, DOI 10.1016/j.apenergy.2016.01.031
   Xiong BB, 2017, INT J CLIM CHANG STR, V9, P666, DOI 10.1108/IJCCSM-08-2016-0112
   Yan XF, 2015, SEEKER, V11, P44, DOI [10.16059/j.cnki.cn43-1008/c.2015.11.009, DOI 10.16059/J.CNKI.CN43-1008/C.2015.11.009]
   Yang X, 2017, EC RES J, V52, P60
   Zhang C, 2016, MODERN BUSINESS, V12, P58, DOI [10.14097/j.cnki.5392/2016.12.028, DOI 10.14097/J.CNKI.5392/2016.12.028]
   Zhang L., 2020, J HARBIN I TECHNOLOG, V22, P152, DOI [10.16822/j.cnki.hitskb.20200514.013, DOI 10.16822/J.CNKI.HITSKB.20200514.013]
   Zhang XM, 2014, ECOLOGICAL EC, V30, P34, DOI 1671-4407(2014)05-034-07
   Zhang YZ., 2015, J BEIJING I TECHNOLO, V17, P46, DOI [10.15918/j.jbitss1009-3370.2015.0506, DOI 10.15918/J.JBITSS1009-3370.2015.0506]
   Zhao YN, 2020, COAL EC RES, V40, P73, DOI [10.13202/j.cnki.cer.2020.08.010, DOI 10.13202/J.CNKI.CER.2020.08.010]
   Zheng TL, 2017, ENVIRON SCI POLLUT R, V24, P27613, DOI 10.1007/s11356-017-0696-5
   [周肖肖 Zhou Xiaoxiao], 2015, [中国人口·资源与环境, China Population Resources and Environment], V25, P35
NR 77
TC 0
Z9 0
U1 23
U2 23
PU IOP Publishing Ltd
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 2515-7620
J9 ENVIRON RES COMMUN
JI Environ. Res. Commun.
PD MAY 1
PY 2022
VL 4
IS 5
AR 055004
DI 10.1088/2515-7620/ac637f
PG 16
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 1A2BT
UT WOS:000791569400001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, P
   Zhu, FW
   Song, HY
   Hou, JH
   Zhang, JL
AF Wang, Peng
   Zhu, Fang-Wei
   Song, Hao-Yang
   Hou, Jian-Hua
   Zhang, Jin-Lan
TI Visualizing the Academic Discipline of Knowledge Management
SO SUSTAINABILITY
LA English
DT Article
DE bibliometric; knowledge management; keywords analysis; intellectual
   structure; emerging trends; knowledge mapping
ID COCITATION; NETWORK; SCIENCE; SYSTEMS; TRENDS; PERSPECTIVE; BENEFITS;
   TAXONOMY; FIRM
AB The aim of this paper was to evaluate the research status of knowledge management (KM) and identify the characteristics of KM in the literature. We selected and studied in detail 7628 original research articles from the Web of Science from 1974 to 2017. Although many studies have contributed to the evolution of the KM domain, our results showed that a comprehensive bibliometric and visualization investigation was required. The literature on KM has grown rapidly since the 1970s. The United States of America, as the original contributing country, has also internationally collaborated the most in this field of study. The National Cheng Kung University has made the highest number of contributions. The majority of authors contributed a small number of publications. Additionally, the most common category in KM research was management. The main publications for KM research include Journal of Knowledge Management, and Knowledge Management Research & Practice. A keywords analysis determined that "knowledge sharing", "innovation", "ontology", and "knowledge management" were consistent hotspots in knowledge management research. Through a document co-citation analysis, the intellectual structures of knowledge management were defined, and four emerging trends were identified that focus on new phenomenon, the practice of knowledge management, small and medium enterprises (SMEs) management based on knowledge perspective, innovation and performance, and big data-enabled KM. We also provide eight research questions for future studies. Our results will benefit academics, researchers, and research students who want to rapidly obtain an overview of knowledge management research. This study can also be a starting point for communication between academics and practitioners.
C1 [Wang, Peng; Zhu, Fang-Wei; Song, Hao-Yang; Zhang, Jin-Lan] Dalian Univ Technol, Fac Management & Econ, Dalian 116024, Peoples R China.
   [Hou, Jian-Hua] Dalian Univ, Res Ctr Sci Technol & Soc, Dalian 116622, Peoples R China.
RP Zhu, FW (通讯作者)，Dalian Univ Technol, Fac Management & Econ, Dalian 116024, Peoples R China.
EM wangpeng26893@126.com; zhufangwei@mail.dlut.edu.cn;
   haoyang@mail.dlut.edu.cn; hqzhixing@gmail.com;
   zhangjinlan530@mail.dlut.edu.cn
FU National Natural Science Foundation of China [71372085]
FX This research is funded by National Natural Science Foundation of China
   (71372085). The authors are very grateful for the valuable comments and
   suggestions of the anonymous reviewers, which significantly improved the
   article.
CR Akhavan P, 2016, SCIENTOMETRICS, V107, P1249, DOI 10.1007/s11192-016-1938-x
   Alavi M, 2001, MIS QUART, V25, P107, DOI 10.2307/3250961
   Anderson ER, 2010, MULTIVARIATE DATA AN
   Andreeva T, 2012, J KNOWL MANAG, V16, P617, DOI 10.1108/13673271211246185
   Barclay B. R. O., 2009, KNOWLEDGE PRAXIS
   Bhatt G. D., 2004, J KNOWL MANAG, V1, P15
   Borgatti S.P., 2002, UCINET WINDOWS SOFTW
   Braun T, 2003, SCIENTOMETRICS, V58, P183, DOI 10.1023/A:1025439910278
   Butler F. A., 2001, LANG TEST, V18, P409, DOI DOI 10.1177/026553220101800406
   CALLON M, 1991, SCIENTOMETRICS, V22, P155, DOI 10.1007/BF02019280
   Centobelli P, 2018, EXPERT SYST APPL, V91, P107, DOI 10.1016/j.eswa.2017.08.032
   Centobelli P, 2017, KNOWL MAN RES PRACT, V15, P294, DOI 10.1057/s41275-017-0054-x
   Centobelli P, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9030361
   Cerchione R, 2017, INT J INFORM MANAGE, V37, P1551, DOI 10.1016/j.ijinfomgt.2016.10.007
   Cerchione R, 2016, INT J PROD ECON, V182, P276, DOI 10.1016/j.ijpe.2016.09.006
   Cerchione R, 2016, KNOWL MAN RES PRACT, V14, P169, DOI 10.1057/kmrp.2015.12
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Cobos C, 2014, INFORM SCIENCES, V281, P248, DOI 10.1016/j.ins.2014.05.047
   Connelly CE, 2012, J ORGAN BEHAV, V33, P64, DOI 10.1002/job.737
   Darroch J., 2005, Journal of Knowledge Management, V9, P101, DOI 10.1108/13673270510602809
   Durst S, 2012, J KNOWL MANAG, V16, P879, DOI 10.1108/13673271211276173
   Dwivedi YK, 2011, INFORM SYST MANAGE, V28, P43, DOI 10.1080/10580530.2011.536112
   Ensign PC, 2010, MIT SLOAN MANAGE REV, V51, P79
   Esposito C, 2015, KNOWL-BASED SYST, V79, P3, DOI 10.1016/j.knosys.2014.05.003
   Fensel D, 2002, COMPUTER, V35, P56, DOI 10.1109/MC.2002.1046975
   Fink K, 2009, INT J KNOWL MANAG, V5, P46, DOI 10.4018/jkm.2009010104
   FREUDENTHAL G, 1984, SOC STUD SCI, V14, P285, DOI 10.1177/030631284014002010
   Garfield E, 2004, J INF SCI, V30, P119, DOI 10.1177/0165551504042802
   Gu YN, 2004, SCIENTOMETRICS, V61, P171, DOI 10.1023/B:SCIE.0000041647.01086.f4
   Haas MR, 2007, STRATEG MANAGE J, V28, P1133, DOI 10.1002/smj.631
   HENRY N, 1975, PUBLIC ADMIN REV, V35, P572, DOI 10.2307/974273
   Inkinen H, 2016, J KNOWL MANAG, V20, P230, DOI 10.1108/JKM-09-2015-0336
   Krogh G.V., 2012, J STRATEGIC INF SYST, V21, P154
   Kuan Yew Wong, 2005, Journal of Knowledge Management, V9, P64, DOI 10.1108/13673270510602773
   Lee M. R., 2007, VISUALIZING TRENDS K, P362
   Lee MR, 2012, KNOWL-BASED SYST, V28, P47, DOI 10.1016/j.knosys.2011.11.016
   Leiponen A, 2010, STRATEGIC MANAGE J, V31, P224, DOI 10.1002/smj.807
   Leydesdorff L, 2004, SCIENTOMETRICS, V60, P159, DOI 10.1023/B:SCIE.0000027678.31097.e0
   Li CL, 2013, SCIENTOMETRICS, V94, P95, DOI 10.1007/s11192-012-0858-7
   Manley K, 2009, J CONSTR ENG M, V135, P764, DOI 10.1061/(ASCE)CO.1943-7862.0000030
   Marks MS, 2013, TRAFFIC, V14, P1, DOI 10.1111/tra.12023
   Money W., 2007, KNOWLEDGE MANAGEMENT, P233
   Nikabadi S.M., 2014, IRAN J INF PROCESS M, V28, P611
   Nissen ME, 1998, MIS QUART, V22, P509, DOI 10.2307/249553
   Persson O., 2009, CELEBRATING SCHOLARL, P9
   Pi SM, 2013, COMPUT HUM BEHAV, V29, P1971, DOI 10.1016/j.chb.2013.04.019
   Pirro G, 2010, FUTURE GENER COMP SY, V26, P38, DOI 10.1016/j.future.2009.06.004
   Rinia EJ, 1998, RES POLICY, V27, P95, DOI 10.1016/S0048-7333(98)00026-2
   Romo-Fernandez LM, 2013, SCIENTOMETRICS, V97, P743, DOI 10.1007/s11192-013-1009-5
   Serenko A, 2015, J KNOWL MANAG, V19, P401, DOI 10.1108/JKM-06-2014-0220
   Serenko A, 2013, J KNOWL MANAG, V17, P773, DOI 10.1108/JKM-05-2013-0166
   Serenko A, 2009, J KNOWL MANAG, V13, P4, DOI 10.1108/13673270910931125
   SKUCE D, 1995, INT J HUM-COMPUT ST, V42, P413, DOI 10.1006/ijhc.1995.1019
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Small H, 2009, SCIENTOMETRICS, V79, P365, DOI 10.1007/s11192-009-0424-0
   Spender JC, 1996, STRATEGIC MANAGE J, V17, P45, DOI 10.1002/smj.4250171106
   Styhre A., 2003, UNDERSTANDING KNOWLE
   Takeda Y, 2009, SCIENTOMETRICS, V80, P23, DOI 10.1007/s11192-007-1897-3
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   von Nordenflycht A, 2010, ACAD MANAGE REV, V35, P155, DOI 10.5465/AMR.2010.45577926
   Wagner CS, 2005, RES POLICY, V34, P1608, DOI 10.1016/j.respol.2005.08.002
   Wallace DP, 2011, INT J INFORM MANAGE, V31, P14, DOI 10.1016/j.ijinfomgt.2010.10.002
   WHITE HD, 1981, J AM SOC INFORM SCI, V32, P163, DOI 10.1002/asi.4630320302
   WHITTAKER J, 1989, SOC STUD SCI, V19, P473, DOI 10.1177/030631289019003004
   Wiig KM, 1997, EXPERT SYST APPL, V13, P1, DOI 10.1016/S0957-4174(97)00018-3
   Xu WW, 2015, QUAL QUANT, V49, P1361, DOI 10.1007/s11135-014-0051-6
   Zhang J, 2009, IEEE T VIS COMPUT GR, V15, P1153, DOI 10.1109/TVCG.2009.202
NR 69
TC 17
Z9 17
U1 2
U2 36
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAR
PY 2018
VL 10
IS 3
AR 682
DI 10.3390/su10030682
PG 28
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA GA8DA
UT WOS:000428567100109
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Wang, SP
   Yang, KL
   Wang, YW
   Xu, LY
   Gu, YW
   Fan, Q
   Zhu, M
   Wang, Q
   Guo, K
   Ren, SW
   Zhao, DQ
AF Wang, Shaopei
   Yang, Kaili
   Wang, Yawen
   Xu, Liyan
   Gu, Yuwei
   Fan, Qi
   Zhu, Meng
   Wang, Qing
   Guo, Kai
   Ren, Shengwei
   Zhao, Dongqing
TI Trends in research on corneal cross linking from 2001 to 2020: a
   bibliometric analysis
SO CLINICAL AND EXPERIMENTAL OPTOMETRY
LA English
DT Article; Early Access
DE Bibliometric analysis; corneal cross linking; keratoconus; mapping
   knowledge domain; VOSviewer
ID REFRACTIVE SURGERY; RISK-FACTORS; KERATOCONUS; ECTASIA; RIBOFLAVIN;
   KERATITIS; ULTRAVIOLET; EFFICACY; OUTCOMES; LASIK
AB Background The purpose of this study was to map the publishing trend on CXL research and explore the research hotspots. Methods A bibliometric analysis was performed using the Web of Science Core Collection to investigate the publishing trend on CXL research. VOSviewer was used to build the knowledge map to visualise the number of annual publications, distribution of countries and institutions, international cooperation, author productivity, source journals and research hotspots in the field of CXL. Results A total of 2061 peer-reviewed articles on CXL research were collected from 2001 to 2020, and the annual research production increased over time. The United States was the country with the largest number of published articles, and the University of Zurich was the most active institution. Hafezi F published the largest number of articles on CXL, while Cornea was the journal with the largest number of studies on CXL. The most frequently cited references mainly focus on CXL in the treatment of keratoconus. The keywords were divided in 5 categories: 1) CXL mechanism, 2) ectasia diseases and refractive surgery, 3) corneal biomechanics, 4) efficacy evaluation, 5) treatment of infectious keratitis. Conclusion The quantity and quality of articles on CXL were evaluated using bibliometric techniques by extracting the data from the Web of Science Core Collection. The research hotspots could provide insights on CXL research, providing valuable information for clinicians to perform research in this field and find potential partners.
C1 [Wang, Shaopei; Zhao, Dongqing] Xinxiang Med Univ, Henan Prov Peoples Hosp, Henan Eye Hosp, Henan Eye Inst, Zhengzhou, Peoples R China.
   [Yang, Kaili; Xu, Liyan; Gu, Yuwei; Fan, Qi; Ren, Shengwei; Zhao, Dongqing] Zhengzhou Univ, Henan Univ Peoples Hosp, Peoples Hosp, Henan Eye Inst,Henan Eye Hosp,Henan Prov Peoples, Zhengzhou, Peoples R China.
   [Wang, Yawen; Wang, Qing] Henan Univ Peoples Hosp, Henan Prov Peoples Hosp, Henan Eye Hosp, Henan Eye Inst, Zhengzhou, Peoples R China.
   [Zhu, Meng; Guo, Kai] Zhengzhou Univ Peoples Hosp, Henan Prov Peoples Hosp, Henan Eye Hosp, Zhengzhou, Peoples R China.
   [Zhu, Meng; Guo, Kai] Henan Eye Inst, Zhengzhou, Peoples R China.
RP Zhao, DQ (通讯作者)，Xinxiang Med Univ, Henan Prov Peoples Hosp, Henan Eye Hosp, Henan Eye Inst, Zhengzhou, Peoples R China.; Ren, SW; Zhao, DQ (通讯作者)，Zhengzhou Univ, Henan Univ Peoples Hosp, Peoples Hosp, Henan Eye Inst,Henan Eye Hosp,Henan Prov Peoples, Zhengzhou, Peoples R China.
EM ysgzz2018@163.com; ykszdq@163.com
FU National Natural Science Foundation of China [81200664]; Basic Research
   and Cultivation Foundation for Young Teachers of Zhengzhou University
   [JC202051049]; Henan Provincial Medical Science Building Key Program
   [SBGJ202002028]; Henan Provincial Medical Science and Technology Joint
   Program [LHGJ20200066, LHGJ20210080]; Open Program of Shandong
   Provincial Key Laboratory of Ophthalmology [2018-04]; Special Program
   for Basic Research of Henan Eye Hospital [20JCZD003]; Henan Young Health
   Science and Technology Innovation Outstanding Program [YXKC2020023];
   Youth Special Program for Basic Research of Henan Eye Hospital
   [21JCQN006, 21JCQN008]
FX This research was supported by the National Natural Science Foundation
   of China [No. 81200664], Basic Research and Cultivation Foundation for
   Young Teachers of Zhengzhou University [No. JC202051049], Henan
   Provincial Medical Science Building Key Program [No. SBGJ202002028],
   Henan Provincial Medical Science and Technology Joint Program[No.
   LHGJ20200066, LHGJ20210080], Open Program of Shandong Provincial Key
   Laboratory of Ophthalmology [No. 2018-04], Special Program for Basic
   Research of Henan Eye Hospital [No. 20JCZD003], Henan Young Health
   Science and Technology Innovation Outstanding Program [No. YXKC2020023],
   Youth Special Program for Basic Research of Henan Eye Hospital [No.
   21JCQN006, 21JCQN008].
CR Abu Ameerh MA, 2019, INT OPHTHALMOL, V39, P1089, DOI 10.1007/s10792-018-0920-4
   Agarwal A, 2016, ASIAN J ANDROL, V18, P296, DOI 10.4103/1008-682X.171582
   Ali NQ, 2014, ASIA-PAC J OPHTHALMO, V3, P67, DOI 10.1097/APO.0b013e3182a4cf92
   Arboleda A, 2014, AM J OPHTHALMOL, V158, P64, DOI 10.1016/j.ajo.2014.04.007
   Boudry C, 2018, OCUL SURF, V16, P173, DOI 10.1016/j.jtos.2017.10.002
   Caporossi A, 2010, AM J OPHTHALMOL, V149, P585, DOI 10.1016/j.ajo.2009.10.021
   Cen YJ, 2020, MEDICINE, V99, DOI 10.1097/MD.0000000000020420
   Chen CM, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0223994
   Fu HZ, 2013, SCI TOTAL ENVIRON, V443, P757, DOI 10.1016/j.scitotenv.2012.11.061
   Green M, 2008, CORNEA, V27, P22, DOI 10.1097/ICO.0b013e318156caf2
   Iqbal M, 2020, ACTA OPHTHALMOL, V98, pE352, DOI 10.1111/aos.14275
   Jabbarvand M, 2021, EUR J OPHTHALMOL, V31, P1577, DOI 10.1177/1120672120944798
   Kankariya VP, 2020, INDIAN J OPHTHALMOL, V68, P2757, DOI 10.4103/ijo.IJO_1841_20
   Khairy HA, 2019, INT J OPHTHALMOL-CHI, V12, P1714, DOI 10.18240/ijo.2019.11.08
   Leonard BC, 2020, BIOENGINEERING-BASEL, V7, DOI 10.3390/bioengineering7010004
   Liang CC, 2018, SAGE OPEN MED, V6, DOI 10.1177/2050312118800199
   Lim L, 2018, OPEN OPHTHALMOL J, V12, P181, DOI 10.2174/1874364101812010181
   Mazzotta C, 2018, CORNEA, V37, P560, DOI 10.1097/ICO.0000000000001505
   Movahed SMA, 2021, SCI TOTAL ENVIRON, V785, DOI 10.1016/j.scitotenv.2021.147362
   Nicula C, 2019, J OPHTHALMOL, V2019, DOI 10.1155/2019/8285649
   Papaioannou L, 2016, CORNEA, V35, P62, DOI 10.1097/ICO.0000000000000644
   Richoz O, 2013, OPHTHALMOLOGY, V120, P1354, DOI 10.1016/j.ophtha.2012.12.027
   Romero L, 2019, FRONT PHARMACOL, V10, DOI 10.3389/fphar.2019.00564
   Saeed A, 2009, CORNEA, V28, P285, DOI 10.1097/ICO.0b013e3181877a52
   Schilde T, 2008, OPHTHALMOLOGE, V105, P165, DOI 10.1007/s00347-007-1587-9
   Sharif W, 2019, INT J OPHTHALMOL-CHI, V12, P333, DOI 10.18240/ijo.2019.02.22
   Spoerl E, 2004, CURR EYE RES, V29, P35, DOI 10.1080/02713680490513182
   Spoerl E, 1999, J REFRACT SURG, V15, P711
   Synnestvedt Marie B, 2005, AMIA Annu Symp Proc, P724
   Ting DSJ, 2019, OCUL SURF, V17, P624, DOI 10.1016/j.jtos.2019.08.006
   Turhan SA, 2020, J REFRACT SURG, V36, P265, DOI 10.3928/1081597X-20200302-01
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vastardis Iraklis, 2017, Open Ophthalmol J, V11, P217, DOI 10.2174/1874364101711010217
   Wollensak G, 2003, J CATARACT REFR SURG, V29, P1780, DOI 10.1016/S0886-3350(03)00407-3
   Wollensak G, 2003, AM J OPHTHALMOL, V135, P620, DOI 10.1016/S0002-9394(02)02220-1
   Wollensak G, 2006, CURR OPIN OPHTHALMOL, V17, P356, DOI 10.1097/01.icu.0000233954.86723.25
   Woodward MA, 2008, J CATARACT REFR SURG, V34, P383, DOI 10.1016/j.jcrs.2007.10.025
   Zhao FK, 2019, CORNEA, V38, P847, DOI 10.1097/ICO.0000000000001984
   Zheng MS, 2017, ENVIRON SCI POLLUT R, V24, P20409, DOI 10.1007/s11356-017-9711-0
   Zhu AY, 2019, OPHTHALMOL THER, V8, pS15, DOI 10.1007/s40123-019-00210-3
NR 40
TC 1
Z9 1
U1 5
U2 5
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0816-4622
EI 1444-0938
J9 CLIN EXP OPTOM
JI Clin. Exp. Optom.
DI 10.1080/08164622.2022.2038013
EA FEB 2022
PG 7
WC Ophthalmology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Ophthalmology
GA ZE4LO
UT WOS:000758856400001
PM 35188092
DA 2022-08-23
ER

PT J
AU Song, YY
   Qin, YZ
   Hao, WQ
   Liu, PK
   Li, JX
   Choudhury, FM
   Wang, X
   Zhang, QP
AF Song, Yanyan
   Qin, Yuzhou
   Hao, Wenqi
   Liu, Pengkai
   Li, Jianxin
   Choudhury, Farhana Murtaza
   Wang, Xin
   Zhang, Qingpeng
TI Optimizing subgraph matching over distributed knowledge graphs using
   partial evaluation
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE Partial evaluation; Subgraph matching; RDF graph
ID SPARQL QUERIES; ENGINE
AB The partial evaluation and assembly framework has recently been applied for processing subgraph matching queries over large-scale knowledge graphs in the distributed environment. The framework is implemented on the master-slave architecture, endowed with outstanding scalability. However, there are two drawbacks of partial evaluation: if the volume of intermediate results is large, a large number of repeated partial matches will be generated; and the assembly computation handled by the master would be a bottleneck. In this paper, we propose an optimal partial evaluation algorithm and a filter method to reduce partial matches by exploring the computing characteristics of partial evaluation and assembly framework. (1) An index structure named inner boundary node index (IBN-Index) is constructed to prune for graph exploration to improve the searching efficiency of the partial evaluation phase. (2) The boundary characteristics of local partial matches are utilized to construct a boundary node index (BN-Index) to reduce the number of local partial matches. (3) The experimental results over benchmark datasets show that our approach outperforms the state-of-the-art methods.
C1 [Song, Yanyan; Qin, Yuzhou; Hao, Wenqi; Liu, Pengkai; Wang, Xin] Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China.
   [Li, Jianxin] Deakin Univ, Sch Informat Technol, Melbourne, Vic, Australia.
   [Choudhury, Farhana Murtaza] Univ Melbourne, Sch Comp & Informat Syst, Melbourne, Vic, Australia.
   [Zhang, Qingpeng] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China.
RP Wang, X (通讯作者)，Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China.
EM songyanyan1895@tju.edu.cn; yuzhou_qin@tju.edu.cn; haowenqi@tju.edu.cn;
   liupengkai@tju.edu.cn; jianxin.li@deakin.edu.cn;
   farhana.choudhury@unimelb.edu.au; wangx@tju.edu.cn;
   qingpeng.zhang@cityu.edu.hk
FU National Key Research and Development Program of China [2019YFE0198600];
   National Natural Science Foundation of China [61972275]; Australian
   Research Council Linkage Project [LP180100750]
FX This work is expanded on the optimal subgraph matching queries over
   distributed knowledge graphs based on partial evaluation [30], and is
   supported by National Key Research and Development Program of China
   (2019YFE0198600); the National Natural Science Foundation of China
   (61972275), partially supported by Australian Research Council Linkage
   Project (LP180100750).
CR Buneman P., 2006, P 32 INT C VER LARG, P211
   Chandra A.K., 1977, P 9 ANN ACM S THEOR, P77
   Cong G, 2012, ACM T DATABASE SYST, V37, DOI 10.1145/2389241.2389251
   Deppisch U., 1986, P ACM C RES DEV INF, P77, DOI DOI 10.1145/253168.253189
   Fan W., 2012, ARXIV 12080091
   Ge YF, 2022, VLDB J, DOI 10.1007/s00778-021-00718-w
   Ge YF, 2021, IEEE T CYBERNETICS, V51, P4808, DOI 10.1109/TCYB.2020.3027962
   Guo YB, 2005, J WEB SEMANT, V3, P158, DOI 10.1016/j.websem.2005.06.005
   He H, 2006, ICDE, P38
   Husain MF, 2011, IEEE T KNOWL DATA EN, V23, P1312, DOI 10.1109/TKDE.2011.103
   Jones ND, 1996, ACM COMPUT SURV, V28, P480, DOI 10.1145/243439.243447
   Ma S., 2012, P 21 INT C WORLD WID, P949
   Neumann T, 2008, PROC VLDB ENDOW, V1, P647, DOI 10.14778/1453856.1453927
   ONEIL PE, 1992, ACTA INFORM, V29, P241, DOI 10.1007/BF01185680
   Peng P, 2019, PROC INT CONF DATA, P112, DOI 10.1109/ICDE.2019.00019
   Peng P, 2016, VLDB J, V25, P243, DOI 10.1007/s00778-015-0415-0
   Perez J, 2006, LECT NOTES COMPUT SC, V4273, P30
   Ren X., 2019, ARXIV 190107747
   Rohloff K, 2011, P 4 INT WORKSH DAT I, P35
   Sasaki Y., 2020, ARXIV 200303079
   Wang X., 2019, J SOFTW, V30, P2140
   Wang X, 2020, WORLD WIDE WEB, V23, P1465, DOI 10.1007/s11280-019-00739-0
   Wang X, 2019, DATA SCI ENG, V4, P24, DOI 10.1007/s41019-019-0090-z
   Wang X, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1933, DOI 10.1145/2983323.2983877
   Weiss C, 2008, PROC VLDB ENDOW, V1, P1008, DOI 10.14778/1453856.1453965
   Xing J., 2021, INT C WEB INFORM SYS, P274
   Zeng K, 2013, PROC VLDB ENDOW, V6, P265, DOI 10.14778/2535570.2488333
   Zhao PX, 2010, PROC VLDB ENDOW, V3, P340, DOI 10.14778/1920841.1920887
   Zou L, 2011, PROC VLDB ENDOW, V4, P482, DOI 10.14778/2002974.2002976
NR 29
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
DI 10.1007/s11280-022-01075-6
EA JUL 2022
PG 21
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2X1MC
UT WOS:000824974800005
OA hybrid
DA 2022-08-23
ER

PT J
AU Kushida, T
   Kozaki, K
   Kawamura, T
   Tateisi, Y
   Yamamoto, Y
   Takagi, T
AF Kushida, Tatsuya
   Kozaki, Kouji
   Kawamura, Takahiro
   Tateisi, Yuka
   Yamamoto, Yasunori
   Takagi, Toshihisa
TI Interconnection of Biological Knowledge Using NikkajiRDF and
   Interlinking Ontology for Biological Concepts
SO NEW GENERATION COMPUTING
LA English
DT Article
DE Chemical compound; Disease; Gene product; Inference; Knowledge Graph;
   Ontology
ID RESOURCE; DATABASE; GENES
AB We investigated the interconnection on knowledge of biological molecules, biological phenomena, and diseases to efficiently collect information regarding the functions of chemical compounds and gene products, roles, applications, and involvements in diseases using knowledge graphs (KGs) developed from Resource Description Framework (RDF) data and ontologies. NikkajiRDF linked open data provide information on approximately 3.5 million chemical compounds and 694 application examples. We integrated NikkajiRDF with Interlinking Ontology for Biological Concepts (IOBC), including approximately 80,000 concepts, information on gene products, drugs, and diseases. Using IOBC's ontological structure, we confirmed that this integration enabled us to infer new information regarding biological and chemical functions, applications, and involvements in diseases for 5038 chemical compounds. Furthermore, we developed KGs from IOBC and added protein, biological phenomena, and disease identifiers used in major biological databases: UniProt, Gene Ontology, and MeSH to the KGs. Using the extended KGs and federated search to the DisGeNET, we discovered more than 60 chemicals and 700 gene products, involved in 32 diseases.
C1 [Kushida, Tatsuya; Tateisi, Yuka; Takagi, Toshihisa] Japan Sci & Technol Agcy, Natl Biosci Database Ctr, Chiyoda Ku, 5-3 Yonbancho, Tokyo 1028666, Japan.
   [Kozaki, Kouji] Osaka Electrocommun Univ, Fac Informat & Commun Engn, Osaka, Japan.
   [Kawamura, Takahiro] Natl Agr & Food Res Org, Tsukuba, Ibaraki, Japan.
   [Yamamoto, Yasunori] Res Org Informat & Syst, Database Ctr Life Sci, Chiba, Japan.
   [Takagi, Toshihisa] Toyama Univ Int Studies, Toyama, Japan.
RP Kushida, T (通讯作者)，Japan Sci & Technol Agcy, Natl Biosci Database Ctr, Chiyoda Ku, 5-3 Yonbancho, Tokyo 1028666, Japan.
EM kushida@biosciencedbc.jp; kozaki@osakac.ac.jp;
   takahiro.kawamura@affrc.go.jp; tateisi@biosciencedbc.jp;
   yy@dbcls.rois.ac.jp; tt@tuins.ac.jp
OI Kawamura, Takahiro/0000-0002-2765-6232; Kushida,
   Tatsuya/0000-0002-0784-4113; Kozaki, Kouji/0000-0003-4578-4980;
   Yamamoto, Yasunori/0000-0002-6943-6887; Tateisi,
   Yuka/0000-0002-3813-5782
FU Japan Science and Technology Agency; JSPS KAKENHI [JP17H01789]
FX This study was supported by an operating grant from the Japan Science
   and Technology Agency and JSPS KAKENHI Grant Number JP17H01789. A part
   of this study was progressed and discussed in Japan BioHackathon 2016
   (BH16.12), which served as a research and development meeting. We are
   grateful to all participants who gave us their valuable advice and
   constructive comments.
CR Alshahrani M, 2017, BIOINFORMATICS, V33, P2723, DOI 10.1093/bioinformatics/btx275
   Ashburner M, 2001, GENOME RES, V11, P1425
   Batchelor C, 2014, LECT NOTES COMPUT SC, V8796, P98, DOI 10.1007/978-3-319-11964-9_7
   Belleau F, 2008, J BIOMED INFORM, V41, P706, DOI 10.1016/j.jbi.2008.03.004
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bodenreider O, 1998, J AM MED INFORM ASSN, P815
   Brown GR, 2015, NUCLEIC ACIDS RES, V43, pD36, DOI 10.1093/nar/gku1055
   Chambers J, 2013, J CHEMINFORMATICS, V5, DOI 10.1186/1758-2946-5-3
   Chen B, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002574
   Chen B, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-255
   Chepelev LL, 2011, J CHEMINFORMATICS, V3, DOI 10.1186/1758-2946-3-16
   Chong J, 2002, PLANT CELL, V14, P1093, DOI 10.1105/tpc.010436
   Davis AP, 2019, NUCLEIC ACIDS RES, V47, pD948, DOI 10.1093/nar/gky868
   Dumontier M, 2014, J BIOMED SEMANT, V5, DOI 10.1186/2041-1480-5-14
   Ertl P, 2015, J CHEMINFORMATICS, V7, DOI 10.1186/s13321-015-0061-y
   Euzenat J., 2013, ONTOLOGY MATCHING
   Faria D, 2018, J BIOMED SEMANT, V9, DOI 10.1186/s13326-017-0170-9
   Forrest ARR, 2014, NATURE, V507, P462, DOI 10.1038/nature13182
   Fu G, 2015, J CHEMINFORMATICS, V7, DOI 10.1186/s13321-015-0084-4
   Ghazvinian Amir, 2009, AMIA Annu Symp Proc, V2009, P198
   Gkoutos GV, 2009, IEEE ENG MED BIO, P7069, DOI 10.1109/IEMBS.2009.5333362
   Hamosh A, 2005, NUCLEIC ACIDS RES, V33, pD514
   Harrow I., 2019, DRUG DISCOV TODAY, pS1359
   Hastings J, 2013, NUCLEIC ACIDS RES, V41, pD456, DOI 10.1093/nar/gks1146
   Hastings J, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0025513
   Heller S, 2013, J CHEMINFORMATICS, V5, DOI 10.1186/1758-2946-5-7
   Jain E, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-136
   Kanehisa M, 2000, NUCLEIC ACIDS RES, V28, P27, DOI 10.1093/nar/28.1.27
   Kimura T, 2015, J INF PROCESS MANAG, V58, P204, DOI [10.1241/johokanri.58.204, DOI 10.1241/JOHOKANRI.58.204]
   Kinjo AR, 2017, NUCLEIC ACIDS RES, V45, pD282, DOI 10.1093/nar/gkw962
   Kobayashi Norio, 2016, Semantic Technology. 6th Joint International Conference, JIST 2016. Revised Selected Papers: LNCS 10055, P99, DOI 10.1007/978-3-319-50112-3_8
   Kushida T., 2017, P 8 INT C BIOM ONT I, V2137, P1
   Kushida T, 2016, P 5 INT EXPL SEM DAT P 5 INT EXPL SEM DAT, P1
   Kushida T, 2017, LECT NOTES COMPUT SC, V10675, P35, DOI 10.1007/978-3-319-70682-5_3
   Noy NF, 2009, NUCLEIC ACIDS RES, V37, pW170, DOI 10.1093/nar/gkp440
   Pinero J, 2015, DATABASE-OXFORD, DOI 10.1093/database/bav028
   Samwald M, 2011, J CHEMINFORMATICS, V3, DOI 10.1186/1758-2946-3-19
   Schriml LM, 2019, NUCLEIC ACIDS RES, V47, pD955, DOI 10.1093/nar/gky1032
   Slenter DN, 2018, NUCLEIC ACIDS RES, V46, pD661, DOI 10.1093/nar/gkx1064
   Tateisi Yuka, 2018, SEM TECHN 8 JOINT IN, V11341, P385
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Willighagen EL, 2013, J CHEMINFORMATICS, V5, DOI 10.1186/1758-2946-5-23
   Wimalaratne SM, 2015, BIOINFORMATICS, V31, P1875, DOI 10.1093/bioinformatics/btv064
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Yoshiki A, 2009, EXP ANIM TOKYO, V58, P85, DOI 10.1538/expanim.58.85
NR 46
TC 3
Z9 3
U1 1
U2 4
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0288-3635
EI 1882-7055
J9 NEW GENERAT COMPUT
JI New Gener. Comput.
PD DEC
PY 2019
VL 37
IS 4
SI SI
BP 525
EP 549
DI 10.1007/s00354-019-00074-y
PG 25
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JY1TV
UT WOS:000504205800008
OA hybrid
DA 2022-08-23
ER

PT J
AU Liu, J
   Wang, YJ
   Zhang, Q
   Wei, JX
   Zhou, HH
AF Liu, Jing
   Wang, Yujie
   Zhang, Qian
   Wei, Jianxiang
   Zhou, Haihua
TI Scientometric Analysis of Public Health Emergencies: 1994-2020
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE public health emergency; CiteSpace; visual analysis; research hotspot;
   scientometric
ID UNITED-STATES; PREPAREDNESS; INFECTION; SCIENCE; WEB
AB The purpose of this paper is to summarize the research hotspots and frontiers in the field of public health emergencies (PHE) between 1994-2020 through the scientometric analysis method. In total, 2247 literature works retrieved from the Web of Science core database were analyzed by CiteSpace software, and the results were displayed in knowledge mapping. The overall characteristics analysis showed that the number of publications and authors in the field of PHE kept an upward trend during the past decades, and the United States was in the leading position, followed by China and England. Switzerland has the highest central value and plays an important intermediary role in promoting the integration and exchange of international PHE research achievements. The keyword co-occurrence analysis indicated that COVID-19 was the most high-frequency keyword in this field, and there had been no new keywords for a long time until the outbreak of COVID-19 in 2019. The burst detection analysis showed that the top five burst keywords in terms of burst intensity were zika virus, Ebola, United States, emergency preparedness and microcephaly. The results indicated that the research theme of PHE is closely related to the major infectious diseases in a specific period. It will continue to develop with more attention paid to public health. The conclusions can provide help and reference for the PHE potential researchers.
C1 [Liu, Jing; Wang, Yujie; Zhang, Qian; Wei, Jianxiang] Nanjing Univ Posts & Telecommun, Sch Management, Nanjing 210003, Peoples R China.
   [Wei, Jianxiang] Management Res Ctr, Jiangsu Informat Ind Integrat Innovat & Emergen, Key Res Base Philosophy & Social Sci, Nanjing 210003, Peoples R China.
   [Zhou, Haihua] Nanjing Xiaozhuang Univ, Business Sch, Nanjing 211171, Peoples R China.
RP Liu, J (通讯作者)，Nanjing Univ Posts & Telecommun, Sch Management, Nanjing 210003, Peoples R China.
EM liuj_m@njupt.edu.cn; b19111515@njupt.edu.cn; zhg_Qian@163.com;
   jxwei@njupt.edu.cn; zhouhaihua@njxzc.edu.cn
OI Liu, Jing/0000-0003-2259-1158; wei, jian xiang/0000-0001-9052-9212
FU Major Project of Philosophy and Social Science Research in Jiangsu
   Universities [2020SJZDA102]
FX FundingThis work was supported by the Major Project of Philosophy and
   Social Science Research in Jiangsu Universities (2020SJZDA102).
CR Boyce MR, 2021, LANCET GLOB HEALTH, V9, pE181, DOI 10.1016/S2214-109X(20)30420-4
   Chen C., 2017, TEXT MINING VISUALIZ, V2
   Chen C.M., 2018, PRACTICE MAPPING SCI
   Chen HB, 2014, INT J LIFE CYCLE ASS, V19, P1674, DOI 10.1007/s11367-014-0777-3
   Chen HL, 2016, J CHIN MED ASSOC, V79, P174, DOI 10.1016/j.jcma.2016.03.001
   Chen W, 2017, RENEW SUST ENERG REV, V73, P1051, DOI 10.1016/j.rser.2017.02.041
   Chen Y., 2015, STUDIES SCI SCI, V33, P242
   Cooke Fiona J., 2003, International Journal of Infectious Diseases, V7, P80, DOI 10.1016/S1201-9712(03)90001-4
   Corrigan PW, 2018, INT J DRUG POLICY, V59, P44, DOI 10.1016/j.drugpo.2018.06.015
   Duncan WJ, 2007, PUBLIC HEALTH, V121, P241, DOI 10.1016/j.puhe.2006.10.014
   Falagas ME, 2008, FASEB J, V22, P338, DOI 10.1096/fj.07-9492LSF
   Gao JQ, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18010273
   Hou Q, 2015, INT J LIFE CYCLE ASS, V20, P541, DOI 10.1007/s11367-015-0846-2
   Hu Q.X., 2021, STAT MANAG, V36, P42
   Jiao Y.M., 2021, CLIN INFECT DIS, V34, P1
   Li N.Q, 2004, CHINA SOC SECUR, V11, P26
   Liu L., 2021, J U ELECT SCI TECHNO, V23, P46
   Ma W.L., 2020, J XINJIANG FINANC EC, V2, P5
   Maldonado J, 2006, VET J, V172, P377, DOI 10.1016/j.tvjl.2005.04.014
   Marsilio M, 2011, PUBLIC MANAG REV, V13, P763, DOI 10.1080/14719037.2010.539112
   McCloskey B, 2017, PUBLIC HEALTH, V150, P87, DOI 10.1016/j.puhe.2017.05.008
   Morrison T, 2011, ISAAC NEWTON'S TEMPLE OF SOLOMON AND HIS RECONSTRUCTION OF SACRED ARCHITECTURE, P1, DOI 10.1007/978-3-0348-0046-4
   Neto DDES, 2016, J CONSTR ENG M, V142, DOI 10.1061/(ASCE)CO.1943-7862.0001163
   Olawumi TO, 2018, J CLEAN PROD, V183, P231, DOI 10.1016/j.jclepro.2018.02.162
   Onyancha OB, 2008, AJAR-AFR J AIDS RES, V7, P55, DOI 10.2989/AJAR.2008.7.1.7.435
   Qin X., 2020, J GUANGXI ACAD SCI, V36, P242
   Savoia E, 2017, AM J PUBLIC HEALTH, V107, pE1, DOI 10.2105/AJPH.2017.304051
   Savoia E, 2014, INT J ENV RES PUB HE, V11, P9713, DOI 10.3390/ijerph110909712
   Savoia E, 2009, AM J PREV MED, V37, P150, DOI 10.1016/j.amepre.2009.03.023
   Sen-Crowe B, 2020, ANN MED SURG, V59, P242, DOI 10.1016/j.amsu.2020.10.007
   Shrivastava S. R. B. L., 2015, Asian Pacific Journal of Tropical Disease, V5, P253, DOI 10.1016/S2222-1808(14)60779-9
   Si HY, 2019, J CLEAN PROD, V213, P415, DOI 10.1016/j.jclepro.2018.12.157
   Tang Z.W., 2017, J MOD INF, V37, P119
   Tavares MP, 2016, CONTRACEPTION, V94, P109, DOI 10.1016/j.contraception.2016.04.006
   Wang CJ, 2008, PUBLIC HEALTH, V122, P471, DOI 10.1016/j.puhe.2007.08.006
   Wang Y.N., 2021, FINANC TRAD RES, V32, P1
   WHO, 11 EB OUTBR DEM REP
   WHO, EB VIR DIS DEM REP C
   World Health Organization, UPD DENG SIT W PAC R
   Zhang J.H., 2013, MODERN INTELLIGENCE, V33, P99
   Zhu Z, 2020, J PSYCHIATR RES, V129, P1, DOI 10.1016/j.jpsychires.2020.05.026
NR 41
TC 1
Z9 1
U1 20
U2 20
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD JAN
PY 2022
VL 19
IS 2
AR 640
DI 10.3390/ijerph19020640
PG 15
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA ZG5IG
UT WOS:000760290700001
PM 35055460
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wang, YZ
   Liao, LQ
   Lin, XX
   Sun, YB
   Wang, N
   Wang, JY
   Luo, F
AF Wang, Yuzheng
   Liao, Lingqiu
   Lin, Xiaoxiao
   Sun, Yabin
   Wang, Ning
   Wang, Jinyan
   Luo, Fei
TI A Bibliometric and Visualization Analysis of Mindfulness and Meditation
   Research from 1900 to 2021
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE mindfulness; meditation; CiteSpace; VOSviewer; bibliometric; well-being;
   health
ID STRESS REDUCTION; THEORETICAL FOUNDATIONS; COMMITMENT THERAPY; KNOWLEDGE
   DOMAIN; SELF-REPORT; ACCEPTANCE; MECHANISMS; INTERVENTIONS; DEPRESSION;
   HEALTH
AB This study comprehensively summarizes research in the field of meditation, especially mindfulness meditation from 1900 to 2021, by analyzing the knowledge map through CiteSpace and VOSviewer software. Using "mindfulness *" or "meditation *" as the topic, articles included in the Science Citation Index Expanded and Social Sciences Citation Index were searched in the web of science core database, resulting in the selection of 19,752 articles. Over half a century ago, Deikman published the field's first article in the Journal of Nervous and Mental Disease in 1963, and publications have soared in subsequent decades. The USA is in the core position in terms of global collaboration, total publication numbers, and total citations. The Mindfulness journal ranked first for the most published articles and citations. "The benefits of being present: Mindfulness and its role in psychological well-being," written by Brown and Ryan, was the most cited article. Mindfulness, meditation, depression, intervention, stress reduction, stress, and anxiety are the top co-occurrence keywords. The timeline of cluster analysis discloses that before 2010, hypertension, cancer, mindfulness, generalized anxiety disorder, and other topics received great attention. In the decade since 2010, scholars have shown interest in meta-analysis, attention, and self-assessment, and keen attention to mindfulness-based interventions. These findings provide an important foundation to direct future research.
C1 [Wang, Yuzheng; Liao, Lingqiu; Lin, Xiaoxiao; Sun, Yabin; Wang, Ning; Wang, Jinyan; Luo, Fei] Chinese Acad Sci, Inst Psychol, CAS Key Lab Mental Hlth, Beijing 100101, Peoples R China.
   [Wang, Yuzheng; Liao, Lingqiu; Lin, Xiaoxiao; Sun, Yabin; Wang, Ning; Wang, Jinyan; Luo, Fei] Univ Chinese Acad Sci, Dept Psychol, Beijing 100049, Peoples R China.
   [Wang, Yuzheng] East China Normal Univ, Shanghai Key Lab Mental Hlth & Psychol Crisis Int, Sch Psychol & Cognit Sci, Shanghai 200062, Peoples R China.
RP Luo, F (通讯作者)，Chinese Acad Sci, Inst Psychol, CAS Key Lab Mental Hlth, Beijing 100101, Peoples R China.; Luo, F (通讯作者)，Univ Chinese Acad Sci, Dept Psychol, Beijing 100049, Peoples R China.
EM wangyz@psych.ac.cn; liaolq@psych.ac.cn; linxx@psych.ac.cn;
   sunyb@psych.ac.cn; wangn@psych.ac.cn; wangjy@psych.ac.cn;
   luof@psych.ac.cn
OI Wang, Yuzheng/0000-0001-8007-9019; luo, fei/0000-0003-2536-8517
FU NNSF (National Natural Science Foundation of China) [31970926];
   Scientific Foundation of Institute of Psychology, Chinese Academy of
   Sciences [Y9CX441005]; Research Project of Shanghai Science and
   Technology Commission [20dz2260300]; Fundamental Research Funds for the
   Central Universities
FX FundingThis research was funded by NNSF (National Natural Science
   Foundation of China) grant number (31970926) to Fei Luo; the Scientific
   Foundation of Institute of Psychology, Chinese Academy of Sciences grant
   number (Y9CX441005), the Research Project of Shanghai Science and
   Technology Commission grant number (20dz2260300) and The Fundamental
   Research Funds for the Central Universities to Yuzheng Wang.
CR Baer RA, 2006, ASSESSMENT, V13, P27, DOI 10.1177/1073191105283504
   Baer RA, 2004, ASSESSMENT, V11, P191, DOI 10.1177/1073191104268029
   Baer RA, 2003, CLIN PSYCHOL-SCI PR, V10, P125, DOI 10.1093/clipsy/bpg015
   Baer RA, 2008, ASSESSMENT, V15, P329, DOI 10.1177/1073191107313003
   Baminiwatta A, 2021, MINDFULNESS, V12, P2099, DOI 10.1007/s12671-021-01681-x
   Bishop SR, 2004, CLIN PSYCHOL-SCI PR, V11, P230, DOI 10.1093/clipsy/bph077
   Borgman CL, 2002, ANNU REV INFORM SCI, V36, P3
   Boyack KW, 2010, J AM SOC INF SCI TEC, V61, P2389, DOI 10.1002/asi.21419
   Brown KW, 2007, PSYCHOL INQ, V18, P211, DOI 10.1080/10478400701598298
   Brown KW, 2003, J PERS SOC PSYCHOL, V84, P822, DOI 10.1037/0022-3514.84.4.822
   Burke CA, 2010, J CHILD FAM STUD, V19, P133, DOI 10.1007/s10826-009-9282-x
   Cahn BR, 2006, PSYCHOL BULL, V132, P180, DOI 10.1037/0033-2909.132.2.180
   Carmody J, 2008, J BEHAV MED, V31, P23, DOI 10.1007/s10865-007-9130-7
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Chiesa A, 2009, J ALTERN COMPLEM MED, V15, P593, DOI 10.1089/acm.2008.0495
   Clarke Tainya C, 2015, Natl Health Stat Report, P1
   Cocker F, 2016, INT J ENV RES PUB HE, V13, DOI 10.3390/ijerph13060618
   COHEN S, 1983, J HEALTH SOC BEHAV, V24, P385, DOI 10.2307/2136404
   Creswell JD, 2017, ANNU REV PSYCHOL, V68, P491, DOI 10.1146/annurev-psych-042716-051139
   Dalen J, 2010, COMPLEMENT THER MED, V18, P260, DOI 10.1016/j.ctim.2010.09.008
   Davidson RJ, 2015, AM PSYCHOL, V70, P581, DOI 10.1037/a0039512
   Davidson RJ, 2003, PSYCHOSOM MED, V65, P564, DOI 10.1097/01.PSY.0000077505.67574.E3
   Deci EL, 2008, CAN PSYCHOL, V49, P182, DOI 10.1037/a0012801
   DEIKMAN AJ, 1963, J NERV MENT DIS, V136, P329, DOI 10.1097/00005053-196304000-00002
   Diamond A, 2011, SCIENCE, V333, P959, DOI 10.1126/science.1204529
   Eberth J, 2012, MINDFULNESS, V3, P174, DOI 10.1007/s12671-012-0101-x
   Fox KCR, 2014, NEUROSCI BIOBEHAV R, V43, P48, DOI 10.1016/j.neubiorev.2014.03.016
   Francis AW, 2016, J CONTEXT BEHAV SCI, V5, P134, DOI 10.1016/j.jcbs.2016.05.003
   Garland E, 2009, EXPLORE-NY, V5, P37, DOI 10.1016/j.explore.2008.10.001
   Goyal M, 2014, JAMA INTERN MED, V174, P357, DOI 10.1001/jamainternmed.2013.13018
   Grant JA, 2014, ANN NY ACAD SCI, V1307, P55, DOI 10.1111/nyas.12282
   Grossman P, 2004, J PSYCHOSOM RES, V57, P35, DOI 10.1016/S0022-3999(03)00573-7
   Gu J, 2015, CLIN PSYCHOL REV, V37, P1, DOI 10.1016/j.cpr.2015.01.006
   Hayes SC, 2006, BEHAV RES THER, V44, P1, DOI 10.1016/j.brat.2005.06.006
   Hayes SC., 2009, ACCEPTANCE COMMITMEN
   Hayes-Skelton S, 2013, BEHAV COGN PSYCHOTH, V41, P317, DOI 10.1017/S1352465812000902
   He XR, 2017, INT J INTELL SYST, V32, P1151, DOI 10.1002/int.21894
   Holzel BK, 2011, PERSPECT PSYCHOL SCI, V6, P537, DOI 10.1177/1745691611419671
   Hofmann SG, 2010, J CONSULT CLIN PSYCH, V78, P169, DOI 10.1037/a0018555
   Howarth A, 2019, MINDFULNESS, V10, P1957, DOI 10.1007/s12671-019-01163-1
   Jain S, 2007, ANN BEHAV MED, V33, P11, DOI 10.1207/s15324796abm3301_2
   Jha AP, 2007, COGN AFFECT BEHAV NE, V7, P109, DOI 10.3758/CABN.7.2.109
   Kabat-Zinn J, 2003, CLIN PSYCHOL-SCI PR, V10, P144, DOI 10.1093/clipsy/bpg016
   Kabat-Zinn J., 2009, FULL CATASTROPHE LIV
   Kabat-Zinn J., 1994, WHEREVER YOU GO THER
   KABATZINN J, 1982, GEN HOSP PSYCHIAT, V4, P33, DOI 10.1016/0163-8343(82)90026-3
   Keng SL, 2011, CLIN PSYCHOL REV, V31, P1041, DOI 10.1016/j.cpr.2011.04.006
   Khalsa SS, 2008, PSYCHOPHYSIOLOGY, V45, P671, DOI 10.1111/j.1469-8986.2008.00666.x
   Khoury B, 2015, J PSYCHOSOM RES, V78, P519, DOI 10.1016/j.jpsychores.2015.03.009
   Khoury B, 2013, CLIN PSYCHOL REV, V33, P763, DOI 10.1016/j.cpr.2013.05.005
   LAWANI SM, 1981, LIBRI, V31, P294
   Lazar SW, 2005, MINDFULNESS AND PSYCHOTHERAPY, P220
   Lengacher CA, 2009, PSYCHO-ONCOLOGY, V18, P1261, DOI 10.1002/pon.1529
   Lettus D, 2010, LIBR J, V135, P30
   Lindsay EK, 2017, CLIN PSYCHOL REV, V51, P48, DOI 10.1016/j.cpr.2016.10.011
   Lutz A, 2008, TRENDS COGN SCI, V12, P163, DOI 10.1016/j.tics.2008.01.005
   Ma SH, 2004, J CONSULT CLIN PSYCH, V72, P31, DOI 10.1037/0022-006X.72.1.31
   Merigo JM, 2017, OMEGA-INT J MANAGE S, V73, P37, DOI 10.1016/j.omega.2016.12.004
   Merigo JM, 2017, INT J INTELL SYST, V32, P526, DOI 10.1002/int.21859
   Merigo JM, 2016, SCIENTOMETRICS, V108, P559, DOI 10.1007/s11192-016-1984-4
   Morgan D, 2003, PSYCHOTHER RES, V13, P123, DOI [10.1093/ptr/kpg004, 10.1080/713869628]
   Mrazek MD, 2013, PSYCHOL SCI, V24, P776, DOI 10.1177/0956797612459659
   Sedlmeier P, 2012, PSYCHOL BULL, V138, P1139, DOI 10.1037/a0028168
   Shapiro SL, 2006, J CLIN PSYCHOL, V62, P373, DOI 10.1002/jclp.20237
   Sweileh WM, 2016, J HEALTH POPUL NUTR, V35, DOI 10.1186/s41043-016-0076-7
   Tang YY, 2015, NAT REV NEUROSCI, V16, P213, DOI 10.1038/nrn3916
   Teasdale JD, 2000, J CONSULT CLIN PSYCH, V68, P615, DOI 10.1037//0022-006X.68.4.615
   Vago DR, 2012, FRONT HUM NEUROSCI, V6, DOI 10.3389/fnhum.2012.00296
   Van Dam NT, 2018, PERSPECT PSYCHOL SCI, V13, P36, DOI 10.1177/1745691617709589
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Walach H, 2006, PERS INDIV DIFFER, V40, P1543, DOI 10.1016/j.paid.2005.11.025
   Walsh R, 2006, AM PSYCHOL, V61, P227, DOI 10.1037/0003-066X.61.3.227
   Wang H, 2018, KNOWL-BASED SYST, V144, P174, DOI 10.1016/j.knosys.2017.12.030
   Wang YZ, 2021, CURR PSYCHOL, DOI 10.1007/s12144-021-01891-5
   Wang YZ, 2021, CURR PSYCHOL, DOI 10.1007/s12144-021-01377-4
   Wang YZ, 2019, MINDFULNESS, V10, P1352, DOI 10.1007/s12671-019-1091-8
   Weber B, 2010, EUR PSYCHIAT, V25, P334, DOI 10.1016/j.eurpsy.2010.03.007
   Yeung AWK, 2017, CURR SCI INDIA, V112, P725, DOI 10.18520/cs/v112/i04/725-734
   Zeidan F, 2011, J NEUROSCI, V31, P5540, DOI 10.1523/JNEUROSCI.5791-10.2011
   Zeidan F, 2010, CONSCIOUS COGN, V19, P597, DOI 10.1016/j.concog.2010.03.014
   Zenner C, 2014, FRONT PSYCHOL, V5, DOI 10.3389/fpsyg.2014.00603
NR 81
TC 2
Z9 2
U1 38
U2 44
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD DEC
PY 2021
VL 18
IS 24
AR 13150
DI 10.3390/ijerph182413150
PG 25
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA XZ9OY
UT WOS:000737976100001
PM 34948760
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Dutta, R
   Morshed, A
   Aryal, J
   D'Este, C
   Das, A
AF Dutta, Ritaban
   Morshed, Ahsan
   Aryal, Jagannath
   D'Este, Claire
   Das, Aruneema
TI Development of an intelligent environmental knowledge system for
   sustainable agricultural decision support
SO ENVIRONMENTAL MODELLING & SOFTWARE
LA English
DT Article
DE Feature; Semantic matching; Knowledge integration; i-EKbase; Linked Open
   Data cloud
ID MODEL
AB The purpose of this research was to develop a knowledge recommendation architecture based on unsupervised machine learning and unified resource description framework (RDF) for integrated environmental sensory data sources. In developing this architecture, which is very useful for agricultural decision support systems, we considered web based large-scale dynamic data mining, contextual knowledge extraction, and integrated knowledge representation methods. Five different environmental data sources were considered to develop and test the proposed knowledge recommendation framework called Intelligent Environmental Knowledgebase (i-EKbase); including Bureau of Meteorology SILO, Australian Water Availability Project, Australian Soil Resource Information System, Australian National Cosmic Ray Soil Moisture Monitoring Facility, and NASA's Moderate Resolution Imaging Spectroradiometer. Unsupervised clustering techniques based on Principal Component Analysis (PCA), Fuzzy-C-Means (FCM) and Self-organizing map (SOM) were used to create a 2D colour knowledge map representing the dynamics of the i-EKbase to provide "prior knowledge" about the integrated knowledgebase. Prior availability of recommendations from the knowledge base could potentially optimize the accessibility and usability issues related to big data sets and minimize the overall application costs. RDF representation has made i-EKbase flexible enough to publish and integrate on the Linked Open Data cloud. This newly developed system was evaluated as an expert agricultural decision support for sustainable water resource management case study in Australia at Tasmania with promising results. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.
C1 [Dutta, Ritaban; Morshed, Ahsan; D'Este, Claire] CSIRO, Intelligent Sensing & Syst Lab, Hobart, Tas 7001, Australia.
   [Aryal, Jagannath] Univ Tasmania, Sch Geog & Environm Studies, Hobart, Tas 7001, Australia.
   [Das, Aruneema] Univ Tasmania, Sch Engn, Hobart, Tas 7001, Australia.
RP Dutta, R (通讯作者)，CSIRO, Intelligent Sensing & Syst Lab, Hobart, Tas 7001, Australia.
EM ritaban_dut@yahoo.com
RI Aryal, Jagannath/E-8529-2012; Morshed, Ahsan/AAK-2685-2021; D'Este,
   Claire/C-1549-2010; Aryal, Jagannath/AAY-7074-2020; Dutta,
   Ritaban/AAV-8279-2021
OI Aryal, Jagannath/0000-0002-4875-2127; Aryal,
   Jagannath/0000-0002-4875-2127; Dutta, Ritaban/0000-0001-6236-0296
CR Akbar M, 2013, ENVIRON MODELL SOFTW, V39, P24, DOI 10.1016/j.envsoft.2011.12.006
   Arnold TR, 2013, ENVIRON MODELL SOFTW, V39, P135, DOI 10.1016/j.envsoft.2012.04.015
   Auer S, 2010, SEMANT WEB, V1, P97, DOI 10.3233/SW-2010-0019
   Berners-Lee T., 2011, LINKED DATA
   Bizer C, 2009, INT J SEMANT WEB INF, V5, P1, DOI 10.4018/jswis.2009081901
   Broekstra J, 2002, LECT NOTES COMPUT SC, V2342, P54
   Dutta R, 2006, SENSOR ACTUAT B-CHEM, V120, P156, DOI 10.1016/j.snb.2006.02.013
   Gilbert R.C., 2010, COMPUT INTELL ARCHIT, P105
   Heath T., 2011, SYNTHESIS LECT SEMAN, DOI [DOI 10.2200/S00334ED1V01Y201102WBE001, 10.2200/s00334ed1v01y201102wbe001]
   Henson C, 2012, IEEE INTERNET COMPUT, V16, P26, DOI 10.1109/MIC.2012.20
   Knapen R, 2013, ENVIRON MODELL SOFTW, V39, P274, DOI 10.1016/j.envsoft.2012.06.011
   KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288
   Lin C-T., 1996, NEURAL FUZZY SYSTEMS
   LUO RC, 1988, IEEE T ROBOTIC AUTOM, V4, P386, DOI 10.1109/56.802
   McIntosh BS, 2011, ENVIRON MODELL SOFTW, V26, P1389, DOI 10.1016/j.envsoft.2011.09.009
   Mendes P., 2012, 1 INT WORKSH LINK WE
   Miles A., 2009, W3C SKOS SIMPLE KNOW
   Morshed A., 2013, 12 IEEE TRUSTCOM 201
   Morshed A, 2013, I C DATA ENGIN WORKS, P27, DOI 10.1109/ICDEW.2013.6547421
   Nino-Ruiz M, 2013, ENVIRON MODELL SOFTW, V39, P70, DOI 10.1016/j.envsoft.2012.06.009
   Sheth A, 2012, DISTRIB PARALLEL DAT, V30, P101, DOI 10.1007/s10619-012-7091-5
   Voinov A, 2013, ENVIRON MODELL SOFTW, V39, P149, DOI 10.1016/j.envsoft.2012.05.014
   Zhang L, 1996, J HYDROL, V185, P147, DOI 10.1016/0022-1694(95)03006-9
NR 23
TC 32
Z9 34
U1 2
U2 59
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1364-8152
EI 1873-6726
J9 ENVIRON MODELL SOFTW
JI Environ. Modell. Softw.
PD FEB
PY 2014
VL 52
BP 264
EP 272
DI 10.1016/j.envsoft.2013.10.004
PG 9
WC Computer Science, Interdisciplinary Applications; Engineering,
   Environmental; Environmental Sciences; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Environmental Sciences & Ecology; Water
   Resources
GA AX2CY
UT WOS:000346751700022
DA 2022-08-23
ER

PT J
AU Yu, WD
   Yang, JB
   Tseng, JCR
   Liu, SJ
   Wu, JW
AF Yu, Wen-der
   Yang, Jyh-bin
   Tseng, Judy C. R.
   Liu, Shen-jung
   Wu, Ji-wei
TI Proactive problem-solver for construction
SO AUTOMATION IN CONSTRUCTION
LA English
DT Article
DE Knowledge management; Problem-solving; Knowledge map
AB Construction is an experience-based discipline. Knowledge or experience accumulated from previous projects plays a very important role in successful performance of new works. More and more construction organizations have adopted commercial Knowledge Management Systems (KMSs) to develop their own Knowledge Management (KM) functionalities. Most of the existing KMSs adopt Communities of Practice (CoPs) for knowledge sharing and exchange. Such an approach is found on the reactive problem-solver (RPS); that is, the problem raised by the questioner in the CoP has to "wait" for the "solution knower" to respond (or reply). Previous research indicated that the RPS approach may suffer in poor time and cost effectiveness. This paper proposes a Proactive Problem-Solver (PPS) approach for the problems encountered in construction engineering and management. Unlike RPS, the PPS proactively solves the problem based on lessons learned from previous projects. Should the solution be not available; the PPS dispatches the problem to the most appropriate domain experts so that the problem can be tackled timely and efficiently. A case A/E consulting firm is selected for implementation of the proposed PPS to demonstrate its applicability. It is shown that the proposed PPS improves more than 89.5% of efficiency both for timeliness and cost-saving of problem-solving. The proposed PPS demonstrates great potentials for improvement of emergent problem solving and enhancement of market competitiveness of a construction organization. (C) 2010 Elsevier B.V. All rights reserved.
C1 [Yu, Wen-der; Yang, Jyh-bin] Chung Hua Univ, Inst Const Management, Hsinchu, Taiwan.
   [Tseng, Judy C. R.] Chung Hua Univ, Dept Comp Sci & Informat Engn, Hsinchu, Taiwan.
   [Liu, Shen-jung] Natl Taiwan Univ, Dept Civil Engn, Taipei 10764, Taiwan.
   [Wu, Ji-wei] Natl Chiao Tung Univ, Dept Comp Sci, Hsinchu, Taiwan.
RP Yu, WD (通讯作者)，Chung Hua Univ, Inst Const Management, Hsinchu, Taiwan.
EM wenderyu@chu.edu.tw
RI Tseng, Judy C. R./AAI-9658-2020; Yang, Jyh-Bin/AAO-2806-2021
FU National Science Council, Taiwan [NSC 97-2221-E-216-039]
FX The funding of this research project was partially supported by the
   National Science Council, Taiwan, under project No. NSC
   97-2221-E-216-039. Sincere appreciations are given to the sponsor by the
   authors. The valuable case study information presented in this paper was
   provided by CECI Engineering Consultants, Inc., Taipei. The authors
   would like to express sincere appreciations to Mr. Chen, G. L. and other
   staffs of the Department of Business and Research and Department of
   Information Systems, CECI Engineering Consultants, Inc., Taipei, Taiwan.
CR Altshuller G. S., 2000, INNOVATION ALGORITHM
   [Anonymous], [No title captured]
   Caldas CH, 2002, J COMPUT CIVIL ENG, V16, P234, DOI 10.1061/(ASCE)0887-3801(2002)16:4(234)
   CLEVERDON C, 1967, ASLIB PROC, V19, P173, DOI 10.1108/eb050097
   *CONSTR IND I, 1993, PUBL CONSTR IND I, V341
   FISHER D, 1998, 12311 CONSTR IND I
   IOANNOU PG, 1992, 84 CONSTR IND I
   JOHANSSON F, 2002, MEDICI EFFECT BREAKT
   KARTAM N, 1994, P COMP CIV ENG NEW Y, P1139
   KIM S, 2003, J KNOWLEDGE MANAGEME, V7, P34, DOI DOI 10.1108/13673270310477270
   Lave J., 1991, SITUATED LEARNING LE
   Li H., 1998, CONSTRUCTION MANAGEM, V16, P721, DOI [10.1080/014461998372015, DOI 10.1080/014461998372015]
   LOSS J, 1987, J PERFORMANCE CONSTR, V1, P11
   Ma W.-Y., 2003, P ACL 2 SIGHAN WORKS, P168
   MCCULLOUCH B, 1994, P 1 C COMP CIV ENG, P1397
   Mezher T, 2005, J MANAGE ENG, V21, P138, DOI 10.1061/(ASCE)0742-597X(2005)21:3(138)
   Mohamed Y, 2005, J CONSTR ENG M ASCE, V131, P993, DOI 10.1061/(ASCE)0733-9364(2005)131:9(993)
   NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14
   SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220
   Salton G., 1983, INTRO MODERN INFORM
   TSENG JCR, 2007, ELECTRON COMMER R A, P18
   WALTZ DL, 1978, COMMUN ACM, V21, P526, DOI 10.1145/359545.359550
   WU JW, 2007, P 2007 WORLD C ED MU
   YU WD, 2006, P INT S AUT ROB CONS
   YU WD, 2007, P CME 25 C U READ RE
   YU WD, 2008, P INT S AUT ROB CONS
   YU WD, 2008, P 6 INT C ENG COMP T
   Yu WD, 2009, CONSTR MANAG ECON, V27, P733, DOI 10.1080/01446190903074978
NR 28
TC 11
Z9 11
U1 0
U2 15
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0926-5805
J9 AUTOMAT CONSTR
JI Autom. Constr.
PD OCT
PY 2010
VL 19
IS 6
BP 808
EP 816
DI 10.1016/j.autcon.2010.05.003
PG 9
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA 645CG
UT WOS:000281427900015
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Blazquez-Ruiz, J
   Guerrero-Bote, VP
   Moya-Anegon, F
AF Blazquez-Ruiz, Jesus
   Guerrero-Bote, Vicente P.
   Moya-Anegon, Felix
TI New Scientometric-Based Knowledge Map of Food Science Research (2003 to
   2014)
SO COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY
LA English
DT Review
DE co-word analysis; food science; food science structure; scientometrics;
   thematic analysis
ID SCIENTIFIC PRODUCTION; CUMULATIVE ADVANTAGE; SCOPUS; INSTITUTIONS;
   TECHNOLOGY
AB This article describes an analysis of keywords which was aimed at revealing publication patterns in the field of Food Science (FS) during the last decade, including the temporal evolution of its different research lines. To this end, the records of the specific subject area of FS were 1st retrieved from Scopus, and then their keywords were processed to resolve the obvious problems of synonymy and to limit the study to those most frequently used. These keywords were grouped into thematic clusters based on a scientometric technique known as co-word analysis. The structure of the clusters, their scientific impact, and their temporal evolution were then analyzed. This type of analysis is of great interest for all researchers in FSfor new researchers because they can form an objective vision of the subject based on the data from papers that have been published in the last decade, and for experienced researchers because they can contrast their own vision of the field with this objective overview. The results showed there to has been a clear increase in scientific production related to FS. This production had a structure corresponding to 5 major clusters which were themselves disaggregated into 18 2nd-level clusters. The cluster that had received most attention was that corresponding to antioxidants in food, being the cluster with the greatest scientific impact and the greatest growth in the period.
C1 [Blazquez-Ruiz, Jesus; Guerrero-Bote, Vicente P.] Univ Extremadura, Scimago Grp, Dept Informat & Commun, E-06071 Badajoz, Spain.
   [Moya-Anegon, Felix] CSIC, Scimago Grp, CCHS, IPP, Madrid, Spain.
RP Guerrero-Bote, VP (通讯作者)，Univ Extremadura, Scimago Grp, Dept Informat & Commun, E-06071 Badajoz, Spain.
EM guerrero@unex.es
RI de Moya Anegón, Félix/C-4004-2009; Guerrero-Bote, Vicente P./E-7546-2016
OI de Moya Anegón, Félix/0000-0002-0255-8628; Guerrero-Bote, Vicente
   P./0000-0003-4821-9768; Blazquez Ruiz, Jesus/0000-0002-3158-1421
FU Junta de Extremadura; Consejeria de Educacion Ciencia Tecnologia;
   European Social Fund [GR15024]; Plan Nacional de Investigacion
   Cientifica, Desarrollo e Innovacion Tecnologica; European Regional
   Development Fund (ERDF) [CSO2013-40530-R]
FX This work was financed by the Junta de Extremadura and the Consejeria de
   Educacion Ciencia & Tecnologia, and the European Social Fund, as part of
   research group grant GR15024, and by the Plan Nacional de Investigacion
   Cientifica, Desarrollo e Innovacion Tecnologica 2012-2015 and the
   European Regional Development Fund (ERDF) as part of research project
   CSO2013-40530-R. We also want to thank Juan Jose Cordoba Ramos for his
   help in labeling the structure of clusters and his comments about the
   results.
CR Alfaraz PH, 2004, SCIENTOMETRICS, V61, P89, DOI 10.1023/B:SCIE.0000037365.53469.91
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], 1999, THESIS
   [Anonymous], [No title captured]
   Bornmann L, 2008, J DOC, V64, P45, DOI 10.1108/00220410810844150
   Bornmann L, 2012, J INFORMETR, V6, P333, DOI 10.1016/j.joi.2011.11.006
   Bornmann L, 2011, ANNU REV INFORM SCI, V45, P199, DOI 10.1002/aris.2011.1440450112
   CALLON M, 1991, SCIENTOMETRICS, V22, P155, DOI 10.1007/BF02019280
   Clauset A, 2004, PHYS REV E, V70, DOI 10.1103/PhysRevE.70.066111
   DAMERAU FJ, 1964, COMMUN ACM, V7, P171, DOI 10.1145/363958.363994
   GARFIELD E, 1955, SCIENCE, V122, P108, DOI 10.1126/science.122.3159.108
   GARFIELD E, 1963, AM DOC, V14, P195, DOI 10.1002/asi.5090140304
   Guerrero-Bote VP, 2016, PROF INFORM, V25, P25, DOI 10.3145/epi.2016.ene.04
   Guerrero-Bote VP, 2016, LWT-FOOD SCI TECHNOL, V67, P133, DOI 10.1016/j.lwt.2015.11.035
   Guerrero-Bote VP, 2015, J FOOD SCI, V80, pR2619, DOI 10.1111/1750-3841.13108
   Hinze S, 1996, SCIENTOMETRICS, V37, P313, DOI 10.1007/BF02093627
   IRVINE J, 1985, NATURE, V316, P587, DOI 10.1038/316587a0
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   LEVENSHT.VI, 1965, DOKL AKAD NAUK SSSR+, V163, P845
   Leydesdorff L, 2010, J AM SOC INF SCI TEC, V61, P352, DOI 10.1002/asi.21250
   Lopez-Illescas C, 2009, J INF SCI, V35, P244, DOI 10.1177/0165551508098603
   MERTON RK, 1968, SCIENCE, V159, P56, DOI 10.1126/science.159.3810.56
   MERTON RK, 1988, ISIS, V79, P606, DOI 10.1086/354848
   Miguel S, 2011, J AM SOC INF SCI TEC, V62, P1130, DOI 10.1002/asi.21532
   Mingers J, 2015, EUR J OPER RES, V246, P1, DOI 10.1016/j.ejor.2015.04.002
   Moed H., 2005, CITATION ANAL RES EV
   Muscio A, 2012, FOOD POLICY, V37, P710, DOI 10.1016/j.foodpol.2012.07.003
   Nalimov Vasily V., 1971, MEASUREMENT SCI STUD
   Neff MW, 2009, SCIENTOMETRICS, V80, P657, DOI 10.1007/s11192-008-2099-3
   Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.066133
   Noack Andreas, 2007, Journal of Graph Algorithms and Applications, V11, P453
   Noack A, 2009, PHYS REV E, V79, DOI 10.1103/PhysRevE.79.026102
   PRICE DJD, 1976, J AM SOC INFORM SCI, V27, P292, DOI 10.1002/asi.4630270505
   PRICE DJD, 1965, SCIENCE, V149, P510
   Rehn C., 2008, BIBLIOMETRIC HDB KAR
   Romo-Fernandez LM, 2013, SCIENTOMETRICS, V97, P743, DOI 10.1007/s11192-013-1009-5
   Seetharam G, 1999, SCIENTOMETRICS, V44, P59, DOI 10.1007/BF02458478
   van Eck NJ, 2010, J AM SOC INF SCI TEC, V61, P2405, DOI 10.1002/asi.21421
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Waltman L, 2010, J INFORMETR, V4, P629, DOI 10.1016/j.joi.2010.07.002
   Wouters P, 2014, BEYOND BIBLIOMETRICS: HARNESSING MULTIDIMENSIONAL INDICATORS OF SCHOLARLY IMPACT, P47
   Zhou GH, 2012, MEAT SCI, V92, P188, DOI 10.1016/j.meatsci.2012.04.016
NR 43
TC 8
Z9 9
U1 0
U2 41
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1541-4337
J9 COMPR REV FOOD SCI F
JI Compr. Rev. Food. Sci. Food Saf.
PD NOV
PY 2016
VL 15
IS 6
BP 1040
EP 1055
DI 10.1111/1541-4337.12223
PG 16
WC Food Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Food Science & Technology
GA DZ4SO
UT WOS:000385850300004
PM 33401834
DA 2022-08-23
ER

PT J
AU Wang, Q
   Wang, JP
   Xue, MM
   Zhang, XF
AF Wang, Qiang
   Wang, Jinping
   Xue, Mingmei
   Zhang, Xifeng
TI Characteristics and Trends of Ocean Remote Sensing Research from 1990 to
   2020: A Bibliometric Network Analysis and Its Implications
SO JOURNAL OF MARINE SCIENCE AND ENGINEERING
LA English
DT Article
DE remote sensing; ocean ecosystem; bibliometric analysis; VOSviewer;
   knowledge mapping
ID CLIMATE-CHANGE; REFLECTANCE PRODUCTS; MARINE; MANAGEMENT; COASTAL;
   CARBON; PHENOLOGY; LANDSAT-8; SYSTEM; IMPACT
AB The ocean is of great significance in the climate system, global resources and strategic decision making. With the continuous improvement in remote sensing technology, ocean remote sensing research has increasingly become an important topic for resource development and environmental protection. This paper uses bibliometric analysis method and VOSviewer visual software to conduct analysis. The analysis focuses on the period from 1990 to 2020. The analysis results show that articles have been steadily increasing over the past two decades. Scholars and researchers form the United States, China and Europe (mainly Western European countries), as well as NASA, Chinese Academy of Sciences and NOAA have bigger influence in this field to some extent. Among them, the United States and NASA holds the core leading position. Moreover, global cooperation in this field presents certain characteristics of geographical distribution. This study also reveals journals that include the most publications and subject categories that are highly relevant to related fields. Cluster analysis shows that remote sensing, ocean color, MODIS (or Moderate Resolution Imaging Spectroradiometer), chlorophy, sea ice and climate change are main research hotspots. In addition, in the context of climate warming, researchers have improved monitoring technology for remote sensing to warn and protect ocean ecosystems in hotspots (the Arctic and Antarctica). The valuable results obtained from this study will help academic professionals keep informed of the latest developments and identify future research directions in the field related to ocean remote sensing.
C1 [Wang, Qiang; Wang, Jinping; Xue, Mingmei] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China.
   [Zhang, Xifeng] Northwest Normal Univ, Coll Geog & Environm Sci, Lanzhou 730070, Peoples R China.
RP Wang, JP (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China.
EM wangqiang@llas.ac.cn; wangjp@llas.ac.cn; xuemm@llas.ac.cn;
   zhangxifeng@nwnu.edu.cn
OI qiang, wang/0000-0001-6516-6202
FU Literature and Information Capacity Building Special Project of Chinese
   Academy of Sciences [E0290001]
FX We wish to thank the anonymous reviewers for valuable comments on the
   manuscript. This work was funded by the Literature and Information
   Capacity Building Special Project of Chinese Academy of Sciences
   (E0290001).
CR Aparicio G, 2019, EUR RES MANAG BUS EC, V25, P105, DOI 10.1016/j.iedeen.2019.04.003
   Barth JA, 2019, FRONT MAR SCI, V6, DOI 10.3389/fmars.2019.00093
   BUETTNER KJ, 1969, EOS T AM GEOPHYS UN, V50, P158
   [陈尚 CHEN Shang], 2006, [地球科学进展, Advance in Earth Sciences], V21, P1127
   Cheng QC, 2021, FRONT MAR SCI, V8, DOI 10.3389/fmars.2021.657263
   Cheung WWL, 2016, SCIENCE, V354, P1591, DOI 10.1126/science.aag2331
   Choi W, 2021, J CLEAN PROD, V301, DOI 10.1016/j.jclepro.2021.126908
   Cui TW, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18093067
   Dong Lina, 2014, Applied Mechanics and Materials, V644-650, P4360, DOI 10.4028/www.scientific.net/AMM.644-650.4360
   Duan PL, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12050772
   Edwards M, 2004, NATURE, V430, P881, DOI 10.1038/nature02808
   El Mahrad B, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12142313
   Fahimnia B, 2015, INT J PROD ECON, V162, P101, DOI 10.1016/j.ijpe.2015.01.003
   Fink P, 2013, ISOT ENVIRON HEALT S, V49, P312, DOI 10.1080/10256016.2013.808197
   [傅明珠 Fu Mingzhu], 2016, [海洋学报, Acta Oceanologica Sinica], V38, P22
   Goddijn-Murphy L, 2018, MAR POLLUT BULL, V126, P255, DOI 10.1016/j.marpolbul.2017.11.011
   Guo JJ, 2001, IEEE T GEOSCI REMOTE, V39, P1019, DOI 10.1109/36.921420
   Hoegh-Guldberg O, 2010, SCIENCE, V328, P1523, DOI 10.1126/science.1189930
   Hou YL, 2021, ENVIRON SCI POLLUT R, V28, P34187, DOI 10.1007/s11356-021-14059-2
   Huang H, 2012, PROC SPIE, V8524, DOI 10.1117/12.977224
   Huang YL, 2016, SCIENTOMETRICS, V109, P1547, DOI 10.1007/s11192-016-2117-9
   Huot Y, 2016, REMOTE SENS ENVIRON, V184, P101, DOI 10.1016/j.rse.2016.06.002
   Jiao H., 2019, INF STUD THEORY APPL, V42, P101
   Kavanaugh MT, 2021, OCEANOGRAPHY, V34, P62, DOI 10.5670/oceanog.2021.215
   Khalid FE, 2021, J MAR SCI ENG, V9, DOI 10.3390/jmse9020155
   Kleidman RG, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005760
   Konstantinos T., 2021, MAR POLLUT BULL, V170
   Kuhn C, 2019, REMOTE SENS ENVIRON, V224, P104, DOI 10.1016/j.rse.2019.01.023
   Leng S.Y., 2016, GEOGRAPHICAL SCI 198
   Li XF, 2020, NATL SCI REV, V7, P1584, DOI 10.1093/nsr/nwaa047
   Li YX, 2017, SCIENTOMETRICS, V112, P1025, DOI 10.1007/s11192-017-2421-z
   Liu FL, 2016, SCIENTOMETRICS, V106, P751, DOI 10.1007/s11192-015-1789-x
   Loisel H, 2018, J GEOPHYS RES-OCEANS, V123, P2141, DOI 10.1002/2017JC013632
   Luo Z, 2020, FRONT MAR SCI, V7, DOI 10.3389/fmars.2020.00041
   Mao ZH, 2016, REMOTE SENS-BASEL, V8, DOI 10.3390/rs8070536
   Vazquez RMM, 2021, J MAR SCI ENG, V9, DOI 10.3390/jmse9010093
   McCarthy MJ, 2017, ENVIRON MANAGE, V60, P323, DOI 10.1007/s00267-017-0880-x
   Mcleod E, 2011, FRONT ECOL ENVIRON, V9, P552, DOI 10.1890/110004
   Md Khudzari J, 2018, BIOCHEM ENG J, V136, P51, DOI 10.1016/j.bej.2018.05.002
   Melin F, 2016, REMOTE SENS ENVIRON, V177, P107, DOI 10.1016/j.rse.2016.02.014
   Meng S, 2018, TROP CONSERV SCI, V11, DOI 10.1177/1940082918806795
   Merigo JM, 2017, OMEGA-INT J MANAGE S, V73, P37, DOI 10.1016/j.omega.2016.12.004
   Moore CJ, 2008, ENVIRON RES, V108, P131, DOI 10.1016/j.envres.2008.07.025
   Muller-Karger F, 2013, IEEE GEOSC REM SEN M, V1, P8, DOI 10.1109/MGRS.2013.2289656
   Nellemann C., 2009, ENV FOOD CRISIS ENV
   Notz D, 2016, SCIENCE, V354, P747, DOI 10.1126/science.aag2345
   Ospina-Mateus H, 2019, SCIENTOMETRICS, V121, P793, DOI 10.1007/s11192-019-03234-5
   Ouyang W, 2018, SCI TOTAL ENVIRON, V637, P208, DOI 10.1016/j.scitotenv.2018.04.434
   Peng B, 2019, J COASTAL RES, P783, DOI 10.2112/SI94-155.1
   Perry AL, 2005, SCIENCE, V308, P1912, DOI 10.1126/science.1111322
   Platt T, 2008, REMOTE SENS ENVIRON, V112, P3426, DOI 10.1016/j.rse.2007.10.016
   PRITCHARD A, 1969, J DOC, V25, P348
   Salgado-Hernanz PM, 2019, REMOTE SENS ENVIRON, V221, P50, DOI 10.1016/j.rse.2018.10.036
   STEHLING KR, 1969, ASTRONAUT AERONAUT, V7, P62
   Su H, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12142294
   Sulich A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13041604
   Tiit K., 2020, REMOTE SENS ENVIRON, V240
   Umbert M, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12071153
   United Nations Environment Programme, 2019, GLOB ENV OUTL GEO 6
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   VINOGRAD.VV, 1969, OCEANOLOGY-USSR, V9, P414
   Waltman L, 2010, J INFORMETR, V4, P629, DOI 10.1016/j.joi.2010.07.002
   Wang BJ, 2021, ECOL INDIC, V125, DOI 10.1016/j.ecolind.2021.107449
   [王福涛 Wang Futao], 2021, [中国科学院院刊, Bulletin of the Chinese Academy of Sciences], V36, P932
   Wang H, 2020, CHIN SCI B-CHIN, V65, P453, DOI 10.1360/TB-2019-0287
   [王金平 Wang Jinping], 2018, [地球科学进展, Advance in Earth Sciences], V33, P653
   Wang XG, 2014, SCIENTOMETRICS, V101, P1253, DOI 10.1007/s11192-014-1347-y
   Wei JW, 2018, REMOTE SENS ENVIRON, V215, P18, DOI 10.1016/j.rse.2018.05.033
   Yang WT, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17176218
   Ye C.L., 2012, INF STUD THEORY APPL, V35, P79
   Yin X.X., 2009, J INTELL-BASEL, V28, P1
   Yun C.X., 1993, ADV EARTH SCI, V8, P14
   Zeng JJ, 2021, J CLEAN PROD, V295, DOI 10.1016/j.jclepro.2021.126468
   Zhang G.B., 2021, LIBR INF SERV, V65, P89
   Zhang JD, 2021, J CLEAN PROD, V300, DOI 10.1016/j.jclepro.2021.126824
   Zhang X, 2019, INT J DIGIT EARTH, V12, P1457, DOI 10.1080/17538947.2019.1574317
   [赵鸿彬 Zhao Hongbin], 2019, [测绘学报, Acta Geodetica et Cartographica Sinica], V48, P1119
   Zhao XB, 2017, AUTOMAT CONSTR, V80, P37, DOI 10.1016/j.autcon.2017.04.002
NR 78
TC 0
Z9 0
U1 9
U2 9
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2077-1312
J9 J MAR SCI ENG
JI J. Mar. Sci. Eng.
PD MAR
PY 2022
VL 10
IS 3
AR 373
DI 10.3390/jmse10030373
PG 16
WC Engineering, Marine; Engineering, Ocean; Oceanography
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Oceanography
GA 0B7UX
UT WOS:000774836500001
OA gold
DA 2022-08-23
ER

PT J
AU Chen, YB
   Tong, XF
   Ren, JG
   Yu, CQ
   Cui, YL
AF Chen, Yi-Bing
   Tong, Xiao-Fang
   Ren, Junge
   Yu, Chun-Quan
   Cui, Yuan-Lu
TI Current Research Trends in Traditional Chinese Medicine Formula: A
   Bibliometric Review from 2000 to 2016
SO EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE
LA English
DT Review
ID DEPRESSION; HEALTH; ANTIOXIDANT; PATHWAYS; SCIENCE; DISEASE; INJURY;
   THREAT; MILD
AB Background. Traditional Chinese Medicine Formula (TCMF) study has been recognized widely by medical scientists around the world. However, few researchers have analyzed and summarized the rapid growth of academic articles of TCMF published in English. The primary aim of this work was to assess the outcome of these research outputs in the TCMF field from 2000 to 2016 and to evaluate the situation and tendency. Methods. Research datasets were acquired from the Web of Science database, which includes all academic articles published from 2000 to 2016; articles were tracked by the keywords Traditional Chinese Medicine, Traditional Chinese Medicine Formula, and Chinese herb formula. Moreover, visualization software CiteSpace V was used to analyze and generate visualization knowledge maps. Results. In total, 26,917 articles appeared in the Web of Science database, and only 2,621 publications met requirement based on reading the abstract or full text. The annual publications total, list of journals, research interests, list of medicine names, disease types, and the top 20 cited articles were given in this research paper. In addition, we compared the research of Japan and Korea TCMF, in the appendix. Conclusion. This review demonstrates that increasingly more researchers have interest in the TCMF and TCMF has great significant advantages over other areas of focus. However, these publications were published rarely in top academic journals and most best-quality papers have bias toward medical analysis rather than pharmacology. To make a breakthrough in TCMF field, further investigation is required to place emphasis on the deepening study of the mechanism of related TCMF.
C1 [Chen, Yi-Bing; Tong, Xiao-Fang; Yu, Chun-Quan; Cui, Yuan-Lu] Tianjin Univ Tradit Chinese Med, Tianjin 301617, Peoples R China.
   [Chen, Yi-Bing; Tong, Xiao-Fang; Cui, Yuan-Lu] Tianjin Univ Tradit Chinese Med, Res Ctr Tradit Chinese Med, Tianjin 301617, Peoples R China.
   [Chen, Yi-Bing; Tong, Xiao-Fang; Cui, Yuan-Lu] Tianjin Univ Tradit Chinese Med, Key Res Lab Prescript Compatibil Components, Tianjin 301617, Peoples R China.
   [Ren, Junge] Tianjin Univ Lib, Tianjin 300072, Peoples R China.
RP Yu, CQ; Cui, YL (通讯作者)，Tianjin Univ Tradit Chinese Med, Tianjin 301617, Peoples R China.; Cui, YL (通讯作者)，Tianjin Univ Tradit Chinese Med, Res Ctr Tradit Chinese Med, Tianjin 301617, Peoples R China.; Cui, YL (通讯作者)，Tianjin Univ Tradit Chinese Med, Key Res Lab Prescript Compatibil Components, Tianjin 301617, Peoples R China.
EM 570016009@qq.com; 15032266399@163.com; renjunge@tju.edu.cn;
   ycq-4@163.com; cuiyl@tju.edu.cn
RI Cui, Yuan-Lu/A-3705-2013
OI Cui, Yuan-Lu/0000-0003-4392-3713; Chen, Yi-Bing/0000-0003-0025-7474
FU National Natural Science Foundation of China [81741119, 81473542]
FX This work was supported by the National Natural Science Foundation of
   China (grant numbers 81741119, 81473542). The authors are thankful to
   Tianjin University of Traditional Chinese Medicine for help in
   conducting this study.
CR Abd El-Wahab Abeer E, 2013, BMC Complement Altern Med, V13, P218, DOI 10.1186/1472-6882-13-218
   Aroor AR, 2013, METABOLISM, V62, P1543, DOI 10.1016/j.metabol.2013.07.001
   Castro-Vazquez L, 2016, OXID MED CELL LONGEV, V2016, DOI 10.1155/2016/8915729
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chiu JH, 2014, EVID-BASED COMPL ALT, V2014, DOI 10.1155/2014/965486
   Danhier F, 2010, J CONTROL RELEASE, V148, P135, DOI 10.1016/j.jconrel.2010.08.027
   Farris P, 2014, J DRUGS DERMATOL, V13, P1467
   Fuster V, 2011, CIRCULATION, V123, P1671, DOI 10.1161/CIRCULATIONAHA.110.009522
   Guo Ping, 2002, Zhongguo Ying Yong Sheng Li Xue Za Zhi, V18, P131
   Hsiao CF, 2008, J FORMOS MED ASSOC, V107, pS74, DOI 10.1016/S0929-6646(09)60011-5
   de Granda-Orive JI, 2011, ARCH BRONCONEUMOL, V47, P213, DOI 10.1016/j.arbres.2010.10.007
   Jaeschke H, 2011, J GASTROEN HEPATOL, V26, P173, DOI 10.1111/j.1440-1746.2010.06592.x
   Jia W, 2004, PHYTOTHER RES, V18, P681, DOI 10.1002/ptr.1506
   Kazakis NA, 2014, SCIENTOMETRICS, V101, P505, DOI 10.1007/s11192-014-1326-3
   Kelleher C, 2011, ENVIRON MODELL SOFTW, V26, P822, DOI 10.1016/j.envsoft.2010.12.006
   King RJ, 2016, HERZ, V41, P184, DOI 10.1007/s00059-016-4414-8
   Leighton SP, 2018, MOL PSYCHIATR, V23, P48, DOI 10.1038/mp.2017.205
   Li F, 2016, SCI REP-UK, V6, DOI 10.1038/srep37114
   Li J., 2016, ARXIV, V1st
   Liu I.-M., 2011, EVIDENCE BASED COMPL, V2011
   Lu J, 2011, J ETHNOPHARMACOL, V134, P911, DOI 10.1016/j.jep.2011.01.049
   Mandegary A, 2012, FOOD CHEM TOXICOL, V50, P2503, DOI 10.1016/j.fct.2012.04.020
   Mitanchez D, 2014, J PEDIATR-US, V164, P445, DOI 10.1016/j.jpeds.2013.10.076
   Mo CF, 2014, ANTIOXID REDOX SIGN, V20, P574, DOI 10.1089/ars.2012.5116
   OECD, 2008, OECD GLOSS STAT TERM, DOI [10.1787/9789264055087-en, DOI 10.1787/9789264055087-EN]
   PETERS HPF, 1991, SCIENTOMETRICS, V20, P235, DOI 10.1007/BF02018157
   Ross S, 2016, J PSYCHOPHARMACOL, V30, P1165, DOI 10.1177/0269881116675512
   SCHUBERT A, 1986, SCIENTOMETRICS, V9, P281, DOI 10.1007/BF02017249
   Wachs TD, 2009, CHILD DEV PERSPECT, V3, P51, DOI 10.1111/j.1750-8606.2008.00077.x
   Wang J, 2012, EVID-BASED COMPL ALT, V2012, DOI 10.1155/2012/697237
   World Health Organization (WHO), 2013, WHO TRAD MED STRAT 2, DOI [10.2471/BLT.08.060814, DOI 10.2471/BLT.08.060814]
   Xi MM, 2008, PHYTOTHER RES, V22, P228, DOI 10.1002/ptr.2297
   Yan L, 2016, SCI REP-UK, V6, DOI 10.1038/srep30014
   Yang XC, 2014, COMPLEMENT THER MED, V22, P391, DOI 10.1016/j.ctim.2014.01.003
   Yu YL, 2009, J ETHNOPHARMACOL, V124, P444, DOI 10.1016/j.jep.2009.05.027
   Zhai X, 2016, LANCET, V387, P1722, DOI 10.1016/S0140-6736(16)30261-6
   Zhang Y, 2012, MATH PROBL ENG, V2012, P2012, DOI DOI 10.1145/2382196.2382230
   Zhou MG, 2016, LANCET, V387, P251, DOI 10.1016/S0140-6736(15)00551-6
   Zyoud SH, 2015, BMC COMPLEM ALTERN M, V15, DOI 10.1186/s12906-015-0840-z
NR 40
TC 40
Z9 42
U1 9
U2 33
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1741-427X
EI 1741-4288
J9 EVID-BASED COMPL ALT
JI Evid.-based Complement Altern. Med.
PY 2019
VL 2019
AR 3961395
DI 10.1155/2019/3961395
PG 13
WC Integrative & Complementary Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Integrative & Complementary Medicine
GA HP4GI
UT WOS:000461634000001
PM 30941195
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Weinzierl, MA
   Harabagiu, SM
AF Weinzierl, Maxwell A.
   Harabagiu, Sanda M.
TI Automatic detection of COVID-19 vaccine misinformation with graph link
   prediction
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Natural Language Processing; Machine learning; COVID-19; vaccine
   misinformation; Social Media; knowledge graph embedding
AB Enormous hope in the efficacy of vaccines became recently a successful reality in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled by exposure to social media misinformation about COVID-19 vaccines became a major hurdle. Therefore, it is essential to automatically detect where misinformation about COVID-19 vaccines on social media is spread and what kind of misinformation is discussed, such that inoculation interventions can be delivered at the right time and in the right place, in addition to interventions designed to address vaccine hesitancy. This paper is addressing the first step in tackling hesitancy against COVID-19 vaccines, namely the automatic detection of known misinformation about the vaccines on Twitter, the social media platform that has the highest volume of conversations about COVID-19 and its vaccines. We present COVAXLIES, a new dataset of tweets judged relevant to several misinformation targets about COVID-19 vaccines on which a novel method of detecting misinformation was developed. Our method organizes COVAXLIES in a Misinformation Knowledge Graph as it casts misinformation detection as a graph link prediction problem. The misinformation detection method detailed in this paper takes advantage of the link scoring functions provided by several knowledge embedding methods. The experimental results demonstrate the superiority of this method when compared with classification-based methods, widely used currently.
C1 [Weinzierl, Maxwell A.; Harabagiu, Sanda M.] Univ Texas Dallas, Human Language Technol Res Inst, Dept Comp Sci, Richardson, TX 75080 USA.
RP Weinzierl, MA (通讯作者)，Univ Texas Dallas, Human Language Technol Res Inst, Dept Comp Sci, Richardson, TX 75080 USA.
EM maxwell.weinzierl@utdallas.edu; sanda@utdallas.edu
OI Harabagiu, Sanda/0000-0002-8186-1501
CR A.S. Foundation,, 1999, AP LUC
   [Anonymous], **NON-TRADITIONAL**
   [Anonymous], DEBUNKING MYTHS COVI
   [Anonymous], 2019, P C EMP METH NAT LAN
   [Anonymous], COVID 19 MISINFORMAT
   [Anonymous], REAL FACTS COMMON CO
   [Anonymous], VACCINE MYTHS DEBUNK
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Beaulieu M. M., 1997, Fifth Text REtrieval Conference (TREC-5) (NIST SP 500-238), P143
   Beltagy I., 2019, P 2019 C EMPIRICAL M, P3615
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Castillo Carlos, 2011, P 20 INT C WORLD WID, P675, DOI 10.1145/1963405.1963500
   Chen E., JMIR PUBLIC HLTH SUR, V6
   Chiu Jason PC, 2016, T ASS COMPUT LINGUIS, V4, P357, DOI 10.1162/tacl_a_00104
   Das A.S., P 16 INT C WORLD WID
   Derczynski L., 2017, PROC INT WORKSHOP SE, P69, DOI [10.18653/v1/S17-2006, DOI 10.18653/V1/S17-2006]
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Figueiredo A., LANCET LONDON ENGLAN, P396
   Goodman F.C, 2020, JACK VACCINE RUMOURS
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hossain T., 2020, P 1 WORKSHOP NLP COV, DOI [10.18653/v1/2020.nlpcovid19-2.11, DOI 10.18653/V1/2020.NLPCOVID19-2.11]
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kai Shu, 2017, ACM SIGKDD Explorations Newsletter, V19, P22, DOI 10.1145/3137597.3137600
   King DB, 2015, ACS SYM SER, V1214, P1
   Kwok KO, 2020, J INFECTION, V80, pE32, DOI 10.1016/j.jinf.2020.03.027
   Kwon S, 2013, IEEE DATA MINING, P1103, DOI 10.1109/ICDM.2013.61
   Lee J., BIOINFORMATICS
   Liu X., 2015, P ACM INT C INF KNOW, P1867, DOI DOI 10.1145/2806416.2806651
   Liu Y, 2018, AAAI CONF ARTIF INTE, P354
   Loomba S, 2021, NAT HUM BEHAV, V5, P337, DOI 10.1038/s41562-021-01056-1
   Lu YJ, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P505
   Ma J., 2016, P 25 INT JOINT C ART, P3818, DOI DOI 10.5555/3061053
   Ma J, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3049, DOI 10.1145/3308558.3313741
   Ma J, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1980
   Ma J, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P708, DOI 10.18653/v1/P17-1066
   McHugh ML, 2012, BIOCHEM MEDICA, V22, P276, DOI 10.11613/bm.2012.031
   Muller M., COVID TWITTER BERT N
   Nguyen AT., 2020, P 2020 C EMP METH NA, P9
   Nguyen D.M., 2019, P 2019 C N AM CHAPTE, V1, P1391
   Nogueira R., ARXIV190104085
   Ou-Yang L, 2014, NEWSPAPER3K ARTICLE
   Saraykar T., WHAT SOCIAL MEDIA IS
   Weinzierl M., 2021, P INT AAI C WEB SOC
   Wu LW, 2019, CHINESE J ELECTRON, V28, P754, DOI 10.1049/cje.2019.05.002
   Yang F., 2012, P ACM SIGKDD WORKSH
   Yang SH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1935
   Zhang Q, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2333, DOI 10.1145/3308558.3313718
   Zhang T., INT C LEARN REPR
   Zhao Z, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1395, DOI 10.1145/2736277.2741637
   Zubiaga A, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0150989
NR 50
TC 1
Z9 1
U1 8
U2 11
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD DEC
PY 2021
VL 124
AR 103955
DI 10.1016/j.jbi.2021.103955
EA NOV 2021
PG 15
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA XW2AP
UT WOS:000735429000001
PM 34800722
OA Green Submitted, Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Chu, JB
   Chen, JB
   Chen, XF
   Dong, W
   Shi, JL
   Huang, ZX
AF Chu, Jiebin
   Chen, Jinbiao
   Chen, Xiaofang
   Dong, Wei
   Shi, Jinlong
   Huang, Zhengxing
TI Knowledge-aware multi-center clinical dataset adaptation: Problem,
   method, and application
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Clinical dataset adaptation; Knowledge graph; Clinical outcome
   prediction; Heart failure; Representation learning
ID PROPENSITY SCORE; MULTIPLE
AB Adaptable utilization of clinical data collected from multiple centers, prompted by the need to overcome the shifts between the dataset distributions, and exploit these different datasets for potential clinical applications, has received significant attention in recent years. In this study, we propose a novel approach to this task by infusing an external knowledge graph (KG) into multi-center clinical data mining. Specifically, we propose an adversarial learning model to capture shared patient feature representations from multi-center heterogeneous clinical datasets, and employ an external KG to enrich the semantics of the patient sample by providing both clinical center-specific and center-general knowledge features, which are trained with a graph convolutional autoencoder. We evaluate the proposed model on a real clinical dataset extracted from the general cardiology wards of a Chinese hospital and a well-known public clinical dataset (MIMIC III, pertaining to ICU clinical settings) for the task of predicting acute kidney injury in patients with heart failure. The achieved experimental results demonstrate the efficacy of our proposed model.
C1 [Chu, Jiebin; Chen, Jinbiao; Chen, Xiaofang; Huang, Zhengxing] Zhejiang Univ, Coll Biomed Engn & Instrument Sci, Hangzhou, Peoples R China.
   [Dong, Wei] Chinese Peoples Liberat Army Gen Hosp, Dept Cardiol, Beijing, Peoples R China.
   [Shi, Jinlong] Chinese Peoples Liberat Army Gen Hosp, Med Big Data Ctr, Dept Med Innovat Res, Beijing, Peoples R China.
RP Huang, ZX (通讯作者)，Zhejiang Univ, Coll Biomed Engn & Instrument Sci, Hangzhou, Peoples R China.
EM zhengxinghuang@zju.edu.cn
FU National Key Research and Development Program of China [2018YFC2001204];
   National Natural Science Foundation of China [61672450]
FX This work was partially supported by the National Key Research and
   Development Program of China under Grant No. 2018YFC2001204 and the
   National Natural Science Foundation of China under Grant No. 61672450.
   The supporting bodies had no influence on the analysis and
   interpretation of data, on the writing of the report, or on the decision
   to submit the paper for publication.
CR Anand I, 2004, CIRCULATION, V110, P149, DOI 10.1161/01.CIR.0000134279.79571.73
   Austin PC, 2011, MULTIVAR BEHAV RES, V46, P119, DOI 10.1080/00273171.2011.540480
   Averitt AJ, 2020, J BIOMED INFORM, V109, DOI 10.1016/j.jbi.2020.103515
   Bakal G, 2018, J BIOMED INFORM, V82, P189, DOI 10.1016/j.jbi.2018.05.003
   Ben-Israel D, 2020, ARTIF INTELL MED, V103, DOI 10.1016/j.artmed.2019.101785
   Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
   Bica I., 2020, INT C LEARN REPR ICL
   Bickel S, 2009, J MACH LEARN RES, V10, P2137
   Bordes A., 2011, AAAI
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Boshnak H., 2019, INT J COMPUT SCI INF, V16, P68
   Chen JF, 2020, ARTIF INTELL MED, V103, DOI 10.1016/j.artmed.2019.101782
   Chen PP, 2019, J BIOMED INFORM, V100, DOI 10.1016/j.jbi.2019.103303
   Chen Y, 2019, COMMUN MATH STAT, V7, P1, DOI 10.1007/s40304-018-0132-2
   Choi E., P 23 ACM SIGKDD INT
   Choi E, 2017, J AM MED INFORM ASSN, V24, P361, DOI 10.1093/jamia/ocw112
   Chu JB, 2020, J BIOMED INFORM, V109, DOI 10.1016/j.jbi.2020.103518
   Dehejia RH, 2002, REV ECON STAT, V84, P151, DOI 10.1162/003465302317331982
   Duan HL, 2020, IEEE J BIOMED HEALTH, V24, P2053, DOI 10.1109/JBHI.2019.2962079
   Fei Wang, 2012, Statistical Analysis and Data Mining, V5, P54, DOI 10.1002/sam.11135
   Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
   Gu YY, 2020, IEEE J BIOMED HEALTH, V24, P1379, DOI 10.1109/JBHI.2019.2942429
   Hong SD, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5888
   Hripcsak G, 2015, STUD HEALTH TECHNOL, V216, P574, DOI 10.3233/978-1-61499-564-7-574
   Hripcsak G, 2016, P NATL ACAD SCI USA, V113, P7329, DOI 10.1073/pnas.1510502113
   Huang Z., 2019, IEEE J BIOMED HLTH I, V23
   Joachims T, 1999, MACHINE LEARNING, PROCEEDINGS, P200
   Johansson FD, 2016, PR MACH LEARN RES, V48
   Kingma D. P., 2017, ARXIV
   Kingma Diederik P., 2014, ADV NEURAL INFORM PR, P3581, DOI DOI 10.5555/2969033.2969226
   Kondylakis H, 2016, J BIOMED INFORM, V62, P32, DOI 10.1016/j.jbi.2016.05.006
   Li H, 2016, DATA BRIEF, V8, P40, DOI 10.1016/j.dib.2016.05.015
   Li LF, 2020, ARTIF INTELL MED, V103, DOI 10.1016/j.artmed.2020.101817
   Lv XB, 2014, J BIOMED INFORM, V52, P55, DOI 10.1016/j.jbi.2014.05.006
   Ma SW, 2020, ARTIF INTELL MED, V102, DOI 10.1016/j.artmed.2019.101745
   Okoli GN, 2014, BRIT J ANAESTH, V112, P13, DOI 10.1093/bja/aet290
   Ostropolets A, 2020, J BIOMED INFORM, V102, DOI 10.1016/j.jbi.2019.103363
   Pirracchio R, 2012, BMC MED RES METHODOL, V12, DOI 10.1186/1471-2288-12-70
   Pokharel S, 2020, ARTIF INTELL MED, V108, DOI 10.1016/j.artmed.2020.101900
   Quionero-Candela J., 2009, DATASET SHIFT MACHIN
   Riano D, 2019, ARTIF INTELL MED, V100, DOI 10.1016/j.artmed.2019.101713
   ROSENBAUM PR, 1983, BIOMETRIKA, V70, P41, DOI 10.1093/biomet/70.1.41
   Rossi E, 2014, J BIOMED INFORM, V52, P92, DOI 10.1016/j.jbi.2014.08.014
   Streiner DL, 2012, CHEST, V142, P1380, DOI 10.1378/chest.12-1920
   Sun J, 2012, SIGKDD EXPLOR, V14, P16, DOI [10.1145/2408736.2408740, DOI 10.1145/2408736.2408740]
   Valgimigli M, 2012, JACC-CARDIOVASC INTE, V5, P23, DOI 10.1016/j.jcin.2011.08.018
   Voss EA, 2015, J AM MED INFORM ASSN, V22, P553, DOI 10.1093/jamia/ocu023
   Wang JH, 2007, CONTEMP MATH, V443, P7
   Waring J, 2020, ARTIF INTELL MED, V104, DOI 10.1016/j.artmed.2020.101822
   Weng C, 2020, J BIOMED INFORM, V105, DOI 10.1016/j.jbi.2020.103433
   Xu X, 2019, SIGNAL PROCESS, V164, P354, DOI 10.1016/j.sigpro.2019.05.022
   Yang B., 2015, P INT C LEARN REPR
   Yao Liuyi, 2018, ADV NEURAL INFORM PR, P2633
   Yoon D, 2016, HEALTHC INFORM RES, V22, P54, DOI 10.4258/hir.2016.22.1.54
   Yoon J, 2018, 6 INT C LEARN REPR I, P1
   Zellinger W, 2017, INT C LEARN REPR
   Zhang ZQ, 2020, J AM MED INFORM ASSN, V27, P99, DOI 10.1093/jamia/ocz161
NR 58
TC 0
Z9 0
U1 8
U2 29
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD MAR
PY 2021
VL 115
AR 103710
DI 10.1016/j.jbi.2021.103710
EA FEB 2021
PG 12
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Medical Informatics
GA RN6WD
UT WOS:000640491300005
PM 33581323
OA Bronze
DA 2022-08-23
ER

PT J
AU Xu, J
   Kim, S
   Song, M
   Jeong, M
   Kim, D
   Kang, J
   Rousseau, JF
   Li, X
   Xu, WJ
   Torvik, VI
   Bu, Y
   Chen, CY
   Ebeid, IA
   Li, DF
   Ding, Y
AF Xu, Jian
   Kim, Sunkyu
   Song, Min
   Jeong, Minbyul
   Kim, Donghyeon
   Kang, Jaewoo
   Rousseau, Justin F.
   Li, Xin
   Xu, Weijia
   Torvik, Vetle I.
   Bu, Yi
   Chen, Chongyan
   Ebeid, Islam Akef
   Li, Daifeng
   Ding, Ying
TI Building a PubMed knowledge graph
SO SCIENTIFIC DATA
LA English
DT Article; Data Paper
ID DATABASE; DISAMBIGUATION; RECOGNITION; SYSTEM; GENES; CGRP
AB PubMed(R)is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguous, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID(R), and identifying fine-grained affiliation data from MapAffil. Through the integration of these credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving an F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities.
C1 [Xu, Jian; Li, Daifeng] Sun Yat Sen Univ, Sch Informat Management, Guangzhou, Peoples R China.
   [Kim, Sunkyu; Jeong, Minbyul; Kim, Donghyeon; Kang, Jaewoo] Korea Univ, Dept Comp Sci & Engn, Seoul, South Korea.
   [Song, Min] Yonsei Univ, Dept Lib & Informat Sci, Seoul, South Korea.
   [Rousseau, Justin F.; Ding, Ying] Univ Texas Austin, Dell Med Sch, Austin, TX 78712 USA.
   [Li, Xin; Chen, Chongyan; Ebeid, Islam Akef; Ding, Ying] Univ Texas Austin, Sch Informat, Austin, TX 78712 USA.
   [Xu, Weijia] Texas Adv Comp Ctr, Austin, TX USA.
   [Torvik, Vetle I.] Univ Illinois, Sch Informat Sci, Champaign, IL USA.
   [Bu, Yi] Peking Univ, Dept Informat Management, Beijing, Peoples R China.
RP Li, DF (通讯作者)，Sun Yat Sen Univ, Sch Informat Management, Guangzhou, Peoples R China.; Ding, Y (通讯作者)，Univ Texas Austin, Dell Med Sch, Austin, TX 78712 USA.; Ding, Y (通讯作者)，Univ Texas Austin, Sch Informat, Austin, TX 78712 USA.
EM lidaifeng@mail.sysu.edu.cn; ying.ding@austin.utexas.edu
RI Bu, Yi/B-4964-2018; Ebeid, Islam Akef/GLQ-8040-2022; Li,
   Xin/ABD-7192-2020; Kim, Sunkyu/AAJ-1052-2021; Rousseau,
   Justin/ABG-4164-2021
OI Bu, Yi/0000-0003-2549-4580; Ebeid, Islam Akef/0000-0001-5345-0775; Li,
   Xin/0000-0002-8169-6059; Kang, Jaewoo/0000-0001-6798-9106; Xu,
   Jian/0000-0003-4886-4708; Kim, Sunkyu/0000-0002-0240-6210; Rousseau,
   Justin/0000-0002-2817-9124
FU National Social Science Fund of China [18BTQ076]; Chinese National Youth
   Foundation Research [61702564]; Natural Science Foundation of Guangdong
   Province [2018A030313981]; Soft Science Foundation of Guangdong Province
   [2019A101002020]; National Research Foundation of Korea
   [NRF2019R1A2C2002577, NRF-2017R1A2A1A17069645]; US National Institutes
   of Health [P01AG039347]
FX This work was supported by National Social Science Fund of China
   [18BTQ076], Chinese National Youth Foundation Research [61702564],
   Natural Science Foundation of Guangdong Province [2018A030313981], Soft
   Science Foundation of Guangdong Province [2019A101002020], National
   Research Foundation of Korea [NRF2019R1A2C2002577] and
   [NRF-2017R1A2A1A17069645], and US National Institutes of Health
   [P01AG039347]. The authors acknowledge the Texas Advanced Computing
   Center (TACC) at The University of Texas at Austin for providing storage
   resources that have contributed to the research results reported within
   this paper. URL: http://www.tacc.utexas.edu.
CR Achakulvisut T, 2017, AFFILIATION PARSER
   Ammar W., 2018, P 2018 C N AM CHAPT, P84, DOI DOI 10.18653/V1/N18-3011
   Bell L, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0021474
   Bertolini F, 2015, NAT REV CLIN ONCOL, V12, P732, DOI 10.1038/nrclinonc.2015.169
   Blackburn R, 2018, ORCID PUBLIC DATA FI, DOI 10.23640/07243.7234028.v1
   Buchholz Sabine, 2006, P 10 C COMP NAT LANG, P149, DOI [DOI 10.3115/1596276.1596305, 10.5555/1596276.1596305]
   Carroll HD, 2010, BIOINFORMATICS, V26, P1708, DOI 10.1093/bioinformatics/btq270
   CUSP, 2019, CUSP SCI PROF
   D'Souza J, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (IJCNLP), VOL 2, P297
   Degtyarenko K, 2008, NUCLEIC ACIDS RES, V36, pD344, DOI 10.1093/nar/gkm791
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dogan RI, 2014, J BIOMED INFORM, V47, P1, DOI 10.1016/j.jbi.2013.12.006
   Donnelly K, 2006, STUD HEALTH TECHNOL, V121, P279
   Durham PL, 2006, HEADACHE, V46, pS3, DOI 10.1111/j.1526-4610.2006.00483.x
   Durham PL, 2004, NEW ENGL J MED, V350, P1073, DOI 10.1056/NEJMp048016
   ELSEVIER, 2019, ELSEVIER FINGERPRINT
   Furlong LI, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-84
   Gerner M, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-85
   Giorgi JM, 2018, BIOINFORMATICS, V34, P4087, DOI 10.1093/bioinformatics/bty449
   Griffin M., 2019, PROFESSIONAL NETWORK
   Habibi M, 2017, BIOINFORMATICS, V33, pI37, DOI 10.1093/bioinformatics/btx228
   Hakala K, 2016, ACL, V2016, P102, DOI DOI 10.18653/V1/W16-2913
   Hamosh A, 2005, NUCLEIC ACIDS RES, V33, pD514
   Kang IS, 2009, INFORM PROCESS MANAG, V45, P84, DOI 10.1016/j.ipm.2008.06.006
   Kawashima H, 2015, SCIENTOMETRICS, V103, P1061, DOI 10.1007/s11192-015-1580-z
   Kim D, 2019, IEEE ACCESS, V7, P73729, DOI 10.1109/ACCESS.2019.2920708
   Kim JK, 2019, SPACE, P70
   Krallinger M., 2015, J CHEMINFORMATICS S1, V7, pS1
   Landrum MJ, 2016, NUCLEIC ACIDS RES, V44, pD862, DOI 10.1093/nar/gkv1222
   Laudel G, 2003, SCIENTOMETRICS, V57, P215, DOI 10.1023/A:1024137718393
   Law V, 2014, NUCLEIC ACIDS RES, V42, pD1091, DOI 10.1093/nar/gkt1068
   Lee J, 2020, BIOINFORMATICS, V36, P1234, DOI 10.1093/bioinformatics/btz682
   Lerchenmueller MJ, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0158731
   Levin M, 2012, J AM SOC INF SCI TEC, V63, P1030, DOI 10.1002/asi.22621
   Li J, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw068
   Li JC, 2019, SCI DATA, V6, DOI 10.1038/s41597-019-0033-6
   Lipscomb CE, 2000, B MED LIBR ASSOC, V88, P265
   Liu WL, 2014, J ASSOC INF SCI TECH, V65, P765, DOI 10.1002/asi.23063
   Liu YF, 2015, NUCLEIC ACIDS RES, V43, pW535, DOI 10.1093/nar/gkv383
   Lu Z, 2011, BMC BIOINFORMATIC S8, V12, pS2
   Maglott D, 2011, NUCLEIC ACIDS RES, V39, pD52, DOI 10.1093/nar/gkq1237
   Morgan AA, 2008, GENOME BIOL, V9, pS3
   NIH, 2018, NIH EXPORTER DAT 201
   NLM, 2019, MEDLINE PUBMED XML E
   NLM, 2019, DOWNL MEDLINE PUBMED
   ORCID, 2019, AB ORCID
   Pafilis E, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0065390
   Pradhan S., 2013, TASK, P1
   Sachan Devendra Singh, 2018, P MACHINE LEARNING R, V85, P1
   Sang EFTK, 1999, NINTH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS, P173
   Sherry ST, 2001, NUCLEIC ACIDS RES, V29, P308, DOI 10.1093/nar/29.1.308
   Shin D, 2014, SCIENTOMETRICS, V100, P15, DOI 10.1007/s11192-014-1289-4
   Shu H, 2020, J AM STAT ASSOC, V115, P292, DOI 10.1080/01621459.2018.1543599
   Smith L., 2008, GENOME BIOL, V9
   Thomas PE, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-S4-S4
   Torvik V. I., 2018, MAPAFFIL 2016 DATASE, DOI [10.13012/B2IDB-4354331_V1, DOI 10.13012/B2IDB-4354331_V1]
   Torvik Vetle I, 2015, Dlib Mag, V21, DOI 10.1045/november2015-torvik
   Torvik VI, 2009, ACM T KNOWL DISCOV D, V3, DOI 10.1145/1552303.1552304
   UCI, 2019, DISC UCI FAC
   Uzuner O, 2011, J AM MED INFORM ASSN, V18, P552, DOI 10.1136/amiajnl-2011-000203
   Wang X, 2019, BIOINFORMATICS, V35, P1745, DOI 10.1093/bioinformatics/bty869
   Warner ET, 2016, J GEN INTERN MED, V31, P60, DOI 10.1007/s11606-015-3463-7
   Wei CH, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0038460
   Wu H, 2014, SCIENTOMETRICS, V101, P1955, DOI 10.1007/s11192-014-1283-x
   Wu J, 2013, SCIENTOMETRICS, V96, P683, DOI 10.1007/s11192-013-0978-8
   Wu Y, 2016, ARXIV160908144
   Xu J, 2020, BUILDING PUBMED KNOW, DOI 10.6084/m9.figshare.c.4773944
   Yoon W, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-2813-6
   Yue W, 2014, CANCER PREV RES, V7, P388, DOI 10.1158/1940-6207.CAPR-13-0337
NR 69
TC 33
Z9 34
U1 18
U2 39
PU NATURE PUBLISHING GROUP
PI LONDON
PA MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 2052-4463
J9 SCI DATA
JI Sci. Data
PD JUN 26
PY 2020
VL 7
IS 1
AR 205
DI 10.1038/s41597-020-0543-2
PG 15
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA MG3XG
UT WOS:000545966200002
PM 32591513
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Blagec, K
   Barbosa-Silva, A
   Ott, S
   Samwald, M
AF Blagec, Kathrin
   Barbosa-Silva, Adriano
   Ott, Simon
   Samwald, Matthias
TI A curated, ontology-based, large-scale knowledge graph of artificial
   intelligence tasks and benchmarks
SO SCIENTIFIC DATA
LA English
DT Article
AB Research in artificial intelligence (AI) is addressing a growing number of tasks through a rapidly growing number of models and methodologies. This makes it difficult to keep track of where novel AI methods are successfully-or still unsuccessfully - applied, how progress is measured, how different advances might synergize with each other, and how future research should be prioritized. To help address these issues, we created the Intelligence Task Ontology and Knowledge Graph (ITO), a comprehensive, richly structured and manually curated resource on artificial intelligence tasks, benchmark results and performance metrics. The current version of ITO contains 685,560 edges, 1,100 classes representing AI processes and 1,995 properties representing performance metrics. The primary goal of ITO is to enable analyses of the global landscape of AI tasks and capabilities. ITO is based on technologies that allow for easy integration and enrichment with external data, automated inference and continuous, collaborative expert curation of underlying ontological models. We make the ITO dataset and a collection of Jupyter notebooks utilizing ITO openly available.
C1 [Blagec, Kathrin; Barbosa-Silva, Adriano; Ott, Simon; Samwald, Matthias] Med Univ Vienna, Ctr Med Stat Informat & Intelligent Syst, Inst Artificial Int, Vienna, Austria.
RP Samwald, M (通讯作者)，Med Univ Vienna, Ctr Med Stat Informat & Intelligent Syst, Inst Artificial Int, Vienna, Austria.
EM matthias.samwald@meduniwien.ac.at
OI Samwald, Matthias/0000-0002-4855-2571
FU netidee [5158]; European Community's Horizon 2020 Programme [668353];
   National Institute of General Medical Sciences of the United States
   National Institutes of Health [GM10331601]
FX The research leading to these results has received funding from netidee
   under grant agreement 5158 ('Web of AI') and the European Community's
   Horizon 2020 Programme under grant agreement No. 668353 ('Ubiquitous
   Pharmacogenomics'). The authors thank the team of Papers with Code for
   making their data available. This work was conducted using the Protege
   resource, which is supported by grant GM10331601 from the National
   Institute of General Medical Sciences of the United States National
   Institutes of Health.
CR Aguado-de-Cea G, 2015, PROCD SOC BEHV, V212, P151, DOI 10.1016/j.sbspro.2015.11.313
   [Anonymous], OWL 2 WEB ONTOLOGY L
   Auer S, 2018, P INT C WEB INT MIN, DOI DOI 10.1186/1475-2859-8-19
   Blagec K., 2022, ARXIV
   Blagec K., 2021, Zenodo, DOI 10.5281/zenodo.4647824
   Blagec K., CRITICAL ANAL METRIC
   Breit A, 2020, BIOINFORMATICS, V36, P4097, DOI 10.1093/bioinformatics/btaa274
   Callahan TJ., 2020, BIORXIV, DOI [10.1101/2020.04.30.071407, DOI 10.1101/2020.04.30.071407]
   Carriero VA, 2021, SEMANT WEB, V12, P313, DOI 10.3233/SW-200422
   Dessi Danilo, 2020, The Semantic Web - ISWC 2020. 19th International Semantic Web Conference. Lecture Notes in Computer Science (LNCS 12507), P127, DOI 10.1007/978-3-030-62466-8_9
   Dublin Core Metadata Initiative, 2012, DUBL COR MET EL SET
   Dumontier M, 2014, J BIOMED SEMANT, V5, DOI 10.1186/2041-1480-5-14
   GOMEZ-PEREZ A., 1999, 12 BANFF KNOWL ACQ K
   Graves M, 2007, CAT CLASSIF Q, V43, P191, DOI [10.1300/J104v43n03_11, 10.1300/J104v43n03_10]
   Han K, 2020, ADBIS TPDL EDA 2020, P207, DOI 10.1007/978-3-030-55814-7_17
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Hogan A., 2020, ARXIV PREPRINT ARXIV
   Horridge M, 2019, COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), P686, DOI 10.1145/3308560.3317707
   Ioannidis JPA, 2018, PLOS BIOL, V16, DOI 10.1371/journal.pbio.2005468
   Ison J, 2013, BIOINFORMATICS, V29, P1325, DOI 10.1093/bioinformatics/btt113
   Jaradeh MY, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P243, DOI 10.1145/3360901.3364435
   Kazakov Y, 2014, J AUTOM REASONING, V53, P1, DOI 10.1007/s10817-013-9296-3
   Krizhevsky A., 2012, ADV NEURAL INFORM PR
   Kuhn T, 2018, P IEEE INT C E-SCI, P83, DOI 10.1109/eScience.2018.00024
   Lantow B, 2016, KEOD: PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL. 2, P186, DOI 10.5220/0006084601860191
   Maguire E, 2013, BIOINFORMATICS, V29, P525, DOI 10.1093/bioinformatics/bts718
   Martinez-Plumed F, 2020, FRONT ARTIF INTEL AP, V325, P2915, DOI 10.3233/FAIA200451
   Musen Mark A, 2015, AI Matters, V1, P4
   Poldrack Russell A, 2011, Front Neuroinform, V5, P17, DOI 10.3389/fninf.2011.00017
   Poveda-Villalon M, 2014, INT J SEMANT WEB INF, V10, P7, DOI 10.4018/ijswis.2014040102
   Raad Joe, 2015, IC3K 2015. 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, P179
   Ruttenberg A, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-S3-S2
   Salatino AA, 2020, DATA INTELLIGENCE, V2, P379, DOI 10.1162/dint_a_00055
   Samwald M., 2022, OPENBIOLINKITO V101, DOI [10.5281/zenodo.6566103, DOI 10.5281/ZENODO.6566103]
   Samwald M., 2021, Zenodo, DOI 10.5281/zenodo.5561989
   Tirmizi Syed Hamid, 2011, J Biomed Semantics, V2 Suppl 1, pS3, DOI 10.1186/2041-1480-2-S1-S3
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Vrandecic D, 2009, HDB ONTOLOGIES, P293, DOI DOI 10.1007/978-3-540-92673-3_13
   w3, RDF 11 CONCEPTS ABST
   w3, SPARQL 11 OVERVIEW
   Zhang D., 2021, INDEX 2021 ANN REPOR
NR 41
TC 0
Z9 0
U1 3
U2 3
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
EI 2052-4463
J9 SCI DATA
JI Sci. Data
PD JUN 17
PY 2022
VL 9
IS 1
AR 322
DI 10.1038/s41597-022-01435-x
PG 10
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 2E8XT
UT WOS:000812506300005
PM 35715466
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, XY
   Zhu, DD
   Lin, DZ
   Cao, DL
AF Chen, Xiangyan
   Zhu, Duoduo
   Lin, Dazhen
   Cao, Donglin
TI Rumor knowledge embedding based data augmentation for imbalanced rumor
   detection
SO INFORMATION SCIENCES
LA English
DT Article
DE Rumor detection; Rumor data augmentation; Knowledge graph
ID FAKE NEWS; REPRESENTATION; SMOTE
AB Rumor detection aims to detect rumors in a timely manner to prevent malicious rumors from misleading the public and disrupting social order. However, rumor detection suffers from the problem of imbalanced data. Existing methods of text generation and imbalanced learning are insufficient in addressing this imbalance because they are not specialized in rumor tasks. We propose a knowledge graph-based rumor data augmentation method: Graph Embedding-based Rumor Data Augmentation (GERDA), which simulates the generation process of rumor from the perspective of knowledge. To model the generation process of false information, we introduce knowledge representation in the process of text generation. To better learn the graph structured rumor data, we propose a graph-based rumor text generative model G2S-AT-GAN, which uses an attention-based graph convolutional neural network and a generative adversarial network for rumor text generation. Experiments show that our method is able to generate high-quality rumors of diverse topics and the generated rumors can further address rumor data imbalance for better performance in rumor detection. (c) 2021 Elsevier Inc. All rights reserved.
C1 [Chen, Xiangyan; Zhu, Duoduo; Lin, Dazhen; Cao, Donglin] Xiamen Univ, Dept Artificial Intelligence, Xiamen, Peoples R China.
   [Cao, Donglin] Wuyi Univ, Key Lab Cognit Comp & Intelligent Informat Proc F, Guangzhou, Guangdong, Peoples R China.
RP Cao, DL (通讯作者)，Xiamen Univ, Dept Artificial Intelligence, Xiamen, Peoples R China.
EM another@xmu.edu.cn
FU Nature Science Foundation of China [62076210]; National Key Research and
   Development Program of China [2018YFC0831402]; Open Project Program of
   The Key Laboratory of Cognitive Computing and Intelligent Information
   Processing of Fujian Education Institutions, Wuyi University
   [KLCCIIP2020203]
FX This work is supported by the Nature Science Foundation of China (No.
   62076210), the National Key Research and Development Program of China
   (No. 2018YFC0831402) and the Open Project Program of The Key Laboratory
   of Cognitive Computing and Intelligent Information Processing of Fujian
   Education Institutions, Wuyi University(No. KLCCIIP2020203)
CR Bachman P., 2015, ADV NEURAL INF PROCE, P3249
   Bahdanau D., 2014, ARXIV
   Belz A., 2011, P 13 EUR WORKSH NAT, P217
   Bian T, 2020, AAAI CONF ARTIF INTE, V34, P549
   Bondielli A, 2019, INFORM SCIENCES, V497, P38, DOI 10.1016/j.ins.2019.05.035
   Chawla NV, 2002, J ARTIF INTELL RES, V16, P321, DOI 10.1613/jair.953
   Chopra S, 2016, P 2016 C N AM CHAPT, P93, DOI [10.18653/v1/N16-1012, DOI 10.18653/V1/N16-1012]
   Ciampaglia GL, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0128193
   Ciravegna F., 2019, DATA AUGMENTATION RU
   Colin Emilie, 2016, P 9 INT NAT LANG GEN, P163
   Fard Amir Ebrahimi, 2020, Computational Science - ICCS 2020. 20th International Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12140), P639, DOI 10.1007/978-3-030-50423-6_48
   Fard AE, 2019, INT ICE CONF ENG
   Gardent C., 2017, P 10 INT C NAT LANG, P124, DOI DOI 10.18653/V1/W17-3518
   Han H, 2005, LECT NOTES COMPUT SC, V3644, P878, DOI 10.1007/11538059_91
   He HB, 2009, IEEE T KNOWL DATA EN, V21, P1263, DOI 10.1109/TKDE.2008.239
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Jin ZW, 2017, PROCEEDINGS OF THE 2017 ACM MULTIMEDIA CONFERENCE (MM'17), P795, DOI 10.1145/3123266.3123454
   Kim Y., 2014, ARXIV14085882, DOI DOI 10.3115/V1/D14-1181
   Kingma D. P., 2017, ARXIV
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Li Quanzhi, 2019, EVENTAI SEMEVAL 2019, P855
   Li Y, 2018, INFORM SCIENCES, V450, P301, DOI 10.1016/j.ins.2018.03.050
   Lin C.-Y., 2004, NTCIR WORKSH
   Liu Y, 2018, AAAI CONF ARTIF INTE, P354
   Liu Z., 2015, SCI SINICA INFORM, V45, P1536
   Luong MT, 2015, EFFECTIVE APPROACHES
   Ma B, 2017, ADV INTELL SYST, V513, P245, DOI 10.1007/978-3-319-46562-3_16
   Ma J, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1980
   Marcheggiani D., 2018, P 11 INT C NAT LANG
   Marcheggiani D., 2018, P 2018 C N AM CHAPT, V2, P486, DOI [1804.08313, DOI 10.18653/V1/N18-2078, 10.18653/ v1, DOI 10.18653/V1]
   Pan JZ, 2018, LECT NOTES COMPUT SC, V11136, P669, DOI 10.1007/978-3-030-00671-6_39
   Papineni K, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P311, DOI 10.3115/1073083.1073135
   Qi P, 2019, IEEE DATA MINING, P518, DOI 10.1109/ICDM.2019.00062
   Rush A.M., 2015, P EMPIRICAL METHODS, P379, DOI DOI 10.18653/V1/D15-1044
   Salvaris M, 2018, ARXIV MACHINE LEARNI, P187, DOI [10.1007/978-1-4842-3679-6 _8, DOI 10.1007/978-1-4842-3679-6, 10.1007/978-1-4842-3679-6_8.]
   Satanjeev Banerjee, 2005, P ACL WORKSH INTR EX, P228
   Sharma Shikhar, 2018, RELEVANCE UNSUPERVIS
   Shiralkar P, 2017, IEEE DATA MINING, P859, DOI 10.1109/ICDM.2017.105
   Song C., 2019, IEEE T KNOWL DATA EN
   Stolcke  A., 2002, P ICSLP
   Sun YM, 2009, INT J PATTERN RECOGN, V23, P687, DOI 10.1142/S0218001409007326
   Sutskever Ilya, 2014, ADV NEURAL INFORM PR, V27, P3104
   Tu KF, 2021, INFORM SCIENCES, V560, P137, DOI 10.1016/j.ins.2020.12.080
   Vaswani A, 2017, ADV NEUR IN, V30
   Vedantam R, 2015, PROC CVPR IEEE, P4566, DOI 10.1109/CVPR.2015.7299087
   Wei J., 2019, ARXIV PREPRINT ARXIV, P6381, DOI 10.18653/v1/D19-1670
   Xiaojin Zhu, 2002, CMUCALD02107
   Yu LT, 2017, AAAI CONF ARTIF INTE, P2852
   Zhang HW, 2019, PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19), P1942, DOI 10.1145/3343031.3350850
NR 49
TC 4
Z9 4
U1 26
U2 48
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD NOV
PY 2021
VL 580
BP 352
EP 370
DI 10.1016/j.ins.2021.08.059
EA SEP 2021
PG 19
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UY0KL
UT WOS:000701222400003
DA 2022-08-23
ER

PT J
AU Delmas, M
   Filangi, O
   Paulhe, N
   Vinson, F
   Duperier, C
   Garrier, W
   Saunier, PE
   Pitarch, Y
   Jourdan, F
   Giacomoni, F
   Frainay, C
AF Delmas, Maxime
   Filangi, Olivier
   Paulhe, Nils
   Vinson, Florence
   Duperier, Christophe
   Garrier, William
   Saunier, Paul-Emeric
   Pitarch, Yoann
   Jourdan, Fabien
   Giacomoni, Franck
   Frainay, Clement
TI FORUM: building a Knowledge Graph from public databases and scientific
   literature to extract associations between chemicals and diseases
SO BIOINFORMATICS
LA English
DT Article
ID TARGET IDENTIFICATION; BIOMEDICAL-RESEARCH; TEXT; TOOL; METABOLOMICS;
   ONTOLOGIES; DISCOVERY; RESOURCE; DRUGS
AB Motivation: Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories.
   Results: The use of a Semantic Web framework on biological data allows us to apply ontological-based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries.
   Supplementary information: Supplementary data are available at Bioinformatics online.
C1 [Delmas, Maxime; Vinson, Florence; Jourdan, Fabien; Frainay, Clement] Univ Toulouse, Toxalim Res Ctr Food Toxicol, UPS, INRAE,ENVT,INP Purpan, F-31300 Toulouse, France.
   [Filangi, Olivier] Univ Rennes, Inst Agro, Domaine Motte, IGEPP,INRAE, F-35653 Le Rheu, France.
   [Paulhe, Nils; Duperier, Christophe; Giacomoni, Franck] Univ Clermont Auvergne, MetaboHUB Clermont, Plateforme Explorat Metab, INRAE,UNH, F-63000 Clermont Ferrand, France.
   [Garrier, William; Saunier, Paul-Emeric] ISIMA, Campus Cezeaux, F-63177 Aubiere, France.
   [Pitarch, Yoann] Univ Toulouse, Cours Rose Dieng Kuntz, IRIT, F-31400 Toulouse, France.
RP Frainay, C (通讯作者)，Univ Toulouse, Toxalim Res Ctr Food Toxicol, UPS, INRAE,ENVT,INP Purpan, F-31300 Toulouse, France.
EM clement.frainay@inrae.fr
RI Frainay, Clement/B-8636-2019
OI Frainay, Clement/0000-0003-4313-2786; Jourdan,
   Fabien/0000-0001-9401-2894; Saunier, Paul-Emeric/0000-0003-4332-9992;
   Giacomoni, Franck/0000-0001-6063-4214; Delmas,
   Maxime/0000-0002-9352-0624
FU INRA SDN (Schema Directeur du Numerique); European Union [825489];
   French Ministry of Research; National Research Agency as part of the
   French MetaboHUB infrastructure [ANR-INBS-0010]
FX This project has received funding from the INRA SDN (Schema Directeur du
   Numerique) and the European Union's Horizon 2020 research and innovation
   program under grant agreement GOLIATH No. 825489. This work was
   supported by the French Ministry of Research and National Research
   Agency as part of the French MetaboHUB infrastructure [Grant
   ANR-INBS-0010].
CR Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Barriot R, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-332
   Bento AP, 2014, NUCLEIC ACIDS RES, V42, pD1083, DOI 10.1093/nar/gkt1031
   Berman HM, 2000, NUCLEIC ACIDS RES, V28, P235, DOI 10.1093/nar/28.1.235
   Bettembourg, 2015, OVIVE INTEGRATION SO, P7
   Blau N, 2014, EXPERT REV MOL DIAGN, V14, P655, DOI 10.1586/14737159.2014.923760
   Brown LD, 2001, STAT SCI, V16, P101, DOI 10.1214/ss/1009213286
   Bushman Barbara, 2015, J Libr Metadata, V15, P157
   Byrne JA, 2019, BIOMARK INSIGHTS, V14, DOI 10.1177/1177271919829162
   Canada A, 2017, NUCLEIC ACIDS RES, V45, pW484, DOI 10.1093/nar/gkx462
   Chambers J, 2013, J CHEMINFORMATICS, V5, DOI 10.1186/1758-2946-5-3
   Cheng D, 2008, NUCLEIC ACIDS RES, V36, pW399, DOI 10.1093/nar/gkn296
   Cheung WA, 2013, BMC MED GENOMICS, V6, DOI 10.1186/1755-8794-6-S2-S3
   Cheung WA, 2012, BMC BIOINFORMATICS, V13, DOI 10.1186/1471-2105-13-249
   Dumontier M, 2014, J BIOMED SEMANT, V5, DOI 10.1186/2041-1480-5-14
   Duran-Frigola M, 2020, NAT BIOTECHNOL, V38, P1087, DOI 10.1038/s41587-020-0502-7
   Errami M, 2008, NATURE, V451, P397, DOI 10.1038/451397a
   Feunang YD, 2016, J CHEMINFORMATICS, V8, DOI 10.1186/s13321-016-0174-y
   Fiehn O, 2002, PLANT MOL BIOL, V48, P155, DOI 10.1023/A:1013713905833
   Fu G, 2015, J CHEMINFORMATICS, V7, DOI 10.1186/s13321-015-0084-4
   Giacomoni F, 2015, BIOINFORMATICS, V31, P1493, DOI 10.1093/bioinformatics/btu813
   Gijon-Correas JA, 2014, NUCLEIC ACIDS RES, V42, pW422, DOI 10.1093/nar/gku432
   Hassani-Pak K, 2021, PLANT BIOTECHNOL J, V19, P1670, DOI 10.1111/pbi.13583
   Hastings J, 2016, NUCLEIC ACIDS RES, V44, pD1214, DOI 10.1093/nar/gkv1031
   Hastings J, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0025513
   Hoehndorf R, 2015, BRIEF BIOINFORM, V16, P1069, DOI 10.1093/bib/bbv011
   Johnson CH, 2016, NAT REV MOL CELL BIO, V17, P451, DOI 10.1038/nrm.2016.25
   Kanza S, 2019, EXPERT OPIN DRUG DIS, V14, P433, DOI 10.1080/17460441.2019.1586880
   Katsila T, 2016, COMPUT STRUCT BIOTEC, V14, P177, DOI 10.1016/j.csbj.2016.04.004
   Kim S, 2006, J AGRIC FOOD INF, V7, P23, DOI 10.1300/J108v07n04_04
   Kim S, 2019, NUCLEIC ACIDS RES, V47, pD1102, DOI 10.1093/nar/gky1033
   Kim S, 2016, J CHEMINFORMATICS, V8, DOI 10.1186/s13321-016-0142-6
   Koch T., 2000, D LIB MAGAZINE, V6, P1082, DOI DOI 10.1045/DECEMBER2000-WEIBEL
   Koscielny G, 2017, NUCLEIC ACIDS RES, V45, pD985, DOI 10.1093/nar/gkw1055
   Lu ZY, 2009, INFORM RETRIEVAL, V12, P69, DOI 10.1007/s10791-008-9074-8
   Ludwig KR, 2017, MOL BIOSYST, V13, P648, DOI 10.1039/c6mb00656f
   Malas TB, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-42806-6
   Mattingly CJ, 2006, J EXP ZOOL PART A, V305A, P689, DOI 10.1002/jez.a.307
   Mi HY, 2019, NUCLEIC ACIDS RES, V47, pD419, DOI 10.1093/nar/gky1038
   Miles A., 2009, SKOS SIMPLE KNOWLEDG
   Moretti S, 2016, NUCLEIC ACIDS RES, V44, pD523, DOI 10.1093/nar/gkv1117
   Neumann E, 2007, BRIEF BIOINFORM, V8, P141, DOI 10.1093/bib/bbm013
   Nigsch F, 2009, EXPERT OPIN DRUG MET, V5, P1, DOI [10.1517/17425250802660467, 10.1517/17425250802660467 ]
   Peroni S, 2012, J WEB SEMANT, V17, P33, DOI 10.1016/j.websem.2012.08.001
   Pons E, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw046
   Rebholz-Schuhmann D, 2012, NAT REV GENET, V13, P829, DOI 10.1038/nrg3337
   Sartor MA, 2012, BIOINFORMATICS, V28, P1408, DOI 10.1093/bioinformatics/bts156
   Slenter DN, 2018, NUCLEIC ACIDS RES, V46, pD661, DOI 10.1093/nar/gkx1064
   Smalheiser NR, 2016, J BIOMED DISCOV COLL, V7, DOI 10.5210/disco.v7i0.6654
   Solla F, 2018, CLIN SPINE SURG, V31, P385, DOI 10.1097/BSD.0000000000000695
   Sosa DN, 2020, PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, P463
   Spielmans GI, 2010, PSYCHOTHER PSYCHOSOM, V79, P97, DOI 10.1159/000270917
   Subramanian A, 2005, P NATL ACAD SCI USA, V102, P15545, DOI 10.1073/pnas.0506580102
   SWANSON DR, 1986, PERSPECT BIOL MED, V30, P7
   TALIB R, 2016, INT J ADV COMPUT SC, V7, P2016
   Tanha Kiarash, 2017, Med J Islam Repub Iran, V31, P65, DOI 10.14196/mjiri.31.65
   Valentini G, 2009, LECT NOTES COMPUT SC, V5519, P232, DOI 10.1007/978-3-642-02326-2_24
   van Iersel MP, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-5
   Wang YL, 2012, NUCLEIC ACIDS RES, V40, pD400, DOI [10.1093/nar/gkr1132, 10.1093/nar/gkr1293]
   Williams AJ, 2012, DRUG DISCOV TODAY, V17, P1188, DOI 10.1016/j.drudis.2012.05.016
   Willighagen EL, 2013, J CHEMINFORMATICS, V5, DOI 10.1186/1758-2946-5-23
   Wishart DS, 2006, NUCLEIC ACIDS RES, V34, pD668, DOI 10.1093/nar/gkj067
   Wu HY, 2014, BIOSCI TRENDS, V8, P192, DOI 10.5582/bst.2014.01048
   Xia JG, 2010, NUCLEIC ACIDS RES, V38, pW71, DOI 10.1093/nar/gkq329
   Zhou XZ, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms5212
NR 65
TC 1
Z9 1
U1 7
U2 11
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD NOV 1
PY 2021
VL 37
IS 21
BP 3896
EP 3904
DI 10.1093/bioinformatics/btab627
EA SEP 2021
PG 9
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA XT8LN
UT WOS:000733832700028
PM 34478489
OA Green Published, hybrid, Green Submitted
DA 2022-08-23
ER

PT J
AU Queralt-Rosinach, N
   Stupp, GS
   Li, TS
   Mayers, M
   Hoatlin, ME
   Might, M
   Good, BM
   Su, AI
AF Queralt-Rosinach, Nuria
   Stupp, Gregory S.
   Li, Tong Shu
   Mayers, Michael
   Hoatlin, Maureen E.
   Might, Matthew
   Good, Benjamin M.
   Su, Andrew I.
TI Structured reviews for data and knowledge-driven research
SO DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
LA English
DT Article
ID NF-KAPPA-B; BOX-BINDING PROTEIN; EXPRESSION; P53; DISORDER; DISEASES;
   DOMAIN; MODEL
AB Hypothesis generation is a critical step in research and a cornerstone in the rare disease field. Research is most efficient when those hypotheses are based on the entirety of knowledge known to date. Systematic review articles are commonly used in biomedicine to summarize existing knowledge and contextualize experimental data. But the information contained within review articles is typically only expressed as free-text, which is difficult to use computationally. Researchers struggle to navigate, collect and remix prior knowledge as it is scattered in several silos without seamless integration and access. This lack of a structured information framework hinders research by both experimental and computational scientists. To better organize knowledge and data, we built a structured review article that is specifically focused on NGLY1 Deficiency, an ultra-rare genetic disease first reported in 2012. We represented this structured review as a knowledge graph and then stored this knowledge graph in a Neo4j database to simplify dissemination, querying and visualization of the network. Relative to free-text, this structured review better promotes the principles of findability, accessibility, interoperability and reusability (FAIR). In collaboration with domain experts in NGLY1 Deficiency, we demonstrate how this resource can improve the efficiency and comprehensiveness of hypothesis generation. We also developed a read-write interface that allows domain experts to contribute FAIR structured knowledge to this community resource. In contrast to traditional free-text review articles, this structured review exists as a living knowledge graph that is curated by humans and accessible to computational analyses. Finally, we have generalized this workflow into modular and repurposable components that can be applied to other domain areas. This NGLY1 Deficiency-focused network is publicly available at http://ngly1graph.org/.
   Availability and implementation: Database URL: http://ngly1graph.org/. Network data files are at: https://github.com/ SuLab/ngly1-graph and source code at: https://github.com/SuLab/bioknowledge-reviewer.
   Contact: asu@scripps.edu
C1 [Queralt-Rosinach, Nuria; Stupp, Gregory S.; Li, Tong Shu; Mayers, Michael; Good, Benjamin M.; Su, Andrew I.] Scripps Res, Dept Integrat Struct & Computat Biol, 10550 N Torrey Pines Rd, La Jolla, CA 92037 USA.
   [Hoatlin, Maureen E.] Oregon Hlth & Sci Univ, Dept Biochem & Mol Biol, 3181 SW Sam Jackson Pkwy, Portland, OR 97239 USA.
   [Might, Matthew] Univ Alabama Birmingham, Hugh Kaul Precis Med Inst, Dept Med, 510 20th St S, Birmingham, AL 35210 USA.
RP Su, AI (通讯作者)，Scripps Res, Dept Integrat Struct & Computat Biol, 10550 N Torrey Pines Rd, La Jolla, CA 92037 USA.
EM asu@scripps.edu
FU National Center for Advancing Translational Sciences Biomedical Data
   Translator program [OT3TR002019]; National Institute of General Medical
   Sciences [R01GM089820]
FX The National Center for Advancing Translational Sciences Biomedical Data
   Translator program (OT3TR002019); National Institute of General Medical
   Sciences (R01GM089820).
CR Albensi BC, 2000, SYNAPSE, V35, P151
   Bateman A, 2019, NUCLEIC ACIDS RES, V47, pD506, DOI 10.1093/nar/gky1049
   Brandizi M, 2018, J INTEGR BIOINFORMAT, V15, DOI 10.1515/jib-2018-0023
   Burgstaller-Muehlbacher S, 2016, DATABASE, V2016
   Callahan Alison, 2011, J Biomed Semantics, V2 Suppl 2, pS3, DOI 10.1186/2041-1480-2-S2-S3
   Carbon S, 2019, NUCLEIC ACIDS RES, V47, pD330, DOI 10.1093/nar/gky1055
   Cohen T, 2010, J BIOMED DISCOV COLL, V5, P21
   Cvekl A, 1999, INVEST OPHTH VIS SCI, V40, P1343
   Dunham I, 2012, NATURE, V489, P57, DOI 10.1038/nature11247
   Elliott JH, 2014, PLOS MED, V11, DOI 10.1371/journal.pmed.1001603
   Enns GM, 2014, GENET MED, V16, P751, DOI 10.1038/gim.2014.22
   Fabregat A, 2018, NUCLEIC ACIDS RES, V46, pD649, DOI [10.1093/nar/gkx1132, 10.1093/nar/gkz1031]
   Gaulton A, 2017, NUCLEIC ACIDS RES, V45, pD945, DOI 10.1093/nar/gkw1074
   Han H, 2015, SCI REP-UK, V5, DOI 10.1038/srep16457
   Hastings J, 2016, NUCLEIC ACIDS RES, V44, pD1214, DOI 10.1093/nar/gkv1031
   Hristovski D, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-014-0365-3
   Jiang C, 2007, NUCLEIC ACIDS RES, V35, pD137, DOI 10.1093/nar/gkl1041
   Jiang Y, 2016, MOL CELLS, V39, P292, DOI 10.14348/molcells.2016.2223
   Jupp S, 2014, BIOINFORMATICS, V30, P1338, DOI 10.1093/bioinformatics/btt765
   Kanehisa M, 2017, NUCLEIC ACIDS RES, V45, pD353, DOI 10.1093/nar/gkw1092
   Kawashima S, 2018, DATABASE J BIOL DATA, V2018
   Kibbe WA, 2015, NUCLEIC ACIDS RES, V43, pD1071, DOI 10.1093/nar/gku1011
   Kilicoglu H, 2012, BIOINFORMATICS, V28, P3158, DOI 10.1093/bioinformatics/bts591
   Kohler S, 2019, NUCLEIC ACIDS RES, V47, pD1018, DOI 10.1093/nar/gky1105
   Lam C, 2017, GENET MED, V19, P160, DOI 10.1038/gim.2016.75
   Leach SM, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000215
   Lehrbach NJ, 2019, CELL, V177, P737, DOI 10.1016/j.cell.2019.03.035
   Lehrbach NJ, 2016, ELIFE, V5, DOI 10.7554/eLife.17721
   Liberzon A, 2015, CELL SYST, V1, P417, DOI 10.1016/j.cels.2015.12.004
   Liekens AML, 2011, GENOME BIOL, V12, DOI 10.1186/gb-2011-12-6-r57
   Livingston KM, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0559-3
   Lysenko A, 2016, BIODATA MIN, V9, DOI 10.1186/s13040-016-0102-8
   Meffert MK, 2003, NAT NEUROSCI, V6, P1329, DOI 10.1038/nn1203-1329b
   Messina A, 2018, BMC SYST BIOL, V12, DOI 10.1186/s12918-018-0616-4
   Mungall CJ, 2017, NUCLEIC ACIDS RES, V45, pD712, DOI 10.1093/nar/gkw1128
   Need AC, 2012, J MED GENET, V49, P353, DOI 10.1136/jmedgenet-2012-100819
   Neph S, 2012, CELL, V150, P1274, DOI 10.1016/j.cell.2012.04.040
   Oughtred R, 2019, NUCLEIC ACIDS RES, V47, pD529, DOI 10.1093/nar/gky1079
   Owings KG, 2018, HUM MOL GENET, V27, P1055, DOI 10.1093/hmg/ddy026
   Prasad TSK, 2009, NUCLEIC ACIDS RES, V37, pD767, DOI 10.1093/nar/gkn892
   Qadri I, 2002, BBA-MOL CELL RES, V1592, P193, DOI 10.1016/S0167-4889(02)00315-4
   Ratnam J, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0115460
   Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003
   SETO E, 1992, P NATL ACAD SCI USA, V89, P12028, DOI 10.1073/pnas.89.24.12028
   Shokraneh F., 2019, WORLD J META-ANAL, V7, P66, DOI DOI 10.13105/WJMA.V7.I3.66
   Smith CL, 2009, WIRES SYST BIOL MED, V1, P390, DOI 10.1002/wsbm.44
   SOMMER MH, 1994, J VIROL, V68, P6223, DOI 10.1128/JVI.68.10.6223-6231.1994
   Subramanian A, 2005, P NATL ACAD SCI USA, V102, P15545, DOI 10.1073/pnas.0506580102
   Szklarczyk D, 2019, NUCLEIC ACIDS RES, V47, pD607, DOI 10.1093/nar/gky1131
   Tambe MA, 2019, CELL REP, V29, P4620, DOI 10.1016/j.celrep.2019.11.097
   Tomlin FM, 2017, ACS CENTRAL SCI, V3, P1143, DOI 10.1021/acscentsci.7b00224
   Townsend J, 1999, J NEUROSCI, V19, P5632
   Vlahopoulos SA, 2017, CANCER BIOL MED, V14, P254, DOI 10.20892/j.issn.2095-3941.2017.0029
   Vlahopoulos SA, 2015, CYTOKINE GROWTH F R, V26, P389, DOI 10.1016/j.cytogfr.2015.06.001
   Vrandecic D., 2012, P 21 INT C WORLD WID, P1063, DOI [DOI 10.1145/2187980.2188242, 10.1145/2187980.2188242]
   Wilkinson Mark D, 2016, Sci Data, V3, P160018, DOI 10.1038/sdata.2016.18
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD608, DOI 10.1093/nar/gkx1089
   Wu CL, 2013, NUCLEIC ACIDS RES, V41, pD561, DOI 10.1093/nar/gks1114
   Wu YL, 2001, ONCOGENE, V20, P240, DOI 10.1038/sj.onc.1204067
   Xie XH, 2005, NATURE, V434, P338, DOI 10.1038/nature03441
   Xin JW, 2016, GENOME BIOL, V17, DOI 10.1186/s13059-016-0953-9
NR 61
TC 2
Z9 2
U1 3
U2 3
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1758-0463
J9 DATABASE-OXFORD
JI Database
PD APR 11
PY 2020
AR baaa015
DI 10.1093/database/baaa015
PG 12
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA LF9BD
UT WOS:000527707300001
PM 32283553
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Jeong, JS
   Garcia-Moruno, L
   Hernandez-Blanco, J
AF Su Jeong, Jin
   Garcia-Moruno, Lorenzo
   Hernandez-Blanco, Julio
TI Integrating buildings into a rural landscape using a multi-criteria
   spatial decision analysis in GIS-enabled web environment
SO BIOSYSTEMS ENGINEERING
LA English
DT Article
ID AGROINDUSTRIAL BUILDINGS; IMPACT
AB There is often a difficult relationship between rural buildings and the landscape. This may be overcome by methodologies that support a decision-making processes for establishing harmonious relationships and sustainable environment integrity within a unique framework. Preliminary results are presented from a continuing broad research project developing a spatial methodology for integrating new rural buildings associated with tourist functions into landscapes and coupling multi-criteria evaluations (MCE) into a web environment that uses a geographic information system (GIS) technique. Use of the internet allows users easy access to diverse GIS data sources and also allows support collaboration amongst planners, stakeholders and the public. The aim of this methodology, which applies an overlay and index method involving several parameters, is to evaluate its suitability in the study region, Hervas, Spain, in order to optimally plan for rural building integration within its landscape. The methodology used intermediate suitability maps classified by five evaluation criteria, namely physical, visual, economic, social, and environmental criteria. A combination of the five intermediate maps resulted in a final composite suitability map for buildings in a rural landscape. The possibility of designing and implemeriting a GIS-enabled web application with the methodology, consisting of a general overview, a multi-criteria spatial decision support system, an interoperable knowledge map and a post-task questionnaire to identify spatial models for the different perceptions of building integration within the rural landscape and to certify the possible economic impact on tourism, is presented. (c) 2012 IAgrE. Published by Elsevier Ltd. All rights reserved.
C1 [Su Jeong, Jin; Garcia-Moruno, Lorenzo] Univ Extremadura, Ctr Univ Merida, Dpto Expres Graf, Merida 06800, Spain.
   [Hernandez-Blanco, Julio] Univ Extremadura, Dpto Expres Graf, Ctr Univ Plasenica, Plasencia 10600, Spain.
RP Jeong, JS (通讯作者)，Univ Extremadura, Ctr Univ Merida, Dpto Expres Graf, Calle Santa Teresa Jornet 38, Merida 06800, Spain.
EM jin@unex.es
RI Jeong, Jin Su/L-4968-2014; Blanco, Julio Hernández/M-5571-2014;
   Hernández Blanco, Julio/GLS-1164-2022; García-Moruno,
   Lorenzo/AAE-7389-2022; García-Moruno, Lorenzo/AAE-8239-2019
OI Jeong, Jin Su/0000-0003-1370-0623; Blanco, Julio
   Hernández/0000-0002-1951-9545; Hernández Blanco,
   Julio/0000-0002-1951-9545; García-Moruno, Lorenzo/0000-0003-4997-4781;
   García-Moruno, Lorenzo/0000-0003-4997-4781
FU Ministerio de Ciencia e Innovacion [BIA 2007-61166]; Captacion y
   Formacion de Recursos Humanos de Excelencia en Investigacion, Desarrollo
   e Innovacion (Universidad de Extremadura)
FX This research has been carried and carrying out with the financial
   support from Ministerio de Ciencia e Innovacion (BIA 2007-61166) and
   Captacion y Formacion de Recursos Humanos de Excelencia en
   Investigacion, Desarrollo e Innovacion (Universidad de Extremadura); the
   support of both institutions is gratefully acknowledged.
CR [Anonymous], COMPLEX MANAGERIAL D
   Ayuga F., 2001, GESTION SOSTENIBLE P
   Council of the European Union, 2001, OFFICIAL J EUROPEA C, VC73
   De Kadt E., 1979, TOURISM PASSPORT DEV
   de Vries S, 2012, LANDSCAPE URBAN PLAN, V105, P118, DOI 10.1016/j.landurbplan.2011.12.005
   Di Fazio S., 1988, RIV INGEGNERIA AGRAR, V10, P379
   Domingo-Santos JM, 2011, LANDSCAPE URBAN PLAN, V101, P52, DOI 10.1016/j.landurbplan.2010.11.018
   Eastman J.R., 2003, IDRISI KILIMANJARO G
   Espanol I., 1995, IMPACTO AMBIENTAL
   Faraji Sabokbar H, 2005, GEOGRAPHICAL RES, V51, P125
   Fountas S, 2006, AGR SYST, V87, P192, DOI 10.1016/j.agsy.2004.12.003
   Garcia L, 2006, LANDSCAPE URBAN PLAN, V74, P110, DOI 10.1016/j.landurbplan.2004.10.007
   Haklay M, 2008, GEOGR COMPASS, V2, P2011, DOI 10.1111/j.1749-8198.2008.00167.x
   Hernandez J, 2004, BIOSYST ENG, V88, P255, DOI 10.1016/j.biosystemseng.2004.02.008
   Hwang CL, 1981, MULTIPLE ATTRIBUTE D
   Jankowski P, 1997, INT J GEOGR INF SCI, V11, P577, DOI 10.1080/136588197242202
   Jeong J. S., 2011, P INT C URB PLANN RE
   LEADER & PRODER, 2011, POL EUR LEAD PROD EX
   Maguire D. J., 1991, OVERVIEW DEFINITION
   Malczewski J., 1999, GIS MULTICRITERIA DE
   Mennella V., 1997, QUALITA DELL AMBIENT
   Montero M.J., 2005, P INT C MOD SIM MELB
   Natura, 2000, EUR COMM ENV NAT BIO
   Nielsen Jakob, 1994, USABILITY ENG
   Nieto A. G., 2001, SIG ANALISIS POBLACI
   Pigram J. J., 1980, Annals of Tourism Research, V7, DOI 10.1016/0160-7383(80)90049-3
   Poggenpohl S., 2003, VISIBLE LANG, V37, P91
   POLANYI M, 1996, TACIT DIMENSION
   Renger Michiel, 2008, International Journal of Simulation & Process Modelling, V4, P248, DOI 10.1504/IJSPM.2008.023686
   Roth M, 2006, LANDSCAPE URBAN PLAN, V78, P179, DOI 10.1016/j.landurbplan.2005.07.005
   Roy B., 1996, MULTICRITERIA METHOD
   Ruiz MC, 2009, AUTOMAT CONSTR, V18, P1135, DOI 10.1016/j.autcon.2009.07.005
   S-J Chen, 1992, FUZZY MULTIPLE ATTRI
   Saaty T.L, 1996, ANAL HIERARCHY PROCE, Vfirst
   Schmitt H., 2003, LANDTECHNIK, V2, P88
   Simao A, 2009, J ENVIRON MANAGE, V90, P2027, DOI 10.1016/j.jenvman.2007.08.032
   Smardon R. C, 1979, PROTOTYPE VISUAL ASS
   Tandy C., 1979, IND PAISAJE
   Tassinari P, 2008, BIOSYST ENG, V100, P435, DOI 10.1016/j.biosystemseng.2008.04.004
   Tassinari P, 2006, T ASABE, V49, P505, DOI 10.13031/2013.20405
   Tassinari P., 2007, AGR ENG INT CI UNPUB, V9
   Umar A., 1997, OBJECT ORIENTED CLIE
   Voinov A, 2010, ENVIRON MODELL SOFTW, V25, P1268, DOI 10.1016/j.envsoft.2010.03.007
   Voloshyn A., 2003, INT J INF THEOR APPL, V10, P243
   Voss A, 2004, COMPUT ENVIRON URBAN, V28, P635, DOI [10.1016/j.compenvurbsys.2003.12.003, DOI 10.1016/J.COMPENVURBSYS.2003.12.003]
   Yoon K., 1995, MULTIPLE ATTRIBUTE D
   [No title captured]
NR 47
TC 38
Z9 39
U1 4
U2 60
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1537-5110
J9 BIOSYST ENG
JI Biosyst. Eng.
PD JUN
PY 2012
VL 112
IS 2
BP 82
EP 92
DI 10.1016/j.biosystemseng.2012.03.002
PG 11
WC Agricultural Engineering; Agriculture, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Agriculture
GA 948LE
UT WOS:000304503900002
DA 2022-08-23
ER

PT J
AU Wang, CS
   Hazen, RM
   Cheng, QM
   Stephenson, MH
   Zhou, CH
   Fox, P
   Shen, SZ
   Oberhansli, R
   Hou, ZQ
   Ma, XG
   Feng, ZQ
   Fan, JX
   Ma, C
   Hu, XM
   Luo, B
   Wang, JL
   Schiffries, CM
AF Wang, Chengshan
   Hazen, Robert M.
   Cheng, Qiuming
   Stephenson, Michael H.
   Zhou, Chenghu
   Fox, Peter
   Shen, Shu-zhong
   Oberhansli, Roland
   Hou, Zengqian
   Ma, Xiaogang
   Feng, Zhiqiang
   Fan, Junxuan
   Ma, Chao
   Hu, Xiumian
   Luo, Bin
   Wang, Juanle
   Schiffries, Craig M.
TI The Deep-Time Digital Earth program: data-driven discovery in
   geosciences
SO NATIONAL SCIENCE REVIEW
LA English
DT Review
DE Deep-Time Digital Earth; data-driven discovery; big data; Earth
   evolution; cyberinfrastructure
ID DATA INTEGRATION; EVOLUTION; SYSTEM; VISUALIZATION; INFORMATION;
   PLATFORM; TRENDS
AB Current barriers hindering data-driven discoveries in deep-time Earth (DE) include: substantial volumes of DE data are not digitized; many DE databases do not adhere to FAIR (findable, accessible, interoperable and reusable) principles; we lack a systematic knowledge graph for DE; existing DE databases are geographically heterogeneous; a significant fraction of DE data is not in open-access formats; tailored tools are needed. These challenges motivate the Deep-Time Digital Earth (DDE) program initiated by the International Union of Geological Sciences and developed in cooperation with national geological surveys, professional associations, academic institutions and scientists around the world. DDE's mission is to build on previous research to develop a systematic DE knowledge graph, a FAIR data infrastructure that links existing databases and makes dark data visible, and tailored tools for DE data, which are universally accessible. DDE aims to harmonize DE data, share global geoscience knowledge and facilitate data-driven discovery in the understanding of Earth's evolution.
C1 [Wang, Chengshan] China Univ Geosci, State Key Lab Biogeol & Environm Geol, Beijing 10008, Peoples R China.
   [Wang, Chengshan] China Univ Geosci, Sch Earth Sci & Resources, Beijing 10008, Peoples R China.
   [Hazen, Robert M.] Carnegie Inst Sci, Earth & Planets Lab, Washington, DC 20015 USA.
   [Cheng, Qiuming] China Univ Geosci, State Key Lab Geol Proc & Mineral Resources, Beijing 100082, Peoples R China.
   [Stephenson, Michael H.] British Geol Survey, Nottingham NG12 5GG, England.
   [Zhou, Chenghu; Luo, Bin; Wang, Juanle] Chinese Acad Sci, Inst Geog Sci & Nat Resources, State Key Lab Resource & Environm Informat Syst, Beijing 100101, Peoples R China.
   [Fox, Peter] Rensselaer Polytech Inst, Tetherless World Constellat, Troy, NY 12180 USA.
   [Shen, Shu-zhong; Fan, Junxuan; Hu, Xiumian] Nanjing Univ, Sch Earth Sci & Engn, Nanjing 210023, Peoples R China.
   [Oberhansli, Roland] Univ Potsdam, Inst Earth & Environm Sci, D-14476 Potsdam, Germany.
   [Hou, Zengqian] Chinese Acad Geol Sci, Inst Geol, Beijing 100037, Peoples R China.
   [Ma, Xiaogang; Ma, Chao] Univ Idaho, Dept Comp Sci, Moscow, ID 83844 USA.
   [Feng, Zhiqiang] SINOPEC, Petr Explorat & Prod Res Inst, Beijing 100083, Peoples R China.
   [Schiffries, Craig M.] DDE Ctr Excellence Suzhou, Kunshan 215300, Peoples R China.
RP Wang, CS (通讯作者)，China Univ Geosci, State Key Lab Biogeol & Environm Geol, Beijing 10008, Peoples R China.; Wang, CS (通讯作者)，China Univ Geosci, Sch Earth Sci & Resources, Beijing 10008, Peoples R China.
EM wang.chengshan@ddeworld.org
RI chen, shu zhong/D-8214-2011; Ma, Xiaogang/C-9286-2011
OI chen, shu zhong/0000-0001-8380-0692; Ma, Xiaogang/0000-0002-9110-7369
FU National Natural Science Foundation of China [41888101]; National Key
   R&D Program of China [2018YFE0204204]
FX The work reported in the paper was supported by the National Natural
   Science Foundation of China (41888101) and the National Key R&D Program
   of China (2018YFE0204204).
CR Alroy J, 2008, SCIENCE, V321, P97, DOI 10.1126/science.1156963
   Bergen KJ, 2019, SCIENCE, V363, P1299, DOI 10.1126/science.aau0323
   Block K, 2009, AGU FALL M
   Boulton G, 2018, BIG EARTH DATA, V2, P1, DOI 10.1080/20964471.2017.1397411
   Callaway E, 2015, NATURE, V523, P115, DOI 10.1038/523115a
   Cheng QM, 2018, GONDWANA RES, V63, P268, DOI 10.1016/j.gr.2018.08.001
   Cox SJD, 2015, EARTH SCI INFORM, V8, P5, DOI 10.1007/s12145-014-0170-6
   Darwin C., 1859, ORIGIN SPECIES
   Fan JX, 2020, SCIENCE, V367, P273, DOI 10.1126/science.aax4953
   Fan JX, 2014, GFF, V136, P70, DOI 10.1080/11035897.2014.880070
   Gil Y, 2019, COMMUN ACM, V62, P76, DOI 10.1145/3192335
   Guo HD, 2017, BIG EARTH DATA, V1, P4, DOI 10.1080/20964471.2017.1403062
   Hazen RM, 2019, ENGINEERING-PRC, V5, P397, DOI 10.1016/j.eng.2019.03.006
   Hazen RM, 2019, AM MINERAL, V104, P810, DOI 10.2138/am-2019-6709CCBYNCND
   Hazen RM, 2014, AM MINERAL, V99, P2165, DOI 10.2138/am-2014-4895
   Hazen RM, 2011, AM MINERAL, V96, P953, DOI 10.2138/am.2011.3725
   Husson JM, 2018, EMERG TOP LIFE SCI, V2, P125, DOI 10.1042/ETLS20170152
   Jackson I, 2008, EPISODES, V31, P344, DOI 10.18814/epiiugs/2008/v31i3/009
   Keller CB, 2012, NATURE, V485, P490, DOI 10.1038/nature11024
   Khider D, 2019, PALEOCEANOGR PALEOCL, V34, P1570, DOI 10.1029/2019PA003632
   Lafuente B., 2016, HIGHLIGHTS MINERALOG, P1
   Lehnert K, AGU FALL M
   Lin DW, 2020, SCI DATA, V7, DOI 10.1038/s41597-020-0486-7
   Liu C, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-02095-x
   Lyell C., 1990, PRINCIPLES GEOLOGY, VI
   Ma XG, 2017, ISPRS INT J GEO-INF, V6, DOI 10.3390/ijgi6110368
   Ma XG, 2013, EARTH SCI INFORM, V6, P31, DOI 10.1007/s12145-013-0110-x
   Ma XG, 2012, COMPUT GEOSCI-UK, V40, P107, DOI 10.1016/j.cageo.2011.07.018
   Mallard C, 2016, NATURE, V535, P140, DOI 10.1038/nature17992
   Michener W, 2011, DLIB MAGAZINE, V17, P12
   Muller RD, 2018, GEOCHEM GEOPHY GEOSY, V19, P2243, DOI 10.1029/2018GC007584
   Muller RD, 2018, SCI ADV, V4, DOI 10.1126/sciadv.aaq0500
   Muscente AD, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-08837-3
   Normile D, 2019, SCIENCE, V363, P917, DOI 10.1126/science.363.6430.917
   Pall J, 2018, CLIM PAST, V14, P857, DOI 10.5194/cp-14-857-2018
   Peckham S, 2014, AGU FALL M
   Peters SE, 2017, EOS, V98, DOI [10.1029/2017EO082377, DOI 10.1029/2017EO082377]
   Peters SE, 2018, GEOCHEM GEOPHY GEOSY, V19, P1393, DOI 10.1029/2018GC007467
   Peters SE, 2017, GEOLOGY, V45, P487, DOI 10.1130/G38931.1
   Peters SE, 2017, GEOLOGY, V45, P323, DOI 10.1130/G38861.1
   Peters SE, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0113523
   Reichstein M, 2019, NATURE, V566, P195, DOI 10.1038/s41586-019-0912-1
   Scotese CR, 2001, ATLAS EARTH HIST, V1, P52
   Sillitoe RH, 2010, ECON GEOL, V105, P3, DOI 10.2113/gsecongeo.105.1.3
   Stall S, 2019, NATURE, V570, P27, DOI 10.1038/d41586-019-01720-7
   Sun WD, 2015, ORE GEOL REV, V65, P97, DOI 10.1016/j.oregeorev.2014.09.004
   Wang CB, 2018, COMPUT GEOSCI-UK, V115, P12, DOI 10.1016/j.cageo.2018.03.004
   [王成善 Wang Chengshan], 2017, [地学前缘, Earth Science Frontiers], V24, P1
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Wright N, 2013, BIOGEOSCIENCES, V10, P1529, DOI 10.5194/bg-10-1529-2013
NR 50
TC 1
Z9 3
U1 23
U2 42
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 2095-5138
EI 2053-714X
J9 NATL SCI REV
JI Natl. Sci. Rev.
PD SEP
PY 2021
VL 8
IS 9
AR nwab027
DI 10.1093/nsr/nwab027
EA FEB 2021
PG 11
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA UY6VO
UT WOS:000701659300014
PM 34691735
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wang, C
   Liu, JP
   Liu, JT
   Wang, W
AF Wang, Chao
   Liu, Jingping
   Liu, Juntao
   Wang, Wei
TI Inference of isA commonsense knowledge with lexical taxonomy
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE isA commonsense knowledge; Knowledge graph; Lexical taxonomy
AB Commonsense knowledge is a crucial resource to help the machine understand the human world. However, the conventional methods of extracting commonsense knowledge with isA relation (or isA commonsense knowledge) from text corpora generally do not work well since commonsense knowledge is typically omitted in communication. In this paper, we mainly focus on the inference of isA commonsense knowledge (the definition of isA here to express a hypernym-hyponym relationship and we concentrate on whether the description of (s, isA, o) is correct based on this relationship, e.g., (mammal, isA, animal), (Hello Kitty, isA, cat)) with a special kind of knowledge graph: lexical taxonomy. Lexical and semantic features of terms are both extracted from three relationships including exclusive, compatible, andinclusive relationships then a simple but effective classification model is further utilized to predict whether isA commonsense holds or not. Besides, we implement our model on a lexical taxonomy: Probase. A series of comparative experiments prove the effectiveness of our approach with an accuracy of over 96%, and we infer 200k isA commonsense knowledge from 1 million new pairs.
C1 [Wang, Chao; Liu, Juntao; Wang, Wei] Fudan Univ, Shanghai Key Lab Data Sci, Sch Comp Sci, Shanghai, Peoples R China.
   [Liu, Jingping] East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China.
RP Liu, JP (通讯作者)，East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China.
EM cwang17@fudan.edu.cn; liujingping@ecust.edu.cn; jtliu19@fudan.edu.cn;
   wangwei1@fudan.edu.cn
FU National Key R&D Program of China [2018YFB1403200]
FX This work was supported by the National Key R&D Program of China under
   Grant 2018YFB1403200.
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Borrego A, 2021, ENG APPL ARTIF INTEL, V103, DOI 10.1016/j.engappai.2021.104302
   Breiman L., 2001, RANDOM FORESTS, V45, P5, DOI 10.1023/A:1010933404324
   Cambria E., 2011, 2011 IEEE International Conference on Data Mining Workshops, P315, DOI 10.1109/ICDMW.2011.106
   Chen HY, 2018, P 2018 C EMP METH NA, P4834
   Chen JD, 2019, AAAI CONF ARTIF INTE, P6252
   Chen JD, 2019, PROC INT CONF DATA, P1706, DOI 10.1109/ICDE.2019.00178
   Dash S, 2020, AAAI CONF ARTIF INTE, V34, P7626
   Devlin J, 2018, P 2019 C N AM CHAPT
   Feng J, 2021, APPL INTELL, P1
   FLEISS JL, 1971, PSYCHOL BULL, V76, P378, DOI 10.1037/h0031619
   Fu RJ, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1199
   Grice HP, 1975, SYNTAX SEMANTICS, P41, DOI DOI 10.1163/9789004368811_003
   Hearst MA, 1992, P 14 C COMP LING, P539, DOI DOI 10.3115/992133.992154
   Herdagdelen A, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2337542.2337544
   Hosmer DW, 2013, WILEY SER PROBAB ST, P1, DOI 10.1002/9781118548387
   Kliegr T, 2016, J WEB SEMANT, V39, P47, DOI 10.1016/j.websem.2016.05.001
   Lee K, 2017, LECT NOTES ARTIF INT, V10235, P814, DOI 10.1007/978-3-319-57529-2_63
   Lenat D. B., 1989, BUILDING LARGE KNOWL
   Li JY, 2015, LECT NOTES COMPUT SC, V9313, P623, DOI 10.1007/978-3-319-25255-1_51
   Li PP, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1401, DOI 10.1145/2505515.2505567
   Liang JQ, 2017, AAAI CONF ARTIF INTE, P1185
   Liang JQ, 2017, IEEE T KNOWL DATA EN, V29, P1281, DOI 10.1109/TKDE.2017.2653115
   Liu JP, 2021, AAAI CONF ARTIF INTE, V35, P6410
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Pasca M, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2832
   Ramage D., 2009, P 2009 WORKSH GRAPH, P23
   RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Tandon N, 2016, AAAI CONF ARTIF INTE, P243
   Tandon N, 2014, AAAI CONF ARTIF INTE, P166
   Tandon N, 2017, SIGMOD REC, V46, P49, DOI 10.1145/3186549.3186562
   Trouillon T, 2017, J MACH LEARN RES, V18
   von Ahn Luis, 2006, P SIGCHI C HUM FACT, DOI DOI 10.1145/1124772.1124784
   Wang CY, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3630
   Wang CY, 2019, KNOWL INF SYST, V58, P585, DOI 10.1007/s10115-018-1166-1
   Wang GC, 2022, ANN OPER RES, V310, P223, DOI 10.1007/s10479-021-03952-1
   Wang HB, 2020, APPL INTELL, V50, P3336, DOI 10.1007/s10489-020-01734-z
   Wu T, 2014, JOINT INT SEM TECHN, P213
   Wu W, 2012, ACM SIGMOD, DOI DOI 10.1145/2213836.2213891
   Yaghoobzadeh Yadollah, 2015, P 2015 C EMP METH NA, P715
   Yan X.-F., 2006, COMPUT ENG DES, V27, P1996
   Yu C, 2020, C EMP METH NAT LANG, P6208
   Zang LJ, 2013, J COMPUT SCI TECH-CH, V28, P689, DOI 10.1007/s11390-013-1369-6
   Zanzotto F. M., 2010, NLP KE, P1
NR 45
TC 0
Z9 0
U1 2
U2 2
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
DI 10.1007/s10489-022-03680-4
EA JUN 2022
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2H7AY
UT WOS:000814444400003
DA 2022-08-23
ER

PT J
AU Melo, A
   Paulheim, H
AF Melo, Andre
   Paulheim, Heiko
TI Automatic detection of relation assertion errors and induction of
   relation constraints
SO SEMANTIC WEB
LA English
DT Article
DE SHACL; SPARQL; ontology learning; error detection; knowledge graph
ID KNOWLEDGE; OWL
AB Although the link prediction problem, where missing relation assertions are predicted, has been widely researched, error detection did not receive as much attention. In this paper, we investigate the problem of error detection in relation assertions of knowledge graphs, and we propose an error detection method which relies on path and type features used by a classifier for every relation in the graph exploiting local feature selection. Furthermore, we propose an approach for automatically correcting detected errors originated from confusions between entities. Moreover, we present an approach that translates decision trees trained for relation assertion error detection into SHACL-SPARQL relation constraints. We perform an extensive evaluation on a variety of datasets comparing our error detection approach with state-of-the-art error detection and knowledge completion methods, backed by a manual evaluation on DBpedia and NELL. We evaluate our error correction approach results on DBpedia and NELL and show that the relation constraint induction approach benefits from the higher expressiveness of SHACL and can detect errors which could not be found by automatically learned OWL constraints.
C1 [Melo, Andre; Paulheim, Heiko] Univ Mannheim, Data & Web Sci Grp, Mannheim, Germany.
RP Paulheim, H (通讯作者)，Univ Mannheim, Data & Web Sci Grp, Mannheim, Germany.
EM heiko@informatik.uni-mannheim.de
OI Paulheim, Heiko/0000-0003-4386-8195
FU Ministry of Science, Research and the Arts Baden-Wurttemberg
FX The work presented in this paper has been partly supported by the
   Ministry of Science, Research and the Arts Baden-Wurttemberg in the
   project SyKo2W2 (Synthesis of Completion and Correction of Knowledge
   Graphs on the Web).
CR Arndt D, 2017, LECT NOTES COMPUT SC, V10364, P22, DOI 10.1007/978-3-319-61252-2_3
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bordes A., 2012, P 15 INT C ART INT S, P127
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2011, AAAI
   BREIMAN L, 2001, MACH LEARN, V0045
   Bryl V, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P1129, DOI 10.1145/2567948.2578999
   Buhmann Lorenz, 2012, Knowledge Engineering and Knowledge Management. 18th International Conference, EKAW 2012. Proceedings, P57, DOI 10.1007/978-3-642-33876-2_8
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Chang K., 2014, PROC C EMPIRICAL MET, P1568
   Cochez M, 2017, LECT NOTES COMPUT SC, V10587, P190, DOI 10.1007/978-3-319-68288-4_12
   CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1007/BF00994018
   Debattista J., 2016, SEMANTIC TECHNOLOGY, P116, DOI DOI 10.1007/978-3-319-50112-3_9
   Demsar J, 2006, J MACH LEARN RES, V7, P1
   Farber M, 2018, SEMANT WEB, V9
   Fleischhacker D, 2014, LECT NOTES COMPUT SC, V8796, P357, DOI 10.1007/978-3-319-11964-9_23
   Galarraga L.A., 2013, P 22 INT C WORLD WID, P413, DOI DOI 10.1145/2488388.2488425
   Gardner M., 2015, P 2015 C EMP METH NA, P1488, DOI DOI 10.18653/V1/D15-1173
   Gayo J.E.L., 2017, VALIDATING DESCRIBIN
   Gayo J. E. Labra, 2017, SYNTHESIS LECT SEMAN, V7, P1, DOI DOI 10.2200/S00786ED1V01Y201707WBE016
   Grau BC, 2008, J WEB SEMANT, V6, P309, DOI 10.1016/j.websem.2008.05.001
   Han X., 2018, P C EMP METH NAT LAN, P139
   Heist N, 2018, INFORMATION, V9, DOI 10.3390/info9040075
   Heist N, 2017, LECT NOTES COMPUT SC, V10587, P383, DOI 10.1007/978-3-319-68288-4_23
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Kadlec R., 2017, KNOWLEDGE BASE COMPL
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Lao N, 2010, MACH LEARN, V81, P53, DOI 10.1007/s10994-010-5205-8
   Lehmann J., 2007, LECT NOTES COMPUTER, V4894, P147, DOI DOI 10.1007/978-3-540-78469-2_17
   Lehmann J, 2014, STUD SEMANTIC WEB, V18, pIX
   Lehmann T, 2012, 2012 IEEE ASIA PACIFIC CONFERENCE ON CIRCUITS AND SYSTEMS (APCCAS), P312, DOI 10.1109/APCCAS.2012.6419034
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu FT, 2008, IEEE DATA MINING, P413, DOI 10.1109/ICDM.2008.17
   Maciejewski R, 2008, IEEE CONF VIS ANAL, P35, DOI 10.1109/VAST.2008.4677354
   Mahdisoltani F., 2015, YAGO3 KNOWLEDGE BASE
   Meilicke C, 2018, LECT NOTES COMPUT SC, V11136, P3, DOI 10.1007/978-3-030-00671-6_1
   Melo A., 2016, P 6 INT C WEB INT MI, DOI 10.1145/2912845.2912861
   Melo A., 2014, P 27 INT C FLOR ART, P345
   Melo A., 2017, 1 INT WORKSH QUAL EN, P1
   Melo A, 2017, K-CAP 2017: PROCEEDINGS OF THE KNOWLEDGE CAPTURE CONFERENCE, DOI 10.1145/3148011.3148033
   Melo A, 2019, ARTIF INTELL REV, V51, P33, DOI 10.1007/s10462-017-9556-4
   Melo A, 2017, LECT NOTES COMPUT SC, V10249, P136, DOI 10.1007/978-3-319-58068-5_9
   Mihov S, 2004, COMPUT LINGUIST, V30, P451, DOI 10.1162/0891201042544938
   Munoz E, 2017, LECT NOTES COMPUT SC, V10438, P447, DOI 10.1007/978-3-319-64468-4_34
   Navarro G, 2001, ACM COMPUT SURV, V33, P31, DOI 10.1145/375360.375365
   Nickel M., 2015, HOLOGRAPHIC EMBEDDIN
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Paulheim H, 2017, LECT NOTES COMPUT SC, V10249, P404, DOI 10.1007/978-3-319-58068-5_25
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Paulheim H, 2016, LECT NOTES COMPUT SC, V9678, P135, DOI 10.1007/978-3-319-34129-3_9
   Paulheim H, 2015, LECT NOTES COMPUT SC, V9366, P180, DOI 10.1007/978-3-319-25007-6_11
   Paulheim H, 2014, INT J SEMANT WEB INF, V10, P63, DOI 10.4018/ijswis.2014040104
   Paulheim H, 2013, LECT NOTES COMPUT SC, V8218, P510, DOI 10.1007/978-3-642-41335-3_32
   Potoniec J, 2017, J WEB SEMANT, V46-47, P31, DOI 10.1016/j.websem.2017.08.001
   QUINLAN JR, 1987, INT J MAN MACH STUD, V27, P221, DOI 10.1016/S0020-7373(87)80053-6
   Ringler D, 2017, LECT NOTES ARTIF INT, V10505, P366, DOI 10.1007/978-3-319-67190-1_33
   Ristoski P, 2019, SEMANT WEB, V10, P721, DOI 10.3233/SW-180317
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9981, P498, DOI 10.1007/978-3-319-46523-4_30
   Shi B., 2017, PROJE EMBEDDING PROJ
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Toepper G., 2012, I SEMANTICS, P33
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Trouillon T., 2016, COMPLEX EMBEDDINGS S
   Volker J, 2011, LECT NOTES COMPUT SC, V6643, P124, DOI 10.1007/978-3-642-21034-1_9
   Wang CY, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P307, DOI 10.1145/2983323.2983705
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weaver G, 2006, OPENING INFORMATION HORIZONS, P358
   Xiao H., 2015, TRANSG GENERATIVE MI
   Yang B., 2014, LEARNING MULTIRELATI
   Yang ZL, 2010, AAAI CONF ARTIF INTE, P1467
NR 73
TC 0
Z9 0
U1 0
U2 1
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2020
VL 11
IS 5
BP 801
EP 830
DI 10.3233/SW-200369
PG 30
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NI2LZ
UT WOS:000565188300005
DA 2022-08-23
ER

PT J
AU Hu, JJ
   Lepore, R
   Dobson, RJB
   Al-Chalabi, A
   Bean, DM
   Iacoangeli, A
AF Hu, Jiajing
   Lepore, Rosalba
   Dobson, Richard J. B.
   Al-Chalabi, Ammar
   Bean, Daniel M.
   Iacoangeli, Alfredo
TI DGLinker: flexible knowledge-graph prediction of disease-gene
   associations
SO NUCLEIC ACIDS RESEARCH
LA English
DT Article
ID ONLINE MENDELIAN INHERITANCE; DATABASE; PRIORITIZATION; ALS; EXPRESSION;
   MUTATIONS; RESOURCE; CURATION; PLATFORM; CATALOG
AB As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration.
C1 [Hu, Jiajing; Dobson, Richard J. B.; Bean, Daniel M.; Iacoangeli, Alfredo] Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Biostat & Hlth Informat, London SE5 8AF, England.
   [Hu, Jiajing; Al-Chalabi, Ammar; Iacoangeli, Alfredo] Kings Coll London, Maurice Wohl Clin Neurosci Inst, Inst Psychiat Psychol & Neurosci, Dept Basic & Clin Neurosci, London SE5 9RT, England.
   [Lepore, Rosalba] BSC CNS Barcelona Supercomp Ctr, Barcelona 08034, Spain.
   [Dobson, Richard J. B.; Bean, Daniel M.] UCL, Hlth Data Res UK London, London WC1E 6BT, England.
   [Dobson, Richard J. B.] UCL, Inst Hlth Informat, London NW1 2DA, England.
   [Al-Chalabi, Ammar] Kings Coll Hosp London, Bessemer Rd,Denmark Hill, London SE5 9RS, England.
   [Iacoangeli, Alfredo] Natl Inst Hlth Res Biomed Res Ctr, London SE5 8AF, England.
   [Iacoangeli, Alfredo] South London & Maudsley NHS Fdn Trust, Dementia Unit, London SE5 8AF, England.
   [Iacoangeli, Alfredo] Kings Coll London, London SE5 8AF, England.
RP Iacoangeli, A (通讯作者)，Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Biostat & Hlth Informat, London SE5 8AF, England.; Iacoangeli, A (通讯作者)，Kings Coll London, Maurice Wohl Clin Neurosci Inst, Inst Psychiat Psychol & Neurosci, Dept Basic & Clin Neurosci, London SE5 9RT, England.; Iacoangeli, A (通讯作者)，Natl Inst Hlth Res Biomed Res Ctr, London SE5 8AF, England.; Iacoangeli, A (通讯作者)，South London & Maudsley NHS Fdn Trust, Dementia Unit, London SE5 8AF, England.; Iacoangeli, A (通讯作者)，Kings Coll London, London SE5 8AF, England.
EM alfredo.iacoangeli@kcl.ac.uk
RI Hu, Jiajing/GLT-8729-2022; Bean, Daniel/AFQ-4601-2022; dobson,
   richard/C-9269-2011; Al-Chalabi, Ammar/E-5361-2010; Lepore,
   Rosalba/I-3074-2014
OI dobson, richard/0000-0003-4224-9245; Al-Chalabi,
   Ammar/0000-0002-4924-7712; Bean, Daniel/0000-0002-8594-7804; Lepore,
   Rosalba/0000-0002-9481-2557
FU UK Research and Innovation; Medical Research Council; South London and
   Maudsley NHS Foundation Trust; MND Scotland; Motor Neurone Disease
   Association; National Institute for Health Research; China Scholarship
   Council; Spastic Paraplegia Foundation; UKRI Innovation Fellowship [UK
   MR/S00310X/1]; MotorNeurone Disease Association; King's-China
   Scholarship Council PhD Scholarship programme; United Kingdom, Medical
   Research Council [MR/L501529/1, MR/R024804/1]; Economic and Social
   Research Council [ES/L008238/1]; National Institute for Health Research
   (NIHR) Biomedical Research Centre at South London and Maudsley NHS
   Foundation Trust and King's College London; European Community's Horizon
   2020 Programme [633413]; Maudsley Charity [980]; Guy's and St Thomas'
   Charity [TR130505]
FX UK Research and Innovation; Medical Research Council; South London and
   Maudsley NHS Foundation Trust; MND Scotland; Motor Neurone Disease
   Association; National Institute for Health Research; China Scholarship
   Council; Spastic Paraplegia Foundation. Funding for open access charge:
   UKRI. D.B. is funded by a UKRI Innovation Fellowship (Health Data
   Research UK MR/S00310X/1). A.I is funded by the MotorNeurone Disease
   Association. J.U. is funded by the King's-China Scholarship Council PhD
   Scholarship programme. This is an EU Joint Programme-Neurodegenerative
   Disease Research (JPND) project. The project is supported through the
   following funding organizations under the aegis of
   JPNDhttp://www.neurodegenerationresearch.eu/(United Kingdom, Medical
   Research Council MR/L501529/1 to A.A.C., principal investigator [PI] and
   MR/R024804/1 to A.A.-C., PI]; Economic and Social Research Council
   ES/L008238/1 to A.A.-C. [co-PI]) and through the Motor Neurone Disease
   Association. This study represents independent research partly funded by
   the National Institute for Health Research (NIHR) Biomedical Research
   Centre at South London and Maudsley NHS Foundation Trust and King's
   College London. The work leading up to this publication was funded by
   the European Community's Horizon 2020 Programme
   (H2020-PHC-2014two-stage; grant 633413). We acknowledge use of the
   research computing facility at King's College London, Rosalind
   (https://rosalind.kcl.ac.uk), which is delivered in partnership with the
   National Institute for Health Research (NIHR) BiomedicalResearch Centres
   at South London & Maudsley and Guy's & St. Thomas' NHS Foundation Trusts
   and part-funded by capital equipment grants from the Maudsley Charity
   (award 980) and Guy's and St Thomas' Charity (TR130505). The views
   expressed are those of the author(s) and not necessarily those of the
   NHS, the NIHR, King's College London, or the Department of Health and
   Social Care.
CR Adibhatla RM, 2007, FUTURE LIPIDOL, V2, P403, DOI 10.2217/17460875.2.4.403
   Agarwala R, 2018, NUCLEIC ACIDS RES, V46, pD8, DOI DOI 10.1093/NAR/GKX1095
   Amberger JS, 2015, NUCLEIC ACIDS RES, V43, pD789, DOI 10.1093/nar/gku1205
   Apweiler R, 2004, NUCLEIC ACIDS RES, V32, pD115, DOI 10.1093/nar/gkh131
   Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Barber SC, 2010, FREE RADICAL BIO MED, V48, P629, DOI 10.1016/j.freeradbiomed.2009.11.018
   Bean DM, 2020, GENES-BASEL, V11, DOI 10.3390/genes11060668
   Bean DM, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-16674-x
   BOWLING AC, 1993, J NEUROCHEM, V61, P2322, DOI 10.1111/j.1471-4159.1993.tb07478.x
   Breuer K, 2013, NUCLEIC ACIDS RES, V41, pD1228, DOI 10.1093/nar/gks1147
   Browne MW, 2000, J MATH PSYCHOL, V44, P108, DOI 10.1006/jmps.1999.1279
   Calderone A, 2013, NAT METHODS, V10, P690, DOI 10.1038/nmeth.2561
   Carbon S, 2021, NUCLEIC ACIDS RES, V49, pD325, DOI 10.1093/nar/gkaa1113
   Chatr-Aryamontri A, 2007, NUCLEIC ACIDS RES, V35, pD572, DOI 10.1093/nar/gkl950
   Chatr-aryamontri A, 2017, NUCLEIC ACIDS RES, V45, pD369, DOI 10.1093/nar/gkw1102
   Chen J, 2009, NUCLEIC ACIDS RES, V37, pW305, DOI 10.1093/nar/gkp427
   Clerc O, 2019, NUCLEIC ACIDS RES, V47, pD376, DOI 10.1093/nar/gky1035
   Cooper-Knock J, 2020, CELL REP, V33, DOI 10.1016/j.celrep.2020.108456
   Cooper-Knock J, 2019, CELL REP, V26, P2298, DOI 10.1016/j.celrep.2019.02.006
   Dabbish L., 2012, P ACM C COMP SUPP CO, P1277, DOI DOI 10.1145/2145204.2145396
   Dunn-Giroux T, 2020, FASEB J, V34, DOI 10.1096/fasebj.2020.34.s1.00143
   Fabregat A, 2018, NUCLEIC ACIDS RES, V46, pD649, DOI [10.1093/nar/gkx1132, 10.1093/nar/gkz1031]
   Foerster BR, 2013, JAMA NEUROL, V70, P1009, DOI 10.1001/jamaneurol.2013.234
   Guala D, 2014, BIOINFORMATICS, V30, P2689, DOI 10.1093/bioinformatics/btu344
   Hamosh A, 2005, NUCLEIC ACIDS RES, V33, pD514
   Hermjakob H, 2004, NUCLEIC ACIDS RES, V32, pD452, DOI 10.1093/nar/gkh052
   Hwang S, 2019, NUCLEIC ACIDS RES, V47, pD573, DOI 10.1093/nar/gky1126
   Iacoangeli A, 2020, CELL REP, V33, DOI 10.1016/j.celrep.2020.108323
   Iacoangeli A, 2019, AMYOTROPH LAT SCL FR, V20, P207, DOI 10.1080/21678421.2018.1562553
   Kanehisa M, 2004, NUCLEIC ACIDS RES, V32, pD277, DOI 10.1093/nar/gkh063
   Kanehisa M, 2021, NUCLEIC ACIDS RES, V49, pD545, DOI 10.1093/nar/gkaa970
   Klopfenstein DV, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-28948-z
   Kohler S, 2021, NUCLEIC ACIDS RES, V49, pD1207, DOI 10.1093/nar/gkaa1043
   Kotera M, 2012, NUCLEIC ACIDS RES, V40, pW162, DOI 10.1093/nar/gks459
   Lachmann A, 2010, BIOINFORMATICS, V26, P2438, DOI 10.1093/bioinformatics/btq466
   Lacoangeli A, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-2791-8
   Landrum MJ, 2018, NUCLEIC ACIDS RES, V46, pD1062, DOI 10.1093/nar/gkx1153
   Landrum MJ, 2014, NUCLEIC ACIDS RES, V42, pD980, DOI 10.1093/nar/gkt1113
   Liscic RM, 2015, ARH HIG RADA TOKSIKO, V66, P285, DOI 10.1515/aiht-2015-66-2679
   Lonsdale J, 2013, NAT GENET, V45, P580, DOI 10.1038/ng.2653
   MacArthur J, 2017, NUCLEIC ACIDS RES, V45, pD896, DOI 10.1093/nar/gkw1133
   Nishimura AL, 2004, AM J HUM GENET, V75, P822, DOI 10.1086/425287
   Obuchowski NA, 2005, AM J ROENTGENOL, V184, P364, DOI 10.2214/ajr.184.2.01840364
   Oosthuyse B, 2001, NAT GENET, V28, P131, DOI 10.1038/88842
   Orchard S, 2014, NUCLEIC ACIDS RES, V42, pD358, DOI 10.1093/nar/gkt1115
   Orchard S, 2012, NAT METHODS, V9, P345, DOI [10.1038/NMETH.1931, 10.1038/nmeth.1931]
   Petryszak R, 2014, NUCLEIC ACIDS RES, V42, pD926, DOI 10.1093/nar/gkt1270
   Pinero J, 2020, NUCLEIC ACIDS RES, V48, pD845, DOI 10.1093/nar/gkz1021
   Pinero J, 2017, NUCLEIC ACIDS RES, V45, pD833, DOI 10.1093/nar/gkw943
   Povey S, 2001, HUM GENET, V109, P678, DOI 10.1007/s00439-001-0615-0
   Radunovic A, 1999, AMYOTROPH LATERAL SC, V1, P45
   Rebhan M, 1998, BIOINFORMATICS, V14, P656, DOI 10.1093/bioinformatics/14.8.656
   Smith EF, 2019, NEUROSCI LETT, V710, DOI 10.1016/j.neulet.2017.06.052
   Su AI, 2004, P NATL ACAD SCI USA, V101, P6062, DOI 10.1073/pnas.0400782101
   Szklarczyk D, 2017, NUCLEIC ACIDS RES, V45, pD362, DOI 10.1093/nar/gkw937
   Tazelaar GHP, 2020, BRAIN COMMUN, V2, DOI 10.1093/braincomms/fcaa064
   Uhlen M, 2017, SCIENCE, V357, P660, DOI 10.1126/science.aan2507
   Wang J, 2013, NUCLEIC ACIDS RES, V41, pD171, DOI 10.1093/nar/gks1221
   Warde-Farley D, 2010, NUCLEIC ACIDS RES, V38, pW214, DOI 10.1093/nar/gkq537
   Wroe R, 2008, AMYOTROPH LATERAL SC, V9, P249, DOI 10.1080/17482960802146106
   Yang H, 2015, NAT METHODS, V12, P841, DOI [10.1038/NMETH.3484, 10.1038/nmeth.3484]
   Yoo M, 2015, BIOINFORMATICS, V31, P3069, DOI 10.1093/bioinformatics/btv313
   Zhao MG, 2020, NAR GENOM BIOINFORM, V2, DOI 10.1093/nargab/lqaa032
   Zolotareva O, 2019, J INTEGR BIOINFORMAT, V16, DOI 10.1515/jib-2018-0069
NR 64
TC 4
Z9 4
U1 5
U2 11
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0305-1048
EI 1362-4962
J9 NUCLEIC ACIDS RES
JI Nucleic Acids Res.
PD JUL 2
PY 2021
VL 49
IS W1
BP W153
EP W161
DI 10.1093/nar/gkab449
EA JUN 2021
PG 9
WC Biochemistry & Molecular Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology
GA TI4NF
UT WOS:000672775800021
PM 34125897
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Liu, Q
   Liu, M
   Zhou, HL
   Yan, F
   Ma, YY
   Shen, WM
AF Liu, Qing
   Liu, Min
   Zhou, Hanlu
   Yan, Feng
   Ma, Yingyi
   Shen, Weiming
TI Intelligent manufacturing system with human-cyber-physical fusion and
   collaboration for process fine control
SO JOURNAL OF MANUFACTURING SYSTEMS
LA English
DT Article
DE Non-ferrous metal industry; Human-cyber-physical system; HCP fusion and
   collaboration; Knowledge-graph; Collaborative decision-making;
   Production, supply and marketing integration plan
AB There are some problems in non-ferrous metal industry, such as low production efficiency, separation of manufacturing information, disconnection between production, supply and marketing and so on, which highlight the need for fine process control and business collaborative decision-making with cost, efficiency, quality and safety as the core. On the basis of Human Cyber Physical fusion intelligence, this paper firstly discusses the mechanism of intelligent manufacturing system with HCP fusion and collaboration, and explores the HCP data fusion mechanism, collaboration mechanism and interaction mechanism. Then, an intelligent manufacturing system with HCP fusion and collaboration in the non-ferrous metal industry is established, in which a knowledge-graph based HCP data fusion framework and a cyber-physical system based HCP business collaborative decision model are discussed to realize HCP fusion and collaboration manufacturing. Finally, a case study for a copper smelting enterprise is explored, in which the intelligent manufacturing system architecture for the copper smelting enterprise is proposed, and validated through the collaborative decision-making of production, supply and marketing integration plan in the world's largest monomer copper smelter enterprise.
C1 [Liu, Qing; Liu, Min; Zhou, Hanlu] Tongji Univ, Sch Elect & Informat Engn, Shanghai 200000, Peoples R China.
   [Yan, Feng] CINF Engn Corp Ltd, Changsha 410000, Peoples R China.
   [Ma, Yingyi] Guixi Smelter, Yingtan 335000, Peoples R China.
   [Shen, Weiming] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Wuhan 430000, Peoples R China.
RP Liu, M (通讯作者)，Tongji Univ, Sch Elect & Informat Engn, Shanghai 200000, Peoples R China.; Yan, F (通讯作者)，CINF Engn Corp Ltd, Changsha 410000, Peoples R China.
EM 2010142@tongji.edu.cn; 2033011@tongji.edu.cn; lmin@tongji.edu.cn;
   cinf_yf@163.com; mayingyi@gy.jxcc.com; wshen@ieee.org
FU National Key R&D Program of China [2019YFB1704700]; National Natural
   Science Foundation of China [61873191]; science and technology
   innovation Program of Hunan Province o funder [2021RC4047]
FX This work was supported by the National Key R&D Program of China under
   Grant 2019YFB1704700, part by the National Natural Science Foundation of
   China under Grant 61873191 and the science and technology innovation
   Program of Hunan Province o funder Grant 2021RC4047.
CR [柴天佑 Chai Tianyou], 2020, [自动化学报, Acta Automatica Sinica], V46, P2005
   Chen S, 2020, IEEE ACCESS, V8, P134233, DOI 10.1109/ACCESS.2020.3011254
   Chen ZG, 2019, J IND MANAG OPTIM, V15, P595, DOI 10.3934/jimo.2018060
   Cimini C, 2020, J MANUF SYST, V54, P258, DOI 10.1016/j.jmsy.2020.01.002
   [丁进良 Ding JinLiang], 2018, [自动化学报, Acta Automatica Sinica], V44, P1931
   Dragomiretskiy K, 2014, IEEE T SIGNAL PROCES, V62, P531, DOI 10.1109/TSP.2013.2288675
   Fantini P, 2020, COMPUT IND ENG, V139, DOI 10.1016/j.cie.2018.01.025
   Gao J, 2015, STRATEG STUDY CAE, V17, P54
   [桂卫华 Gui Weihua], 2020, [中国科学. 信息科学, Scientia Sinica Informationis], V50, P1345
   [桂卫华 Gui Weihua], 2015, [中国科学基金, Bulletin of National Natural Science Foundation of China], V29, P337
   Haenlein M, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619864925
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Huang KK, 2021, IEEE T NEUR NET LEAR, DOI 10.1109/TNNLS.2021.3083401
   Huang KK, 2021, IEEE T IND INFORM, V17, P558, DOI 10.1109/TII.2020.2992728
   Kaasinen E, 2020, COMPUT IND ENG, V139, DOI 10.1016/j.cie.2019.01.052
   Kazancoglu Y, 2018, J ENTERP INF MANAG, V31, P891, DOI 10.1108/JEIM-01-2017-0015
   Liang H, 2021, IEEE T INSTRUM MEAS, V70, P1
   Lim KYH, 2020, J INTELL MANUF, V31, P1313, DOI 10.1007/s10845-019-01512-w
   Nunes D., 2018, PRACTICAL INTRO HUMA
   Nunes DS, 2015, IEEE COMMUN SURV TUT, V17, P944, DOI 10.1109/COMST.2015.2398816
   Oussous A, 2018, J KING SAUD UNIV-COM, V30, P431, DOI 10.1016/j.jksuci.2017.06.001
   Oztemel E, 2020, J INTELL MANUF, V31, P127, DOI 10.1007/s10845-018-1433-8
   Pereira AC, 2017, PROCEDIA MANUF, V13, P1206, DOI 10.1016/j.promfg.2017.09.032
   Perrusquia A, 2020, J INTELL ROBOT SYST, V97, P271, DOI 10.1007/s10846-019-01058-2
   Peruzzini M, 2020, COMPUT IND ENG, V139, DOI 10.1016/j.cie.2018.12.047
   Qian F, 2019, ENGINEERING, V5, P981, DOI 10.1016/j.eng.2019.10.002
   [钱锋 Qian Feng], 2017, [自动化学报, Acta Automatica Sinica], V43, P893
   Rauch E, 2020, COMPUT IND ENG, V139, DOI 10.1016/j.cie.2019.01.018
   Romero D, 2016, IFIP ADV INF COMM TE, V488, P677, DOI 10.1007/978-3-319-51133-7_80
   Sisinni E, 2018, IEEE T IND INFORM, V14, P4724, DOI 10.1109/TII.2018.2852491
   Sun SJ, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20072011
   Torres ME, 2011, INT CONF ACOUST SPEE, P4144
   Wang C, 2018, J INTELL MANUF, V29, P937, DOI [10.1007/s10845-015-1153-2, 10.1007/s12652-015-0301-z]
   Wang DS, 2018, SOFT COMPUT, V22, P387, DOI 10.1007/s00500-016-2474-6
   Wang J, 2018, APPL ENERG, V228, P2195, DOI 10.1016/j.apenergy.2018.07.039
   Wang LH, 2016, J MANUF SYST, V40, P25, DOI 10.1016/j.jmsy.2016.05.002
   Yin YY, 2021, CONTROL ENG PRACT, V113, DOI 10.1016/j.conengprac.2021.104855
   [袁小锋 Yuan Xiaofeng], 2018, [中国工程科学, Engineering Science], V20, P59
   Zhong M, 2019, PHYS STAT MECH ITS A, V528
   Zhou J, 2018, ENGINEERING-PRC, V4, P11, DOI 10.1016/j.eng.2018.01.002
   Zhou XJ, 2020, IEEE T IND INFORM, V16, P7255, DOI 10.1109/TII.2019.2943500
NR 41
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0278-6125
EI 1878-6642
J9 J MANUF SYST
JI J. Manuf. Syst.
PD JUL
PY 2022
VL 64
BP 149
EP 169
DI 10.1016/j.jmsy.2022.06.004
PG 21
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA 3I8TK
UT WOS:000832981500001
DA 2022-08-23
ER

PT J
AU Gogleva, A
   Polychronopoulos, D
   Pfeifer, M
   Poroshin, V
   Ughetto, M
   Martin, MJ
   Thorpe, H
   Bornot, A
   Smith, PD
   Sidders, B
   Dry, JR
   Ahdesmaki, M
   McDermott, U
   Papa, E
   Bulusu, KC
AF Gogleva, Anna
   Polychronopoulos, Dimitris
   Pfeifer, Matthias
   Poroshin, Vladimir
   Ughetto, Michael
   Martin, Matthew J.
   Thorpe, Hannah
   Bornot, Aurelie
   Smith, Paul D.
   Sidders, Ben
   Dry, Jonathan R.
   Ahdesmaki, Miika
   McDermott, Ultan
   Papa, Eliseo
   Bulusu, Krishna C.
TI Knowledge graph-based recommendation framework identifies drivers of
   resistance in EGFR mutant non-small cell lung cancer
SO NATURE COMMUNICATIONS
LA English
DT Article
ID ACQUIRED-RESISTANCE; DRUG-RESISTANCE; OSIMERTINIB; INHIBITION;
   COMBINATION; MUTATIONS; THERAPY; AZD9291
AB Resistance to EGFR inhibitors presents a major obstacle in treating non-small cell lung cancer. Here, the authors develop a recommender system ranking genes based on trade-offs between diverse types of evidence linking them to potential mechanisms of EGFRi resistance.
   Resistance to EGFR inhibitors (EGFRi) presents a major obstacle in treating non-small cell lung cancer (NSCLC). One of the most exciting new ways to find potential resistance markers involves running functional genetic screens, such as CRISPR, followed by manual triage of significantly enriched genes. This triage process to identify 'high value' hits resulting from the CRISPR screen involves manual curation that requires specialized knowledge and can take even experts several months to comprehensively complete. To find key drivers of resistance faster we build a recommendation system on top of a heterogeneous biomedical knowledge graph integrating pre-clinical, clinical, and literature evidence. The recommender system ranks genes based on trade-offs between diverse types of evidence linking them to potential mechanisms of EGFRi resistance. This unbiased approach identifies 57 resistance markers from >3,000 genes, reducing hit identification time from months to minutes. In addition to reproducing known resistance markers, our method identifies previously unexplored resistance mechanisms that we prospectively validate.
C1 [Gogleva, Anna; Papa, Eliseo] AstraZeneca, AI Engn, R&D IT, Biol Insight Knowledge Graph BIKG, Cambridge, England.
   [Polychronopoulos, Dimitris; Sidders, Ben; Ahdesmaki, Miika; Bulusu, Krishna C.] AstraZeneca, Oncol R&D, Early Computat Oncol, Res & Early Dev, Cambridge, England.
   [Pfeifer, Matthias; Martin, Matthew J.; Thorpe, Hannah; Smith, Paul D.; McDermott, Ultan] AstraZeneca, Oncol R&D, Biosci Res & Early Dev, Cambridge, England.
   [Poroshin, Vladimir] AstraZeneca, Enterprise AI Serv, IGNITE, NLP Lab, Cambridge, England.
   [Ughetto, Michael] AstraZeneca, R&D IT, AI Engn, Biol Insight Knowledge Graph BIKG, Gothenburg, Sweden.
   [Bornot, Aurelie] AstraZeneca, R&D, Discovery Sci, Data Sci & Quantitat Biol, Cambridge, Sweden.
   [Dry, Jonathan R.] AstraZeneca, Oncol R&D, Early Computat Oncol, Res & Early Dev, Waltham, MA USA.
RP Papa, E (通讯作者)，AstraZeneca, AI Engn, R&D IT, Biol Insight Knowledge Graph BIKG, Cambridge, England.; Bulusu, KC (通讯作者)，AstraZeneca, Oncol R&D, Early Computat Oncol, Res & Early Dev, Cambridge, England.
EM elipapa@alum.mit.edu; krishna.bulusu@astrazeneca.com
RI ; Polychronopoulos, Dimitris/G-8214-2014
OI Bulusu, Krishna/0000-0002-5092-6640; , Matthew/0000-0002-8948-961X;
   Polychronopoulos, Dimitris/0000-0001-9427-3082
CR Andrews S., 2010, FASTQC QUALITY CONTR
   Angrisani A, 2021, CELL COMMUN SIGNAL, V19, DOI 10.1186/s12964-021-00737-8
   Behan FM, 2019, NATURE, V568, P511, DOI 10.1038/s41586-019-1103-9
   Brockmann M, 2017, NATURE, V546, P307, DOI 10.1038/nature22376
   Brown KK, 2018, MEDCHEMCOMM, V9, P606, DOI 10.1039/c7md00633k
   Chakravarty Debyani, 2017, JCO Precis Oncol, V2017, DOI 10.1200/PO.17.00011
   Chang JW, 2019, CARCINOGENESIS, V40, P624, DOI 10.1093/carcin/bgz044
   Chang W., 2021, SHINY WEB APPL FRAME
   Cross DAE, 2014, CANCER DISCOV, V4, P1046, DOI 10.1158/2159-8290.CD-14-0337
   de Bruin EC, 2014, CANCER DISCOV, V4, P606, DOI 10.1158/2159-8290.CD-13-0741
   De Vree JML, 1998, P NATL ACAD SCI USA, V95, P282, DOI 10.1073/pnas.95.1.282
   Deb D, 2017, CANCER RES, V77, P3070, DOI 10.1158/0008-5472.CAN-15-3052
   DepMap B., 2019, FIGSHARE, V10, pm9
   Dias MB, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P291
   Diederichs S, 2016, EMBO MOL MED, V8, P442, DOI 10.15252/emmm.201506055
   Dutta J, 2011, OPTIMIZATION, V60, P1091, DOI 10.1080/02331934.2011.587006
   Eberlein CA, 2015, CANCER RES, V75, P2489, DOI 10.1158/0008-5472.CAN-14-3167
   Elahi M, 2016, COMPUT SCI REV, V20, P29, DOI 10.1016/j.cosrev.2016.05.002
   Ewels P, 2016, BIOINFORMATICS, V32, P3047, DOI 10.1093/bioinformatics/btw354
   Frainay C, 2019, BIOINFORMATICS, V35, P274, DOI 10.1093/bioinformatics/bty577
   FREEMAN LC, 1977, SOCIOMETRY, V40, P35, DOI 10.2307/3033543
   Garcia-Perez J, 2015, ENVIRON RES, V140, P542, DOI 10.1016/j.envres.2015.05.014
   Geleta D.., 2022, WSDM 2022 INT C WEB
   Georgiou A, 2020, MOL CANCER RES, V18, P835, DOI 10.1158/1541-7786.MCR-19-1201
   Germain A, 2013, IEEE INT WORKSH MULT, P25, DOI 10.1109/MMSP.2013.6659258
   Gogleva A, 2021, 15TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS 2021), P548, DOI 10.1145/3460231.3474598
   Gong H, 2020, BMC CANCER, V20, DOI 10.1186/s12885-020-07667-7
   Goss G, 2016, LANCET ONCOL, V17, P1643, DOI 10.1016/S1470-2045(16)30508-3
   Greenwell B, 2020, FASTSHAP FAST APPROX
   Guala D, 2017, SCI REP-UK, V7, DOI 10.1038/srep46598
   Gunawardana A., 2009, P 3 ACM C REC SYST, P117
   Gupta P., 2013, P 22 INT C WORLD WID, P505, DOI DOI 10.1145/2488388.2488433
   Han J, 2021, J HEMATOL ONCOL, V14, DOI 10.1186/s13045-020-01025-7
   Hart T, 2017, G3-GENES GENOM GENET, V7, P2719, DOI 10.1534/g3.117.041277
   Hart T, 2016, BMC BIOINFORMATICS, V17, DOI 10.1186/s12859-016-1015-8
   Himmelstein DS, 2015, PLOS COMPUT BIOL, V11, DOI 10.1371/journal.pcbi.1004259
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Iorio F, 2016, CELL, V166, P740, DOI 10.1016/j.cell.2016.06.017
   Jiangzhou Liu, 2021, 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), P2450, DOI 10.1109/IAEAC50856.2021.9390863
   Johnson J, 2021, IEEE T BIG DATA, V7, P535, DOI 10.1109/TBDATA.2019.2921572
   Kang M, 2018, CLIN TRANSL ONCOL, V20, P457, DOI 10.1007/s12094-017-1730-2
   Kelland L, 2007, NAT REV CANCER, V7, P573, DOI 10.1038/nrc2167
   Kobayashi S, 2005, NEW ENGL J MED, V352, P786, DOI 10.1056/NEJMoa044238
   Konermann S, 2015, NATURE, V517, P583, DOI 10.1038/nature14136
   Kurppa KJ, 2020, CANCER CELL, V37, P104, DOI 10.1016/j.ccell.2019.12.006
   Lau MT, 2020, GENOME BIOL, V21, DOI 10.1186/s13059-020-1940-8
   Lazic SE, 2015, PEERJ, V3, DOI 10.7717/peerj.1444
   Leonetti A, 2019, BRIT J CANCER, V121, P725, DOI 10.1038/s41416-019-0573-8
   Li G, 2016, MATH PROBL ENG, V2016, DOI 10.1155/2016/2535329
   Li W, 2014, GENOME BIOL, V15, DOI 10.1186/s13059-014-0554-4
   Lizotte DJ, 2016, J MACH LEARN RES, V17
   Lo Sardo F, 2018, CANCERS, V10, DOI 10.3390/cancers10050137
   Lops P., 2011, RECOMMENDER SYSTEMS, V73
   Lundberg SM, 2017, ADV NEUR IN, V30
   Lynch TJ, 2004, NEW ENGL J MED, V350, P2129, DOI 10.1056/NEJMoa040938
   Marler RT, 2004, STRUCT MULTIDISCIP O, V26, P369, DOI 10.1007/s00158-003-0368-6
   Michael Ughetto A. G.., KNOWLEDGE GRAPH BASE
   Montani I., 2018, ARTIF INTELL
   Mroz EA, 2017, CANCER-AM CANCER SOC, V123, P917, DOI 10.1002/cncr.30430
   Mu YX, 2020, J CANCER RES CLIN, V146, P2427, DOI 10.1007/s00432-020-03239-1
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Ohata Y, 2017, MOL CANCER THER, V16, P1155, DOI 10.1158/1535-7163.MCT-16-0728
   Ozsoy MG., 2018, BMC BIOINFORMATICS, V19
   Page L., 1999, 199966 STANF INFOLAB
   Park SH, 2018, FASEB J, V32, P5862, DOI 10.1096/fj.201800011R
   Pfeifer M, 2021, MOL CANCER THER, V20, DOI 10.1158/1535-7163.TARG-21-P066
   Pham TH, 2021, CANCER DISCOV, V11, P778, DOI 10.1158/2159-8290.CD-20-0706
   Phuchareon J, 2015, P NATL ACAD SCI USA, V112, pE3855, DOI 10.1073/pnas.1510733112
   Radivojevic T, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-18008-4
   Ramalingam SS, 2020, NEW ENGL J MED, V382, P41, DOI 10.1056/NEJMoa1913662
   Ricordel C, 2018, ANN ONCOL, V29, pI28, DOI 10.1093/annonc/mdx705
   Rong XZ, 2019, J THORAC ONCOL, V14, P1766, DOI 10.1016/j.jtho.2019.06.014
   Roocks P, 2016, R J, V8, P393
   Sampath D, 2006, BLOOD, V107, P2517, DOI 10.1182/blood-2005-08-3351
   Smith B, 2017, IEEE INTERNET COMPUT, V21, P12, DOI 10.1109/MIC.2017.72
   Su Xiaoyuan, 2009, ADV ARTIFICIAL INTEL, DOI DOI 10.1155/2009/421425
   Suphavilai C, 2018, BIOINFORMATICS, V34, P3907, DOI 10.1093/bioinformatics/bty452
   Tran YZ, 2020, MOL CELL PROTEOMICS, V19, P928, DOI 10.1074/mcp.RA120.002036
   Tsherniak A, 2017, CELL, V170, P564, DOI 10.1016/j.cell.2017.06.010
   Tzelepis K, 2016, CELL REP, V17, P1193, DOI 10.1016/j.celrep.2016.09.079
   Vasan N, 2019, NATURE, V575, P299, DOI 10.1038/s41586-019-1730-1
   Verbinnen I, 2020, ONCOGENESIS, V9, DOI 10.1038/s41389-020-0214-3
   Voorneveld M, 2003, OPER RES LETT, V31, P7, DOI 10.1016/S0167-6377(02)00189-X
   Wang F, 2019, SWARM EVOL COMPUT, V49, P220, DOI 10.1016/j.swevo.2019.06.009
   Wang HW, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P968, DOI 10.1145/3292500.3330836
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wei T, R PACKAGE CORRPLOT V
   Wright MN, 2017, J STAT SOFTW, V77, P1, DOI 10.18637/jss.v077.i01
   Yang JCH, 2017, J CLIN ONCOL, V35, P1288, DOI 10.1200/JCO.2016.70.3223
   Yu H, 2019, J THORAC ONCOL, V14, pS647, DOI 10.1016/j.jtho.2019.08.1366
   Zampieri G, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2025-5
   Zhao TY, 2020, BIOINFORMATICS, V36, P4466, DOI 10.1093/bioinformatics/btaa428
   Zhou ZH, 2018, NATL SCI REV, V5, P44, DOI 10.1093/nsr/nwx106
   Zitnik M, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-018-04948-5
NR 95
TC 0
Z9 0
U1 12
U2 12
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
EI 2041-1723
J9 NAT COMMUN
JI Nat. Commun.
PD MAR 29
PY 2022
VL 13
IS 1
AR 1667
DI 10.1038/s41467-022-29292-7
PG 14
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 0D5AM
UT WOS:000776008000011
PM 35351890
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Bagheri, E
   Ensan, F
   Al-Obeidat, F
AF Bagheri, Ebrahim
   Ensan, Faezeh
   Al-Obeidat, Feras
TI Neural word and entity embeddings for ad hoc retrieval
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Neural embeddings; Ad hoc document retrieval; TREC; Knowledge graph
AB Learning low dimensional dense representations of the vocabularies of a corpus, known as neural embeddings, has gained much attention in the information retrieval community. While there have been several successful attempts at integrating embeddings within the ad hoc document retrieval task, yet, no systematic study has been reported that explores the various aspects of neural embeddings and how they impact retrieval performance. In this paper, we perform a methodical study on how neural embeddings influence the ad hoc document retrieval task. More specifically, we systematically explore the following research questions: (i) do methods solely based on neural embeddings perform competitively with state of the art retrieval methods with and without interpolation? (ii) are there any statistically significant difference between the performance of retrieval models when based on word embeddings compared to when knowledge graph entity embeddings are used? and (iii) is there significant difference between using locally trained neural embeddings compared to when globally trained neural embeddings are used? We examine these three research questions across both hard and all queries. Our study finds that word embeddings do not show competitive performance to any of the baselines. In contrast, entity embeddings show competitive performance to the baselines and when interpolated, outperform the best baselines for both hard and soft queries.
C1 [Bagheri, Ebrahim] Ryerson Univ, Dept Elect & Comp Engn, Lab Syst Software & Semant LS3, Toronto, ON, Canada.
   [Ensan, Faezeh] Ferdowsi Univ Mashhad, Dept Comp Engn, Mashhad, Iran.
   [Al-Obeidat, Feras] Zayed Univ, Coll Technol Innovat, Dubai, U Arab Emirates.
RP Bagheri, E (通讯作者)，Ryerson Univ, Dept Elect & Comp Engn, Lab Syst Software & Semant LS3, Toronto, ON, Canada.
EM bagheri@ryerson.ca; ensan@um.ac.ir
CR Atasu K., 2017, 2017 IEEE INT C BIG, DOI 10.1109/BigData.2017.8258005
   Capannini G, 2016, INFORM PROCESS MANAG, V52, P1161, DOI 10.1016/j.ipm.2016.05.004
   Carmel D., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P390, DOI 10.1145/1148170.1148238
   Croft WB, 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204
   Dalton J, 2014, SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P365, DOI 10.1145/2600428.2609628
   Diaz F, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P367
   Ensan F., 2017, P 26 ACM INT C INF K, P2059
   Ensan F, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P181, DOI 10.1145/3018661.3018692
   Fernandez-Reyes FC, 2018, INFORM PROCESS MANAG, V54, P1, DOI 10.1016/j.ipm.2017.09.001
   Ferragina P., 2010, P 19 ACM INT C INF K, P1625, DOI DOI 10.1145/1871437.1871689
   Foley J, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2061, DOI 10.1145/2983323.2983909
   Ganguly D, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P795, DOI 10.1145/2766462.2767780
   Guo JF, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P701, DOI 10.1145/2983323.2983768
   Guo JF, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P55, DOI 10.1145/2983323.2983769
   Hasibi F., 2016, P 2016 ACM INT C THE, P209, DOI DOI 10.1145/2970398.2970406
   Hasibi F, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1289, DOI 10.1145/3077136.3084149
   Huston S., 2014, P CIKM, P111
   Jovanovic J, 2014, IT PROF, V16, P38, DOI 10.1109/MITP.2014.85
   Karisani P, 2016, INFORM PROCESS MANAG, V52, P478, DOI 10.1016/j.ipm.2015.09.002
   Kusner MJ, 2015, PR MACH LEARN RES, V37, P957
   Kuzi S, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1929, DOI 10.1145/2983323.2983876
   Lavrenko V., 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972
   Le Q.V., 2014, COMPUT RES REPOS COR, P1, DOI [DOI 10.1145/2740908.2742760, 10.1145/2740908.2742760]
   LI Y, 2007, P 30 ANN INT ACM SIG, P797, DOI DOI 10.1145/1277741
   Li Y., 2016, P COLING
   Lin Y., 2015, P AAAI C ART INT, P2181
   LIU S, 2004, P 27 ANN INT ACM SIG, P266, DOI DOI 10.1145/1008992.1009039
   Liu XT, 2015, INFORM RETRIEVAL J, V18, P473, DOI 10.1007/s10791-015-9267-x
   Lv Y., 2009, P ACM C INF KNOWL MA, P1895, DOI [10.1145/1645953.1646259, DOI 10.1145/1645953.1646259]
   Lv YH, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P579
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Nikolaev F, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P435, DOI 10.1145/2911451.2911545
   Pantel Patrick, 2012, ACL ASS P COMP LING, P563
   Ponte J. M., 1998, P 21 ANN INT ACM SIG, P275, DOI DOI 10.1145/290941.291008
   Raviv H, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P65, DOI 10.1145/2911451.2911508
   Rekabsaz N., 2017, P 2017 ANN INT ACM S
   Ren YF, 2016, INFORM SCIENCES, V369, P188, DOI 10.1016/j.ins.2016.06.040
   Su JS, 2018, INFORM SCIENCES, V424, P287, DOI 10.1016/j.ins.2017.09.070
   Sugawara K., 2016, P ANN M ASS COMP LIN
   Toutanova K., 2015, P 2015 C EMP METH NA, P1499
   Wang P, 2016, NEUROCOMPUTING, V174, P806, DOI 10.1016/j.neucom.2015.09.096
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xiong CY, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P763, DOI 10.1145/3077136.3080768
   Xiong CY, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1271, DOI 10.1145/3038912.3052558
   Zamani H., 2017, P ANN INT ACM SIGIR, P1505
   Zamani  H., 2016, P 2016 ACM INT C THE, P147, DOI DOI 10.1145/2970398.2970405
   ZHAI C, 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019
   Zhiltsov N, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P253, DOI 10.1145/2766462.2767756
   Zhu GG, 2017, IEEE T KNOWL DATA EN, V29, P72, DOI 10.1109/TKDE.2016.2610428
   Zuccon Guido, 2015, P 20 AUSTR DOC COMP, P12
NR 50
TC 14
Z9 14
U1 0
U2 16
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD JUL
PY 2018
VL 54
IS 4
BP 657
EP 673
DI 10.1016/j.ipm.2018.04.007
PG 17
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA GI8AH
UT WOS:000434743700011
DA 2022-08-23
ER

PT J
AU Mi, JX
   Li, AD
   Zhou, LF
AF Mi, Jian-Xun
   Li, An-Di
   Zhou, Li-Fang
TI Review Study of Interpretation Methods for Future Interpretable Machine
   Learning
SO IEEE ACCESS
LA English
DT Review
DE Deep learning; interpretable machine learning; interpretable methods
   with the self-explanatory model; interpretable methods with external
   co-explanation; influential instance; knowledge graph
ID EXPLANATIONS; RULES
AB In recent years, black-box models have developed rapidly because of their high accuracy. Balancing the interpretability and accuracy is increasingly important. The lack of interpretability severely limits the application of the model in academia and industry. Despite the various interpretable machine learning methods, the perspective and meaning of the interpretation are also different. We provide a review of the current interpretable methods and divide them based on the model being applied. We divide them into two categories: interpretable methods with the self-explanatory model and interpretable methods with external co-explanation. And the interpretable methods with external co-explanation are further divided into subbranch methods based on instances, SHAP, knowledge graph, deep learning, and clustering model. The classification aims to help us understand the model characteristics applied in the interpretable method better. This survey makes the researcher find a suitable model to solve interpretability problems more easily. And the comparison experiments contribute to discovering complementary features from different methods. At the same time, we explore the future challenges and trends of interpretable machine learning to promote the development of interpretable machine learning.
C1 [Mi, Jian-Xun; Li, An-Di; Zhou, Li-Fang] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Image Cognit, Chongqing 400065, Peoples R China.
   [Mi, Jian-Xun; Li, An-Di] Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China.
   [Zhou, Li-Fang] Chongqing Univ Posts & Telecommun, Sch Software Engn, Chongqing 400065, Peoples R China.
RP Mi, JX (通讯作者)，Chongqing Univ Posts & Telecommun, Chongqing Key Lab Image Cognit, Chongqing 400065, Peoples R China.; Mi, JX (通讯作者)，Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China.
EM mijianxun@gmail.com
RI Mi, Jianxun/J-9670-2014
OI Mi, Jianxun/0000-0002-7531-4341; Li, An-Di/0000-0003-1677-3553
FU Natural Science Foundation of Chongqing [cstc2018jcyjAX0532,
   cstc2019jcyj-msxmX0461]; National Natural Science Foundation of China
   [61906024]
FX This work was supported in part by the Natural Science Foundation of
   Chongqing under Grant cstc2018jcyjAX0532 and Grant
   cstc2019jcyj-msxmX0461, and in part by the National Natural Science
   Foundation of China under Grant 61906024.
CR Alaa AM, 2019, ADV NEUR IN, V32
   Gomez JA, 2018, ENVIRON SCI POLLUT R, V25, P977, DOI 10.1007/s11356-016-8339-9
   Andrews R, 1995, KNOWL-BASED SYST, V8, P373, DOI 10.1016/0950-7051(96)81920-4
   Antwarg L., 2019, ARXIV190302407
   Aupetit M, 2007, NEUROCOMPUTING, V70, P1304, DOI 10.1016/j.neucom.2006.11.018
   Badanidiyuru A, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P671, DOI 10.1145/2623330.2623637
   Bascol K, 2016, LECT NOTES COMPUT SC, V10029, P427, DOI 10.1007/978-3-319-49055-7_38
   Bau D, 2017, PROC CVPR IEEE, P3319, DOI 10.1109/CVPR.2017.354
   Bertsimas D, 2018, ARXIV181200539
   Bichindaritz I, 2006, ARTIF INTELL MED, V36, P127, DOI 10.1016/j.artmed.2005.10.008
   Binder Alexander, 2016, International Conference on Information Science and Applications (ICISA) 2016. LNEE 376, P913, DOI 10.1007/978-981-10-0557-2_87
   Boz O., 2002, P 8 ACM SIGKDD INT C, P456
   Carvalho DV, 2019, ELECTRONICS-SWITZ, V8, DOI 10.3390/electronics8080832
   Catherine R, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P288, DOI 10.1145/3109859.3109878
   Chatfield K, 2011, PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE 2011, DOI 10.5244/C.25.76
   Chen JX, 2016, IEEE DATA MINING, P823, DOI [10.1109/ICDM.2016.0097, 10.1109/ICDM.2016.166]
   Cohen MS, 1996, HUM FACTORS, V38, P206, DOI 10.1518/001872096779048020
   Cook RD, 2000, TECHNOMETRICS, V42, P65, DOI 10.2307/1271434
   Craven MW, 1996, ADV NEUR IN, V8, P24
   Dehkordi M. N., 2006, P 3 INT IEEE C INT S, P483
   Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Doshi-Velez F, 2017, RIGOROUS SCI INTERPR
   Du MN, 2020, COMMUN ACM, V63, P68, DOI 10.1145/3359786
   Erhan D., 2009, TR1341 U MONTR BRANC
   Fong RC, 2017, IEEE I CONF COMP VIS, P3449, DOI 10.1109/ICCV.2017.371
   Fraiman R, 2013, ADV DATA ANAL CLASSI, V7, P125, DOI 10.1007/s11634-013-0129-3
   Galarraga L, 2015, VLDB J, V24, P707, DOI 10.1007/s00778-015-0394-1
   Geng Y., 2019, ARXIV190108547
   Gilpin LH, 2018, PR INT CONF DATA SC, P80, DOI 10.1109/DSAA.2018.00018
   Giurgiu I, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P2245, DOI 10.1145/3357384.3358121
   Guidotti R, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3236009
   Guo WB, 2018, PROCEEDINGS OF THE 2018 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'18), P364, DOI 10.1145/3243734.3243792
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   He XN, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P355, DOI 10.1145/3077136.3080777
   Hendricks LA, 2016, LECT NOTES COMPUT SC, V9908, P3, DOI 10.1007/978-3-319-46493-0_1
   Hsu W. N., 2017, P ADV NEUR INF PROC, P1879
   Huang G, 2017, PROC CVPR IEEE, P2261, DOI 10.1109/CVPR.2017.243
   Huysmans J, 2011, DECIS SUPPORT SYST, V51, P141, DOI 10.1016/j.dss.2010.12.003
   Ilyas A, 2019, ADV NEUR IN, V32
   Janzing D., 2019, ARXIV191013413
   Kim B, 2016, ADV NEUR IN, V29
   Kim Been, 2014, ADV NEURAL INFORM PR, V27, P1952
   Kim Been, 2018, 35 INT C MACH LEARN, V6, P4186
   Kinkeldey C., 2019, P EUROVIS WORKSH TRU, P1, DOI [10.2312/trvis.20191183, DOI 10.2312/TRVIS.20191183]
   Koh PW, 2017, PR MACH LEARN RES, V70
   Krishnan R, 1999, PATTERN RECOGN, V32, P1999, DOI 10.1016/S0031-3203(98)00181-2
   Krizhevsky Alex, 2017, Communications of the ACM, V60, P84, DOI 10.1145/3065386
   Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791
   LeCun Y., 1999, MNIST DATABASE HANDW
   Liu MC, 2018, IEEE CONF VIS ANAL, P60, DOI 10.1109/VAST.2018.8802509
   Lou Y., 2012, P 18 ACM SIGKDD INT, P150
   Lully V, 2018, PROCEDIA COMPUT SCI, V137, P211, DOI 10.1016/j.procs.2018.09.020
   Lundberg S., 2018, ARXIV180203888
   Lundberg S. M., 2017, ADV NEURAL INFORM PR, V27, P4766
   Ma WZ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1210, DOI 10.1145/3308558.3313607
   Martins RM, 2014, COMPUT GRAPH-UK, V41, P26, DOI 10.1016/j.cag.2014.01.006
   Miller T, 2019, ARTIF INTELL, V267, P1, DOI 10.1016/j.artint.2018.07.007
   Minervini P, 2017, LECT NOTES ARTIF INT, V10534, P668, DOI 10.1007/978-3-319-71249-9_40
   Mohseni S., 2018, ARXIV181111839
   Molnar C., 2020, INTERPRETABLE MACHIN
   Murdoch W. J., 2019, ARXIV190104592
   O'Grady NP, 2002, CLIN INFECT DIS, V35, P1281, DOI 10.1086/344188
   Olugbade TA, 2019, ACM T COMPUT-HUM INT, V26, DOI 10.1145/3299095
   Oramas J., 2019, PROC INT C LEARN REP, P1
   Peake G, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2060, DOI 10.1145/3219819.3220072
   Proenca HM, 2020, INFORM SCIENCES, V512, P1372, DOI 10.1016/j.ins.2019.10.050
   Quinlan J. R., 1993, Machine Learning: ECML-93. European Conference on Machine Learning Proceedings, P3
   Raghu M., 2017, ADV NEURAL INFORM PR, P6077
   Ravikumar P, 2009, J R STAT SOC B, V71, P1009, DOI 10.1111/j.1467-9868.2009.00718.x
   Ribeiro MT, 2018, AAAI CONF ARTIF INTE, P1527
   Ribeiro MT, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1135, DOI 10.1145/2939672.2939778
   Robnik-Sikonja M, 2018, HUM-COMPUT INT-SPRIN, P159, DOI 10.1007/978-3-319-90403-0_9
   Sagi O, 2020, INFORM FUSION, V61, P124, DOI 10.1016/j.inffus.2020.03.013
   Saisubramanian Sandhya, 2020, AIES '20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, P351, DOI 10.1145/3375627.3375843
   Sarker M. K., 2017, P CEUR WORKSH LOND U, P1
   Sarker MK, 2019, AAAI CONF ARTIF INTE, P3036
   Selvaraju RR, 2020, INT J COMPUT VISION, V128, P336, DOI 10.1007/s11263-019-01228-7
   Shankaranarayana S. M., 2019, LECT NOTES COMPUTER, P454, DOI DOI 10.1007/978-3-030-33607-349
   Shrikumar A, 2017, PR MACH LEARN RES, V70
   Simonyan K., 2015, CORR ABS14091556
   Springenberg JT, 2015, P INT C LEARN REPR I
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Sundararajan Mukund, 2019, ARXIV190808474
   Szegedy C, 2016, PROC CVPR IEEE, P2818, DOI 10.1109/CVPR.2016.308
   Szegedy C, 2015, PROC CVPR IEEE, P1, DOI 10.1109/CVPR.2015.7298594
   Udell M, 2016, FOUND TRENDS MACH LE, V9, P2, DOI 10.1561/2200000055
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Wang F, 2015, JMLR WORKSH CONF PRO, V38, P1013
   Wang MN, 2020, IEEE ACCESS, V8, P73127, DOI 10.1109/ACCESS.2020.2988359
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Yang Q, 2019, ACM T INTEL SYST TEC, V10, DOI 10.1145/3298981
   Yang TY, 2019, IEEE WIREL COMMUNN
   Yoon J, 2019, J COMMUN NETW-S KOR, V21, P1, DOI 10.1109/JCN.2019.000001
   Yosinski J., 2015, DEEP LEARN WORKSH IN
   Zeiler MD, 2014, LECT NOTES COMPUT SC, V8689, P818, DOI 10.1007/978-3-319-10590-1_53
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhang QS, 2018, AAAI CONF ARTIF INTE, P4454
   Zhang QS, 2018, PROC CVPR IEEE, P8827, DOI 10.1109/CVPR.2018.00920
   Zharov Y., 2018, ARXIV181102783
   Zhou BL, 2018, LECT NOTES COMPUT SC, V11212, P122, DOI 10.1007/978-3-030-01237-3_8
   Zhou BL, 2019, IEEE T PATTERN ANAL, V41, P2131, DOI 10.1109/TPAMI.2018.2858759
   Zhou BL, 2017, PROC CVPR IEEE, P5122, DOI 10.1109/CVPR.2017.544
   Zhou B, 2016, PROC CVPR IEEE, P2921, DOI 10.1109/CVPR.2016.319
   Zhou ZH, 2003, AI COMMUN, V16, P3
   Zoph B, 2018, PROC CVPR IEEE, P8697, DOI 10.1109/CVPR.2018.00907
NR 106
TC 13
Z9 13
U1 32
U2 100
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 191969
EP 191985
DI 10.1109/ACCESS.2020.3032756
PG 17
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA OK6MC
UT WOS:000584761200001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, HF
   Du, HF
   Qi, GL
   Chen, HJ
   Hu, W
   Chen, Z
AF Wang, Haofen
   Du, Huifang
   Qi, Guilin
   Chen, Huajun
   Hu, Wei
   Chen, Zhuo
TI Construction of a Linked Data Set of COVID-19 Knowledge Graphs:
   Development and Applications
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE knowledge graph; linked data; COVID-19; knowledge extraction; knowledge
   fusion; natural language processing; artificial intelligence; data set;
   schema modeling; semantic search
AB Background: With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data. Objective: To improve the utilization value of the information and effectively aid researchers to combat COVID-19, we have constructed and successively released a unified linked data set named OpenKG-COVID19, which is one of the largest existing KGs related to COVID-19. OpenKG-COVID19 includes 10 interlinked COVID-19 subgraphs covering the topics of encyclopedia, concept, medical, research, event, health, epidemiology, goods, prevention, and character. Methods: In this paper, we introduce the key techniques exploited in building COVID-19 KGs in a top-down manner. First, the schema of the modeling process for each KG in OpenKG-COVID19 is described. Second, we propose different methods for extracting knowledge from open government sites, professional texts, public domain-specific sources, and public encyclopedia sites. The curated 10 COVID-19 KGs are further linked together at both the schema and data levels. In addition, we present the naming convention for OpenKG-COVID19. Results: OpenKG-COVID19 has more than 2572 concepts, 329,600 entities, 513 properties, and 2,687,329 facts, and the data set will be updated continuously. Each COVID-19 KG was evaluated, and the average precision was found to be above 93%. We have developed search and browse interfaces and a SPARQL endpoint to improve user access. Possible intelligent applications based on OpenKG-COVID19 for further development are also described. Conclusions: A KG is useful for intelligent question-answering, semantic searches, recommendation systems, visualization analysis, and decision-making support. Research related to COVID-19, biomedicine, and many other communities can benefit from OpenKG-COVID19. Furthermore, the 10 KGs will be continuously updated to ensure that the public will have access to sufficient and up-to-date knowledge.
C1 [Wang, Haofen; Du, Huifang] Tongji Univ, Coll Design & Innovat, 281 Fuxin Rd, Shanghai 200092, Peoples R China.
   [Qi, Guilin] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Chen, Huajun; Chen, Zhuo] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China.
   [Hu, Wei] Nanjing Univ, Natl Inst Healthcare Data Sci, Nanjing, Peoples R China.
RP Wang, HF (通讯作者)，Tongji Univ, Coll Design & Innovat, 281 Fuxin Rd, Shanghai 200092, Peoples R China.
EM carter.whfcarter@gmail.com
OI Chen, Zhuo/0000-0001-9991-6892; Hu, Wei/0000-0003-3635-6335
FU Fundamental Research Funds for the Central Universities [22120220069];
   National Nature Science Foundation of China [62176185]
FX This work was supported by the Fundamental Research Funds for the
   Central Universities (grant 22120220069) and the National Nature Science
   Foundation of China (grant 62176185) .
CR Alzubi JA, 2021, ARAB J SCI ENG, DOI 10.1007/s13369-021-05810-5
   [Anonymous], COVIDGRAPH A COVID 1
   [Anonymous], DATABASE COMMONS CAT
   [Anonymous], VIRHOSTNET 30 SYSTEM
   [Anonymous], Query endpoint
   [Anonymous], COVID-19 schema
   [Anonymous], VIPR VIRUS PATHOGEN
   [Anonymous], Coronavirus dataset
   [Anonymous], VISDB VIRAL INTEGRAT
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   BabelNet, US
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Callahan Alison, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P200
   Chai CL, 2018, VLDB J, V27, P745, DOI 10.1007/s00778-018-0509-6
   Chiarcos C., 2013, 2 WORKSHOP LINKED DA, DOI [10.1007/978-3-030-30225-2_1, DOI 10.1007/978-3-030-30225-2_1]
   Colavizza G, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0244839
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Ding K., 2021, 40 CHINESE CONTROL C, P4659
   Domingo-Fernandez D, 2021, BIOINFORMATICS, V37, P1332, DOI 10.1093/bioinformatics/btaa834
   Drug-Bank, BUILD FDN BETT HLTH
   GISAID, CHINA CDC SHARES LAT
   Gong F, 2019, BMC MED INFORM DECIS, V19, DOI 10.1186/s12911-019-0771-6
   Gorbalenya AE., 2020, BIORXIV, DOI [10.1101/2020.02.07.937862, DOI 10.1101/2020.02.07.937862V1]
   Hadfield J, 2018, BIOINFORMATICS, V34, P4121, DOI 10.1093/bioinformatics/bty407
   Hu W, 2008, J WEB SEMANT, V6, P237, DOI 10.1016/j.websem.2008.02.006
   Leskovec J, STANFORD BIOMEDICAL
   Li X, 2020, P 58 ANN M ASS COMP, P5849, DOI DOI 10.18653/V1/2020.ACL-MAIN.519
   Li XY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1340
   Lu Wang L., 2020, ARXIV200410706
   Miller G. A, 1998, WORDNET ELECT LEXICA
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Mohamed SK, 2020, BIOINFORMATICS, V36, P603, DOI 10.1093/bioinformatics/btz600
   National Center for Biotechnology Information, ABOUTUS
   Navigli R, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P216
   Niu X, 2011, LECT NOTES COMPUT SC, V7032, P205, DOI 10.1007/978-3-642-25093-4_14
   OpenBase, US
   OpenKG, US
   Payne S, 2000, LANCET, V355, P1792, DOI 10.1016/S0140-6736(00)02272-8
   Samwald M, 2011, J CHEMINFORMATICS, V3, DOI 10.1186/1758-2946-3-19
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Swanson-Kauffman K, 1988, J Perinat Neonatal Nurs, V2, P78
   Tao YF, 2018, PODS'18: PROCEEDINGS OF THE 37TH ACM SIGMOD-SIGACT-SIGAI SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS, P49, DOI 10.1145/3196959.3196984
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang LD, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-76550-z
   Williams AJ, 2012, DRUG DISCOV TODAY, V17, P1188, DOI 10.1016/j.drudis.2012.05.016
   Wu TX, 2018, J WEB SEMANT, V51, P39, DOI 10.1016/j.websem.2018.05.002
   Yang ZF, 2020, J THORAC DIS, V12, P165, DOI 10.21037/jtd.2020.02.64
   Zhang P, 2021, GENES-BASEL, V12, DOI 10.3390/genes12070998
   Zhang Y., 2021, P 14 ACM INT C WEB S, P898
   Zou L, 2014, VLDB J, V23, P565, DOI 10.1007/s00778-013-0337-7
NR 50
TC 1
Z9 1
U1 3
U2 3
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
EI 2291-9694
J9 JMIR MED INF
JI JMIR Med. Inf.
PD MAY
PY 2022
VL 10
IS 5
AR e37215
DI 10.2196/37215
PG 14
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA 2A2JQ
UT WOS:000809334500018
PM 35476822
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Lamba, YS
   Sood, SK
   Rawat, KS
   Chopra, M
   Singh, AK
AF Lamba, Yogveer S.
   Sood, Sandeep K.
   Rawat, Keshav S.
   Chopra, Mayank
   Singh, Ashutosh K.
TI Knowledge mapping of 4D printing technologies in computer engineering
SO COMPUTER APPLICATIONS IN ENGINEERING EDUCATION
LA English
DT Review; Early Access
DE 4D printing; computer engineering; COVID-19; Industry 5; 0; smart flood
   management
ID OF-THE-ART; SHAPE-MEMORY; 3D; INDEX
AB Four-dimensional (4D) printing has received an amplified consideration in the research community for fabrication and manufacturing in industry, medical field, and smart flood management. Disruptive technologies such as Internet of Things, Artificial Intelligence, and 4D printing are highly recommended during disaster management activities. The 4D printing technology is an evolving field that is an advancement of three-dimensional printing technologies in which the materials are programmed to change shape over time responding to external stimulus without human intervention, thereby adding a fourth dimension. The technology had a great impact during the coronavirus disease 2019 pandemic and flood management activities. This paper, with the aim of a scientometric review, recognizes the recent research hotspots, trends, and worldwide scope of 4D printing for the period of 2007-2021. The bibliographic data fetched from the Scopus in comma-separated values format is used in the study for extracting the masked information by discussing the in-depth visualization of the attributes of the index documents. The study examines the growth of publications, subject categorizations, global distributions, citation analysis, and the influence of the institutions and authors using the bibliographic data. VOSviewer, a java-based tool, is used to study the keyword co-occurrence, which yields hotspots and emerging trends in the various applications of 4D printing, including smart flood management. This study provides the implementation, latest trends, and universal research topography over the past decade, which assists to recognize primitive research and also provides supervision for the upcoming research.
C1 [Lamba, Yogveer S.; Sood, Sandeep K.; Singh, Ashutosh K.] Natl Inst Technol, Dept Comp Applicat, Kurukshetra, Haryana, India.
   [Rawat, Keshav S.] Cent Univ Haryana, Dept Comp Sci & Informat Technol, Mahendergarh, India.
   [Chopra, Mayank] Cent Univ Himachal Pradesh, Dept Comp Sci & Informat, Dharamshala, India.
RP Rawat, KS (通讯作者)，Cent Univ Haryana, Dept Comp Sci & Informat Technol, Mahendergarh, India.
EM keshav@cuh.ac.in
OI Rawat, Dr. Keshav Singh/0000-0002-1497-985X
CR Ahmed A, 2021, POLYMER, V228, DOI 10.1016/j.polymer.2021.123926
   Ali A, 2020, J IND INTEGR MANAG, V5, P393, DOI 10.1142/S2424862219500179
   Ambulo CP, 2017, ACS APPL MATER INTER, V9, P37332, DOI 10.1021/acsami.7b11851
   Bakarich SE, 2015, MACROMOL RAPID COMM, V36, P1211, DOI 10.1002/marc.201500079
   Bar-Ilan J, 2008, SCIENTOMETRICS, V74, P257, DOI 10.1007/s11192-008-0216-y
   Bayart M, 2021, POLYMER, V217, DOI 10.1016/j.polymer.2021.123440
   Bodaghi M., 2016, SMART MATER STRUCT, V25
   Burian T, 2016, INT MULTI SCI GEOCO, P547
   Castro NJ, 2017, CURR OPIN BIOMED ENG, V2, P67, DOI 10.1016/j.cobme.2017.04.002
   Chadegani A.A., 2013, ASIAN SOC SCI, V9, P18, DOI 10.5539/ass.v9n5p18
   Chae MP, 2015, J RECONSTR MICROSURG, V31, P458, DOI 10.1055/s-0035-1549006
   Choi J, 2015, 3D PRINT ADDIT MANUF, V2, P159, DOI 10.1089/3dp.2015.0039
   DAVIES r, 2022, US
   de Marco C, 2019, ADV MATER TECHNOL-US, V4, DOI 10.1002/admt.201900332
   Deng DP, 2016, 2016 INTERNATIONAL SYMPOSIUM ON FLEXIBLE AUTOMATION (ISFA), P286, DOI 10.1109/ISFA.2016.7790176
   Dimassi S, 2021, COMPUT IND, V126, DOI 10.1016/j.compind.2020.103374
   Ding Z, 2017, SCI ADV, V3, DOI 10.1126/sciadv.1602890
   Esworthy TJ, 2019, INT J SMART NANO MAT, V10, P177, DOI 10.1080/19475411.2019.1631899
   Gardan J, 2016, INT J PROD RES, V54, P3118, DOI 10.1080/00207543.2015.1115909
   Ge Q, 2016, SCI REP-UK, V6, DOI 10.1038/srep31110
   Ge Q, 2014, SMART MATER STRUCT, V23, DOI 10.1088/0964-1726/23/9/094007
   Ghawana T, 2013, URBAN AND REGIONAL DATA MANAGEMENT, P211
   Gladman AS, 2016, NAT MATER, V15, P413, DOI [10.1038/NMAT4544, 10.1038/nmat4544]
   Haleem Abid, 2018, J Clin Orthop Trauma, V9, P275, DOI 10.1016/j.jcot.2018.08.016
   Hassan SU, 2014, SCIENTOMETRICS, V99, P549, DOI 10.1007/s11192-013-1193-3
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Huang J., 2021, J MAT SCI TECHNOL
   Huang L, 2020, ENVIRON SCI POLLUT R, V27, P8740, DOI 10.1007/s11356-019-07489-6
   Jacomy M, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0098679
   Javaid M, 2020, J IND INTEGR MANAG, V5, P481, DOI 10.1142/S2424862220500219
   Javaid M, 2019, CLIN EPIDEMIOL GLOB, V7, P317, DOI 10.1016/j.cegh.2018.09.007
   Kaur A, 2020, EARTH SCI INFORM, V13, P1, DOI 10.1007/s12145-019-00408-w
   Kaur M, 2021, NAT HAZARDS, V109, P2053, DOI 10.1007/s11069-021-04910-7
   Kaur M, 2020, EARTH SCI INFORM, V13, P1201, DOI 10.1007/s12145-020-00495-0
   Khoo ZX, 2015, VIRTUAL PHYS PROTOTY, V10, P103, DOI 10.1080/17452759.2015.1097054
   Khorsandi D, 2021, ACTA BIOMATER, V122, P26, DOI 10.1016/j.actbio.2020.12.044
   Kim SH, 2020, BIOMATERIALS, V260, DOI 10.1016/j.biomaterials.2020.120281
   Kuang X, 2019, ADV FUNCT MATER, V29, DOI 10.1002/adfm.201805290
   Kuang X, 2018, ACS APPL MATER INTER, V10, P7381, DOI 10.1021/acsami.7b18265
   Kumar RS, 2015, SCIENTOMETRICS, V105, P921, DOI 10.1007/s11192-015-1710-7
   Labonnote N, 2016, AUTOMAT CONSTR, V72, P347, DOI 10.1016/j.autcon.2016.08.026
   Le Duigou A, 2016, MATER DESIGN, V96, P106, DOI 10.1016/j.matdes.2016.02.018
   Li YC, 2017, BIOFABRICATION, V9, DOI 10.1088/1758-5090/9/1/012001
   Li YJ, 2020, SCI CHINA TECHNOL SC, V63, P545, DOI 10.1007/s11431-019-1494-0
   Mallakpour S, 2021, ADV COLLOID INTERFAC, V294, DOI 10.1016/j.cis.2021.102482
   Miao SD, 2017, MATER TODAY, V20, P577, DOI 10.1016/j.mattod.2017.06.005
   Miao SD, 2016, SCI REP-UK, V6, DOI 10.1038/srep27226
   Mingers J, 2015, EUR J OPER RES, V246, P1, DOI 10.1016/j.ejor.2015.04.002
   Momeni F, 2017, MATER DESIGN, V122, P42, DOI 10.1016/j.matdes.2017.02.068
   Mulakkal MC, 2018, MATER DESIGN, V160, P108, DOI 10.1016/j.matdes.2018.09.009
   Ntouanoglou K., 2018, PROCEDIA MANUF, V18, P120, DOI DOI 10.1016/J.PROMFG.2018.11.016
   Ozturk GB, 2020, AUTOMAT CONSTR, V113, DOI 10.1016/j.autcon.2020.103122
   Pan Y, 2021, AUTOMAT CONSTR, V122, DOI 10.1016/j.autcon.2020.103517
   Parandoush P, 2017, COMPOS STRUCT, V182, P36, DOI 10.1016/j.compstruct.2017.08.088
   Ramesh S., 2018, IOP Conference Series: Materials Science and Engineering, V376, DOI 10.1088/1757-899X/376/1/012123
   Rawat K.S., 2021, QUAL QUANT, V55, P1371, DOI [10.1007/s11135-020-01061-y, DOI 10.1007/S11135-020-01061-Y]
   Rawat KS, 2021, COMPUT APPL ENG EDUC, V29, P1324, DOI 10.1002/cae.22388
   Sanchez-Somolinos C, 2020, MECHANICALLY RESPONSIVE MATERIALS FOR SOFT ROBOTICS, P347
   Solla M, 2020, COMPUT APPL ENG EDUC, V28, P477, DOI 10.1002/cae.22211
   Sood SK, 2018, SUSTAIN COMPUT-INFOR, V20, P102, DOI 10.1016/j.suscom.2017.12.001
   Sood SK, 2022, IEEE T ENG MANAGE, DOI 10.1109/TEM.2021.3134128
   Sood SK, 2021, ARTIF INTELL REV, V54, P6309, DOI 10.1007/s10462-021-09980-4
   Sood SK, 2021, NAT HAZARDS, V106, P2863, DOI 10.1007/s11069-021-04512-3
   Subeshan B, 2021, PROG ADDIT MANUF, V6, P495, DOI 10.1007/s40964-021-00182-6
   Taylor DL, 2016, ADV MATER, V28, P9060, DOI 10.1002/adma.201601613
   Tibbits S, 2014, ARCHIT DESIGN, V84, P116, DOI 10.1002/ad.1710
   UNICEF, 2022, US
   Valvez S, 2021, POLYMERS-BASEL, V13, DOI 10.3390/polym13050701
   Van Eck N., 2014, MEASURING SCHOLARLY, P285, DOI [10.1007/978-3-319-10377-8_13, 10.1007/978-3-319-10377-8_13(InEng.)]
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Wang C, 2018, COMPUT APPL ENG EDUC, V26, P2116, DOI 10.1002/cae.22004
   Wang MX, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17082963
   Wang WH, 2020, SOFT COMPUT, V24, P8173, DOI 10.1007/s00500-019-04384-7
   Wei HQ, 2017, ACS APPL MATER INTER, V9, P876, DOI 10.1021/acsami.6b12824
   Wu J, 2016, SCI REP-UK, V6, DOI 10.1038/srep19007
   Wu Y, 2015, INT J MENT HEALTH SY, V9, DOI 10.1186/1752-4458-9-2
   Zarek M, 2017, MACROMOL RAPID COMM, V38, DOI 10.1002/marc.201600628
   Zhou Y, 2015, J MECH SCI TECHNOL, V29, P4281, DOI 10.1007/s12206-015-0925-0
NR 78
TC 0
Z9 0
U1 0
U2 0
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1061-3773
EI 1099-0542
J9 COMPUT APPL ENG EDUC
JI Comput. Appl. Eng. Educ.
DI 10.1002/cae.22561
EA AUG 2022
PG 20
WC Computer Science, Interdisciplinary Applications; Education, Scientific
   Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Education & Educational Research; Engineering
GA 3N7ED
UT WOS:000836307300001
DA 2022-08-23
ER

PT J
AU Hemmerling, SA
   DeMyers, CA
   Carruthers, TJB
AF Hemmerling, Scott A.
   DeMyers, Christine A.
   Carruthers, Tim J. B.
TI Building Resilience through Collaborative Management of Coastal
   Protection and Restoration Planning in Plaquemines Parish, Louisiana,
   USA
SO SUSTAINABILITY
LA English
DT Article
DE coastal restoration; Louisiana; collaborative management; participatory
   modeling; nature-based solutions
ID PUBLIC-PARTICIPATION; CLIMATE-CHANGE; KEY ISSUES; KNOWLEDGE; EVOLUTION;
   SCIENCE
AB This paper presents the results of a collaborative planning process to develop an integrated coastal restoration plan for Plaquemines Parish, Louisiana that recognizes the knowledge, experience, and priorities of residents and local stakeholders. To achieve this goal, the research team convened a broad group of stakeholders who live and work in Plaquemines Parish, including representatives of the seafood, navigation, and oil and gas industries, as well as residents, landowners, and those who are indigenous to the region, all of whom rely upon the ecosystem services provided by the wetlands, bays, and waterways for sustenance and wellbeing. Using a combination of local knowledge mapping and participatory modeling, the group worked with scientists to develop a restoration plan consisting of a suite of interlinked natural and nature-based solutions. The approach was intentionally interactive and iterative, creating a venue for open dialogue between residents, scientists, and resource users where no one source of knowledge was given primacy over another. Residents were able to contribute information regarding coastal restoration planning within their own communities, and a consensus plan for prioritizing restoration efforts in clusters was submitted for consideration as part of the State of Louisiana's Coastal Master Plan process. Providing local stakeholders with direct access to scientists allowed their local knowledge to be translated into data products that could be more readily ingested into numerical models and other scientific planning tools.
C1 [Hemmerling, Scott A.; DeMyers, Christine A.; Carruthers, Tim J. B.] Water Inst Gulf, Baton Rouge, LA 70802 USA.
RP Hemmerling, SA (通讯作者)，Water Inst Gulf, Baton Rouge, LA 70802 USA.
EM shemmerling@thewaterinstitute.org; cdemyers@asu.edu;
   tcarruthers@thewaterinstitute.org
OI Hemmerling, Scott/0000-0003-0192-6473
CR Barra M., 2017, NEW CARTOGRAPHERS CO
   Barra MP, 2020, PROF GEOGR, V72, P511, DOI 10.1080/00330124.2020.1777574
   Baustian M.M., 2020, ECOL ENG, V143, DOI [10.1016/j.ecoena.2019.100015, DOI 10.1016/J.ECOENA.2019.100015]
   Berkes F, 2009, J ENVIRON MANAGE, V90, P1692, DOI 10.1016/j.jenvman.2008.12.001
   Bethel M.B., 2015, SCI TEK MAPPING TRAD, P252
   Bethel MB, 2014, J COASTAL RES, V30, P1081, DOI 10.2112/JCOASTRES-D-13-00214.1
   Bethel MB, 2011, J COASTAL RES, V27, P555, DOI 10.2112/JCOASTRES-D-10-00138.1
   Bianchette TA, 2016, WATER-SUI, V8, DOI 10.3390/w8010001
   Boesch D.F., 2020, LOUISIANAS RESPONSE, P35
   Brown G, 2014, APPL GEOGR, V46, P122, DOI 10.1016/j.apgeog.2013.11.004
   Cantasano N, 2020, OCEAN COAST MANAGE, V183, DOI 10.1016/j.ocecoaman.2019.104962
   Cantasano N, 2017, J COAST CONSERV, V21, P317, DOI 10.1007/s11852-016-0479-z
   Carruthers T.J., 2017, THIS IS YOUR SHIELD, P48
   Clipp A., 2016, 2017 COASTAL MASTER, P42
   Colten C.E, 2015, LABOR ENG, V9, P6, DOI [10.20396/lobore.v9i4.8639479, DOI 10.20396/LOBORE.V9I4.8639479]
   Colten CE, 2012, ECOL SOC, V17, DOI 10.5751/ES-05047-170305
   Coquet M, 2018, GEOENVIRONMENTAL DIS, V5, DOI [10.1186/s40677-018-0107-3, DOI 10.1186/S40677-018-0107-3]
   Couvillion B.R., 2017, LAND AREA CHANGE COA, P26
   CPRA, 2017, LOUIS COMPR MAST PLA, P392
   CPRA (Coastal Protection and Restoration Authority of Louisiana), 2012, LOUIS COMPR MAST PLA
   Curtis J.W., 2018, TSUNAMIS DETECTION R, P245
   Curtis JW, 2016, GEOGR REV, V106, P338, DOI 10.1111/j.1931-0846.2016.12193.x
   Day JW, 2000, ESTUARIES, V23, P425, DOI 10.2307/1353136
   DeCuir-Gunby JT, 2011, FIELD METHOD, V23, P136, DOI 10.1177/1525822X10388468
   Erdman, 2018, MISSISSIPPI DELTA RE, P113, DOI DOI 10.1007/978-3-319-65663-2_8
   Fischbach JR, 2019, ENVIRON RES COMMUN, V1, DOI 10.1088/2515-7620/ab4b25
   Folke C, 2005, ANNU REV ENV RESOUR, V30, P441, DOI 10.1146/annurev.energy.30.050504.144511
   Friess DA, 2016, CONSERV BIOL, V30, P933, DOI 10.1111/cobi.12784
   Garcia-Ayllon S, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11041039
   Gotham KF, 2016, SOCIOL FORUM, V31, P787, DOI 10.1111/socf.12273
   Hai NT, 2020, ANN FOREST SCI, V77, DOI 10.1007/s13595-020-0921-0
   Hedelin B, 2017, ENVIRON SCI POLICY, V76, P185, DOI 10.1016/j.envsci.2017.07.001
   Hemmerling S.A., 2020, LOUISIANAS RESPONSE
   Hemmerling S.A., 2020, COMMUNITY INFORM FRA
   Hemmerling S.A., 2017, SER NEWS, V31, P14
   Hemmerling SA, 2020, J GEOGR SYST, V22, P241, DOI 10.1007/s10109-019-00313-2
   Jessee N., 2020, LOUISIANAS RESPONSE, DOI 10.1007/978-3-030-27205-0_6
   Johnson M, 2009, SOC NATUR RESOUR, V22, P79, DOI 10.1080/08941920802220347
   Jurjonas M, 2018, OCEAN COAST MANAGE, V162, P137, DOI 10.1016/j.ocecoaman.2017.10.010
   Landstrom C, 2011, ENVIRON PLANN A, V43, P1617, DOI 10.1068/a43482
   Louisiana Coastal Protection and Restoration Authority, 2018, 2023 COAST MAST PLAN
   Meselhe E, 2020, J HYDRAUL ENG, V146, DOI 10.1061/(ASCE)HY.1943-7900.0001659
   Velez JMM, 2018, ENVIRON SCI POLICY, V88, P72, DOI 10.1016/j.envsci.2018.06.016
   Morris RL, 2018, GLOBAL CHANGE BIOL, V24, P1827, DOI 10.1111/gcb.14063
   Narayan S, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0154735
   Newaz MW, 2019, ENVIRON DEV, V32, DOI 10.1016/j.envdev.2019.06.001
   Parker H, 2021, NEW ORLEANS TIMES PI
   Peyronnin N, 2013, J COASTAL RES, P1, DOI 10.2112/SI_67_1.1
   Peyronnin NS, 2017, WATER-SUI, V9, DOI 10.3390/w9060368
   Plaquemines Parish Government, 2010, PLAQ PAR COMPR MAST
   Plummer R, 2012, ECOL SOC, V17, DOI 10.5751/ES-04952-170311
   Pueyo-Ros J, 2019, RESTOR ECOL, V27, P626, DOI 10.1111/rec.12896
   Renschler C.S., 2010, FRAMEWORK DEFINING M, P73
   Scavia D, 2002, ESTUARIES, V25, P149, DOI 10.1007/BF02691304
   Schoolmaster DR, 2018, ECOSYSTEMS, V21, P1335, DOI 10.1007/s10021-018-0223-7
   Speyrer N., 2017, 2017 COASTAL MASTER, P31
   Stave K., 2010, J SUSTAINABILITY, V2, P2762, DOI 10.3390/su2092762
   Voinov A, 2008, ECOL MODEL, V216, P197, DOI 10.1016/j.ecolmodel.2008.03.010
   Voinov A, 2018, ENVIRON MODELL SOFTW, V109, P232, DOI 10.1016/j.envsoft.2018.08.028
   Wells J.T., 2015, 5 WAT I GULF BATON R, P1
   Whatmore SJ, 2009, PROG HUM GEOG, V33, P587, DOI 10.1177/0309132509339841
   Zou L, 2016, WATER-SUI, V8, DOI 10.3390/w8010010
NR 62
TC 0
Z9 0
U1 2
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAR
PY 2022
VL 14
IS 5
AR 2974
DI 10.3390/su14052974
PG 24
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA ZT3YT
UT WOS:000769096400001
OA gold
DA 2022-08-23
ER

PT J
AU Nelson, HR
AF Nelson, HR
TI Virtual seminars
SO COMPUTERS & GEOSCIENCES
LA English
DT Article
DE distance learning; information; Internet; Intranet; knowledge; learning;
   media; mentors; virtual seminars; virtual reality
AB A virtual seminar (SM) is an economic and effective instructional tool for teaching students who are at a distance from their instructor. Like conventional class room teaching, a virtual seminar requires an instructor, a student, and a method of communication. Teleconferencing, video conferencing, intranets and the Internet give learners in a Virtual Seminar the ability to interact immediately with their mentors and receive real and relevant answers. This paper shows how industry and academia can benefit from using methods developed and experience gained in presenting the first virtual seminars to academic and petroleum industry participants in mid-1996. The information explosion in industry means that business or technical information is worthless until it is assimilated into a corporate knowledge management system. A search for specific information often turns into a filtering exercise or an attempt to find patterns and classify retrieved material. In the setting of an interactive corporate information system, virtual seminars meet the need for a productive new relationship between creative people and the Aux of corporate knowledge. Experience shows that it is more efficient to circulate time-sensitive and confidential information electronically through a virtual seminar. Automating the classification of information and removing that task from the usual work load creates an electronic corporate memory and enhances the value of the knowledge to both users and a corporation. Catalogued benchmarks, best-practice standards, and Knowledge Maps (SM) of experience serve as key aids to communicating knowledge through virtual seminars and converting that knowledge into a profit-making asset. (C) 1997 Elsevier Science Ltd.
RP Nelson, HR (通讯作者)，WALDEN 3D INC,POB 382,BARKER,TX 77413, USA.
CR BANKE RE, 1995, P 1995 CONV AM ASS H
   BUSH V, 1945, ATLANTIC MONTHLY JUL
   HAECKEL S, 1993, HARVARD BUSINESS SEP, P122
   LOFTIN B, 1995, SHARED VIRTUAL ENV T
   NELSON HR, 1996, ACCESSING MANAGING K
   PERAYA D, 1994, DISTANCE ED WWW
   SHERRY L, 1994, ISSUES DISTANCE LEAR
   TAYLOR G, 1996, AAPG BULL, V17, P22
   UPDEGROVE KH, 1995, TEACHING INTERNET
NR 9
TC 7
Z9 7
U1 0
U2 3
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB
SN 0098-3004
J9 COMPUT GEOSCI
JI Comput. Geosci.
PD JUN
PY 1997
VL 23
IS 5
BP 601
EP 606
DI 10.1016/S0098-3004(97)00029-0
PG 6
WC Computer Science, Interdisciplinary Applications; Geosciences,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Geology
GA XP417
UT WOS:A1997XP41700014
DA 2022-08-23
ER

PT J
AU Kalayci, TE
   Ozegovic, G
   Bricelj, B
   Lah, M
   Stocker, A
AF Kalayci, Tahir Emre
   Ozegovic, Gabriela
   Bricelj, Bor
   Lah, Marko
   Stocker, Alexander
TI Object Detection in Driving Datasets Using a High-Performance Computing
   Platform: A Benchmark Study
SO IEEE ACCESS
LA English
DT Article
DE Object detection; Computational modeling; Hardware; Data models; Big
   Data; Training; Data integration; Big data; driving datasets; EVOLVE;
   high performance computing; nuScenes; object detection; transfer
   learning
AB Nowadays, machine learning methods are increasingly used in different parts of autonomous driving and driving assistance systems. Yet, data and computational requirements can be enormous with these methods. Thus, providing several datasets containing many and diverse cases for the target problem and sufficient hardware for training and application of ML methods are too critical for achieving accurate results when applying them. Hence, we present an object detection benchmark study implementing the knowledge graph-based data integration framework to meet the data requirements and run the implementation on a big data and high-performance computing (HPC) platform, namely the EVOLVE. We applied different object detection models to widely known open datasets and compared the results on three different hardware setups, including EVOLVE. We also performed a small-scale transfer learning experiment. The results show that EVOLVE allowed the exploitation of much bigger data leading to a more efficient application of the object detection models with the help of the knowledge graph-based data integration framework. EVOLVE significantly improved the execution times compared to running them on a local laptop and a virtual machine and provided the easy-to-use and ready-to-use means to store large datasets and apply different methods with its hardware and software stack.
C1 [Kalayci, Tahir Emre; Ozegovic, Gabriela; Bricelj, Bor; Lah, Marko; Stocker, Alexander] Virtual Vehicle Res GmbH, A-8010 Graz, Austria.
RP Kalayci, TE (通讯作者)，Virtual Vehicle Res GmbH, A-8010 Graz, Austria.
EM emre.kalayci@v2c2.at
RI Kalayci, T. E./B-2365-2012
OI Kalayci, T. E./0000-0001-6228-1221; Stocker,
   Alexander/0000-0002-3758-1617
FU European Union's Horizon 2020 Research and Innovation Program through
   the EVOLVE Project [825061]; Virtual Vehicle Research GmbH, Graz;
   Competence Centers for Excellent Technologies (COMET K2) from the
   Austrian Federal Ministry for Climate Action (BMK); Austrian Federal
   Ministry for Digital and Economic Affairs (BMDW); Province of Styria
   (Department 12); Styrian Business Promotion Agency (SFG); Austrian
   Research Promotion Agency (FFG)
FX This work was supported in part by the European Union's Horizon 2020
   Research and Innovation Program through the EVOLVE Project under Grant
   825061; in part by Virtual Vehicle Research GmbH, Graz; in part by the
   Competence Centers for Excellent Technologies (COMET K2) from the
   Austrian Federal Ministry for Climate Action (BMK); in part by the
   Austrian Federal Ministry for Digital and Economic Affairs (BMDW); in
   part by the Province of Styria (Department 12); in part by the Styrian
   Business Promotion Agency (SFG); and in part by the Austrian Research
   Promotion Agency (FFG) (authorized for the program management).
CR Abadi M., 2015, TENSORFLOW LARGE SCA
   Apache Software Foundation, 2021, AP KAFK
   Apache Software Foundation, 2021, AP SPARK
   Barbiero P., 2020, ARXIV200615680
   Arrieta AB, 2020, INFORM FUSION, V58, P82, DOI 10.1016/j.inffus.2019.12.012
   Behrendt Karsten, 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA), P1370, DOI 10.1109/ICRA.2017.7989163
   Bochkovskiy A., 2020, ARXIV200410934
   Brewer E.A., 2015, P 6 ACM S CLOUD COMP, P167, DOI DOI 10.1145/2806777.2809955
   Caesar H., 2020, NUSCENES DATA FORMAT
   Caino-Lores S, 2019, IEEE ACCESS, V7, P156929, DOI 10.1109/ACCESS.2019.2949836
   Chazapis Antony, 2021, CF '21: Proceedings of the 18th ACM International Conference on Computing Frontiers, P200, DOI 10.1145/3457388.3458621
   Chen YR, 2018, INFORM SCIENCES, V432, P559, DOI 10.1016/j.ins.2017.08.035
   Chen ZH, 2019, 2019 EIGHTH INTERNATIONAL CONFERENCE ON EMERGING SECURITY TECHNOLOGIES (EST)
   Cloud Native Computing Foundation, 2021, VOLC KUB NAT BATCH S
   Collins A., 2022, ARGO WORKOWS WOROW E
   Computer Architecture and VLSI Systems (CARV) Laboratory, 2021, H3 EMB OBJ STOR
   Computer Architecture and VLSI Systems (CARV) Laboratory, 2021, KARVD KUB CARV DASHB
   Creative Commons, 2021, CREAT COMM ATTR NONC
   Crotty A., 2016, P WORKSH HUM INTH LO, P1, DOI [10.1145/2939502.2939513, DOI 10.1145/2939502.2939513]
   Dask Core Developers, 2021, DASK SCAL AN PYTH
   DataDirect Networks, 2021, INF MEM ENG FLASH NA
   Dosovitskiy A., 2017, P 1 C ROB LEARN MOUN, P1
   ECMA International, 2017, JSON DATA INTERCHANG
   Elshawi Radwa, 2020, Big Data Management and Analytics. 9th European Summer School, eBISS 2019. Revised Selected Papers. Lecture Notes in Business Information Processing (LNBIP 390), P40, DOI 10.1007/978-3-030-61627-4_3
   Erhan D, 2014, PROC CVPR IEEE, P2155, DOI 10.1109/CVPR.2014.276
   Forrest I., 2016, ARXIV PREPRINT ARXIV
   Francis N, 2018, INT CONF MANAGE DATA, P1433, DOI 10.1145/3183713.3190657
   Gkoufas Y., 2021, DATASHIM A KUBERNETE
   Google, 2019, TENSORFLOW HUB
   He K, 2015, PROC CVPR IEEE, DOI [10.1109/CVPR.2016.90, DOI 10.1109/CVPR.2016.90]
   Heer J, 2012, COMMUN ACM, V55, P45, DOI 10.1145/2133806.2133821
   Howard A.G., 2017, CORR
   Huang G, 2017, PROC CVPR IEEE, P2261, DOI 10.1109/CVPR.2017.243
   Iandola F. N., SQUEEZENET ALEXNET L
   Johansson L, 2020, RABBITMQ ESSENTIALS, V2nd
   Kalayci TE, 2021, J IND INF INTEGR, V21, DOI 10.1016/j.jii.2020.100186
   Kalayci TE, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13031583
   Kesten R., 2019, LYFT LEVEL 5 DATASET
   Kluyver T, 2016, POSITIONING AND POWER IN ACADEMIC PUBLISHING: PLAYERS, AGENTS AND AGENDAS, P87, DOI 10.3233/978-1-61499-649-1-87
   Lee KM, 2019, INT J INFORM MANAGE, V49, P491, DOI 10.1016/j.ijinfomgt.2019.07.003
   Lenzerini M., 2002, P 21 ACM SIGMOD SIGA, V21, P233, DOI DOI 10.1145/543613.543644
   Li X., 2021, AUTOMATED DATA ENG P
   Lin TY, 2014, LECT NOTES COMPUT SC, V8693, P740, DOI 10.1007/978-3-319-10602-1_48
   Lin Tsung-Yi, 2017, ARXIV170802002
   Liu S, 2018, PROC CVPR IEEE, P8759, DOI 10.1109/CVPR.2018.00913
   Message Passing Interface Forum, 2021, MPI MESSAGE PASSING
   Newell A, 2016, LECT NOTES COMPUT SC, V9912, P483, DOI 10.1007/978-3-319-46484-8_29
   OpenSFS and EOFS, 2021, LUSTR FIL
   Ren SQ, 2015, ADV NEUR IN, V28, DOI 10.1109/TPAMI.2016.2577031
   Robinson I., 2015, GRAPH DATABASES NEW
   SchedMD, SLURM WORKL MAN
   Sfakianakis Y, 2018, ACM T ARCHIT CODE OP, V15, DOI 10.1145/3210560
   Simonyan K., 2014, ARXIV
   Solem A., 2018, CELERY DISTRIBUTED T
   Tan MX, 2019, PR MACH LEARN RES, V97
   Tang C., 2021, ARXIV210211905
   The Kubernetes Authors and The Linux Foundation, 2021, KUBERNETES
   Tzenetopoulos Achilleas, 2020, High Performance Computing. ISC High Performance 2020 International Workshops. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 12321), P321, DOI 10.1007/978-3-030-59851-8_21
   Tzenetopoulos A, 2022, DES AUT TEST EUROPE, P975, DOI 10.23919/DATE54114.2022.9774698
   VMware, 2021, RABBITMQ MESS JUST W
   Vora, 2019, ARXIV190311027
   Wang D., 2021, ARXIV210103970
   Woo SH, 2018, LECT NOTES COMPUT SC, V11211, P3, DOI 10.1007/978-3-030-01234-2_1
   Xie SN, 2017, PROC CVPR IEEE, P5987, DOI 10.1109/CVPR.2017.634
   Xu L.D., 2014, ENTERPRISE INTEGRATI, DOI [10.1201/b17156., DOI 10.1201/B17156]
   Yao Z., 2020, ARXIV200205712
   Zhang X, 2018, PROC CVPR IEEE, P6848, DOI 10.1109/CVPR.2018.00716
   Zhou Xingyi, 2019, ARXIV190407850
   Zoller MA., 2019, ARXIV PREPRINT ARXIV
NR 69
TC 0
Z9 0
U1 0
U2 0
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 61666
EP 61677
DI 10.1109/ACCESS.2022.3180788
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 2D4UB
UT WOS:000811543100001
OA gold
DA 2022-08-23
ER

PT J
AU Frischmuth, P
   Martin, M
   Tramp, S
   Riechert, T
   Auer, S
AF Frischmuth, Philipp
   Martin, Michael
   Tramp, Sebastian
   Riechert, Thomas
   Auer, Soeren
TI OntoWiki - An authoring, publication and visualization interface for the
   Data Web
SO SEMANTIC WEB
LA English
DT Article
DE Linked Data; Data Web; RDF; SPARQL; Wiki
AB OntoWiki is a front-end application for the Semantic Data Web, which was originally developed to support distributed knowledge engineering scenarios. Due to its extensibility it also serves as a development framework for knowledge intensive applications. On the surface, OntoWiki is a generic user interface for arbitrary RDF knowledge graphs. It supports the navigation through RDF knowledge bases using SPARQL-generated lists, tables and trees (e.g. class trees and taxonomies). All resources are automatically represented as hyperlinks and backlinks are created whenever feasible, thus enabling users to easily traverse entire knowledge graphs. Since all collections of resources displayed in OntoWiki are generated by SPARQL queries, they can be further refined by applying additional filters. In order to explore large datasets, a comprehensive statistical data management and visualization method was integrated. We render an architectural overview and explain the navigation, exploration and visualization features provided for RDF based information and derived statistics. To showcase the versatility of OntoWiki and its various deployments in the Linked Data Web, we describe some large-scale use cases in the domains of enterprise data integration, governmental data publication and digital humanities. This article is the first comprehensive presentation of OntoWiki summarizing the advancements after the initial publication in 2006 [4].
C1 [Frischmuth, Philipp; Martin, Michael; Tramp, Sebastian; Riechert, Thomas; Auer, Soeren] Univ Leipzig, Inst Comp Sci, AKSW Grp, D-04009 Leipzig, Germany.
RP Frischmuth, P (通讯作者)，Univ Leipzig, Inst Comp Sci, AKSW Grp, Augustuspl 10, D-04009 Leipzig, Germany.
EM frischmuth@informatik.uni-leipzig.de; martin@informatik.uni-leipzig.de;
   tramp@informatik.uni-leipzig.de; riechert@informatik.uni-leipzig.de;
   auer@informatik.uni-leipzig.de
RI Auer, Sören/AAP-9163-2020
OI Auer, Sören/0000-0002-0698-2864; Martin, Michael/0000-0003-0762-8688;
   Riechert, Thomas/0000-0003-2053-5347
FU European Union [257943, 601043]
FX We are grateful to the numerous students, contributors, colleagues
   without whom the development of OntoWiki would not have been possible.
   In particular, we would like to thank Norman Heino, who was one of the
   main initial OntoWiki developers, and Amrapali Zaveri for supporting the
   completion of this paper. This work was supported by a grant from the
   European Union's 7th Framework Programme provided for the projects LOD2
   (GA no. 257943) and DIACHRON (GA no. 601043).
CR Adida B., 2008, W3C RECOMMENDATION
   [Anonymous], 2013, W3C RECOMMENDATION
   [Anonymous], 2000, TECHNICAL REPORT
   Auer Soren, 2013, Reasoning Web. Semantic Technologies for Intelligent Data Access. 9th International Summer School 2013. Proceedings. LNCS 8067, P1, DOI 10.1007/978-3-642-39784-4_1
   Auer Soren, 2012, Knowledge Engineering and Knowledge Management. 18th International Conference, EKAW 2012. Proceedings, P353, DOI 10.1007/978-3-642-33876-2_31
   Auer Soren, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P1, DOI 10.1007/978-3-642-35173-0_1
   Auer S, 2006, LECT NOTES COMPUT SC, V4273, P736
   Aumuller D., 2005, 2 EUR SEM WEB C ESWC
   Beckett D., 2014, RDF 1 1 TURTLE
   Beckett D., 2004, W3C RECOMMENDATION
   BernersLee T., 2006, LINKED DATA W3C DESI
   Breslin JG, 2005, LECT NOTES COMPUT SC, V3532, P500
   Brickley D., 2004, W3C RECOMMENDATION
   Carroll J.J., 2005, P 14 INT C WORLD WID, P613, DOI [10.1145/1060745.1060835, DOI 10.1145/1060745.1060835]
   Cyganiak R., 2010, LDOW 2010 LINKED DAT, V628
   Cyganiak R., 2014, W3C RECOMMENDATION
   Erling O., 2007, LNI, P59
   Feigenbaum L, 2013, W3C RECOMMENDATION
   Gearon P., 2013, W3C RECOMMENDATION
   Hausenblas M, 2009, LECT NOTES COMPUT SC, V5554, P708, DOI 10.1007/978-3-642-02121-3_52
   Heim P, 2009, LECT NOTES COMPUT SC, V5887, P182, DOI 10.1007/978-3-642-10543-2_21
   Heino N., 2009, NETWORKED KNOWLEDGE, V221, P61
   Inkster T., 2009, NAMED GRAPHS RDFA RD
   *ISO, 2005, 173692005 ISOTS
   Krotzsch M, 2007, J WEB SEMANT, V5, P251, DOI 10.1016/j.websem.2007.09.001
   Leuf B., 2001, WIKI WAY QUICK COLLA
   Martin M, 2014, SEMANT WEB, V5, P157, DOI 10.3233/SW-130116
   Martin M, 2010, LECT NOTES COMPUT SC, V6089, P304, DOI 10.1007/978-3-642-13489-0_21
   Martin Michael, 2010, P 4 INT C ADV SEM PR
   McGuinness D.L., 2004, OWL WEB ONTOLOGY LAN, V10
   Miles A., 2009, SKOS SIMPLE KNOWLEDG
   OECD, 2006, TECHNICAL REPORT
   Oren E., 2005, SEM DESKT WORKSH P I, V175
   Riechert T, 2010, LECT NOTES COMPUT SC, V6497, P225, DOI 10.1007/978-3-642-17749-1_15
   Riess C, 2010, LECT NOTES COMPUT SC, V6496, P647, DOI 10.1007/978-3-642-17746-0_41
   Schaffert S., 2006, P 1 INT WORKSH SEM T
   Souzis A, 2005, IEEE INTELL SYST, V20, P87, DOI 10.1109/MIS.2005.83
   Tramp S, 2010, LECT NOTES ARTIF INT, V6317, P90, DOI 10.1007/978-3-642-16438-5_7
   Tramp S, 2010, LECT NOTES ARTIF INT, V6317, P135, DOI 10.1007/978-3-642-16438-5_10
   Tummarello G, 2007, LECT NOTES COMPUT SC, V4825, P552
   Unbehauen J., 2013, LECT NOTES COMPUTER, V7774, P65
NR 41
TC 11
Z9 11
U1 0
U2 19
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2015
VL 6
IS 3
BP 215
EP 240
DI 10.3233/SW-140145
PG 26
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA CM7LW
UT WOS:000357875500002
DA 2022-08-23
ER

PT J
AU Huang, YJ
   Cheng, S
   Yang, FQ
   Chen, C
AF Huang, Yu-Jie
   Cheng, Shuo
   Yang, Fu-Qiang
   Chen, Chao
TI Analysis and Visualization of Research on Resilient Cities and
   Communities Based on VOSviewer
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Review
DE resilient cities; resilient communities; bibliometrics; knowledge graph;
   VOSviewer
ID BIBLIOMETRIC ANALYSIS; ECOLOGICAL RESILIENCE; URBAN RESILIENCE;
   CLIMATE-CHANGE; SAFETY; QUALITY; SCIENCE; IMPACT
AB To objectively grasp the current situation and development trend of resilient cities or communities (RC) research. The articles in Web of Science (WOS) Core Collection databases from 1995 to 2022 were used as a sample, and bibliometrics was used to statistically analyze the year of publication and number of articles, highly cited documents and keyword hotness in this field. VOSviewer was used to explore the knowledge graph of RC research documents. The results show that: the development process is roughly divided into 3 periods: no attention (1995-2004), starting (2005-2014), and rapid growth (2015-2021). The journal "Sustainability" and "International journal of disaster risk reduction" are the key journals publishing RC research. Serre and Shaw are the most productive authors. The USA is still the leading country in this field of RC. Colorado State Univ, Texas a&m Univ, and Delft Univ Technol are the main research institutions. The keyword analysis indicates the hot topics in different periods. Moreover, several limitations and some recommendations for future research on RC are also given based on this.
C1 [Huang, Yu-Jie; Cheng, Shuo; Yang, Fu-Qiang] Fuzhou Univ, Coll Environm & Safety Engn, Fuzhou 350116, Peoples R China.
   [Chen, Chao] Delft Univ Technol, Fac Technol Policy & Management, Safety & Secur Sci Grp, NL-2628 BX Delft, Netherlands.
   [Chen, Chao] Southwest Petr Univ, Sch Petr Engn, Chengdu 610500, Peoples R China.
RP Yang, FQ (通讯作者)，Fuzhou Univ, Coll Environm & Safety Engn, Fuzhou 350116, Peoples R China.; Chen, C (通讯作者)，Delft Univ Technol, Fac Technol Policy & Management, Safety & Secur Sci Grp, NL-2628 BX Delft, Netherlands.; Chen, C (通讯作者)，Southwest Petr Univ, Sch Petr Engn, Chengdu 610500, Peoples R China.
EM huangyujie199871@163.com; jessecheng712@gmail.com; fqouyang82@163.com;
   c.chen-1@tudelft.nl
OI huang, yujie/0000-0002-8938-8245; Chen, Chao/0000-0002-1443-8017
FU National Natural Science Foundation of China [51874100]
FX This research study was funded by the National Natural Science
   Foundation of China, grant number 51874100.
CR Adger WN, 2000, PROG HUM GEOG, V24, P347, DOI 10.1191/030913200701540465
   Anugerah AR, 2022, HELIYON, V8, DOI 10.1016/j.heliyon.2022.e09270
   Barnett J, 2001, WORLD DEV, V29, P977, DOI 10.1016/S0305-750X(01)00022-5
   Bond A, 2015, J ENVIRON MANAGE, V151, P97, DOI 10.1016/j.jenvman.2014.12.030
   Chen D, 2016, ECOL ENG, V90, P285, DOI 10.1016/j.ecoleng.2016.01.026
   Dastjerdi MS, 2021, URBAN CLIM, V36, DOI 10.1016/j.uclim.2021.100794
   Eljaoued Wissem, 2020, Procedia Computer Science, V176, P2625, DOI 10.1016/j.procs.2020.09.305
   Evans JP, 2011, T I BRIT GEOGR, V36, P223, DOI 10.1111/j.1475-5661.2010.00420.x
   Fletcher D, 2013, EUR PSYCHOL, V18, P12, DOI 10.1027/1016-9040/a000124
   Folke C, 2006, GLOBAL ENVIRON CHANG, V16, P253, DOI 10.1016/j.gloenvcha.2006.04.002
   Godschalk DR, 2003, NAT HAZARDS REV, V4, P136, DOI 10.1061/(ASCE)1527-6988(2003)4:3(136)
   Gunderson LH, 2000, ANNU REV ECOL SYST, V31, P425, DOI 10.1146/annurev.ecolsys.31.1.425
   Hayes S, 2019, TRANSPORT REV, V39, P677, DOI 10.1080/01441647.2019.1612480
   Holling C.S., 1973, Annual Rev Ecol Syst, V4, P1, DOI 10.1146/annurev.es.04.110173.000245
   ICLEI, RES CIT 2018 EB OL
   Jennings Barbara J., 2013, Environment Systems & Decisions, V33, P184, DOI 10.1007/s10669-013-9440-y
   Klimek P, 2019, SAFETY SCI, V113, P404, DOI 10.1016/j.ssci.2018.12.017
   Lee AV, 2013, NAT HAZARDS REV, V14, P29, DOI 10.1061/(ASCE)NH.1527-6996.0000075
   Li J, 2015, SAFETY SCI, V74, P70, DOI 10.1016/j.ssci.2014.12.003
   Mcglade J., 2006, IND RESILIENCE DECLI, P47
   Meerow S, 2016, LANDSCAPE URBAN PLAN, V147, P38, DOI 10.1016/j.landurbplan.2015.11.011
   Nunes DM, 2019, J ENVIRON MANAGE, V244, P422, DOI 10.1016/j.jenvman.2019.05.027
   Pan XL, 2018, J INFORMETR, V12, P481, DOI 10.1016/j.joi.2018.03.005
   Pawar B, 2021, J LOSS PREVENT PROC, V69, DOI 10.1016/j.jlp.2020.104366
   Pickett STA, 2014, BUILD RES INF, V42, P143, DOI 10.1080/09613218.2014.850600
   [邱东阳 Qiu Dongyang], 2020, [中国安全科学学报, China Safety Science Journal（CSSJ）], V30, P27
   Romanelli JP, 2018, ECOL ENG, V120, P311, DOI 10.1016/j.ecoleng.2018.06.015
   Shao Y., 2015, URBAN PLAN INT, V30, P48
   Smith DR, 2007, IND HEALTH, V45, P730, DOI 10.2486/indhealth.45.730
   Su M, 2021, EXPERT SYST APPL, V186, DOI 10.1016/j.eswa.2021.115728
   Taddeo R, 2019, SCI TOTAL ENVIRON, V653, P765, DOI 10.1016/j.scitotenv.2018.10.412
   Thomalla F, 2006, DISASTERS, V30, P39, DOI 10.1111/j.1467-9523.2006.00305.x
   Ugolini D, 2015, ENVIRON SCI POLLUT R, V22, P6957, DOI 10.1007/s11356-014-3925-1
   van Nunen K, 2018, SAFETY SCI, V108, P248, DOI 10.1016/j.ssci.2017.08.011
   Walker B., 2006, RESILIENCE THINKING
   Walter G, 2003, MED J AUSTRALIA, V178, P280, DOI 10.5694/j.1326-5377.2003.tb05196.x
   Whipple EC, 2013, INFORM HEALTH SOC CA, V38, P1, DOI 10.3109/17538157.2012.678451
   Wildavsky A., 1988, SEARCHING SAFETY
   Wu F, 2018, J CLEAN PROD, V204, P344, DOI 10.1016/j.jclepro.2018.09.067
   Xie L, 2020, WORLD NEUROSURG, V137, P435, DOI 10.1016/j.wneu.2020.01.171
   Xu Y.Y., 2018, ACTA ECOL SIN, V38, P5297, DOI DOI 10.5846/stxb201709081620
   Yang F.Q, 2021, J FUZHOU U, V49, P121
   Yang QY, 2021, DISCRETE DYN NAT SOC, V2021, DOI 10.1155/2021/5558497
   Yang YF, 2019, SAFETY SCI, V120, P14, DOI 10.1016/j.ssci.2019.06.022
   Zou X, 2018, ACCIDENT ANAL PREV, V118, P131, DOI 10.1016/j.aap.2018.06.010
NR 45
TC 1
Z9 1
U1 16
U2 16
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD JUN
PY 2022
VL 19
IS 12
AR 7068
DI 10.3390/ijerph19127068
PG 14
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA 2K1VY
UT WOS:000816133100001
PM 35742316
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Jiang, JC
   Li, XL
   Zhao, C
   Guan, Y
   Yu, QB
AF Jiang, Jingchi
   Li, Xueli
   Zhao, Chao
   Guan, Yi
   Yu, Qiubin
TI Learning and inference in knowledge-based probabilistic model for
   medical diagnosis
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Probabilistic model; First-order knowledge; Markov network; Gradient
   descent; Markov logic network
ID NETWORK; SYSTEM
AB Based on a weighted knowledge graph to represent first-order knowledge and combining it with a probabilistic model, we propose a methodology for creating a medical knowledge network (MKN) in medical diagnosis. When a set of evidence is activated for a specific patient, we can generate a ground medical knowledge network that is composed of evidence nodes and potential disease nodes. By incorporating a Boltzmann machine into the potential function of a Markov network, we investigated the joint probability distribution of the MKN. To consider numerical evidence, a multivariate inference model is presented that uses conditional probability. In addition, the weights for the knowledge graph are efficiently learned from manually annotated Chinese Electronic Medical Records (CEMRs) and Blood Examination Records (BERs). In our experiments, we found numerically that an improved expression of evidence variables is necessary for medical diagnosis. Our experimental results comparing a Markov logic network and six kinds of classic machine learning algorithms on the actual CEMR database and BER database indicate that our method holds promise and that MKN can facilitate studies of intelligent diagnosis. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Jiang, Jingchi; Zhao, Chao; Guan, Yi] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China.
   [Li, Xueli] EBAONET Healthcare Informat Technol Beijing CO LT, Beijing 100028, Peoples R China.
   [Yu, Qiubin] Harbin Med Univ, Affiliated Hosp 2, Med Record Room, Harbin 150086, Heilongjiang, Peoples R China.
RP Guan, Y (通讯作者)，Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China.
EM jiangjingchi0118@163.com; xueli.li@ebaonet.cn; guanyi@hit.edu.cn;
   yuqiubin6695@163.com
FU National Natural Science Foundation of China [NSFC71531007]
FX This work was supported by the Key Program of National Natural Science
   Foundation of China under grant no. NSFC71531007. The Chinese Electronic
   Medical Records and the Blood Examination Records used in this paper
   were provided by Second Affiliated Hospital of Harbin Medical University
   and XingYi People's Hospital, respectively.
CR Amato F, 2013, J APPL BIOMED, V11, P47, DOI 10.2478/v10136-012-0031-x
   Sanz JA, 2014, APPL SOFT COMPUT, V20, P103, DOI 10.1016/j.asoc.2013.11.009
   Artikis A., 2012, P 6 ACM INT C DISTR, P32, DOI DOI 10.1145/2335484.2335488
   Azar AT, 2013, NEURAL COMPUT APPL, V23, P2387, DOI 10.1007/s00521-012-1196-7
   Bhardwaj A, 2015, EXPERT SYST APPL, V42, P4611, DOI 10.1016/j.eswa.2015.01.065
   Constantinou AC, 2016, ARTIF INTELL MED, V67, P75, DOI 10.1016/j.artmed.2016.01.002
   Domingos P, 2006, P 21 NAT C ART INT, P458
   Goh WP, 2016, KNOWL-BASED SYST, V104, P195, DOI 10.1016/j.knosys.2016.04.022
   Gutmann B., 2010, LECT NOTES COMPUTER, P76
   He B, 2017, J BIOMED INFORM
   Hinton G. E., 1983, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, P448
   Huang Y, 2007, ARTIF INTELL MED, V41, P251, DOI 10.1016/j.artmed.2007.07.002
   I2B2, INF INT BIOL BEDS
   Ising E, 1925, Z PHYS, V31, P253, DOI 10.1007/BF02980577
   Jain A., 2015, INT J ARTIF INTELL K, V5, P11
   Jin B, 2007, INFORM SCIENCES, V177, P476, DOI 10.1016/j.ins.2006.03.015
   Kazama J., 2007, P 2007 JOINT C EMP M, P698
   Korenevskiy N. A., 2015, Biomedical Engineering, V49, P46, DOI 10.1007/s10527-015-9494-x
   Lavanya D., 2012, INT J INFORM TECHNOL, V2, P17, DOI DOI 10.5121/ijitcs.2012.2103
   Liu XM, 2016, IEEE J BIOMED HEALTH, V20, P655, DOI 10.1109/JBHI.2015.2407157
   LOWD D, 2007, EUR C PRINC DATA, V4702, P200
   Palaniappan S, 2008, I C COMP SYST APPLIC, P108, DOI 10.1109/AICCSA.2008.4493524
   Pearl J., 2014, PROBABILISTIC REASON, DOI DOI 10.1080/01621459.2014.951443
   Peker M, 2016, J MED SYST, V40, DOI 10.1007/s10916-016-0477-6
   Pena-Reyes CA, 2000, ARTIF INTELL MED, V19, P1, DOI 10.1016/S0933-3657(99)00047-0
   PRIMARY PSYCHIATRY, ELECT MED RECORDS
   Project Tuffy, MEET TUFF
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Sari AK, 2012, KNOWL-BASED SYST, V26, P75, DOI 10.1016/j.knosys.2011.07.004
   Song M, 2015, J BIOMED INFORM, V57, P320, DOI 10.1016/j.jbi.2015.08.008
   Ting SL, 2011, KNOWL-BASED SYST, V24, P444, DOI 10.1016/j.knosys.2010.12.011
   Uzuner O, 2007, J AM MED INFORM ASSN, V14, P550, DOI 10.1197/jamia.M2444
   Vassis D, 2015, AIP CONF PROC, V1644, P32, DOI 10.1063/1.4907814
   Wang J., 2008, P 23 AAAI C ART INT, V8, P1106
   Wee YY, 2015, EXPERT SYST APPL, V42, P468, DOI 10.1016/j.eswa.2014.06.037
   who, CANCER
   WHO, CARDIOVASCULAR DIS
   WILAB-HIT, RESOURCES
   World Health Organization, DIAB PROGR
   YILMAZ E, 2008, P 31 ANN INT ACM SIG
NR 40
TC 12
Z9 12
U1 1
U2 38
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD DEC 15
PY 2017
VL 138
BP 58
EP 68
DI 10.1016/j.knosys.2017.09.030
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FP6ZM
UT WOS:000417776100005
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, TL
   Zeng, Y
   Pan, RH
   Shi, MT
   Lu, EM
AF Zhang, Tielin
   Zeng, Yi
   Pan, Ruihan
   Shi, Mengting
   Lu, Enmeng
TI Brain-Inspired Active Learning Architecture for Procedural Knowledge
   Understanding Based on Human-Robot Interaction
SO COGNITIVE COMPUTATION
LA English
DT Article
DE Brain-inspired architecture; Procedural knowledge; Deep neural network;
   Reinforcement learning; Knowledge graph
AB Improving robots with self-learning ability is one of the critical challenges for the researchers in the area of cognitive robotics and artificial general intelligence. This robot will decide when, where, and what to learn in a continuous visual environment by itself. Here we focus on the procedural knowledge learning, which is sequential and considered harder to understand compared with declarative knowledge in the cognitive system. Inspired by the architecture of the human brain which has integrated well different kinds of cognitive functions, a Brain-inspired Active Learning Architecture (BALA) is proposed for procedural knowledge understanding based on Baxter robot and human interaction. The BALA model contains four main parts: inspired by Primary Visual Pathway, a Convolutional Neural Network (CNN) is constructed for spatial information abstraction; inspired by the Hippocampus Pathway (especially the recurrent loops in CA3 sub-region), a Recurrent Neural Network (RNN) is built for sequential information processing related with procedural knowledge; inspired by the Prefrontal Cortex, a Knowledge Graph based on Bag Of Words (BOW) is constructed for declarative knowledge generation and association; inspired by the Basal Ganglia Pathway, we select Q matrix for Reinforcement Learning (RL). The CNN and RNN parts will be firstly pre-trained on ImageNet dataset and standard Youtube Video-Scene dataset respectively. Then, the RNN, Knowledge Graph, and Q matrix will be dynamically updated in the Baxter robot's interactive learning procedure with human cooperators. The BALA could actively and incrementally recognize different kinds of procedural knowledge. In 22-type daily-life videos with procedure knowledge (e.g., opening the door, wiping the table, or taking the phone), the BALA model gets the best performance compared with standard CNN, RNN, RL, and other integrative methods. The BALA model is a small step on integrative intelligence interaction between the Baxter robot and human cooperator.
C1 [Zhang, Tielin; Zeng, Yi; Shi, Mengting; Lu, Enmeng] Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, Beijing, Peoples R China.
   [Zeng, Yi] Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China.
   [Zeng, Yi] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China.
   [Zeng, Yi; Shi, Mengting] Univ Chinese Acad Sci, Beijing, Peoples R China.
   [Pan, Ruihan] Chinese Acad Sci, Res Ctr Intelligent Secur Technol, Chongqing Inst Green & Intelligent Technol, Beijing, Peoples R China.
RP Zhang, TL; Zeng, Y (通讯作者)，Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, Beijing, Peoples R China.; Zeng, Y (通讯作者)，Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China.; Zeng, Y (通讯作者)，Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China.; Zeng, Y (通讯作者)，Univ Chinese Acad Sci, Beijing, Peoples R China.
EM tielin.zhang@ia.ac.cn; yi.zeng@ia.ac.cn
OI Zhang, Tielin/0000-0002-5111-9891
FU National Natural Science Foundation of China [61806195]; Strategic
   Priority Research Program of Chinese Academy of Sciences [XDB32070100];
   Beijing Municipality of Science and Technology [Z181100001518006]; Major
   Research Program of Shandong Province [2018CXGC1503]; CETC Joint Fund
   [6141B08010103]
FX This study is supported by National Natural Science Foundation of China
   (No. 61806195), the Strategic Priority Research Program of Chinese
   Academy of Sciences (Grant No. XDB32070100), the Beijing Municipality of
   Science and Technology (Grant No. Z181100001518006), the Major Research
   Program of Shandong Province (Grant No. 2018CXGC1503) and the CETC Joint
   Fund (Grant No. 6141B08010103).
CR Amato C., 2018, IJCAI, P5662
   Bhat AA, 2018, COGN COMPUT, V10, P558, DOI 10.1007/s12559-018-9553-1
   Camacho-Collados J, 2018, J ARTIF INTELL RES, V63, P743, DOI 10.1613/jair.1.11259
   Chernova S, 2009, J ARTIF INTELL RES, V34, P1, DOI 10.1613/jair.2584
   Cohn DA, 1996, J ARTIF INTELL RES, V4, P129, DOI 10.1613/jair.295
   Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
   Hao WL, 2018, COGN COMPUT, V10, P321, DOI 10.1007/s12559-017-9514-0
   He KM, 2015, IEEE I CONF COMP VIS, P1026, DOI 10.1109/ICCV.2015.123
   Hester T, 2018, AAAI CONF ARTIF INTE, P3223
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Kaliukhovich DA, 2018, J NEUROPHYSIOL, V120, P926, DOI 10.1152/jn.00673.2017
   Konyushkova K., 2017, ADV NEURAL INFORM PR, P4225
   Mnih V, 2015, NATURE, V518, P529, DOI 10.1038/nature14236
   Oh J., 2015, ADV NEURAL INFORM PR, P2863
   Osa T., 2018, FDN TRENDS ROBOT, V7, P1
   Oudeyer PY, 2007, IEEE T EVOLUT COMPUT, V11, P265, DOI 10.1109/TEVC.2006.890271
   Parisi G. I., 2019, NEURAL NETWORKS
   Perez-Sanchez B, 2018, ARTIF INTELL REV, V49, P281, DOI 10.1007/s10462-016-9526-2
   Reyes O, 2018, KNOWL-BASED SYST, V145, P274, DOI 10.1016/j.knosys.2018.01.033
   Rohrbach A, 2015, PROC CVPR IEEE, P3202, DOI 10.1109/CVPR.2015.7298940
   Schembri M, 2007, EVOLVING INTERNAL RE, P282
   Shaw D, 2011, IMPACT OF THE ECONOMIC CRISIS ON EAST ASIA: POLICY RESPONSES FROM FOUR ECONOMIES, P190
   Shen YY, 2018, COGN COMPUT, V10, P334, DOI 10.1007/s12559-017-9491-3
   Silver D, 2016, NATURE, V529, P484, DOI 10.1038/nature16961
   Tebbe J., 2018, GERM C PATT REC, P33
   Toprak S, 2018, COGN COMPUT, V10, P408, DOI 10.1007/s12559-017-9536-7
   Torabi A., 2015, CORR150301070
   Ugur E, 2007, INT C DEVEL LEARN, P128
   Venugopalan S, 2015, IEEE I CONF COMP VIS, P4534, DOI 10.1109/ICCV.2015.515
   Yang Y, 2015, 29 AAAI C ART INT
   Yang YZ, 2018, PATTERN RECOGN, V83, P401, DOI 10.1016/j.patcog.2018.06.004
   Zhang T., 2018, 32 AAAI C ART INT
   Zhang TL, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1653
   Zhang TL, 2017, J INTEGR NEUROSCI, V16, P291, DOI 10.3233/JIN-170019
   Zhang TL, 2016, IEEE SYS MAN CYBERN, P2301, DOI 10.1109/SMC.2016.7844581
   Zhang TL, 2016, INT J PATTERN RECOGN, V30, DOI 10.1142/S0218001416550041
   Zhao FF, 2018, COGN COMPUT, V10, P296, DOI 10.1007/s12559-017-9511-3
   Zhou ZW, 2017, PROC CVPR IEEE, P4761, DOI 10.1109/CVPR.2017.506
   Zhu J.-J., 2017, ARXIV170207956
   Zhu ZY, 2018, ROBOTICS, V7, DOI 10.3390/robotics7020017
   Zlatintsi A, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), P3171, DOI 10.1109/ICASSP.2018.8461568
NR 41
TC 4
Z9 4
U1 2
U2 19
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1866-9956
EI 1866-9964
J9 COGN COMPUT
JI Cogn. Comput.
PD MAR
PY 2021
VL 13
IS 2
BP 381
EP 393
DI 10.1007/s12559-020-09753-1
EA JUL 2020
PG 13
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Neurosciences & Neurology
GA QU0DI
UT WOS:000548792300001
DA 2022-08-23
ER

PT J
AU Qin, AN
   Sun, J
   Gao, C
   Li, CY
AF Qin, Aining
   Sun, Jing
   Gao, Chao
   Li, Chunying
TI Bibliometrics analysis on the research status and trends of adult-onset
   Still's disease: 1921-2021
SO FRONTIERS IN IMMUNOLOGY
LA English
DT Article
DE Still's disease; adult-onset; scopus; bibliometrics [MeSH]; trends;
   epidemiology
ID MACROPHAGE ACTIVATION SYNDROME; HEMOPHAGOCYTIC SYNDROME; NLRP3
   INFLAMMASOME; DIAGNOSIS; MANIFESTATIONS; FEATURES; CLASSIFICATION;
   CLOTRIMAZOLE; PATHOGENESIS; ASSOCIATION
AB Objectives: The aim of this research is to discuss the research status, hotspots, frontiers and development trends in the field of adult-onset Still's disease (AOSD) based on bibliometrics and visual analysis by CiteSpace software. Methods: The relevant research articles on AOSD from 1921 to 2021 were retrieved from the Scopus database. CiteSpace software was used to form a visual knowledge map and conduct analysis for the countries/regions, journals, authors, keywords, clusters, research hotspots and frontiers of the included articles. Results: There were 2,373 articles included, and the number of articles published during 1921-2021 is increasing. The country with the highest number of articles published was Japan (355, 14.96%), followed by the United States (329, 13.86%) and France (215, 9.06%). The author with the highest number of publications is Ansell, Barbara M. (30, 1.26%), and the author with the highest co-citation frequency is Yamaguchi, Masaya (703). Clinical Rheumatology is the journal with the highest publication frequency. The top five cluster groups were "joint ", "differential diagnosis ", "prednisolone ", "methotrexate " and "macrophage activation syndrome ". The diagnosis, treatment and pathogenesis of AOSD form the main research fields, and prognosis and complications are the research hotspots and trends. Conclusions: The global research field in AOSD has expanded in the past 100 years. The complications and new pathogenesis of AOSD are hotspots in this field and need further study in the future.
C1 [Qin, Aining] Peking Univ, Sch Nursing, Beijing, Peoples R China.
   [Sun, Jing] Peking Univ, Sch Nursing, Dept Community Nursing, Beijing, Peoples R China.
   [Gao, Chao] Peking Univ, Peoples Hosp, Dept Rheumatol & Immunol, Beijing, Peoples R China.
   [Li, Chunying] Peking Univ Hlth Sci Lib, Informat & Reference Dept, Beijing, Peoples R China.
RP Sun, J (通讯作者)，Peking Univ, Sch Nursing, Dept Community Nursing, Beijing, Peoples R China.
EM sunjing99@bjmu.edu.cn
CR [Anonymous], 2022, SCOPUS
   Arlet JB, 2006, ANN RHEUM DIS, V65, P1596, DOI 10.1136/ard.2005.046904
   AYDINTUG AO, 1992, J RHEUMATOL, V19, P431
   Bae CB, 2014, J RHEUMATOL, V41, P2403, DOI 10.3899/jrheum.140651
   Baerlecken NT, 2012, Z RHEUMATOL, V71, P174, DOI 10.1007/s00393-011-0859-6
   Bazzani C, 2010, J CARDIOVASC MED, V11, P906, DOI 10.2459/JCM.0b013e32833cdca8
   Ben Salah Raida, 2022, Clin Case Rep, V10, pe05474, DOI 10.1002/ccr3.5474
   Bogdan M, 2021, ANN AGR ENV MED, V28, P250, DOI 10.26444/aaem/132451
   Bornmann L, 2020, QUANT SCI STUD, V1, P171, DOI 10.1162/qss_a_00012
   Carter SJ, 2019, RHEUMATOLOGY, V58, P5, DOI 10.1093/rheumatology/key006
   Chen BY, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18041663
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Chen DY, 2018, MOL MED REP, V17, P3364, DOI 10.3892/mmr.2017.8275
   Chen DY, 2004, ANN RHEUM DIS, V63, P1300, DOI 10.1136/ard.2003.013680
   Chen PK, 2021, FRONT IMMUNOL, V12, DOI 10.3389/fimmu.2021.719544
   Chen PK, 2020, CLIN RHEUMATOL, V39, P1945, DOI 10.1007/s10067-020-04946-3
   Chen PK, 2020, J IMMUNOL RES, V2020, DOI 10.1155/2020/9473497
   Cheng T, 2013, RHEUMATOLOGY, V52, P1630, DOI 10.1093/rheumatology/ket008
   Chi HH, 2020, CLIN RHEUMATOL, V39, P3723, DOI 10.1007/s10067-020-05094-4
   Crayne CB, 2019, FRONT IMMUNOL, V10, DOI 10.3389/fimmu.2019.00119
   Crowley PD, 2014, J APPL MICROBIOL, V117, P611, DOI 10.1111/jam.12554
   CUSH JJ, 1987, ARTHRITIS RHEUM, V30, P186, DOI 10.1002/art.1780300209
   Delplanque M, 2021, J CLIN MED, V10, DOI 10.3390/jcm10235586
   Efthimiou P, 2006, ANN RHEUM DIS, V65, P564, DOI 10.1136/ard.2005.042143
   Efthimiou P, 2014, CLIN RHEUMATOL, V33, P305, DOI 10.1007/s10067-014-2487-4
   Fauter M, 2020, REV MED INTERNE, V41, P168, DOI 10.1016/j.revmed.2019.12.003
   Fautrel B, 2008, BEST PRACT RES CL RH, V22, P773, DOI 10.1016/j.berh.2008.08.006
   Feist E, 2018, NAT REV RHEUMATOL, V14, P603, DOI 10.1038/s41584-018-0081-x
   Franchini S, 2010, ARTHRITIS RHEUM-US, V62, P2530, DOI 10.1002/art.27532
   Gabay C, 2018, ANN RHEUM DIS, V77, P840, DOI 10.1136/annrheumdis-2017-212608
   Gerfaud-Valentin M, 2014, AUTOIMMUN REV, V13, P708, DOI 10.1016/j.autrev.2014.01.058
   Gerfaud-Valentin M, 2014, MEDICINE, V93, P91, DOI 10.1097/MD.0000000000000021
   Giacomelli R, 2018, J AUTOIMMUN, V93, P24, DOI 10.1016/j.jaut.2018.07.018
   Girard-Guyonvarc'h C, 2019, JOINT BONE SPINE, V86, P5, DOI 10.1016/j.jbspin.2018.03.010
   Gracia-Ramos AE, 2020, CLIN RHEUMATOL, V39, P933, DOI 10.1007/s10067-019-04814-9
   Han JH, 2015, ARTHRITIS RES THER, V17, DOI 10.1186/s13075-015-0773-4
   Hofheinz K, 2016, SEMIN ARTHRITIS RHEU, V45, P621, DOI 10.1016/j.semarthrit.2015.10.003
   Hsieh CW, 2017, J RHEUMATOL, V44, P1142, DOI 10.3899/jrheum.161354
   Hu QY, 2020, ANN RHEUM DIS, V79, P842, DOI 10.1136/annrheumdis-2019-216699
   Husni ME, 2002, ARTHRITIS RHEUM, V46, P1171, DOI 10.1002/art.10231
   de Granda-Orive JI, 2013, REV ESP DOC CIENT, V36, DOI 10.3989/redc.2013.2.941
   Iwamoto M, 2002, ARTHRITIS RHEUM-US, V46, P3388, DOI 10.1002/art.10620
   Jamilloux Y, 2015, IMMUNOL RES, V61, P53, DOI 10.1007/s12026-014-8561-9
   Jamilloux Y, 2015, THER CLIN RISK MANAG, V11, P33, DOI 10.2147/TCRM.S64951
   Jensen B S, 2002, Expert Opin Ther Targets, V6, P623, DOI 10.1517/14728222.6.6.623
   Jia JC, 2021, RHEUMATOLOGY, V60, P304, DOI 10.1093/rheumatology/keaa368
   Joung CI, 2003, CLIN EXP RHEUMATOL, V21, P489
   Kadavath S, 2015, ANN MED, V47, P6, DOI 10.3109/07853890.2014.971052
   Kaneko Y, 2022, MOD RHEUMATOL, V32, P12, DOI 10.1093/mr/roab004
   Kaneko Y, 2022, EXPERT OPIN BIOL TH, V22, P79, DOI 10.1080/14712598.2021.1942832
   KHRAISHI M, 1991, J RHEUMATOL, V18, P1088
   Kim Sehee, 2021, [The Korean Journal of Medicine, 대한내과학회지], V96, P30, DOI 10.3904/kjm.2021.96.1.30
   Kishida D, 2011, MOD RHEUMATOL, V21, P215, DOI 10.1007/s10165-010-0365-8
   Lebrun D, 2018, SEMIN ARTHRITIS RHEU, V47, P578, DOI 10.1016/j.semarthrit.2017.07.005
   Liao TL, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-95028-0
   Liao TL, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-04086-w
   Lin SJ, 2000, CLIN RHEUMATOL, V19, P127, DOI 10.1007/s100670050030
   Lin YJ, 2020, CELLS-BASEL, V9, DOI 10.3390/cells9040880
   Liu S, 2019, NEURAL REGEN RES, V14, P1643, DOI 10.4103/1673-5374.255995
   Lowther GH, 2017, PULM CIRC, V7, P797, DOI 10.1177/2045893217712710
   MAGADURJOLY G, 1995, ANN RHEUM DIS, V54, P587, DOI 10.1136/ard.54.7.587
   Mahmood K, 2018, CUREUS, V10, DOI 10.7759/cureus.3255
   Mehta BY, 2019, SEMIN ARTHRITIS RHEU, V49, P469, DOI 10.1016/j.semarthrit.2019.04.004
   Meng JF, 2021, RHEUMATOLOGY, V60, P4520, DOI 10.1093/rheumatology/keab023
   Mimura Tetsuhiko, 2014, BMC Res Notes, V7, P940, DOI 10.1186/1756-0500-7-940
   Mitrovic S, 2018, JOINT BONE SPINE, V85, P285, DOI 10.1016/j.jbspin.2017.05.011
   Mitrovic S, 2018, EXPERT REV CLIN IMMU, V14, P351, DOI 10.1080/1744666X.2018.1465821
   Nakagawa Y, 2005, INTERNAL MED, V44, P994, DOI 10.2169/internalmedicine.44.994
   Neel A, 2018, CRIT CARE, V22, DOI 10.1186/s13054-018-2012-2
   POUCHOT J, 1991, MEDICINE, V70, P118, DOI 10.1097/00005792-199103000-00004
   Priori R, 2010, AUTOIMMUN HIGHLIGHTS, V1, P53, DOI 10.1007/s13317-010-0009-1
   Crnogaj MR, 2020, RHEUMATOL INT, V40, P1317, DOI 10.1007/s00296-020-04622-4
   Rosario C, 2013, BMC MED, V11, DOI 10.1186/1741-7015-11-185
   Ruddell RG, 2009, HEPATOLOGY, V49, P887, DOI 10.1002/hep.22716
   Sakata N, 2016, RHEUMATOL INT, V36, P1399, DOI 10.1007/s00296-016-3546-8
   Scarazzati S, 2019, SCIENTOMETRICS, V121, P839, DOI 10.1007/s11192-019-03220-x
   SCHALLER JG, 1974, ARTHRITIS RHEUM, V17, P409, DOI 10.1002/art.1780170411
   Seo JY, 2017, MEDICINE, V96, DOI 10.1097/MD.0000000000007546
   Sfriso P, 2018, DRUGS, V78, P1187, DOI 10.1007/s40265-018-0956-9
   Sfriso P, 2016, CLIN RHEUMATOL, V35, P1683, DOI 10.1007/s10067-016-3308-8
   Siddiqui M, 2016, OPEN ACCESS RHEUMATO, V8, P17, DOI 10.2147/OARRR.S83948
   Smolen JS, 2016, LANCET, V388, P2023, DOI 10.1016/S0140-6736(16)30173-8
   Sugiyama T, 2022, ARTHRITIS RES THER, V24, DOI 10.1186/s13075-021-02708-3
   TORRIGIANI G, 1969, ANN RHEUM DIS, V28, P424, DOI 10.1136/ard.28.4.424
   Tsimikas S, 2017, J AM COLL CARDIOL, V69, P692, DOI 10.1016/j.jacc.2016.11.042
   Wakai K, 1997, J Epidemiol, V7, P221
   Wang FF, 2013, ARTHRITIS RES THER, V15, DOI 10.1186/ar4239
   Wang H, 2021, FRONT PSYCHIATRY, V12, DOI 10.3389/fpsyt.2021.705749
   Wang MY, 2019, CHINESE MED J-PEKING, V132, P2856, DOI 10.1097/CM9.0000000000000538
   Wang ZH, 2020, RHEUMATOLOGY, V59, P3293, DOI 10.1093/rheumatology/keaa135
   WOJTULEWSKI JA, 1980, ANN RHEUM DIS, V39, P469, DOI 10.1136/ard.39.5.469
   WOUTERS JMGW, 1986, Q J MED, V61, P1055
   YAMAGUCHI M, 1992, J RHEUMATOL, V19, P424
   Yanai H, 2013, AM J HEMATOL, V88, P828, DOI 10.1002/ajh.23517
   Yu DJ, 2018, KNOWL-BASED SYST, V141, P188, DOI 10.1016/j.knosys.2017.11.018
   Zhang X, 2022, RHEUMATOL INT, V42, P707, DOI 10.1007/s00296-022-05097-1
   [朱善邦 Zhu Shanbang], 2020, [第二军医大学学报, Academic Journal of Second Military Medical University], V41, P455
   Zupic I, 2015, ORGAN RES METHODS, V18, P429, DOI 10.1177/1094428114562629
   ZUTSHI DW, 1971, ARCH DIS CHILD, V46, P584, DOI 10.1136/adc.46.249.584
   ZUTSHI DW, 1969, ANN RHEUM DIS, V28, P541, DOI 10.1136/ard.28.5.541
NR 100
TC 0
Z9 0
U1 0
U2 0
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-3224
J9 FRONT IMMUNOL
JI Front. Immunol.
PD JUL 18
PY 2022
VL 13
AR 950641
DI 10.3389/fimmu.2022.950641
PG 14
WC Immunology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Immunology
GA 3L1DH
UT WOS:000834508700001
PM 35924251
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhang, YQ
   You, XL
   Huang, SJ
   Wang, MH
   Dong, JW
AF Zhang, Yanqin
   You, Xianli
   Huang, Shanjun
   Wang, Minhua
   Dong, Jianwen
TI Knowledge Atlas on the Relationship between Water Management and
   Constructed Wetlands-A Bibliometric Analysis Based on CiteSpace
SO SUSTAINABILITY
LA English
DT Article
DE water management; sustainable development; knowledge map; quantitative
   analysis
ID VEGETATED DRAINAGE DITCHES; LOW-GRADE WEIRS; WASTE-WATER;
   NUTRIENT-REMOVAL; AGRICULTURAL RUNOFF; FLOATING WETLAND; SURFACE-FLOW;
   NITROGEN REMOVAL; EMERGING TRENDS; DESIGN
AB Water management is a crucial resource conservation challenge that mankind faces, and encouraging the creation of manmade wetlands with the goal of achieving long-term water management is the key to long-term urban development. To summarise and analyse the status of the research on the relationship between water management and constructed wetlands, this paper makes use of the advantages of the bibliometric visualization of CiteSpace to generate country/region maps and author-collaboration maps, and to analyse research hotspots and research dynamics by using keywords and literature co-citations based on 1248 pieces of related literature in the core collection in the Web of Science (WoS) database. The existing research shows that the research content and methods in the field of constructed-wetland and water-management research are constantly being enriched and deepened, including the research methods frequently used in constructed wetlands in water management and in the research content under concern, the functions and roles of constructed wetlands, the relevant measurement indicators of the purification impact of constructed wetlands on water bodies, and the types of water bodies treated by constructed wetlands in water management. We summarise the impact pathways of constructed wetlands on water management, as well as the impact factors of constructed wetlands under water-management objectives, by analysing the future concerns in the research field to provide references for research.
C1 [Zhang, Yanqin; You, Xianli; Huang, Shanjun; Wang, Minhua; Dong, Jianwen] Fujian Agr & Forestry Univ, Coll Landscape Architecture, Fuzhou 350002, Peoples R China.
RP Dong, JW (通讯作者)，Fujian Agr & Forestry Univ, Coll Landscape Architecture, Fuzhou 350002, Peoples R China.
EM zyq97120530@163.com; yxianli0709@163.com; hsj7174@163.com;
   wmh699@163.com; fjdjw@fafu.edu.cn
FU Forest Park Engineering Technology Research Center of the State Forestry
   Administration [PTJH15002]; Wuyishan National Park Research Institute
   Special Project [KJg20009A]
FX This research received no external funding. This study was funded by the
   Forest Park Engineering Technology Research Center of the State Forestry
   Administration (PTJH15002), and by the Wuyishan National Park Research
   Institute Special Project (KJg20009A).
CR Abbassi B., 2018, CONSTRUCTED WETLANDS
   Addo-Bankas O, 2021, ECOL ENG, V169, DOI 10.1016/j.ecoleng.2021.106321
   Adyel TM, 2017, SCI TOTAL ENVIRON, V598, P1001, DOI 10.1016/j.scitotenv.2017.04.044
   Afzal M, 2019, NPJ CLEAN WATER, V2, DOI 10.1038/s41545-018-0025-7
   Aguado R, 2022, J WATER PROCESS ENG, V47, DOI 10.1016/j.jwpe.2022.102676
   Almuktar SAAAN, 2021, AGRONOMY-BASEL, V11, DOI 10.3390/agronomy11091817
   Altamira-Algarra B, 2022, ECOL ENG, V177, DOI 10.1016/j.ecoleng.2022.106571
   Anawar HM, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12177017
   Anawar HM, 2019, EMERGING AND NANOMATERIAL CONTAMINANTS IN WASTEWATER: ADVANCED TREATMENT TECHNOLOGIES, P43, DOI 10.1016/B978-0-12-814673-6.00002-4
   Andreo-Martinez P, 2017, BIORESOURCE TECHNOL, V233, P236, DOI 10.1016/j.biortech.2017.02.123
   [Anonymous], 2018, NAT BAS SOL WAT
   APHA, 2012, AM PUBL HLTH ASS AM
   Arden S, 2018, SCI TOTAL ENVIRON, V630, P587, DOI 10.1016/j.scitotenv.2018.02.218
   Aristeidis K., 2015, Silva Balcanica, V16, P71
   Ashraf A, 2017, INT J PHYTOREMEDIAT, V19, P605, DOI 10.1080/15226514.2016.1256372
   Avila C, 2021, J HAZARD MATER, V412, DOI 10.1016/j.jhazmat.2021.125231
   Awad J, 2022, J HAZARD MATER, V429, DOI 10.1016/j.jhazmat.2022.128326
   Bai XY, 2020, WATER-SUI, V12, DOI 10.3390/w12041054
   Batool A, 2020, ECOTOX ENVIRON SAFE, V189, DOI 10.1016/j.ecoenv.2019.109924
   Benvenuti T, 2018, J ENVIRON CHEM ENG, V6, P5706, DOI 10.1016/j.jece.2018.08.067
   Bernhardt ES, 2008, ANN NY ACAD SCI, V1134, P61, DOI 10.1196/annals.1439.014
   Cao Z, 2021, J CLEAN PROD, V288, DOI 10.1016/j.jclepro.2020.125551
   Sandoval-Herazo LC, 2020, MOLECULES, V25, DOI 10.3390/molecules25225273
   Carvalho P, 2022, J CLEAN PROD, V338, DOI 10.1016/j.jclepro.2022.130652
   Castonguay AC, 2018, WATER RES, V142, P301, DOI 10.1016/j.watres.2018.05.037
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen CM, 2014, J ASSOC INF SCI TECH, V65, P334, DOI 10.1002/asi.22968
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Chen HJ, 2011, WETLANDS, V31, P805, DOI 10.1007/s13157-011-0186-3
   Cheng SY, 2021, ECOTOX ENVIRON SAFE, V221, DOI 10.1016/j.ecoenv.2021.112451
   Chimney MJ, 2006, ECOL ENG, V27, P268, DOI 10.1016/j.ecoleng.2006.05.015
   Choi H, 2021, WATER-SUI, V13, DOI 10.3390/w13091242
   Collins KA, 2010, ECOL ENG, V36, P1507, DOI 10.1016/j.ecoleng.2010.03.015
   Connor R., UN WORLD WATER DEV R
   Dal Ferro N, 2021, SCI TOTAL ENVIRON, V757, DOI 10.1016/j.scitotenv.2020.144189
   Dell'Osbel N, 2020, ECOL ENG, V158, DOI 10.1016/j.ecoleng.2020.106072
   Dharmasena T, 2021, ECOL ENG, V173, DOI 10.1016/j.ecoleng.2021.106441
   Ding XW, 2018, J CLEAN PROD, V172, P541, DOI 10.1016/j.jclepro.2017.10.170
   Dong X, 2017, WATER RES, V124, P280, DOI 10.1016/j.watres.2017.07.038
   Dotro G, 2017, TREATMENT WETLANDS
   DuPoldt C., 1996, ECOL ENG, V1, P53
   Everard M, 2012, ECOSYST SERV, V2, P27, DOI 10.1016/j.ecoser.2012.08.001
   Flora C, 2014, AQUACULT ENG, V60, P68, DOI 10.1016/j.aquaeng.2014.04.007
   Flora C, 2014, AQUACULT ENG, V60, P56, DOI 10.1016/j.aquaeng.2014.04.006
   Fuchs VJ, 2011, WATER RES, V45, P2073, DOI 10.1016/j.watres.2010.12.021
   Gao L, 2017, BIORESOURCE TECHNOL, V234, P243, DOI 10.1016/j.biortech.2017.03.036
   Getahun E., 2016, SUSTAIN WATER QUAL E, V8, P14, DOI [10.1016/j.swaqe.2016.06.002, DOI 10.1016/J.SWAQE.2016.06.002]
   Gholipour A, 2020, CHEMOSPHERE, V247, DOI 10.1016/j.chemosphere.2020.125966
   Gikas GD, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14074056
   Gordon BA, 2021, J SOIL WATER CONSERV, V76, P446, DOI 10.2489/jswc.2021.02155
   Guittonny-Philippe A, 2015, WATER RES, V68, P328, DOI 10.1016/j.watres.2014.10.014
   Haddis A, 2020, ECOHYDROL HYDROBIOL, V20, P38, DOI 10.1016/j.ecohyd.2019.03.001
   Hammer D. A., 1989, CONSTRUCTED WETLANDS
   Hansen DJ, 2022, BIOLOGY-BASEL, V11, DOI 10.3390/biology11020188
   Harris-Lovett S, 2019, J ENVIRON MANAGE, V233, P218, DOI 10.1016/j.jenvman.2018.11.112
   Hdidou M, 2022, ENERGIES, V15, DOI 10.3390/en15010156
   He CY, 2021, NAT COMMUN, V12, DOI 10.1038/s41467-021-25026-3
   House CH, 1999, ECOL ENG, V12, P27, DOI 10.1016/S0925-8574(98)00052-4
   Hsu CY, 2021, WATER-SUI, V13, DOI 10.3390/w13212973
   Hunt PG, 2002, T ASAE, V45, P639
   Ibekwe AM, 2007, J APPL MICROBIOL, V102, P921, DOI 10.1111/j.1365-2672.2006.03181.x
   Ingrao Carlo, 2020, Current Opinion in Environmental Science & Health, V13, P35, DOI 10.1016/j.coesh.2019.10.007
   Jesus JM, 2018, ENVIRON SCI POLLUT R, V25, P4149, DOI 10.1007/s11356-017-0982-2
   Jing YY, 2021, WATER RES, V207, DOI 10.1016/j.watres.2021.117776
   Kadlec R.H., 2008, TREATMENT WETLANDS
   Kataki S, 2021, J ENVIRON MANAGE, V283, DOI 10.1016/j.jenvman.2021.111986
   Knapp S, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11205846
   Kumwimba MN, 2021, ENVIRON TECHNOL INNO, V22, DOI 10.1016/j.eti.2021.101542
   Langergraber G, 2020, BLUE-GREEN SYST, V2, P173, DOI 10.2166/bgs.2020.933
   Lei MT, 2020, CHEMOSPHERE, V245, DOI 10.1016/j.chemosphere.2019.125615
   Leigh NG, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11030918
   Li D, 2021, SCI TOTAL ENVIRON, V797, DOI 10.1016/j.scitotenv.2021.148989
   Li D, 2018, APPL MICROBIOL BIOT, V102, P5355, DOI 10.1007/s00253-018-9011-8
   Li X, 2022, ENVIRON DEV SUSTAIN, V24, P112, DOI 10.1007/s10668-021-01495-w
   Li X, 2020, BIORESOURCE TECHNOL, V318, DOI 10.1016/j.biortech.2020.123858
   Li YC, 2017, CLEAN-SOIL AIR WATER, V45, DOI 10.1002/clen.201600740
   Liquete C, 2016, ECOSYST SERV, V22, P392, DOI 10.1016/j.ecoser.2016.09.011
   Liu M, 2022, ENVIRON SCI POLLUT R, V29, P30169, DOI 10.1007/s11356-021-17454-x
   Lizotte RE, 2012, SCI TOTAL ENVIRON, V427, P373, DOI 10.1016/j.scitotenv.2012.04.025
   Lord J, 2021, NATURE, V598, P611, DOI 10.1038/s41586-021-03900-w
   Lu SB, 2016, J CLEAN PROD, V127, P325, DOI 10.1016/j.jclepro.2016.03.166
   Lucke T, 2019, SCI TOTAL ENVIRON, V660, P199, DOI 10.1016/j.scitotenv.2019.01.018
   Marin-Muniz JL, 2020, ECOL ENG, V147, DOI 10.1016/j.ecoleng.2019.105658
   Lv J, 2019, HUM ECOL RISK ASSESS, V25, P1802, DOI 10.1080/10807039.2018.1475214
   Lyu CJ, 2021, J ENVIRON SCI, V106, P171, DOI 10.1016/j.jes.2020.12.018
   Maas CM, 2021, WATER-SUI, V13, DOI 10.3390/w13111492
   Rosendo JCM, 2022, REV ENVIRON SCI BIO, V21, P371, DOI 10.1007/s11157-022-09616-1
   Martinez-Guerra E, 2020, WATER ENVIRON RES, V92, P1677, DOI 10.1002/wer.1422
   Marzo A, 2018, SCI TOTAL ENVIRON, V636, P94, DOI 10.1016/j.scitotenv.2018.04.228
   Masi E, 2018, J ENVIRON MANAGE, V216, P275, DOI 10.1016/j.jenvman.2017.11.086
   Massoud MA, 2009, J ENVIRON MANAGE, V90, P652, DOI 10.1016/j.jenvman.2008.07.001
   McCalla LB, 2022, ARCH ENVIRON CON TOX, V82, P317, DOI 10.1007/s00244-021-00909-0
   Mench M, 2010, J SOIL SEDIMENT, V10, P1039, DOI 10.1007/s11368-010-0190-x
   Perrone SM, 2022, AQUAT CONSERV, V32, P591, DOI 10.1002/aqc.3792
   Mohammed A, 2017, ECOL MODEL, V354, P62, DOI 10.1016/j.ecolmodel.2017.03.012
   Moore MT, 2011, WATER AIR SOIL POLL, V217, P445, DOI 10.1007/s11270-010-0599-2
   Moore TLC, 2012, WATER RES, V46, P6811, DOI 10.1016/j.watres.2011.11.026
   Mustafa A, 2009, ECOL ENG, V35, P779, DOI 10.1016/j.ecoleng.2008.12.008
   Nuamah LA, 2020, J CLEAN PROD, V269, DOI 10.1016/j.jclepro.2020.122340
   Oral HV, 2020, BLUE-GREEN SYST, V2, P112, DOI 10.2166/bgs.2020.932
   Packer JG, 2017, J ECOL, V105, P1123, DOI 10.1111/1365-2745.12797
   Pahunang RR, 2021, SCI TOTAL ENVIRON, V792, DOI 10.1016/j.scitotenv.2021.148479
   Pan T, 2011, ECOL ENG, V37, P248, DOI 10.1016/j.ecoleng.2010.11.014
   Penacoba-Antona L, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18105415
   Perdana MC, 2018, IOP C SER EARTH ENV, V148, DOI 10.1088/1755-1315/148/1/012025
   Qiu JL, 2019, ENVIRON MONIT ASSESS, V191, DOI 10.1007/s10661-019-7747-y
   Ramirez S, 2019, CHEMOSPHERE, V234, P496, DOI 10.1016/j.chemosphere.2019.06.081
   Ramos A, 2019, ENVIRON CHEM, V16, P563, DOI 10.1071/EN19026
   Roth JJ, 2021, SCI TOTAL ENVIRON, V758, DOI 10.1016/j.scitotenv.2020.143652
   Russo N, 2019, SCI TOTAL ENVIRON, V656, P558, DOI 10.1016/j.scitotenv.2018.11.417
   Saeed T, 2021, J CLEAN PROD, V282, DOI 10.1016/j.jclepro.2020.125427
   Saeed T, 2019, J ENVIRON CHEM ENG, V7, DOI 10.1016/j.jece.2019.103042
   Salimi S, 2021, J ENVIRON MANAGE, V289, DOI 10.1016/j.jenvman.2021.112459
   Shi BQ, 2019, J ENVIRON MANAGE, V246, P203, DOI 10.1016/j.jenvman.2019.06.006
   Sijimol M., 2021, INT J ENERGY WATER R, V5, P357, DOI [10.1007/s42108-021-00129-1, DOI 10.1007/S42108-021-00129-1]
   de Queiroz RDS, 2020, INT J PHYTOREMEDIAT, V22, P518, DOI 10.1080/15226514.2019.1686603
   Spangler JT, 2019, ECOL ENG, V127, P468, DOI 10.1016/j.ecoleng.2018.12.017
   Spangler JT, 2019, ENVIRON SCI POLLUT R, V26, P5751, DOI 10.1007/s11356-018-3964-0
   Srivastava P, 2021, SUSTAIN ENERGY TECHN, V46, DOI 10.1016/j.seta.2021.101282
   Stefanakis AI, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11246981
   Sun Y, 2021, J ENVIRON MANAGE, V298, DOI 10.1016/j.jenvman.2021.113401
   Takavakoglou V, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14063145
   Toet S, 2005, ECOL ENG, V25, P101, DOI 10.1016/j.ecoleng.2005.03.004
   Upadhyay AK, 2016, J ENVIRON MANAGE, V169, P285, DOI 10.1016/j.jenvman.2016.01.004
   Vymazal J, 2007, SCI TOTAL ENVIRON, V380, P48, DOI 10.1016/j.scitotenv.2006.09.014
   Vymazal J, 2015, ENVIRON INT, V75, P11, DOI 10.1016/j.envint.2014.10.026
   Vymazal J, 2014, ECOL ENG, V73, P724, DOI [10.1016/j.ecoleng.2014.09.0, 10.1016/j.ecoleng.2014.09.034]
   Vymazal J, 2011, ENVIRON SCI TECHNOL, V45, P61, DOI 10.1021/es101403q
   Vymazal J, 2010, WATER-SUI, V2, P530, DOI 10.3390/w2030530
   Walaszek M, 2018, SCI TOTAL ENVIRON, V637, P443, DOI 10.1016/j.scitotenv.2018.04.325
   Wang G, 2019, WATER-SUI, V11, DOI 10.3390/w11030620
   Wang HX, 2021, J CLEAN PROD, V295, DOI 10.1016/j.jclepro.2021.126455
   Wang JQ, 2021, J ENVIRON MANAGE, V280, DOI 10.1016/j.jenvman.2020.111794
   Wang LW, 2020, ENVIRON INT, V134, DOI 10.1016/j.envint.2019.105281
   Wang M, 2021, FRONT ENV SCI-SWITZ, V9, DOI 10.3389/fenvs.2021.713831
   Wang QT, 2020, SCI TOTAL ENVIRON, V721, DOI 10.1016/j.scitotenv.2020.137785
   Wang YT, 2020, SCI TOTAL ENVIRON, V701, DOI 10.1016/j.scitotenv.2019.134736
   Wei DB, 2020, ENVIRON SCI POLLUT R, V27, P24902, DOI 10.1007/s11356-020-08393-0
   White SA, 2013, ECOL ENG, V61, P207, DOI 10.1016/j.ecoleng.2013.09.020
   Wong THF, 2009, WATER SCI TECHNOL, V60, P673, DOI 10.2166/wst.2009.436
   Wu HM, 2015, BIORESOURCE TECHNOL, V175, P594, DOI 10.1016/j.biortech.2014.10.068
   Wu SB, 2015, ENVIRON POLLUT, V201, P107, DOI 10.1016/j.envpol.2015.03.006
   Xi JF, 2022, CHEMOSPHERE, V290, DOI 10.1016/j.chemosphere.2021.133314
   Xia YQ, 2020, J GEOPHYS RES-BIOGEO, V125, DOI 10.1029/2019JG005610
   Xiao L, 2019, APPL ECOL ENV RES, V17, P8931, DOI 10.15666/aeer/1704_89318940
   Xu XY, 2021, ENVIRON SCI POLLUT R, V28, P61526, DOI 10.1007/s11356-021-14418-z
   Yan LB, 2021, WETLANDS, V41, DOI 10.1007/s13157-021-01428-3
   Yan WL, 2019, URBAN PLAN, V4, P123, DOI 10.17645/up.v4i1.1739
   Yang Y, 2018, BIORESOURCE TECHNOL, V261, P441, DOI 10.1016/j.biortech.2018.03.085
   Yeh N, 2015, RENEW SUST ENERG REV, V47, P616, DOI 10.1016/j.rser.2015.03.090
   Yu Q, 2022, BIORESOURCE TECHNOL, V348, DOI 10.1016/j.biortech.2022.126755
   Zamora S, 2019, WATER-SUI, V11, DOI 10.3390/w11112344
   Zhang XM, 2018, SCI TOTAL ENVIRON, V639, P640, DOI 10.1016/j.scitotenv.2018.05.198
   Zheng ZH, 2021, SCI TOTAL ENVIRON, V771, DOI 10.1016/j.scitotenv.2020.144475
   Zhou JB, 2009, COMMUN NONLINEAR SCI, V14, P1781, DOI 10.1016/j.cnsns.2007.08.010
   Zhou TT, 2021, WATER ENVIRON J, V35, P1133, DOI 10.1111/wej.12705
   Zhou W, 2018, ENERGY REP, V4, P724, DOI 10.1016/j.egyr.2018.10.012
   Zhu TD, 2021, J ENVIRON MANAGE, V278, DOI 10.1016/j.jenvman.2020.111564
NR 160
TC 0
Z9 0
U1 7
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUL
PY 2022
VL 14
IS 14
AR 8288
DI 10.3390/su14148288
PG 28
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA 3I0JQ
UT WOS:000832413300001
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, P
   Bu, Y
   Jiang, P
   Shi, XW
   Lun, B
   Chen, CY
   Syafiandini, AF
   Ding, Y
   Song, M
AF Zhang, Peng
   Bu, Yi
   Jiang, Peng
   Shi, Xiaowen
   Lun, Bing
   Chen, Chongyan
   Syafiandini, Arida Ferti
   Ding, Ying
   Song, Min
TI Toward a Coronavirus Knowledge Graph
SO GENES
LA English
DT Article
DE knowledge management applications; knowledge base management; knowledge
   engineering methodologies
ID VIRUS-INFECTION; COVID-19; DRUG
AB This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG's usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.
C1 [Zhang, Peng] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China.
   [Bu, Yi] Peking Univ, Dept Informat Management, Beijing 100871, Peoples R China.
   [Jiang, Peng; Shi, Xiaowen; Lun, Bing] Beijing Knowledge Atlas Technol Co Ltd, Beijing 100043, Peoples R China.
   [Chen, Chongyan; Ding, Ying] Univ Texas Austin, Sch Informat, Austin, TX 78701 USA.
   [Syafiandini, Arida Ferti; Song, Min] Yonsei Univ, Dept Lib & Informat Sci, Seoul 03722, South Korea.
   [Ding, Ying] Univ Texas Austin, Dell Med Sch, Austin, TX 78712 USA.
RP Song, M (通讯作者)，Yonsei Univ, Dept Lib & Informat Sci, Seoul 03722, South Korea.
EM zhangp31@tsinghua.edu.cn; buyi@pku.edu.cn; peng.jiang@aminer.cn;
   shixiaowen_win@163.com; bing.lun@aminer.cn; chongyanchen_hci@utexas.edu;
   afsyafiandini@yonsei.ac.kr; ying.ding@ischool.utexas.edu;
   min.song@yonsei.ac.kr
RI Bu, Yi/B-4964-2018
OI Bu, Yi/0000-0003-2549-4580; Chen, Chongyan/0000-0003-3188-6652
FU National Research Foundation of Korea (NRF) grant by the Korean
   government (MSIT) [NRF-2019R1A2C2002577]; National Science Foundation in
   the United States [NSF Rapid 2028717]
FX This research was funded by the National Research Foundation of Korea
   (NRF) grant by the Korean government (MSIT) with grant number
   NRF-2019R1A2C2002577 and the National Science Foundation in the United
   States with grant number NSF Rapid 2028717.
CR Apweiler R, 2004, NUCLEIC ACIDS RES, V32, pD115, DOI 10.1093/nar/gkh131
   Arora S, 2020, GENE, V762, DOI 10.1016/j.gene.2020.145057
   Baize S, 2002, CLIN EXP IMMUNOL, V128, P163, DOI 10.1046/j.1365-2249.2002.01800.x
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bullock J, 2020, J ARTIF INTELL RES, V69, P807
   Campioli CC, 2020, J CLIN VIROL, V130, DOI 10.1016/j.jcv.2020.104577
   Cao Yang, 2020, J Allergy Clin Immunol, V146, P137, DOI 10.1016/j.jaci.2020.05.019
   Chahal M., 2016, INT J COMPUTER TREND, V36, P140, DOI [10.14445/22312803/IJCTT-V36P124, DOI 10.14445/22312803/IJCTT-V36P124]
   Chahrour M, 2020, CUREUS J MED SCIENCE, V12, DOI 10.7759/cureus.7357
   Chambers J, 2013, J CHEMINFORMATICS, V5, DOI 10.1186/1758-2946-5-3
   Church KW, 2017, NAT LANG ENG, V23, P155, DOI 10.1017/S1351324916000334
   Colalto C, 2020, DRUG DEVELOP RES, V81, P950, DOI 10.1002/ddr.21716
   De Spiegeleer, 2019, REFERENCE MODULE BIO
   Di Maria E, 2020, HUM GENOMICS, V14, DOI 10.1186/s40246-020-00280-6
   Domingo-Fernandez D, 2021, BIOINFORMATICS, V37, P1332, DOI 10.1093/bioinformatics/btaa834
   Fabregat A, 2018, NUCLEIC ACIDS RES, V46, pD649, DOI [10.1093/nar/gkx1132, 10.1093/nar/gkz1031]
   Gaudet P, 2017, NUCLEIC ACIDS RES, V45, pD177, DOI 10.1093/nar/gkw1062
   Gaulton A, 2012, NUCLEIC ACIDS RES, V40, pD1100, DOI 10.1093/nar/gkr777
   Ge Y, 2020, BIORXIV, DOI [DOI 10.1101/2020.03.11.986836, 10.1101/2020.03.11.986836]
   Haque A, 2015, ANTIMICROB AGENTS CH, V59, P5892, DOI 10.1128/AAC.01105-15
   Hussein MIH, 2020, MALARIA J, V19, DOI 10.1186/s12936-020-03541-w
   Joshi AY, 2020, ALLERGY ASTHMA PROC, V41, P296, DOI 10.2500/aap.2020.41.200044
   Kim S, 2016, NUCLEIC ACIDS RES, V44, pD1202, DOI 10.1093/nar/gkv951
   Kuhn M, 2008, NUCLEIC ACIDS RES, V36, pD684, DOI 10.1093/nar/gkm795
   Kuhn M, 2016, NUCLEIC ACIDS RES, V44, pD1075, DOI 10.1093/nar/gkv1075
   Li CF, 2017, EBIOMEDICINE, V24, P189, DOI 10.1016/j.ebiom.2017.09.034
   Liu Y, 2021, CURR OPIN RHEUMATOL, V33, P155, DOI 10.1097/BOR.0000000000000776
   Lu Wang L., 2020, ARXIV200410706
   McDonald AG, 2009, NUCLEIC ACIDS RES, V37, pD593, DOI 10.1093/nar/gkn582
   Mu JF, 2020, CELL DISCOV, V6, DOI 10.1038/s41421-020-00208-3
   Mungall CJ, 2012, GENOME BIOL, V13, DOI 10.1186/gb-2012-13-1-r5
   Naili M, 2017, PROCEDIA COMPUT SCI, V112, P340, DOI 10.1016/j.procs.2017.08.009
   Pinero J, 2020, NUCLEIC ACIDS RES, V48, pD845, DOI 10.1093/nar/gkz1021
   Prasad K, 2020, INT J BIOL MACROMOL, V163, P1, DOI 10.1016/j.ijbiomac.2020.06.228
   Pyysalo S., 2013, P LBM, P39
   Richardson P, 2020, LANCET, V395, pE30, DOI 10.1016/S0140-6736(20)30304-4
   Sallinger, 2004, ARXIV200410119
   Siso-Almirall A, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0237960
   South AM, 2020, AM J PHYSIOL-HEART C, V318, pH1084, DOI 10.1152/ajpheart.00217.2020
   Sun H., 2018, P 2018 C EMP METH NA, P4231, DOI DOI 10.18653/V1/D18-1455
   Szklarczyk D, 2019, NUCLEIC ACIDS RES, V47, pD607, DOI 10.1093/nar/gky1131
   Tatonetti NP, 2012, SCI TRANSL MED, V4, DOI 10.1126/scitranslmed.3003377
   The University of New Mexico, TARG CENTR RES DAT
   Torgerson, 2016, PEDIAT ALLERGY PRINC, P124
   van Eck NJ, 2009, J AM SOC INF SCI TEC, V60, P1635, DOI 10.1002/asi.21075
   vor der Bruck Tim, 2019, P 2019 C N AM CHAPT, P1827, DOI [DOI 10.18653/V1/N19-1181, 10.18653/v1/n19-1181]
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wei CH, 2013, NUCLEIC ACIDS RES, V41, pW518, DOI 10.1093/nar/gkt441
   Wishart DS, 2006, NUCLEIC ACIDS RES, V34, pD668, DOI 10.1093/nar/gkj067
   World Health Organization, COR DIS COVID 19
   Wu J H, 2020, Zhonghua Bing Li Xue Za Zhi, V49, P568, DOI 10.3760/cma.j.cn112151-20200405-00291
   Xu J, 2020, SCI DATA, V7, DOI 10.1038/s41597-020-0543-2
   Yi Wei, 2020, Zhonghua Wei Zhong Bing Ji Jiu Yi Xue, V32, P279, DOI 10.3760/cma.j.cn121430-20200225-00200
   Zerbino DR, 2018, NUCLEIC ACIDS RES, V46, pD754, DOI 10.1093/nar/gkx1098
   Zisman LS, 2005, EUR HEART J, V26, P322, DOI 10.1093/eurheartj/ehi043
NR 55
TC 2
Z9 2
U1 10
U2 23
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-4425
J9 GENES-BASEL
JI Genes
PD JUL
PY 2021
VL 12
IS 7
AR 998
DI 10.3390/genes12070998
PG 17
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA TO2ZJ
UT WOS:000676786200001
PM 34209818
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Hosseini, H
   Bagheri, E
AF Hosseini, Hawre
   Bagheri, Ebrahim
TI Learning to rank implicit entities on Twitter
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Knowledge graph; Entity linking; DBpedia; Learn to rank
ID LINKING; TWEETS
AB Linking textual content to entities from the knowledge graph has received increasing attention in the context of which surface form representations of entities, e.g., terms or phrases, are disambiguated and linked to appropriate entities. This allows textual content, e.g., social usergenerated content, to be interpreted and reasoned on at a higher semantic level. However, recent research has shown that at least 15% of social user-generated content do not have explicit surface form representation of entities that they discuss. In other words, the subject of the content is only implied. For such cases, existing entity linking methods, known as explicit entity linking, cannot perform linking because entity surface form is missing. In this paper, we investigate how implicit entities within social content can be identified and linked. The contributions of our work include (1) modeling the problem of implicit entity linking as a learn to rank problem where knowledge graph entities are ranked based on their relevance to the input tweet, (2) the introduction and systematic classification of appropriate features for identifying implicit entities, (3) extensive evaluation of the proposed approach in comparison with existing state of the art as well as performing feature analysis over proposed features, and (4) the qualitative assessment of the root causes for mislabeled instances in our experiments and careful discussion on how mislabeled entity links can be addressed as a part of future work. In our experiments, we show that our proposed features are able to improve the state of the art over the standard Precision at 1 (P@1) metric.
C1 [Hosseini, Hawre; Bagheri, Ebrahim] Ryerson Univ, Lab Syst Software & Semant LS3, Toronto, ON, Canada.
RP Bagheri, E (通讯作者)，Ryerson Univ, Lab Syst Software & Semant LS3, Toronto, ON, Canada.
EM hawre.hosseini@ryerson.ca; bagheri@ryerson.ca
FU Natural Science and Engineering Research Council of Canada
   [RGPIN-2015-06118]
FX Funding from Natural Science and Engineering Research Council of Canada
   (RGPIN-2015-06118) is graciously acknowledged.
CR Anastacio I., 2011, TEXT AN C TAC TEXT AN C TAC
   Audeh B., 2014, P 29 ANN ACM S APPL, P929
   Bagheri E., 2020, INFORM PROCESS MANAG, V57
   Bagheri E, 2018, INFORM PROCESS MANAG, V54, P657, DOI 10.1016/j.ipm.2018.04.007
   Ceccarelli D, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P139, DOI 10.1145/2505515.2505711
   Charton E, 2014, LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P4609
   Cornolti M, 2013, P 22 INT C WORLD WID, P249, DOI DOI 10.1145/2488388.2488411
   Dalton J, 2014, SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P365, DOI 10.1145/2600428.2609628
   Derczynski L, 2015, INFORM PROCESS MANAG, V51, P32, DOI 10.1016/j.ipm.2014.10.006
   Vo DT, 2019, INFORM PROCESS MANAG, V56, P424, DOI 10.1016/j.ipm.2018.10.011
   Ensan F, 2019, INFORM PROCESS MANAG, V56, P1645, DOI 10.1016/j.ipm.2019.05.005
   Ensan F, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P181, DOI 10.1145/3018661.3018692
   Esquivel J, 2017, LECT NOTES COMPUT SC, V10193, P691, DOI 10.1007/978-3-319-56608-5_67
   Fang Y., 2014, T ACL, V2, P259, DOI DOI 10.1162/TACL_
   Feng Y, 2018, SOC NETW ANAL MIN, V8, DOI 10.1007/s13278-018-0523-0
   Ferragina P., 2010, P 19 ACM INT C INF K, P1625, DOI DOI 10.1145/1871437.1871689
   Guo S., 2013, HLT NAACL, V2013, P1020
   Habib M. B., 2012, P WORKSH SEM WEB INF, P1
   Hachey B, 2013, ARTIF INTELL, V194, P130, DOI 10.1016/j.artint.2012.04.005
   Hasibi F, 2017, LECT NOTES COMPUT SC, V10193, P40, DOI 10.1007/978-3-319-56608-5_4
   Honnibal M., 2015, P C EMP METH NAT LAN, P1373
   Hosseini H, 2019, APPL ONTOL, V14, P451, DOI 10.3233/AO-190215
   Hosseini H, 2018, 2018 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), P326, DOI 10.1109/ASONAM.2018.8508612
   Hua W, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1761, DOI 10.1145/2723372.2751522
   Huang HZ, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P380
   Huang L., 2020, P 43 INT ACM SIGIR C, P901
   Huang ZP, 2018, ACM T KNOWL DISCOV D, V12, DOI 10.1145/3233186
   Ibrahim Y., 2014, P 7 INT WORKSH EXPL, P17
   Leacock C, 1998, COMPUT LINGUIST, V24, P147
   Li Y., 2016, COLING
   Li Y, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P1261, DOI 10.1145/2872427.2883068
   Ling X., 2015, T ASS COMPUT LING, V3, P315, DOI DOI 10.1162/TACL_A_00141
   Liu X., 2013, ACL, P1304
   Masud MM, 2008, IEEE DATA MINING, P929, DOI 10.1109/ICDM.2008.152
   Meij E., 2012, P 5 ACM INT C WEB SE, P563, DOI DOI 10.1145/2124295.2124364
   Metzler D., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P472
   Nikolaev Fedor, 2020, Advances in Information Retrieval, 42nd European Conference on IR Research, ECIR 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12035), P141, DOI 10.1007/978-3-030-45439-5_10
   Perera S, 2016, LECT NOTES COMPUT SC, V9678, P118, DOI 10.1007/978-3-319-34129-3_8
   Ran CW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1135, DOI 10.1145/3178876.3186012
   Sarmento L, 2009, LECT NOTES ARTIF INT, V5632, P689, DOI 10.1007/978-3-642-03070-3_52
   Shen W, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P68
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Tran T., 2015, P 2015 C EMP METH NA P 2015 C EMP METH NA, P97
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   WU ZB, 1994, 32ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P133
   Xiong CY, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2391, DOI 10.1145/3132847.3133048
   Yu Y., 2014, INT J DATABASE THEOR, V7, P1
   Zhao G, 2016, INFORM PROCESS MANAG, V52, P1247, DOI 10.1016/j.ipm.2016.06.002
   Zhao WNX, 2011, LECT NOTES COMPUT SC, V6611, P338, DOI 10.1007/978-3-642-20161-5_34
   Zou X., 2014, NATURAL LANGUAGE PRO, P368
NR 50
TC 2
Z9 2
U1 5
U2 8
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD MAY
PY 2021
VL 58
IS 3
AR 102503
DI 10.1016/j.ipm.2021.102503
EA JAN 2021
PG 21
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA TY5IJ
UT WOS:000683817700004
DA 2022-08-23
ER

PT J
AU Sun, YT
   Jiang, LQ
   Wen, T
   Guo, XY
   Shao, XY
   Qu, H
   Chen, X
   Song, YJ
   Wang, F
   Qu, XJ
   Li, Z
AF Sun, Yiting
   Jiang, Liqing
   Wen, Ti
   Guo, Xiaoyu
   Shao, Xinye
   Qu, Hui
   Chen, Xi
   Song, Yujia
   Wang, Fang
   Qu, Xiujuan
   Li, Zhi
TI Trends in the Research Into Immune Checkpoint Blockade by Anti-PD1/PDL1
   Antibodies in Cancer Immunotherapy: A Bibliometric Study
SO FRONTIERS IN PHARMACOLOGY
LA English
DT Article
DE PD1; PDL1; immune checkpoint blockade; bibliometrics; randomized
   clinical trials; meta-analysis
ID CELL LUNG-CANCER; B7 FAMILY; PD-1; PEMBROLIZUMAB; MEMBER; B7-H1;
   COMBINATION; SURVIVAL; EFFICACY; RECEPTOR
AB The programmed death receptor 1 (PD1) and its ligand programmed death receptor ligand 1 (PDL1) are the most widely used immune checkpoints in cancer immunotherapy. The related literature shows the explosive growth trends due to the promising outcomes of tumor regression. The present study aimed to provide a comprehensive bibliometric analysis of the literature on anti-PD1/PDL1 from three perspectives including molecular mechanisms, randomized clinical trials (RCT), and meta-analysis, thus producing a knowledge map reflecting the status of the research, its historical evolution, and developmental trends in related research from 2000 to 2020. We included 11,971, 191, and 335 documents from the Web of Science Core Collection database, respectively, and adopted various bibliometric methods and techniques thereto. The study revealed the major research themes and emergent hotspots based on literature and citation data and outlined the top contributors in terms of journals and countries. The co-occurrence overlay of keywords and terms pertaining to the PD1/PDL1 molecule reflected the progress from the discovery of the PD1/PDL1 molecule to the clinical application of anti-PD1/PDL1. Immune-related adverse events (irAEs) formed a unique cluster in the term co-occurrence analysis of meta-analysis. The historical direct citation network of RCT indicated the development and transformation of cancers and therapy strategies. irAEs and the strategies of combination therapy might become a future focus of research in this cognate area. In summary, the bibliometric study provides a general overview of the landscape on anti-PD1/PDL1 research, allowing researchers to identify the potential opportunities and challenges therein.
C1 [Sun, Yiting; Jiang, Liqing; Wen, Ti; Guo, Xiaoyu; Shao, Xinye; Qu, Hui; Chen, Xi; Song, Yujia; Wang, Fang; Qu, Xiujuan; Li, Zhi] China Med Univ, Dept Med Oncol, Hosp 1, Shenyang, Peoples R China.
   [Sun, Yiting; Jiang, Liqing; Wen, Ti; Guo, Xiaoyu; Shao, Xinye; Qu, Hui; Chen, Xi; Song, Yujia; Wang, Fang; Qu, Xiujuan; Li, Zhi] China Med Univ, Key Lab Anticanc Drugs & Biotherapy Liaoning Prov, Hosp 1, Shenyang, Peoples R China.
   [Sun, Yiting; Jiang, Liqing; Wen, Ti; Guo, Xiaoyu; Shao, Xinye; Qu, Hui; Chen, Xi; Song, Yujia; Wang, Fang; Qu, Xiujuan; Li, Zhi] China Med Univ, Liaoning Prov Clin Res Ctr Canc, Hosp 1, Shenyang, Peoples R China.
   [Sun, Yiting; Jiang, Liqing; Wen, Ti; Guo, Xiaoyu; Shao, Xinye; Qu, Hui; Chen, Xi; Song, Yujia; Wang, Fang; Qu, Xiujuan; Li, Zhi] China Med Univ, Key Lab Precis Diag & Treatment Gastrointestinal, Hosp 1, Minist Educ, Shenyang, Peoples R China.
RP Qu, XJ; Li, Z (通讯作者)，China Med Univ, Dept Med Oncol, Hosp 1, Shenyang, Peoples R China.; Qu, XJ; Li, Z (通讯作者)，China Med Univ, Key Lab Anticanc Drugs & Biotherapy Liaoning Prov, Hosp 1, Shenyang, Peoples R China.; Qu, XJ; Li, Z (通讯作者)，China Med Univ, Liaoning Prov Clin Res Ctr Canc, Hosp 1, Shenyang, Peoples R China.; Qu, XJ; Li, Z (通讯作者)，China Med Univ, Key Lab Precis Diag & Treatment Gastrointestinal, Hosp 1, Minist Educ, Shenyang, Peoples R China.
EM xiujuanqu@yahoo.com; zli@cmu.edu.cn
RI Li, Zhi/ABE-5902-2022
OI Li, Zhi/0000-0002-6437-0348
FU Major science and technology project of "major new drug development"
   [2017ZX09304025]; National Natural Science Foundation of China
   [31770963]; General Projects of Liaoning Province Colleges and
   Universities [LFWK201706]; Beijing Xisike Clinical Oncology Research
   Foundation [Y-2019Roche-140]; 2017 Subject Enhancement Programme of
   China Medical University [3110117058]; Natural Science Foundation of
   Liaoning Province [20170540986]
FX This work was supported by the Major science and technology project of
   "major new drug development" (2017ZX09304025), National Natural Science
   Foundation of China (No. 31770963), The General Projects of Liaoning
   Province Colleges and Universities (LFWK201706), Beijing Xisike Clinical
   Oncology Research Foundation (Y-2019Roche-140) and 2017 Subject
   Enhancement Programme of China Medical University (3110117058) and
   Natural Science Foundation of Liaoning Province (No. 20170540986).
CR Ahn SK, 2020, J ONCOL, V2020, DOI 10.1155/2020/4708394
   Alsaab HO, 2017, FRONT PHARMACOL, V8, DOI 10.3389/fphar.2017.00561
   Amaria RN, 2018, NAT MED, V24, P1649, DOI 10.1038/s41591-018-0197-1
   Anderson AC, 2016, IMMUNITY, V44, P989, DOI 10.1016/j.immuni.2016.05.001
   Andrews LP, 2019, NAT IMMUNOL, V20, P1425, DOI 10.1038/s41590-019-0512-0
   Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007
   Ballot E, 2020, CANCERS, V12, DOI 10.3390/cancers12092418
   Bang YJ, 2019, FUTURE ONCOL, V15, P943, DOI 10.2217/fon-2018-0581
   Bas Y, 2019, CANCER INFORM, V18, DOI 10.1177/1176935119852620
   Bassez A, 2021, NAT MED, V27, P820, DOI 10.1038/s41591-021-01323-8
   Blank C, 2004, CANCER RES, V64, P1140, DOI 10.1158/0008-5472.CAN-03-3259
   Blank CU, 2018, NAT MED, V24, P1655, DOI 10.1038/s41591-018-0198-0
   Brahmer JR, 2018, J CLIN ONCOL, V36, P1714, DOI 10.1200/JCO.2017.77.6385
   Brummelman J, 2018, J EXP MED, V215, P2520, DOI 10.1084/jem.20180684
   Carter LL, 2002, EUR J IMMUNOL, V32, P634, DOI 10.1002/1521-4141(200203)32:3<634::AID-IMMU634>3.0.CO;2-9
   Chang LC, 2018, DIS MARKERS, V2018, DOI 10.1155/2018/5068701
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen DW, 2020, J IMMUNOTHER CANCER, V8, DOI 10.1136/jitc-2019-000492
   Conforti F, 2019, JNCI-J NATL CANCER I, V111, P772, DOI 10.1093/jnci/djz094
   Conforti F, 2018, LANCET ONCOL, V19, P737, DOI 10.1016/S1470-2045(18)30261-4
   Das S, 2020, ONCOLOGIST, V25, P669, DOI 10.1634/theoncologist.2019-0637
   de Erauso LC, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.00441
   Ding Y., 2016, MEASURING SCHOLARLY
   Dolladille C, 2020, JAMA ONCOL, V6, P865, DOI 10.1001/jamaoncol.2020.0726
   Dominguez CX, 2020, CANCER DISCOV, V10, P232, DOI 10.1158/2159-8290.CD-19-0644
   Dong HD, 1999, NAT MED, V5, P1365
   Dong HD, 2002, NAT MED, V8, P793, DOI 10.1038/nm730
   Dupont R, 2020, ONCOIMMUNOLOGY, V9, DOI 10.1080/2162402X.2019.1682383
   Freeman GJ, 2000, J EXP MED, V192, P1027, DOI 10.1084/jem.192.7.1027
   Fukuoka S, 2020, J CLIN ONCOL, V38, P2053, DOI 10.1200/JCO.19.03296
   Gainor JF, 2016, CLIN CANCER RES, V22, P4585, DOI 10.1158/1078-0432.CCR-15-3101
   Gao Y, 2019, INT IMMUNOPHARMACOL, V72, P374, DOI 10.1016/j.intimp.2019.03.045
   Gohil SH, 2021, NAT REV CLIN ONCOL, V18, P244, DOI 10.1038/s41571-020-00449-x
   Gray KD, 2020, CLIN CANCER RES, V26, P6003, DOI 10.1158/1078-0432.CCR-20-1523
   Griffiths JI, 2020, P NATL ACAD SCI USA, V117, P16072, DOI 10.1073/pnas.1918937117
   Guo YQ, 2020, J MED INTERNET RES, V22, DOI 10.2196/18228
   Hack SP, 2020, FRONT IMMUNOL, V11, DOI 10.3389/fimmu.2020.598877
   Haile ST, 2013, J IMMUNOL, V191, P2829, DOI 10.4049/jimmunol.1202777
   Hirano F, 2005, CANCER RES, V65, P1089
   ISHIDA Y, 1992, EMBO J, V11, P3887, DOI 10.1002/j.1460-2075.1992.tb05481.x
   Jafarzadeh L, 2021, CRIT REV ONCOL HEMAT, V157, DOI 10.1016/j.critrevonc.2020.103161
   Jing Ying, 2021, J Natl Cancer Inst, V113, P1396, DOI 10.1093/jnci/djab035
   Joseph A, 2020, ANN INTENSIVE CARE, V10, DOI 10.1186/s13613-020-00761-w
   Khalil DN, 2016, NAT REV CLIN ONCOL, V13, P273, DOI 10.1038/nrclinonc.2016.25
   Kieffer Y, 2020, CANCER DISCOV, V10, P1330, DOI 10.1158/2159-8290.CD-19-1384
   Klein SL, 2016, NAT REV IMMUNOL, V16, P626, DOI 10.1038/nri.2016.90
   Kwok G, 2016, HUM VACC IMMUNOTHER, V12, P2777, DOI 10.1080/21645515.2016.1199310
   Latchman Y, 2001, NAT IMMUNOL, V2, P261, DOI 10.1038/85330
   Lei QY, 2020, FRONT CELL DEV BIOL, V8, DOI 10.3389/fcell.2020.00672
   Lei YL, 2021, J HEMATOL ONCOL, V14, DOI 10.1186/s13045-021-01105-2
   Leydesdorff L, 2017, J INFORMETR, V11, P117, DOI 10.1016/j.joi.2016.11.007
   Li Y, 2021, CANCER MANAG RES, V13, P1383, DOI 10.2147/CMAR.S270099
   Lin M, 2020, J CLIN INVEST, V130, P2560, DOI 10.1172/JCI132712
   Lisberg A, 2018, J THORAC ONCOL, V13, P1138, DOI 10.1016/j.jtho.2018.03.035
   Liu ZD, 2020, CELL MOL IMMUNOL, V17, P13, DOI 10.1038/s41423-019-0341-y
   Lowe JR, 2016, J IMMUNOTHER CANCER, V4, DOI 10.1186/s40425-016-0196-z
   Luoma AM, 2020, CELL, V182, P655, DOI 10.1016/j.cell.2020.06.001
   Lurienne L, 2020, J THORAC ONCOL, V15, P1147, DOI 10.1016/j.jtho.2020.03.002
   Maillet D, 2020, EUR J CANCER, V132, P61, DOI 10.1016/j.ejca.2020.03.017
   Martins F, 2019, NAT REV CLIN ONCOL, V16, P563, DOI 10.1038/s41571-019-0218-0
   Marur S, 2018, SEMIN ONCOL, V45, P220, DOI 10.1053/j.seminoncol.2018.08.007
   McElroy T, 2020, ANTIOXIDANTS-BASEL, V9, DOI 10.3390/antiox9050439
   McGrail DJ, 2021, ANN ONCOL, V32, P661, DOI 10.1016/j.annonc.2021.02.006
   Mellman I, 2011, NATURE, V480, P480, DOI 10.1038/nature10673
   Merigo JM, 2019, TOURISM GEOGR, V21, P881, DOI 10.1080/14616688.2019.1666913
   Motzer RJ, 2019, NEW ENGL J MED, V380, P1103, DOI 10.1056/NEJMoa1816047
   Mulet-Forteza C, 2019, J BUS RES, V101, P819, DOI 10.1016/j.jbusres.2018.12.002
   Mulet-Forteza C, 2019, INT J CONTEMP HOSP M, V31, P4574, DOI 10.1108/IJCHM-10-2018-0828
   Nosaki K, 2019, LUNG CANCER, V135, P188, DOI 10.1016/j.lungcan.2019.07.004
   Osipov A, 2019, ADV CANCER RES, V143, P63, DOI 10.1016/bs.acr.2019.03.002
   Pardoll DM, 2012, NAT REV CANCER, V12, P252, DOI 10.1038/nrc3239
   Penter L, 2019, ONCOIMMUNOLOGY, V8, DOI 10.1080/2162402X.2019.1586409
   Pinato DJ, 2019, JAMA ONCOL, V5, P1774, DOI 10.1001/jamaoncol.2019.2785
   Rozeman EA, 2021, NAT MED, V27, P256, DOI 10.1038/s41591-020-01211-7
   Sinkovics N, 2016, INT MARKET REV, V33, P327, DOI 10.1108/IMR-10-2014-0341
   Spigel DR, 2018, J THORAC ONCOL, V13, P682, DOI 10.1016/j.jtho.2018.02.022
   Su HN, 2010, SCIENTOMETRICS, V85, P65, DOI 10.1007/s11192-010-0259-8
   Sullivan MR, 2020, CELL DEATH DIS, V11, DOI 10.1038/s41419-020-03173-7
   Sun JY, 2020, BIOMARK RES, V8, DOI 10.1186/s40364-020-00212-5
   Svensson G, 2010, EUR J MARKETING, V44, P23, DOI 10.1108/03090561011008583
   Teng FF, 2018, CANCER LETT, V414, P166, DOI 10.1016/j.canlet.2017.11.014
   Tseng SY, 2001, J EXP MED, V193, P839, DOI 10.1084/jem.193.7.839
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   vom Steeg LG, 2016, PLOS PATHOG, V12, DOI 10.1371/journal.ppat.1005374
   Wallis CJD, 2019, JAMA ONCOL, V5, P529, DOI 10.1001/jamaoncol.2018.5904
   Wang S, 2021, FRONT IMMUNOL, V12, DOI 10.3389/fimmu.2021.669539
   Wang YX, 2020, FRONT IMMUNOL, V11, DOI 10.3389/fimmu.2020.01187
   Wilson BE, 2020, CANCER IMMUNOL IMMUN, V69, P343, DOI 10.1007/s00262-019-02453-2
   Yan XY, 2018, FRONT PHARMACOL, V9, DOI 10.3389/fphar.2018.01050
   Yang JCH, 2019, J THORAC ONCOL, V14, P553, DOI 10.1016/j.jtho.2018.11.028
   Yang MX, 2020, INT IMMUNOPHARMACOL, V88, DOI 10.1016/j.intimp.2020.106876
   Ye YQ, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-15679-x
   Yi M, 2018, MOL CANCER, V17, DOI 10.1186/s12943-018-0864-3
   Zhao PF, 2019, J HEMATOL ONCOL, V12, DOI 10.1186/s13045-019-0738-1
   Zhao XQ, 2018, MEDICINE, V97, DOI 10.1097/MD.0000000000010276
   Zuazo M, 2017, ANN TRANSL MED, V5, DOI 10.21037/atm.2017.06.11
NR 96
TC 2
Z9 2
U1 20
U2 40
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1663-9812
J9 FRONT PHARMACOL
JI Front. Pharmacol.
PD AUG 17
PY 2021
VL 12
DI 10.3389/fphar.2021.670900
PG 18
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA UN2DR
UT WOS:000693831000001
PM 34489691
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wang, BJ
   Zhang, Q
   Cui, FQ
AF Wang, Bojie
   Zhang, Qin
   Cui, Fengqi
TI Scientific research on ecosystem services and human well-being: A
   bibliometric analysis
SO ECOLOGICAL INDICATORS
LA English
DT Article
DE Ecosystem services; Human well-being; Bibliometric analysis; CiteSpace;
   Knowledge mapping
ID POVERTY ALLEVIATION; CLIMATE-CHANGE; TRENDS; BIODIVERSITY; MANAGEMENT;
   SCIENCE; AGRICULTURE; TRADEOFFS; XINJIANG; AREAS
AB Ecosystem services and human well-being have received increasing scientific interest owing to their importance in influencing sustainability. Yet, quantitative and visual analyses of the scientific literature on ecosystem services and human well-being remain scarce. Here, we explored the scientific literature on ecosystem services and human well-being at a global scale to investigate the temporal development, scientific collaboration, research hotspots and emerging trends over the last 27 years (1992?2018) using CiteSpace. The number of publications has substantially increased since the Millennium Ecosystem Assessment (MA). The majority of publications (79.86%) were occurred during the period of 2013?2018, with Ecosystem Services being the dominant journal and most research originating from the USA, England and Australia. And the Chinese Academy of Sciences was the most productive institution. The top-ranking category was environmental sciences. Much more work is needed to strengthen the cohesion among environmental, economic and social science in this field. Three key research themes prevailed: biodiversity, conservation, and management. The hotspots of early studies focused on the descriptive and quantitative analysis of ecosystem services, including ?contingent value?, ?model? and ?dynamics?. The emerging active topics were?poverty alleviation?, ?cultural ecosystem service?, ?perception? and?green infrastructure?. Integrative research on socio-cultural and biophysical dimensions is expected to be used to increasingly support policymaking in the future. The assessment of subjective well-being, the relations between supply-demand and human well-being and sustainable management represent challenging areas for future research on ecosystem services and human well-being.
C1 [Wang, Bojie] Inner Mongolia Univ, Sch Ecol & Environm, Hohhot 010021, Peoples R China.
   [Zhang, Qin] Beijing Int Studies Univ, Belt & Rd Initiat, China Acad, Beijing 100024, Peoples R China.
   [Cui, Fengqi] Univ Ghent, Dept Geog, B-9000 Ghent, Belgium.
RP Wang, BJ (通讯作者)，Inner Mongolia Univ, Sch Ecol & Environm, Hohhot 010021, Peoples R China.
EM wbj8383@163.com
FU Natural science foundation of Inner Mongolia project [2019BS04008];
   Applied technology research and development project of Inner Mongolia
   [2019GG012]; National Natural Science Foundation of China [32060316]
FX The authors would like to acknowledge the Natural science foundation of
   Inner Mongolia project (No. 2019BS04008), the Applied technology
   research and development project of Inner Mongolia (No. 2019GG012) and
   the National Natural Science Foundation of China (No. 32060316) for
   support. We also wish to thank the anonymous reviewers for valuable
   comments on the manuscript.
CR Aleixandre-Benavent R, 2017, J CLEAN PROD, V147, P406, DOI 10.1016/j.jclepro.2017.01.112
   Aznar-Sanchez JA, 2019, ECOL INDIC, V99, P310, DOI 10.1016/j.ecolind.2018.12.045
   Aznar-Sanchez JA, 2018, FORESTS, V9, DOI 10.3390/f9080453
   Balvanera P, 2006, ECOL LETT, V9, P1146, DOI 10.1111/j.1461-0248.2006.00963.x
   Bennett EM, 2015, CURR OPIN ENV SUST, V14, P76, DOI 10.1016/j.cosust.2015.03.007
   Bieling C, 2014, ECOL ECON, V105, P19, DOI 10.1016/j.ecolecon.2014.05.013
   Carpenter SR, 2009, P NATL ACAD SCI USA, V106, P1305, DOI 10.1073/pnas.0808772106
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Costanza R, 1998, ECOL ECON, V25, P3, DOI 10.1016/S0921-8009(98)00020-2
   Costanza R, 2017, ECOSYST SERV, V28, P1, DOI 10.1016/j.ecoser.2017.09.008
   Daniel TC, 2012, P NATL ACAD SCI USA, V109, P8812, DOI 10.1073/pnas.1114773109
   Daw T, 2011, ENVIRON CONSERV, V38, P370, DOI 10.1017/S0376892911000506
   de Groot RS, 2010, ECOL COMPLEX, V7, P260, DOI 10.1016/j.ecocom.2009.10.006
   Diaz S, 2015, CURR OPIN ENV SUST, V14, P1, DOI 10.1016/j.cosust.2014.11.002
   Elwell TL, 2018, SCI TOTAL ENVIRON, V637, P1014, DOI 10.1016/j.scitotenv.2018.04.052
   Fahimnia B, 2015, INT J PROD ECON, V162, P101, DOI 10.1016/j.ijpe.2015.01.003
   Fang XN, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10051478
   Fisher B, 2009, ECOL ECON, V68, P643, DOI 10.1016/j.ecolecon.2008.09.014
   Fisher JA, 2013, GLOBAL ENVIRON CHANG, V23, P1098, DOI 10.1016/j.gloenvcha.2013.04.002
   Fu BJ, 2013, CURR OPIN ENV SUST, V5, P4, DOI 10.1016/j.cosust.2012.12.002
   Gladkikh TM, 2019, ECOSYST SERV, V40, DOI 10.1016/j.ecoser.2019.101036
   Hausmann A, 2016, ENVIRON CONSERV, V43, P117, DOI 10.1017/S0376892915000314
   Huang L, 2020, J CLEAN PROD, V252, DOI 10.1016/j.jclepro.2019.119908
   Isbell F, 2011, NATURE, V477, P199, DOI 10.1038/nature10282
   Laurans Y, 2013, J ENVIRON MANAGE, V119, P208, DOI 10.1016/j.jenvman.2013.01.008
   Liu SA, 2010, ECOL ECON, V69, P1387, DOI 10.1016/j.ecolecon.2010.03.010
   Liu WJ, 2019, ECOL ECON, V156, P293, DOI 10.1016/j.ecolecon.2018.09.001
   Mauser W, 2013, CURR OPIN ENV SUST, V5, P420, DOI 10.1016/j.cosust.2013.07.001
   MEA (Millennium Ecosystem Assessment), 2005, PHYS TEACH
   Milcu AI, 2013, ECOL SOC, V18, DOI 10.5751/ES-05790-180344
   Mongeon P, 2016, SCIENTOMETRICS, V106, P213, DOI 10.1007/s11192-015-1765-5
   Murayama Y., 2019, PLOS ONE, V14
   Ouyang W, 2018, SCI TOTAL ENVIRON, V637, P208, DOI 10.1016/j.scitotenv.2018.04.434
   Pauna VH, 2018, ECOL QUEST, V29, P53, DOI 10.12775/EQ.2018.022
   Pires APF, 2018, ECOSYST SERV, V34, P68, DOI 10.1016/j.ecoser.2018.10.001
   Power AG, 2010, PHILOS T R SOC B, V365, P2959, DOI 10.1098/rstb.2010.0143
   Raudsepp-Hearne C, 2010, P NATL ACAD SCI USA, V107, P5242, DOI 10.1073/pnas.0907284107
   Saier MH, 2007, WATER AIR SOIL POLL, V181, P1, DOI 10.1007/s11270-007-9372-6
   Summers JK, 2014, AMBIO, V43, P718, DOI 10.1007/s13280-013-0483-6
   Tancoigne E, 2014, ECOSYST SERV, V10, P35, DOI 10.1016/j.ecoser.2014.07.004
   Vihervaara P, 2010, AMBIO, V39, P314, DOI 10.1007/s13280-010-0048-x
   Wang BJ, 2017, ECOSYST SERV, V27, P58, DOI 10.1016/j.ecoser.2017.07.018
   Wang XC, 2017, ECOSYST SERV, V27, P113, DOI 10.1016/j.ecoser.2017.08.013
   Wei HJ, 2018, ECOSYST SERV, V31, P44, DOI 10.1016/j.ecoser.2018.03.012
   Wu JG, 2013, LANDSCAPE ECOL, V28, P999, DOI 10.1007/s10980-013-9894-9
   Xu WH, 2017, P NATL ACAD SCI USA, V114, P1601, DOI 10.1073/pnas.1620503114
   Yan Y., 2017, SUSTAINABILITY-BASEL, V9, P12
   Yang Y, 2019, ECOL INDIC, V102, P650, DOI 10.1016/j.ecolind.2019.03.031
   Yu HJ, 2019, J CLEAN PROD, V219, P833, DOI 10.1016/j.jclepro.2019.02.014
   Zhang HJ, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16193602
   Zheng CJ, 2020, J CLEAN PROD, V258, DOI 10.1016/j.jclepro.2020.120689
NR 51
TC 24
Z9 24
U1 58
U2 156
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1470-160X
EI 1872-7034
J9 ECOL INDIC
JI Ecol. Indic.
PD JUN
PY 2021
VL 125
AR 107449
DI 10.1016/j.ecolind.2021.107449
EA FEB 2021
PG 9
WC Biodiversity Conservation; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biodiversity & Conservation; Environmental Sciences & Ecology
GA RI3XX
UT WOS:000636844900003
OA gold
DA 2022-08-23
ER

PT J
AU Liang, LM
   Zhong, Z
   Chen, Y
AF Liang, Liming
   Zhong, Zhen
   Chen, Yue
TI A Chinese professor's academic career rhythm
SO SCIENTOMETRICS
LA English
DT Article
DE Citation rhythm indicator; Compound rhythm indicator; Scientist's
   academic career rhythm; Professor Zeyuan Liu; Whole counting; Fractional
   counting
ID SCIENCE
AB The famous Chinese scientist Professor Zeyuan Liu passed away on February 8, 2020, aged 80. He was a productive and influential scientist, both in quantitative and in qualitative studies. To cherish the memory of Professor Liu and in praise of his scientific thoughts and scientific spirit, we explored his academic career rhythm based on our rhythm indicator of science. The rhythm indicator is created based on a publication-citation matrix, eliminating the impact of length of citation windows, making citations comparable for different years. In Liu's rhythm study the publication indicator and citation indicator are combined. Data were retrieved from China National Knowledge Infrastructure and WoS. The most important finding is that Liu's academic career rhythm is totally different from that of two famous western scientists T. Braun and R. Rousseau. Because of the delayed scientific research by China's Cultural Revolution, it was only in 1979 that Liu published his first scientific paper and launched his academic career. In the first 20 years he published 41 papers, mostly single-authored. Their research topics were scattered and mainly used qualitative methods. In 1999 Liu changed his research paradigm from qualitative to quantitative studies, focusing on scientometrics, citation analysis and knowledge mapping. New topics, new methods and multi-authored collaboration greatly increased his productivity (293 articles) and influence. The above exploration is based on both whole counting of publications/citations and fractional counting. Three problems, including the normalization of the p-c matrix, are discussed. The rhythm indicator of science is a contribution to the evaluation methodology for scientist-level and journal-level scientific impact.
C1 [Liang, Liming] Henan Normal Univ, Inst Sci Technol & Soc, Xinxiang, Henan, Peoples R China.
   [Zhong, Zhen] Henan Univ Technol, Sch Management, Zhengzhou, Peoples R China.
   [Chen, Yue] Dalian Univ Technol, WISE Lab, Dalian, Peoples R China.
RP Liang, LM (通讯作者)，Henan Normal Univ, Inst Sci Technol & Soc, Xinxiang, Henan, Peoples R China.
EM liangliming1949@sina.com; zhongzhen@haut.edu.cn; chenyuedlut@163.com
FU National Natural Science Foundation of China; NSFC [71603073]
FX This work is sponsored by the National Natural Science Foundation of
   China. NSFC project 71603073. We greatly appreciate Professor Ronald
   Rousseau for his valuable suggestions and linguistic help.
CR Chen Y., 2015, STUDIES SCI SCI, V33, P242
   Egghe L, 2008, J AM SOC INF SCI TEC, V59, P1469, DOI 10.1002/asi.20844
   Hu X., 2020, ISSI NEWSLETT, V16, P32
   Jin BH, 2004, J AM SOC INF SCI TEC, V55, P544, DOI 10.1002/asi.20010
   Leydesdorff L, 1992, SCIENTOMETRICS, V60, P19
   Leydesdorff L, 2002, GLOBALIZATION AUTHOR
   Liang LM, 2006, SCIENTOMETRICS, V68, P535, DOI 10.1007/s11192-006-0128-7
   Liang LM, 2005, J AM SOC INF SCI TEC, V56, P1045, DOI 10.1002/asi.20196
   Liang LM, 2010, J CHINESE SOC SCI TE, V29, P145
   Liming LM, 2015, RONALD ROUSSEAU DEV, P260
   Liu ZY, 1981, SCI RES MANAGEMENT, V2, P9
   Persson O., 2007, SPECIAL VOLUME ZINE, P35
   Rousseau R., 2007, SPECIAL VOLUME ZINE, P23
   van Raan T, 2002, INHONOUR TIBOR BRAUN
   Wang GS, 2007, PROCEEDINGS OF THE 26TH CHINESE CONTROL CONFERENCE, VOL 2, P5
NR 15
TC 0
Z9 0
U1 2
U2 50
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUL
PY 2021
VL 126
IS 7
SI SI
BP 6169
EP 6186
DI 10.1007/s11192-020-03767-0
EA NOV 2020
PG 18
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA SY3VS
UT WOS:000591546700002
DA 2022-08-23
ER

PT J
AU Chinazzi, M
   Goncalves, B
   Zhang, Q
   Vespignani, A
AF Chinazzi, Matteo
   Goncalves, Bruno
   Zhang, Qian
   Vespignani, Alessandro
TI Mapping the physics research space: a machine learning approach
SO EPJ DATA SCIENCE
LA English
DT Article
DE Embeddings; Research space; Principle of relatedness; Fingerprinting
   scientific production; Revealed comparative advantage; Predicting
   research specialization
ID COMPARATIVE ADVANTAGE; TEAM SCIENCE; KNOWLEDGE; IMPACT; COLLABORATION;
   RELATEDNESS; TECHNOLOGY; TIME; INTERDISCIPLINARITY; PERSISTENCE
AB Scientific discoveries do not occur in vacuum but rather by connecting existing pieces of knowledge in new and creative ways. Mapping the relation and structure of scientific knowledge is therefore central to our understanding of the dynamics of scientific production. Here we introduce a new approach to generate scientific knowledge maps based on a machine learning approach that, starting from the observed publication patterns of authors, generates an N-dimensional space where it is possible to measure the similarity or distance between different research topics and knowledge domains. We provide an implementation of the proposed approach that considers the American Physical Society publications database and generates a map of the research space in Physics that characterizes the relation among research topics over time. We use this map to measure two indicators, the research capacity fingerprint and the knowledge density, to profile the research activity in physical sciences of more than 400 urban areas across the world. We show that these indicators can be used to analyze and predict the evolution over time of the research capacity and specialization of specific geographical areas. Furthermore we provide an extensive analysis of the relation between socio-economic development indicators and the ability to produce new knowledge for 67 countries, as measured by our approach, highlighting some key correlates of scientific production capacity. The proposed approach is scalable to very large datasets and can be extended to study other disciplines and research areas without having to rely on ad-hoc science classification schemes.
C1 [Chinazzi, Matteo; Zhang, Qian; Vespignani, Alessandro] Northeastern Univ, MOBS Lab, Boston, MA 02115 USA.
   [Goncalves, Bruno] Inst Sci Interchange Fdn, Turin, Italy.
RP Chinazzi, M (通讯作者)，Northeastern Univ, MOBS Lab, Boston, MA 02115 USA.
EM m.chinazzi@northeastern.edu
OI Vespignani, Alessandro/0000-0003-3419-4205; Chinazzi,
   Matteo/0000-0002-5955-1929
FU Air Force Office of Scientific Research [FA9550-15-1-0077]
FX This work has been partially supported by Air Force Office of Scientific
   Research grant FA9550-15-1-0077. We acknowledge the American Physical
   Society for providing access to the publication data.
CR Abramo G, 2019, SCIENTOMETRICS, V118, P215, DOI 10.1007/s11192-018-2970-9
   Amighini A, 2011, REG STUD, V45, P381, DOI 10.1080/00343401003604655
   Amiti M, 1999, WELTWIRTSCH ARCH, V135, P573, DOI 10.1007/BF02707385
   AQUINO A, 1981, EUR ECON REV, V15, P41, DOI 10.1016/0014-2921(81)90066-0
   Arrieta OAD, 2017, SCI ADV, V3, DOI 10.1126/sciadv.1602232
   Bahar D, 2014, J INT ECON, V92, P111, DOI 10.1016/j.jinteco.2013.11.001
   BALASSA B, 1965, MANCH SCH ECON SOC, V33, P99, DOI 10.1111/j.1467-9957.1965.tb00050.x
   Battiston F, 2019, NAT REV PHYS, V1, P89, DOI 10.1038/s42254-018-0005-3
   Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223
   Bettencourt LMA, 2009, J INFORMETR, V3, P210, DOI 10.1016/j.joi.2009.03.001
   Bojanowski P., 2017, T ASSOC COMPUT LING, V5, P135, DOI [DOI 10.1162/TACL_A_00051, 10.1162/tacl_a_00051]
   Bollen J, 2009, PLOS ONE, V4, DOI [10.1371/journal.pone.0004803, 10.1371/journal.pone.0006022]
   Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106
   Boschma R, 2010, HANDBOOK OF EVOLUTIONARY ECONOMIC GEOGRAPHY, P1
   Boschma R., 2012, TERRITORY DYNAMIC GE, P64
   Boschma RA, 2005, REG STUD, V39, P61, DOI 10.1080/0034340052000320887
   Boschma R, 2015, IND CORP CHANGE, V24, P223, DOI 10.1093/icc/dtu012
   Boschma R, 2014, RES POLICY, V43, P107, DOI 10.1016/j.respol.2013.07.009
   Boschma R, 2013, ECON GEOGR, V89, P29, DOI 10.1111/j.1944-8287.2012.01170.x
   Boyack KW, 2005, SCIENTOMETRICS, V64, P351, DOI 10.1007/s11192-005-0255-6
   Bromham L, 2016, NATURE, V534, P684, DOI 10.1038/nature18315
   Bu Y, 2018, SCIENTOMETRICS, V114, P463, DOI 10.1007/s11192-017-2599-0
   CANTWELL J, 1995, CAMBRIDGE J ECON, V19, P155
   Cheng Q, 2015, SCIENTOMETRICS, V103, P453, DOI 10.1007/s11192-015-1559-9
   Clauset A, 2017, SCIENCE, V355, P477, DOI 10.1126/science.aal4217
   COHEN WM, 1990, ADMIN SCI QUART, V35, P128, DOI 10.2307/2393553
   Conneau A, 2017, ARXIV171004087
   Corrado G. S, 2013, NIPS 13, V3111, P3119
   CRAFTS NFR, 1986, ECON J, V96, P629, DOI 10.2307/2232982
   Cristelli M, 2017, WPS8117
   Czaika M, 2018, APPL GEOGR, V96, P1, DOI 10.1016/j.apgeog.2018.04.017
   D'Agostino LM, 2013, J ECON GEOGR, V13, P145, DOI 10.1093/jeg/lbs012
   De Benedictis L, 2008, APPL ECON LETT, V15, P301, DOI 10.1080/13504850500428299
   Deville P, 2014, SCI REP-UK, V4, DOI 10.1038/srep04770
   Duchi J, 2011, J MACH LEARN RES, V12, P2121
   Effendy S, 2017, WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P1245, DOI 10.1145/3041021.3053064
   Essletzbichler J, 2015, REG STUD, V49, P752, DOI 10.1080/00343404.2013.806793
   Fortunato S, 2018, SCIENCE, V359, DOI 10.1126/science.aao0185
   Foster JG, 2015, AM SOCIOL REV, V80, P875, DOI 10.1177/0003122415601618
   Franzoni C, 2014, ECON LETT, V122, P89, DOI 10.1016/j.econlet.2013.10.040
   Freund C, 2015, REV ECON STAT, V97, P1023, DOI 10.1162/REST_a_00511
   Frome Andrea, 2013, ADV NEURAL INFORM PR, P2121
   Guevara MR, 2016, SCIENTOMETRICS, V109, P1695, DOI 10.1007/s11192-016-2125-9
   Hall KL, 2018, AM PSYCHOL, V73, P532, DOI 10.1037/amp0000319
   Hamilton W.L., 2016, ARXIV160509096
   Hidalgo CA, 2007, SCIENCE, V317, P482, DOI 10.1126/science.1144581
   Hidalgo C.A., 2018, P INT C COMPL SYST J, P451
   Hoonlor A, 2013, COMMUN ACM, V56, P74, DOI 10.1145/2500892
   Huang E. H., 2012, P 50 ANN M ASS COMP, V1
   Iapadre L. P, 2001, INT ADV ECON RES, V7, P173, DOI DOI 10.1007/BF02296007
   James C, 2018, ARXIV180204830
   Jia T, 2017, NAT HUM BEHAV, V1, DOI 10.1038/s41562-017-0078
   Kogler DF, 2013, EUR PLAN STUD, V21, P1374, DOI 10.1080/09654313.2012.755832
   Lample G., 2017, ARXIV171100043
   Lariviere V, 2015, J ASSOC INF SCI TECH, V66, P1323, DOI 10.1002/asi.23266
   Leydesdorff L, 2019, J INFORMETR, V13, P255, DOI 10.1016/j.joi.2018.12.006
   Leydesdorff L, 2011, J INFORMETR, V5, P87, DOI 10.1016/j.joi.2010.09.002
   Leydesdorff L, 2009, J AM SOC INF SCI TEC, V60, P348, DOI 10.1002/asi.20967
   Liegsalz J, 2013, RES POLICY, V42, P552, DOI 10.1016/j.respol.2012.06.003
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Milojevic S, 2014, P NATL ACAD SCI USA, V111, P3984, DOI 10.1073/pnas.1309723111
   Neffke F, 2011, ECON GEOGR, V87, P237, DOI 10.1111/j.1944-8287.2011.01121.x
   OECD, 2011, GLOB COMP ADV CHANG
   Pan RK, 2018, J INFORMETR, V12, P656, DOI 10.1016/j.joi.2018.06.005
   Parolo PD, 2015, J INFORMETR, V9, P734, DOI 10.1016/j.joi.2015.07.006
   Penner O, 2013, SCI REP-UK, V3, DOI 10.1038/srep03052
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Petersen AM, 2012, P NATL ACAD SCI USA, V109, P5213, DOI 10.1073/pnas.1121429109
   Petersen AM, 2015, P NATL ACAD SCI USA, V112, pE4671, DOI 10.1073/pnas.1501444112
   Petersen AM, 2014, P NATL ACAD SCI USA, V111, P15316, DOI 10.1073/pnas.1323111111
   Radicchi F, 2009, PHYS REV E, V80, DOI 10.1103/PhysRevE.80.056103
   Rafols I, 2010, J AM SOC INF SCI TEC, V61, P1871, DOI 10.1002/asi.21368
   Rigby DL, 2015, REG STUD, V49, P1922, DOI 10.1080/00343404.2013.854878
   SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220
   Scellato G, 2015, RES POLICY, V44, P108, DOI 10.1016/j.respol.2014.07.014
   Sinatra R, 2016, SCIENCE, V354, DOI 10.1126/science.aaf5239
   Sinatra R, 2015, NAT PHYS, V11, P791, DOI 10.1038/nphys3494
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   SOETE LG, 1983, SCIENTOMETRICS, V5, P31, DOI 10.1007/BF02097176
   Szymanski T, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2, P448, DOI 10.18653/v1/P17-2071
   Tacchella A, 2012, SCI REP-UK, V2, DOI 10.1038/srep00723
   The World Bank, 2019, EC GROWTH
   Vaccario G, 2018, ARXIV181107229
   VANHULST N, 1991, WELTWIRTSCH ARCH, V127, P246
   Veugelers R, 2019, RES POLICY, V48, P1362, DOI 10.1016/j.respol.2019.01.019
   Wagner CS, 2019, RES POLICY, V48, P1260, DOI 10.1016/j.respol.2019.01.002
   Wagner CS, 2011, J INFORMETR, V5, P14, DOI 10.1016/j.joi.2010.06.004
   Wang DS, 2013, SCIENCE, V342, P127, DOI 10.1126/science.1237825
   Wu L., 2017, ARXIV170903856
   Wuchty S, 2007, SCIENCE, V316, P1036, DOI 10.1126/science.1136099
   Yin Y, 2017, J INFORMETR, V11, P608, DOI 10.1016/j.joi.2017.04.002
   Yu S, 2019, COMPUT SCI REV, V31, P72, DOI 10.1016/j.cosrev.2018.12.001
   Zeng A, 2017, PHYS REP, V714, P1, DOI 10.1016/j.physrep.2017.10.001
   Zeng XHT, 2016, PLOS BIOL, V14, DOI 10.1371/journal.pbio.1002573
   Zhang Q, 2013, SCI REP-UK, V3, DOI 10.1038/srep01640
NR 95
TC 11
Z9 11
U1 3
U2 10
PU SPRINGEROPEN
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
EI 2193-1127
J9 EPJ DATA SCI
JI EPJ Data Sci.
PD DEC
PY 2019
VL 8
IS 1
AR 33
DI 10.1140/epjds/s13688-019-0210-z
PG 18
WC Mathematics, Interdisciplinary Applications; Social Sciences,
   Mathematical Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematics; Mathematical Methods In Social Sciences
GA JK4NJ
UT WOS:000494820000001
OA gold
DA 2022-08-23
ER

PT J
AU Fang, H
   Chen, CC
   Long, YF
   Xu, G
   Xiao, YQ
AF Fang, Hui
   Chen, Chongcheng
   Long, Yunfei
   Xu, Ge
   Xiao, Yongqiang
TI DTCRSKG: A Deep Travel Conversational Recommender System Incorporating
   Knowledge Graph
SO MATHEMATICS
LA English
DT Article
DE tourism domain; conversational recommender; knowledge graph; BERT-based
AB In the era of information explosion, it is difficult for people to obtain their desired information effectively. In tourism, a travel recommender system based on big travel data has been developing rapidly over the last decade. However, most work focuses on click logs, visit history, or ratings, and dynamic prediction is absent. As a result, there are significant gaps in both dataset and recommender models. To address these gaps, in the first step of this study, we constructed two human-annotated datasets for the travel conversational recommender system. We provided two linked data sets, namely, interaction sequence and dialogue data sets. The usage of the former data set was done to fully explore the static preference characteristics of users based on it, while the latter identified the dynamics changes in user preference from it. Then, we proposed and evaluated BERT-based baseline models for the travel conversational recommender system and compared them with several representative non-conversational and conversational recommender system models. Extensive experiments demonstrated the effectiveness and robustness of our approach regarding conversational recommendation tasks. Our work can extend the scope of the travel conversational recommender system and our annotated data can also facilitate related research.
C1 [Fang, Hui; Chen, Chongcheng] Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing MOE, Fuzhou 350108, Peoples R China.
   [Fang, Hui; Chen, Chongcheng] Fuzhou Univ, Acad Digital China Fujian, Fuzhou 350108, Peoples R China.
   [Fang, Hui; Xu, Ge; Xiao, Yongqiang] Minjiang Univ, Coll Comp & Control Engn, Fujian Prov Key Lab Informat Proc & Intelligent C, Fuzhou 350108, Peoples R China.
   [Long, Yunfei] Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England.
RP Chen, CC (通讯作者)，Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing MOE, Fuzhou 350108, Peoples R China.; Chen, CC (通讯作者)，Fuzhou Univ, Acad Digital China Fujian, Fuzhou 350108, Peoples R China.
EM fanghui_ce@mju.edu.cn; chencc@fzu.edu.cn; yl20051@essex.ac.uk;
   xuge@pku.edu.cn; xyqhappy12@sina.com
FU National Key Research and Development Program of China [2017YFB0504202];
   Leading Talents of Scientific and Technological Innovation in Fujian
   Province; Central Leading Local Project "Fujian Mental Health
   Human-Computer Interaction Technology Research Center" [2020L3024]; Big
   Data Analysis System National Engineering Laboratory Open Project
   "Emotion Based Intelligent Question Answering System" [CASNDST202006];
   Fujian Provincial Department of Science and Technology Leading Science
   and Technology Project "Personalized Intelligent Question Answering
   System" [2019H0026]
FX This research was funded by the National Key Research and Development
   Program of China under grant no. 2017YFB0504202 and the Leading Talents
   of Scientific and Technological Innovation in Fujian Province. This
   study was also jointly supported by the following projects: the Central
   Leading Local Project "Fujian Mental Health Human-Computer Interaction
   Technology Research Center" (project no. 2020L3024), the Big Data
   Analysis System National Engineering Laboratory Open Project "Emotion
   Based Intelligent Question Answering System" (project no.
   CASNDST202006), and the Fujian Provincial Department of Science and
   Technology Leading Science and Technology Project "Personalized
   Intelligent Question Answering System" (project no. 2019H0026).
CR Abbasi-Moud Z, 2021, EXPERT SYST APPL, V167, DOI 10.1016/j.eswa.2020.114324
   Bin CZ, 2020, MULTIMED TOOLS APPL, V79, P14951, DOI 10.1007/s11042-019-08554-5
   Budzianowski P., 2018, ARXIV, DOI [10.18653/v1/D18-1547, DOI 10.18653/V1/D18-1547]
   Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Chen ZX, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2994
   Cheng H.T., 2016, P 1 WORKSHOP DEEP LE, P7, DOI DOI 10.1145/2988450.2988454
   Fu Z., 2021, P 44 INT ACM SIGIR C, DOI [10.1145/3404835.3463247, DOI 10.1145/3404835.3463247]
   Gao C., 2021, ARXIV, DOI [10.1016/j.aiopen.2021.06.002, DOI 10.1016/J.AIOPEN.2021.06.002]
   Guo H., 2017, ARXIV, DOI [10.5555/3172077.3172127, DOI 10.5555/3172077.3172127]
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   Huang XW, 2019, PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19), P548, DOI 10.1145/3343031.3350893
   Janarthanam S., 2012, P SEMDIAL 2012 SEIN
   Jannach D., 2007, ENTER, V7, P43, DOI [10.1007/978-3-211-69566-1_5, DOI 10.1007/978-3-211-69566-1_5]
   Jannach D, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3453154
   Kang D., 2019, ARXIV, DOI [10.48550/arXiv.1909.03922, DOI 10.48550/ARXIV.1909.03922]
   Kang WC, 2018, IEEE DATA MINING, P197, DOI 10.1109/ICDM.2018.00035
   Koren Y, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P447
   Lei WQ, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2073, DOI 10.1145/3394486.3403258
   Lei WQ, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P304, DOI 10.1145/3336191.3371769
   Li R., 2018, ARXIV, DOI [10.5555/3327546.3327641, DOI 10.5555/3327546.3327641]
   Liu WJ, 2020, AAAI CONF ARTIF INTE, V34, P2901
   Liu Z., 2021, ARXIV, DOI [10.18653/v1/2021.emnlp-main.356, DOI 10.18653/V1/2021.EMNLP-MAIN.356]
   Liu Z., 2020, ARXIV, DOI [10.48550/arXiv.2005.03954, DOI 10.48550/ARXIV.2005.03954]
   Lyu DD, 2020, APPL INTELL, V50, P924, DOI 10.1007/s10489-019-01566-6
   Mahmood T., 2009, Information Technology and Tourism, V11, P285, DOI 10.3727/109830510X12670455864203
   Mahmood T, 2009, 20TH ACM CONFERENCE ON HYPERTEXT AND HYPERMEDIA (HYPERTEXT 2009), P73
   Pan HL, 2021, J SIGNAL PROCESS SYS, V93, P147, DOI 10.1007/s11265-019-01504-2
   Pazzani M. J., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P325
   Ren PJ, 2021, SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P533, DOI 10.1145/3404835.3462897
   Sarkar R., P 28 INT C COMP LING, DOI [10.18653/v1/2020.coling-main.369, DOI 10.18653/V1/2020.COLING-MAIN.369]
   Schafer J. B., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P291
   Shi C, 2019, IEEE T KNOWL DATA EN, V31, P357, DOI 10.1109/TKDE.2018.2833443
   Shunyao Zhang, 2020, Journal of Physics: Conference Series, V1437, DOI 10.1088/1742-6596/1437/1/012047
   Song W., 2019, ARXIV, DOI [10.48550/arXiv.1906.09506, DOI 10.48550/ARXIV.1906.09506]
   Sun F, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P1441, DOI 10.1145/3357384.3357895
   Sun YM, 2018, ACM/SIGIR PROCEEDINGS 2018, P235, DOI 10.1145/3209978.3210002
   ter Hoeve M, 2020, CHIIR'20: PROCEEDINGS OF THE 2020 CONFERENCE ON HUMAN INFORMATION INTERACTION AND RETRIEVAL, P43, DOI 10.1145/3343413.3377971
   Vakulenko S., 2021, ARXIV, DOI [10.1145/3466796, DOI 10.1145/3466796]
   Wong C.M., 2021, ARXIV, DOI [10.48550/arXiv.2104.14899, DOI 10.48550/ARXIV.2104.14899]
   Xue HJ, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3203
   Yang DQ, 2018, IEEE DATA MINING, P1368, DOI 10.1109/ICDM.2018.00187
   Ye YT, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P679, DOI 10.1145/3357384.3358014
   Yilmaz E., 2008, P 31 ANN INT ACM SIG, P603, DOI DOI 10.1145/1390334.1390437
   Zhang Y., 2019, P CHIN C PATT REC CO, DOI [10.1007/978-3-030-31726-3_9, DOI 10.1007/978-3-030-31726-3_9]
   Zhou H., 2020, ARXIV, DOI [10.18653/v1/2020.acl-main.635, DOI 10.18653/V1/2020.ACL-MAIN.635]
   Zhou K., 2020, ARXIV, DOI [10.48550/arXiv.2010.04125, DOI 10.48550/ARXIV.2010.04125]
   Zhu Q, 2020, T ASSOC COMPUT LING, V8, P281, DOI 10.1162/tacl_a_00314
NR 47
TC 1
Z9 1
U1 3
U2 3
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD MAY
PY 2022
VL 10
IS 9
AR 1402
DI 10.3390/math10091402
PG 16
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA 1E7OI
UT WOS:000794672600001
OA gold
DA 2022-08-23
ER

PT J
AU Vickers, CJ
   Fraga, D
   Patrick, WM
AF Vickers, Chelsea J.
   Fraga, Dean
   Patrick, Wayne M.
TI Quantifying the taxonomic bias in enzymology
SO PROTEIN SCIENCE
LA English
DT Article
DE archaeal and bacterial phyla; enzymatic dark matter; eukaryotic
   supergroups; kinetic parameters; phylogenetics; tree of life
ID EVOLUTIONARY; DIVERSITY; BACTERIA; ENZYMES; GENOMES; TREE
AB The ongoing biotechnological revolution is rooted in our knowledge of enzymes. However, metagenomics is showing how little we know about Earth's enzyme repertoire. Deep sequencing has revolutionized our view of the tree of life. The genomes of newly-discovered organisms are replete with novel sequences, emphasizing the trove of enzyme structures and functions waiting to be explored by biochemists. Here, we sought to draw attention to the vastness of the "enzymatic dark matter" within the tree of life by placing enzymological knowledge in the context of phylogeny. We used kinetic parameters from the BRaunschweig ENzyme DAtabase (BRENDA) as our proxy for enzymological knowledge. Mapping 12,677 BRENDA entries onto the phylogenetic tree revealed that 55% of these data were from eukaryotes, even though they are the least diverse part of the tree. At the next taxonomic level, only four of 18 archaeal phyla and 24 of 111 bacterial phyla are represented in the BRENDA dataset. One phylum, the Proteobacteria, accounts for over half of all bacterial entries. Similarly, the supergroup Amorphea, which includes animals and fungi, contains over half the data on eukaryotes. Many major taxonomic groups are notable for their complete absence from BRENDA, including the ultra-diverse bacterial Candidate Phyla Radiation. At the species level, five mammals (including human) contribute 15% of BRENDA entries. The taxonomic bias in enzymology is strong, but in the era of gene synthesis we now have the tools to address it. Doing so promises to enrich our biochemical understanding of life and uncover powerful new biocatalysts.
C1 [Vickers, Chelsea J.; Patrick, Wayne M.] Victoria Univ Wellington, Sch Biol Sci, Ctr Biodiscovery, Wellington, New Zealand.
   [Fraga, Dean] Coll Wooster, Dept Biol, Wooster, OH 44691 USA.
RP Patrick, WM (通讯作者)，Victoria Univ Wellington, Sch Biol Sci, POB 600, Wellington 6140, New Zealand.
EM wayne.patrick@vuw.ac.nz
FU Marsden Fund [18-VUW-050]; Henry Luce III Fund for Distinguished
   Scholarship; James and Jean McClung Endowment for International Research
FX This work was supported via a grant from the Marsden Fund to WMP [grant
   number 18-VUW-050]. DF was supported by the Henry Luce III Fund for
   Distinguished Scholarship and the James and Jean McClung Endowment for
   International Research.
CR Akiva E, 2017, P NATL ACAD SCI USA, V114, pE9549, DOI 10.1073/pnas.1706849114
   Baker BJ, 2020, NAT MICROBIOL, V5, P887, DOI 10.1038/s41564-020-0715-z
   Bar-Even A, 2011, BIOCHEMISTRY-US, V50, P4402, DOI 10.1021/bi2002289
   Buchner E., 1897, BER DTSCH CHEM GES, V30, P117, DOI [10.1002/cber.18970300121, DOI 10.1002/CBER.18970300121]
   Burki F, 2020, TRENDS ECOL EVOL, V35, P43, DOI 10.1016/j.tree.2019.08.008
   Castelle CJ, 2018, NAT REV MICROBIOL, V16, P629, DOI 10.1038/s41579-018-0076-2
   Castelle CJ, 2018, CELL, V172, P1181, DOI 10.1016/j.cell.2018.02.016
   Chang A, 2021, NUCLEIC ACIDS RES, V49, pD498, DOI 10.1093/nar/gkaa1025
   Clark JA, 2002, SCIENCE, V297, P191
   Copp JN, 2018, BIOCHEMISTRY-US, V57, P4651, DOI 10.1021/acs.biochem.8b00473
   Davidi D, 2020, EMBO J, V39, DOI 10.15252/embj.2019104081
   Davidi D, 2018, CHEM REV, V118, P8786, DOI 10.1021/acs.chemrev.8b00039
   Davidi D, 2017, CURR OPIN BIOTECH, V46, P81, DOI 10.1016/j.copbio.2017.02.007
   de Vargas C, 2015, SCIENCE, V348, DOI 10.1126/science.1261605
   del Campo J, 2014, TRENDS ECOL EVOL, V29, P252, DOI 10.1016/j.tree.2014.03.006
   dos Santos JW, 2020, ANIM CONSERV, V23, P679, DOI 10.1111/acv.12586
   Ferla MP, 2017, MOL MICROBIOL, V105, P508, DOI 10.1111/mmi.13737
   Friedmann HC, 1997, NEW BEER OLD BOTTLE, P25
   Gawryluk RMR, 2019, NATURE, V572, P240, DOI 10.1038/s41586-019-1398-6
   Gerlt JA, 2017, BIOCHEMISTRY-US, V56, P4293, DOI 10.1021/acs.biochem.7b00614
   Heckmann CM, 2020, CHEMCATCHEM, V12, P6082, DOI 10.1002/cctc.202001107
   Hochkirch A, 2021, CONSERV BIOL, V35, P502, DOI 10.1111/cobi.13589
   Hon J, 2020, NUCLEIC ACIDS RES, V48, pW104, DOI 10.1093/nar/gkaa372
   Kunin V, 2003, GENOME BIOL, V4, DOI 10.1186/gb-2003-4-2-401
   Larsen BB, 2017, Q REV BIOL, V92, P229, DOI 10.1086/693564
   Leon-Zayas R, 2017, ENVIRON MICROBIOL, V19, P2769, DOI 10.1111/1462-2920.13789
   Letunic I, 2019, NUCLEIC ACIDS RES, V47, pW256, DOI 10.1093/nar/gkz239
   Lloyd KG, 2018, MSYSTEMS, V3, DOI 10.1128/mSystems.00055-18
   Mahe F, 2017, NAT ECOL EVOL, V1, DOI [10.1038/s41559-017-00911, 10.1038/s41559-017-0091]
   Mak WS, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms10005
   Mendler K, 2019, NUCLEIC ACIDS RES, V47, P4442, DOI 10.1093/nar/gkz246
   Mukherjee S, 2017, NAT BIOTECHNOL, V35, P676, DOI 10.1038/nbt.3886
   Newton MS, 2018, CURR OPIN STRUC BIOL, V48, P110, DOI 10.1016/j.sbi.2017.11.007
   Ort DR, 2015, P NATL ACAD SCI USA, V112, P8529, DOI 10.1073/pnas.1424031112
   Parks DH, 2020, NAT BIOTECHNOL, V38, P1079, DOI 10.1038/s41587-020-0501-8
   Parks DH, 2018, NAT BIOTECHNOL, V36, P996, DOI 10.1038/nbt.4229
   Renata H, 2015, ANGEW CHEM INT EDIT, V54, P3351, DOI 10.1002/anie.201409470
   Schoch CL, 2020, DATABASE-OXFORD, DOI 10.1093/database/baaa062
   Tawfik DS, 2016, CURR OPIN CHEM BIOL, V31, pV, DOI 10.1016/j.cbpa.2016.03.002
   Troudet J, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-09084-6
   Vickers CJ, 2021, PROTEIN SCI, P1
   Zhu QY, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-13443-4
NR 42
TC 4
Z9 4
U1 2
U2 6
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0961-8368
EI 1469-896X
J9 PROTEIN SCI
JI Protein Sci.
PD APR
PY 2021
VL 30
IS 4
BP 914
EP 921
DI 10.1002/pro.4041
EA FEB 2021
PG 8
WC Biochemistry & Molecular Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology
GA RA8OA
UT WOS:000621518100001
PM 33583070
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Dori, D
   Shpitalni, M
AF Dori, D
   Shpitalni, M
TI Mapping knowledge about product lifecycle engineering for ontology
   construction via object-process methodology
SO CIRP ANNALS-MANUFACTURING TECHNOLOGY
LA English
DT Article
DE knowledge management; lifecycle ontology; object-process methodology
ID SYSTEM
AB Knowledge mapping is a first and mandatory step in ontology definition. This paper considers the lifecycle of products and systems, and discusses the creation of a knowledge-based ontology. With respect to the life cycle of products and systems, knowledge refers to the processes involved in their creation (design manufacturing and assembly), use and maintenance, and end of life (EOL). Hence, this knowledge should consider what a product is comprised of (its structure), how it operates (its dynamics), and how it interacts with the environment. A clearly defined and consistent mapping of knowledge regarding structure, operation and interaction is necessary to construct an effective and useful ontology. Yet, in order to obtain the required knowledge and to organize it in a consistent and useful form, an appropriate ontology must be used. An interactive, iterative and consistent method is needed to cope with this complex and circular problem. In this paper, the Object-Process Methodology (OPM) approach is considered, along with OPCAT [1], a tool for OPM-based knowledge modeling. OPM is a systems-modeling approach that represents knowledge about systems concurrently and bi-modally through graphics (a set of Object-Process Diagrams, OPDs) and text (Object-Process Language, OPL, a subset of English), yielding a single, unified and consistent view. In this paper we propose a foundational modeling and ontology construction approach for a generic product that incorporates hardware and software components. The ontology can serve as a basis for a knowledge model to cover the entire product lifecycle, from inception to EOL, and can be applied in the VRL-KCiP Network of Excellence.
C1 Technion Israel Inst Technol, Fac Mech Engn, Lab CAD & LCE, IL-32000 Haifa, Israel.
   Technion Israel Inst Technol, Fac Ind Engn & Management, IL-32000 Haifa, Israel.
RP Dori, D (通讯作者)，Technion Israel Inst Technol, Fac Mech Engn, Lab CAD & LCE, IL-32000 Haifa, Israel.
CR ALTING L, 1993, ANN CIRP, V42, P163
   BarCohen A, 1995, MECH ENG, V117, P66
   Brissaud D, 2001, CIRP ANN-MANUF TECHN, V50, P105, DOI 10.1016/S0007-8506(07)62082-4
   Dori D, 2002, COMMUN ACM, V45, P82
   Dori D., 2002, OBJECT PROCESS METHO
   DORI D, 2003, P IEEE ACM 5 INT C E, P286
   Duflou J, 2003, CIRP ANN-MANUF TECHN, V52, P29, DOI 10.1016/S0007-8506(07)60523-X
   HAUSCHILD M, 1999, ANN CIRP, V0048, P00393
   Kaebernick H, 2003, CIRP ANN-MANUF TECHN, V52, P25, DOI 10.1016/S0007-8506(07)60522-8
   Kimura F, 1998, CIRP ANNALS 1998 - MANUFACTURING TECHNOLOGY, VOL 47, NO 1, V47, P87
   KIMURA F, 1995, LIFE CYCLE MODELING, P80
   KRAUSE FL, 1995, P IFIP WG5 3 INT C L, P14
   Mayer R.E., 2001, MULTIMEDIA LEARNING
   Park JH, 2002, CIRP ANN-MANUF TECHN, V51, P421, DOI 10.1016/S0007-8506(07)61551-0
   SHPITALNI M, 2004, ARCH MECH TECHNOLOGY, V24, P251
   Takata S, 2004, CIRP ANN-MANUF TECHN, V53, P643, DOI 10.1016/S0007-8506(07)60033-X
   Takata S, 2003, CIRP ANN-MANUF TECHN, V52, P37, DOI 10.1016/S0007-8506(07)60525-3
   TIPNIS VA, 1991, ANN CIRP, V40, P463
   Westkamper E, 2002, CIRP ANN-MANUF TECHN, V51, P33, DOI 10.1016/S0007-8506(07)61459-0
   Zhang HC, 2004, CIRP ANN-MANUF TECHN, V53, P5, DOI 10.1016/S0007-8506(07)60632-5
   2004, MANUFUTURE VISION 20
NR 21
TC 18
Z9 19
U1 0
U2 10
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-8506
EI 1726-0604
J9 CIRP ANN-MANUF TECHN
JI CIRP Ann-Manuf. Technol.
PY 2005
VL 54
IS 1
BP 117
EP 122
DI 10.1016/S0007-8506(07)60063-8
PG 6
WC Engineering, Industrial; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 942ZS
UT WOS:000230322000028
DA 2022-08-23
ER

PT J
AU Hua, YF
   Dong, F
AF Hua, Yifei
   Dong, Feng
TI How can new energy vehicles become qualified relays from the perspective
   of carbon neutralization? Literature review and research prospect based
   on the CiteSpace knowledge map
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Review; Early Access
DE Fuel vehicle transition path; Carbon neutralization; New energy
   vehicles; Citespace; Hotspot and research edge
ID BATTERY ELECTRIC VEHICLES; PLUG-IN HYBRID; CONSUMER PURCHASE INTENTIONS;
   EMERGING TRENDS; REGENERATIVE MEDICINE; ROAD TRANSPORT; EARLY ADOPTERS;
   ADOPTION; FUEL; BEHAVIOR
AB Analyzing the feasibility of fuel vehicle transition will be conducive to the realization of the carbon neutralization goal. However, at present, there are few studies specifically aimed at the transition of fuel vehicles. Therefore, this study first analyzed the necessity for the transition of fuel vehicles and then used CiteSpace to analyze 2081 articles in the core Web of Science database in the past decade with "fuel vehicle emission reduction" as the search keyword. After clarifying the research context and development frontier of fuel vehicle emission reduction, we found that most of the literature with the research theme on this topic ends with the research of electric vehicles. Therefore, we took new energy vehicles represented by electric vehicles as the starting point to explore the realization path of carbon neutralization by analyzing the development dilemma and residents' feedback on electric vehicles. Finally, the research review and research prospects were carried out. The study found that although the development of new energy vehicles has made obvious progress at this stage, there are still some problems in comprehensively promoting electric vehicles, such as battery power, charging facilities, and the weak willingness of consumers to accept electric vehicles. Therefore, improving the usage efficiency of new energy vehicles can more effectively force fuel vehicles and new energy vehicles to complete the relay from the perspective of market attraction. This study will provide a more scientific solution and implementation path for the transition of fuel vehicles in various countries.
C1 [Hua, Yifei; Dong, Feng] China Univ Min & Technol, Sch Econ & Management, Xuzhou 221116, Jiangsu, Peoples R China.
RP Dong, F (通讯作者)，China Univ Min & Technol, Sch Econ & Management, Xuzhou 221116, Jiangsu, Peoples R China.
EM dongfeng2008@126.com
OI Dong, Feng/0000-0002-5177-6453
FU National Natural Science Foundation of China [71974188]; Major Project
   of National Social Science Foundation of China [21ZDA086]; Postgraduate
   Research & Practice Innovation Program of Jiangsu Province
   [KYCX19_2146]; Postgraduate Research & Practice Innovation Program of
   China University of Mining and Technology [KYCX19_2146]; Humanities and
   Social Sciences Special Research Fund of Ministry of Education in China
   [19JDGC011]
FX This work was supported by the National Natural Science Foundation of
   China (Grant No. 71974188), the Major Project of National Social Science
   Foundation of China (Grant No. 21ZDA086), Postgraduate Research &
   Practice Innovation Program of Jiangsu Province (KYCX19_2146),
   Postgraduate Research & Practice Innovation Program of China University
   of Mining and Technology (KYCX19_2146), and the Humanities and Social
   Sciences Special Research Fund of Ministry of Education in China
   (Research on Talents Training for Engineering Science and Technology,
   Grant No. 19JDGC011).
CR Abotalebi E, 2019, J TRANSP GEOGR, V74, P289, DOI 10.1016/j.jtrangeo.2018.12.001
   Ali MU, 2019, ENERGIES, V12, DOI 10.3390/en12030446
   Anandarajah G, 2013, INT J HYDROGEN ENERG, V38, P3419, DOI 10.1016/j.ijhydene.2012.12.110
   Axsen J, 2013, ENERG POLICY, V61, P532, DOI 10.1016/j.enpol.2013.05.122
   Baek D, 2020, ENERGIES, V13, DOI 10.3390/en13030709
   Baker E, 2015, ENERG POLICY, V80, P219, DOI 10.1016/j.enpol.2014.10.008
   Barfod MB, 2016, RES TRANSP ECON, V55, P3, DOI 10.1016/j.retrec.2016.04.005
   Barth M, 2016, TRANSPORT RES F-TRAF, V37, P64, DOI 10.1016/j.trf.2015.11.011
   Bennett R, 2018, TRANSPORT RES D-TR E, V62, P330, DOI 10.1016/j.trd.2018.03.017
   Biresselioglu ME, 2018, TRANSPORT RES A-POL, V109, P1, DOI 10.1016/j.tra.2018.01.017
   Bonges HA, 2016, TRANSPORT RES A-POL, V83, P63, DOI 10.1016/j.tra.2015.09.011
   Brooker RP, 2015, J POWER SOURCES, V299, P76, DOI 10.1016/j.jpowsour.2015.08.097
   Browne D, 2012, J CLEAN PROD, V35, P140, DOI 10.1016/j.jclepro.2012.05.019
   Bubna P, 2012, J POWER SOURCES, V199, P360, DOI 10.1016/j.jpowsour.2011.09.097
   Caggiani L, 2021, TRANSP LETT, V13, P219, DOI 10.1080/19427867.2020.1861501
   CALFEE JE, 1985, TRANSPORT RES B-METH, V19, P287, DOI 10.1016/0191-2615(85)90037-2
   Carley S, 2013, TRANSPORT RES D-TR E, V18, P39, DOI 10.1016/j.trd.2012.09.007
   Chen CM, 2014, EXPERT OPIN BIOL TH, V14, P1295, DOI 10.1517/14712598.2014.920813
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Conway T, 2017, IEEE T INTELL TRANSP, V18, P2311, DOI 10.1109/TITS.2016.2641981
   Cox B, 2020, APPL ENERG, V269, DOI 10.1016/j.apenergy.2020.115021
   Cui DS, 2022, ENERGY, V250, DOI 10.1016/j.energy.2022.123837
   Tran DD, 2020, RENEW SUST ENERG REV, V119, DOI 10.1016/j.rser.2019.109596
   Daziano RA, 2013, RESOUR ENERGY ECON, V35, P429, DOI 10.1016/j.reseneeco.2013.05.001
   Degirmenci K, 2017, TRANSPORT RES D-TR E, V51, P250, DOI 10.1016/j.trd.2017.01.001
   Ding ZH, 2018, IEEE T IND APPL, V54, P5590, DOI 10.1109/TIA.2018.2851205
   Diouf B, 2015, RENEW ENERG, V76, P375, DOI 10.1016/j.renene.2014.11.058
   Dong F, 2022, RESOUR CONSERV RECY, V177, DOI 10.1016/j.resconrec.2021.106002
   Dong F, 2021, J ENVIRON MANAGE, V300, DOI 10.1016/j.jenvman.2021.113738
   Dong F, 2021, J CLEAN PROD, V316, DOI 10.1016/j.jclepro.2021.128316
   Dong F, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10082895
   Du ZL, 2019, RESOUR CONSERV RECY, V143, P17, DOI 10.1016/j.resconrec.2018.12.007
   Dubarry M, 2017, J POWER SOURCES, V358, P39, DOI 10.1016/j.jpowsour.2017.05.015
   EEA, 2019, GREENHOUSE GAS EMISS
   Franke T, 2017, APPL ERGON, V65, P191, DOI 10.1016/j.apergo.2017.06.013
   Franzo S, 2021, J CLEAN PROD, V315, DOI 10.1016/j.jelepro.2021.128005
   Garcia A, 2020, ENERG CONVERS MANAGE, V224, DOI 10.1016/j.enconman.2020.113320
   Giansoldati M, 2018, ENERGY, V165, P267, DOI 10.1016/j.energy.2018.09.095
   Golob TF, 1998, TRANSPORT RES B-METH, V32, P441, DOI 10.1016/S0191-2615(98)00001-0
   Gurbuz H, 2021, FUEL, V306, DOI 10.1016/j.fuel.2021.121735
   Habich-Sobiegalla S, 2018, J CLEAN PROD, V205, P188, DOI 10.1016/j.jclepro.2018.08.318
   Han L, 2017, TRANSPORT RES A-POL, V103, P185, DOI 10.1016/j.tra.2017.05.033
   Hao Y, 2018, J CLEAN PROD, V172, P743, DOI 10.1016/j.jclepro.2017.10.195
   Hardman S, 2019, TRANSPORT RES A-POL, V119, P1, DOI 10.1016/j.tra.2018.11.002
   Hardman S, 2018, TRANSPORT RES D-TR E, V62, P508, DOI 10.1016/j.trd.2018.04.002
   He XH, 2018, J CLEAN PROD, V172, P3546, DOI 10.1016/j.jclepro.2017.05.088
   Heffner RR, 2007, TRANSPORT RES D-TR E, V12, P396, DOI 10.1016/j.trd.2007.04.003
   Helfand G, 2016, ENERG POLICY, V98, P590, DOI 10.1016/j.enpol.2016.09.014
   Hidrue MK, 2011, RESOUR ENERGY ECON, V33, P686, DOI 10.1016/j.reseneeco.2011.02.002
   Higgins CD, 2017, TRANSPORT RES A-POL, V100, P182, DOI 10.1016/j.tra.2017.04.014
   Hou JH, 2018, SCIENTOMETRICS, V115, P869, DOI 10.1007/s11192-018-2695-9
   Hsu CI, 2013, TRANSPORT RES D-TR E, V20, P21, DOI 10.1016/j.trd.2013.01.001
   Hua YF, 2021, J CLEAN PROD, V280, DOI 10.1016/j.jclepro.2020.124376
   Jansson J, 2017, J CLEAN PROD, V154, P176, DOI 10.1016/j.jclepro.2017.03.186
   Ji Q, 2019, ENERG ECON, V77, P80, DOI 10.1016/j.eneco.2018.07.012
   Junquera B, 2016, TECHNOL FORECAST SOC, V109, P6, DOI 10.1016/j.techfore.2016.05.006
   Kaplan S, 2016, RES TRANSP ECON, V55, P12, DOI 10.1016/j.retrec.2016.04.006
   Kees J, 2011, J CONSUM AFF, V45, P7, DOI 10.1111/j.1745-6606.2010.01190.x
   Keller V, 2019, ENERGY, V172, P740, DOI 10.1016/j.energy.2019.01.160
   Kim J, 2014, TRANSPORT RES A-POL, V69, P71, DOI 10.1016/j.tra.2014.08.016
   Kim MK, 2018, ENERGY, V159, P799, DOI 10.1016/j.energy.2018.06.064
   Kirsten V., 2018, ELECT J, V31, P48
   Klockner CA, 2014, TRANSPORT RES F-TRAF, V24, P103, DOI 10.1016/j.trf.2014.04.015
   Klockner CA, 2013, TRANSPORT RES D-TR E, V21, P32, DOI 10.1016/j.trd.2013.02.007
   Kong DY, 2020, APPL ENERG, V266, DOI 10.1016/j.apenergy.2020.114887
   Kotilainen K, 2019, POLICY SCI, V52, P573, DOI 10.1007/s11077-019-09361-3
   Langbroek JHM, 2018, TRAVEL BEHAV SOC, V13, P144, DOI 10.1016/j.tbs.2018.08.001
   Lee M, 2014, 2014 IEEE 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), P2486, DOI 10.1109/ITSC.2014.6958088
   Li P, 2019, SCIENTOMETRICS, V121, P1753, DOI 10.1007/s11192-019-03239-0
   Li WB, 2018, ENERG ENVIRON-UK, V29, P1512, DOI 10.1177/0958305X18781898
   Li WB, 2017, NAT HAZARDS, V87, P945, DOI 10.1007/s11069-017-2803-9
   Li XG, 2013, TRANSPORT RES D-TR E, V18, P9, DOI 10.1016/j.trd.2012.08.001
   Li Y, 2016, RENEW SUST ENERG REV, V65, P685, DOI 10.1016/j.rser.2016.07.063
   Li Z., 2011, J JIANGSU NORM U PSY, V37
   Liao WZ, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16173120
   Liu FQ, 2021, INT J HYDROGEN ENERG, V46, P17982, DOI 10.1016/j.ijhydene.2021.02.198
   Liu FQ, 2018, INT J HYDROGEN ENERG, V43, P22604, DOI 10.1016/j.ijhydene.2018.10.088
   Liu YJ, 2021, RESOUR CONSERV RECY, V169, DOI 10.1016/j.resconrec.2021.105534
   Liu ZG, 2015, SCIENTOMETRICS, V103, P135, DOI 10.1007/s11192-014-1517-y
   Liu ZW, 2018, ENERG POLICY, V115, P92, DOI 10.1016/j.enpol.2018.01.006
   Loustric I, 2020, EUR TRANSP RES REV, V12, DOI 10.1186/s12544-020-00416-8
   Lu M., 2010, CENTRAL CHINA ELECT, V23, P16
   Lu X, 2020, IEEE T POWER SYST, V35, P2212, DOI 10.1109/TPWRS.2019.2951108
   Ma JH, 2021, ENERG POLICY, V148, DOI 10.1016/j.enpol.2020.111919
   Ma Y, 2017, J CLEAN PROD, V145, P395, DOI 10.1016/j.jclepro.2016.12.131
   McCaffery C, 2021, SCI TOTAL ENVIRON, V784, DOI 10.1016/j.scitotenv.2021.147224
   McJeon HC, 2011, ENERG ECON, V33, P619, DOI 10.1016/j.eneco.2010.10.007
   McLeay F, 2018, J CLEAN PROD, V189, P519, DOI 10.1016/j.jclepro.2018.04.031
   Mohamed M, 2016, TRANSPORT POLICY, V52, P100, DOI 10.1016/j.tranpol.2016.07.006
   Morganti E, 2018, TRANSPORT POLICY, V63, P90, DOI 10.1016/j.tranpol.2017.12.010
   Mounce R, 2019, TRANSPORT RES A-POL, V120, P17, DOI 10.1016/j.tra.2018.12.003
   Mourato S, 2004, ENERG POLICY, V32, P685, DOI 10.1016/S0301-4215(02)00335-X
   Nastjuk I, 2018, TRANSPORT RES F-TRAF, V58, P855, DOI 10.1016/j.trf.2018.07.016
   Ng M, 2018, AUSTRALAS MARK J, V26, P272, DOI 10.1016/j.ausmj.2018.05.015
   Niazi M, 2011, SCIENTOMETRICS, V89, P479, DOI 10.1007/s11192-011-0468-9
   de la Rosa LN, 2017, J EXP ZOOL PART B, V328, P395, DOI 10.1002/jez.b.22741
   Okada T, 2019, TRANSPORT RES D-TR E, V67, P503, DOI 10.1016/j.trd.2019.01.012
   Ou X., 2010, CLIM CHANGE RES, V6, P136
   Pan YL, 2022, TECHNOL FORECAST SOC, V176, DOI 10.1016/j.techfore.2022.121475
   Pan YL, 2022, ENERG POLICY, V160, DOI 10.1016/j.enpol.2021.112668
   Peng F., 2017, AUTOPART, V23, P6
   Peterson SB, 2013, ENERG POLICY, V52, P429, DOI 10.1016/j.enpol.2012.09.059
   Plenter F, 2018, TRANSPORT RES D-TR E, V64, P178, DOI 10.1016/j.trd.2018.02.013
   Plotz P, 2014, TRANSPORT RES A-POL, V67, P96, DOI 10.1016/j.tra.2014.06.006
   Qi L., 2018, CHINA EC TRADE HER, V035, P19
   Qian LX, 2017, TRANSPORT RES D-TR E, V56, P175, DOI 10.1016/j.trd.2017.07.029
   [秦晓楠 Qin Xiaonan], 2014, [生态学报, Acta Ecologica Sinica], V34, P3693
   Raslavicius L, 2015, RENEW SUST ENERG REV, V42, P786, DOI 10.1016/j.rser.2014.10.076
   Rezvani Z, 2015, TRANSPORT RES D-TR E, V34, P122, DOI 10.1016/j.trd.2014.10.010
   Romejko K, 2017, J CLEAN PROD, V142, P39, DOI 10.1016/j.jclepro.2016.09.029
   Saleem MA, 2018, TRANSPORT RES D-TR E, V59, P68, DOI 10.1016/j.trd.2017.12.023
   Sang YN, 2015, J CLEAN PROD, V92, P75, DOI 10.1016/j.jclepro.2014.12.045
   Schneidereit T, 2015, ENERGY RES SOC SCI, V8, P198, DOI 10.1016/j.erss.2015.06.001
   Schuitema G, 2013, TRANSPORT RES A-POL, V48, P39, DOI 10.1016/j.tra.2012.10.004
   Semeijn J, 2019, TRANSPORT RES D-TR E, V67, P197, DOI 10.1016/j.trd.2018.11.016
   She ZY, 2017, TRANSPORT POLICY, V56, P29, DOI 10.1016/j.tranpol.2017.03.001
   Shen H, 2019, SUSTAIN ENERG FUELS, V3, P1647, DOI 10.1039/c9se00119k
   Sheng Y, 2013, APPL ENERG, V104, P319, DOI 10.1016/j.apenergy.2012.10.043
   Shi XQ, 2016, J CLEAN PROD, V137, P449, DOI 10.1016/j.jclepro.2016.07.096
   Simsekoglu O, 2019, TRANSPORT RES F-TRAF, V60, P1, DOI 10.1016/j.trf.2018.10.001
   Smith B, 2017, TRANSPORT RES D-TR E, V51, P290, DOI 10.1016/j.trd.2017.01.008
   Sovacool BK, 2018, GLOBAL ENVIRON CHANG, V52, P86, DOI 10.1016/j.gloenvcha.2018.06.008
   Sovacool BK, 2017, ENERGY RES SOC SCI, V27, P78, DOI 10.1016/j.erss.2017.02.014
   Sundstrom O, 2012, IEEE T SMART GRID, V3, P26, DOI 10.1109/TSG.2011.2168431
   Tan KM, 2016, ENERGY, V112, P1060, DOI 10.1016/j.energy.2016.07.008
   Temporelli A, 2020, ENERGIES, V13, DOI 10.3390/en13112864
   Tong HY, 2019, SUSTAIN CITIES SOC, V48, DOI 10.1016/j.scs.2019.101588
   Tseng CM, 2019, IEEE T INTELL TRANSP, V20, P817, DOI 10.1109/TITS.2018.2839265
   US Environmental Protection Agency (USEPA), 2018, INV US GREENH GAS EM
   Vaideeswaran V., 2019, BATTERY MANAGEMENT S
   Vysotska O, 2019, IEEE ROBOT AUTOM LET, V4, P1730, DOI 10.1109/LRA.2019.2897160
   Wang L, 2020, APPL ENERG, V275, DOI 10.1016/j.apenergy.2020.115328
   Wang MX, 2021, ENVIRON SCI POLLUT R, V28, P42401, DOI 10.1007/s11356-021-13632-z
   Wang N., 2015, CHINA SOFT SCI, V10, P70
   Wang Q, 2020, NAT ENERGY, V5, P703, DOI 10.1038/s41560-020-0678-6
   Wang Q, 2022, SUSTAIN CITIES SOC, V76, DOI 10.1016/j.scs.2021.103382
   Wang Q, 2021, ENVIRON RES, V202, DOI 10.1016/j.envres.2021.111637
   Wang Q, 2019, ENERGY, V169, P762, DOI 10.1016/j.energy.2018.12.045
   Wang SY, 2018, TRANSPORT RES A-POL, V117, P58, DOI 10.1016/j.tra.2018.08.014
   Wang ZH, 2017, J CLEAN PROD, V161, P1000, DOI 10.1016/j.jclepro.2017.05.154
   Wen JP, 2020, RENEW ENERG, V162, P1629, DOI 10.1016/j.renene.2020.09.055
   Westin K, 2018, TRAVEL BEHAV SOC, V13, P118, DOI 10.1016/j.tbs.2018.07.004
   Winkler A, 2021, ENVIRON POLLUT, V291, DOI 10.1016/j.envpol.2021.118191
   Wolbertus R, 2018, TRANSPORT RES D-TR E, V62, P283, DOI 10.1016/j.trd.2018.03.012
   Wolfram P, 2021, ENVIRON SCI TECHNOL, V55, P65, DOI 10.1021/acs.est.0c03796
   Wu HT, 2020, ENERG ECON, V91, DOI 10.1016/j.eneco.2020.104880
   Wu XF, 2019, ENERG POLICY, V128, P476, DOI 10.1016/j.enpol.2019.01.022
   Wu ZB, 2015, J CLEAN PROD, V91, P305, DOI 10.1016/j.jclepro.2014.12.001
   Xie PP, 2016, J ELECTR ENG TECHNOL, V11, P300, DOI 10.5370/JEET.2016.11.2.300
   Xu BJ, 2021, SUSTAIN PROD CONSUMP, V27, P1205, DOI 10.1016/j.spc.2021.03.002
   Yang C, 2022, APPL ENERG, V306, DOI 10.1016/j.apenergy.2021.118116
   Yang Y., 2016, SCI TEC REV, V6, P23
   Yang Y., 2010, ADV MAT IND, V03, P11
   Yao ZH, 2021, ENERGY, V230, DOI 10.1016/j.energy.2021.120766
   Yue ML, 2021, INT J HYDROGEN ENERG, V46, P13206, DOI 10.1016/j.ijhydene.2021.01.095
   Zhang HR, 2018, APPL ENERG, V220, P527, DOI 10.1016/j.apenergy.2018.03.105
   Zhang SL, 2021, J ENVIRON MANAGE, V296, DOI 10.1016/j.jenvman.2021.113159
   Zhang XQ, 2020, IEEE INT POWER ELEC, P3216, DOI 10.1109/IPEMC-ECCEAsia48364.2020.9367968
   Zhou M, 2019, TRANSPORT RES D-TR E, V74, P65, DOI 10.1016/j.trd.2019.07.024
   Zito P, 2004, EUR TRANSP, V5, P685
NR 161
TC 0
Z9 0
U1 21
U2 21
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
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J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
DI 10.1007/s11356-022-21096-y
EA JUN 2022
PG 19
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 1Y9DB
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PM 35678969
DA 2022-08-23
ER

PT J
AU Jiang, CM
   Bhat, CR
   Lam, WHK
AF Jiang, Chenming
   Bhat, Chandra R.
   Lam, William H. K.
TI A bibliometric overview of Transportation Research Part B:
   Methodological in the past forty years (1979-2019)
SO TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
LA English
DT Article
DE Transportation Research Part B: Methodological; Bibliometrics;
   Development review; Web of science; VOSviewer
ID CELL TRANSMISSION MODEL; KINEMATIC WAVES; SCIENTIFIC COLLABORATION;
   INFORMATION SCIENCES; ASSIGNMENT MODEL; NETWORK; EQUILIBRIUM;
   FORMULATION; CONGESTION; EXISTENCE
AB In 1979, Transportation Research Part B: Methodological (TR-Part B) split from its mother journal Transportation Research, which was founded by Dr. Frank A. Haight in 1967, to set off on a journey as an independent journal with a theme on methodological development of transportation science. Since then, TR-Part B has been a leading international journal in the field of transportation research. Motivated by the 40th anniversary of TR-Part B (1979-2019), this paper utilized a bibliometric analysis technique to present a lifetime overview of the development characteristics and contribution details of TR-Part B in the past 40 years. The data used in this analysis were derived from the Core Collection Database of Web of Science, producing a total of 2,697 papers that were regular articles and reviews published in TR-Part B during the study period from 1979 to 2019. A systematic bibliometric analysis was conducted using VOSviewer software around two main aspects. First, analysis was conducted on publication and citation structures, the leading authors, organizations, and regions/countries with the most number of academic publications in TR-Part B, as well as those individuals and sources citing TR-Part B the most. Second, a visualization of the bibliometric characteristics of TR-Part B was presented in a knowledge mapping network analysis of co-authorship and bibliographic coupling. This paper reviews the contributions made to the lifetime development of TR-Part B during the past four decades and reveals insightful bibliometric findings. (C) 2020 Elsevier Ltd. All rights reserved.
C1 [Jiang, Chenming] Shanghai Maritime Univ, China Inst FTZ Supply Chain, Shanghai, Peoples R China.
   [Bhat, Chandra R.] Univ Texas Austin, Dept Civil Architectural & Environm Engn, Austin, TX 78712 USA.
   [Bhat, Chandra R.; Lam, William H. K.] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China.
RP Lam, WHK (通讯作者)，Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China.
EM william.lam@polyu.edu.hk
RI Bhat, Chandra/AAC-6965-2019
OI Bhat, Chandra/0000-0002-0715-8121; Jiang, Chenming/0000-0002-4960-4580
FU National Natural Science Foundation of China [41571149]; Research Grants
   Council of the Hong Kong Special Administrative Region, China
   [R5029-18]; Research Committee of the Hong Kong Polytechnic University
   [4-ZZFY]; Data-Supported Transportation Operations and Planning (D-STOP)
   Center [DTRT13GUTC58]; Tier 1 University Transportation Center - U.S.
   Department of Transportation at The University of Texas at Austin
FX The work described in this paper was supported by the National Natural
   Science Foundation of China (No. 41571149), the Research Grants Council
   of the Hong Kong Special Administrative Region, China (No. R5029-18),
   and the Research Committee of the Hong Kong Polytechnic University (No.
   4-ZZFY). The research was also partially supported by the Data-Supported
   Transportation Operations and Planning (D-STOP) Center (Grant No.
   DTRT13GUTC58), Tier 1 University Transportation Center sponsored by the
   U.S. Department of Transportation at The University of Texas at Austin.
   The authors are indebted to Professor Chi Xie from Tongji University for
   his valuable suggestions, comments and edits on the earlier version of
   this paper, and for his assistance in granting authors access to the
   full WoS dataset used in this study. The authors also extend their
   thanks to Dr. Jie Li from Shanghai Maritime University for his technical
   collaboration with the VOSviewer software.
CR Alonso S, 2009, J INFORMETR, V3, P273, DOI 10.1016/j.joi.2009.04.001
   [Anonymous], 1985, DISCRETE CHOICE ANAL
   ARNOTT R, 1990, J URBAN ECON, V27, P111, DOI 10.1016/0094-1190(90)90028-L
   ARNOTT R, 1993, AM ECON REV, V83, P161
   Beaver D. deB., 1978, Scientometrics, V1, P65, DOI 10.1007/BF02016840
   BEAVER DD, 1979, SCIENTOMETRICS, V1, P231, DOI 10.1007/BF02016308
   BEAVER DD, 1979, SCIENTOMETRICS, V1, P133, DOI 10.1007/BF02016966
   Beckmann M., 1956, STUDIES EC TRANSPORT
   Bektas T, 2011, TRANSPORT RES B-METH, V45, P1232, DOI 10.1016/j.trb.2011.02.004
   Bhat C, 2019, TRANSPORT RES B-METH, V120, pA1, DOI 10.1016/j.trb.2019.01.006
   BROADUS RN, 1987, SCIENTOMETRICS, V12, P373, DOI 10.1007/BF02016680
   Cancino CA, 2017, COMPUT IND ENG, V113, P614, DOI 10.1016/j.cie.2017.08.033
   Clarivate Analytics, 2019, 2018JOURNAL CIT REP
   Cobo MJ, 2015, KNOWL-BASED SYST, V80, P3, DOI 10.1016/j.knosys.2014.12.035
   DAFERMOS S, 1980, TRANSPORT SCI, V14, P42, DOI 10.1287/trsc.14.1.42
   Daganzo C. F., 1977, Transportation Science, V11, P253, DOI 10.1287/trsc.11.3.253
   Daganzo CF, 2007, TRANSPORT RES B-METH, V41, P49, DOI 10.1016/j.trb.2006.03.001
   Daganzo CF, 2005, TRANSPORT RES B-METH, V39, P187, DOI 10.1016/j.trb.2004.04.003
   DAGANZO CF, 1994, TRANSPORT RES B-METH, V28, P269, DOI 10.1016/0191-2615(94)90002-7
   DAGANZO CF, 1995, TRANSPORT RES B-METH, V29, P79, DOI 10.1016/0191-2615(94)00022-R
   DIAL RB, 1971, TRANSPORT RES, V5, P83, DOI 10.1016/0041-1647(71)90012-8
   Franceschini F, 2010, EUR J OPER RES, V203, P494, DOI 10.1016/j.ejor.2009.08.001
   FRIESZ TL, 1993, OPER RES, V41, P179, DOI 10.1287/opre.41.1.179
   Furuhata M, 2013, TRANSPORT RES B-METH, V57, P28, DOI 10.1016/j.trb.2013.08.012
   Geroliminis N, 2008, TRANSPORT RES B-METH, V42, P759, DOI 10.1016/j.trb.2008.02.002
   Geroliminis N, 2011, TRANSPORT RES B-METH, V45, P605, DOI 10.1016/j.trb.2010.11.004
   Grafton, 1994, TOPICS TRANSPORTATIO
   Hagenzieker MP, 2014, TRANSPORT RES F-TRAF, V25, P150, DOI 10.1016/j.trf.2013.10.004
   KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103
   Laengle S, 2017, EUR J OPER RES, V262, P803, DOI 10.1016/j.ejor.2017.04.027
   LEBLANC LJ, 1975, TRANSPORT RES, V9, P309, DOI 10.1016/0041-1647(75)90030-1
   LIGHTHILL MJ, 1955, PROC R SOC LON SER-A, V229, P281, DOI 10.1098/rspa.1955.0088
   LIGHTHILL MJ, 1955, PROC R SOC LON SER-A, V229, P317, DOI 10.1098/rspa.1955.0089
   Valenzuela L, 2017, J BUS IND MARK, V32, P1, DOI 10.1108/JBIM-04-2016-0079
   Merchant D. K., 1978, Transportation Science, V12, P183, DOI 10.1287/trsc.12.3.183
   Merigo JM, 2018, INFORM SCIENCES, V432, P245, DOI 10.1016/j.ins.2017.11.054
   Merigo JM, 2017, OMEGA-INT J MANAGE S, V73, P37, DOI 10.1016/j.omega.2016.12.004
   Merigo JM, 2017, INT J INTELL SYST, V32, P526, DOI 10.1002/int.21859
   Merigo JM, 2016, SCIENTOMETRICS, V108, P559, DOI 10.1007/s11192-016-1984-4
   Merigo JM, 2015, J BUS RES, V68, P2645, DOI 10.1016/j.jbusres.2015.04.006
   Modak NM, 2019, TRANSPORT RES A-POL, V120, P188, DOI 10.1016/j.tra.2018.11.015
   Monastersky R, 2019, NATURE, V575, P22, DOI 10.1038/d41586-019-03305-w
   NEWELL GF, 1993, TRANSPORT RES B-METH, V27, P281, DOI 10.1016/0191-2615(93)90038-C
   Newell GF, 2002, TRANSPORT RES B-METH, V36, P195, DOI 10.1016/S0191-2615(00)00044-8
   Peeta S, 2001, NETW SPAT ECON, V1, P233, DOI [DOI 10.1023/A:1012827724856, 10.1023/A:1012827724856]
   PRITCHARD A, 1969, J DOC, V25, P348
   Rawls CG, 2010, TRANSPORT RES B-METH, V44, P521, DOI 10.1016/j.trb.2009.08.003
   RICHARDS PI, 1956, OPER RES, V4, P42, DOI 10.1287/opre.4.1.42
   Sheffi Y., 1984, URBAN TRANSPORTATION
   SMALL KA, 1982, AM ECON REV, V72, P467
   SMITH MJ, 1979, TRANSPORT RES B-METH, V13, P295, DOI 10.1016/0191-2615(79)90022-5
   SPIESS H, 1989, TRANSPORT RES B-METH, V23, P83, DOI 10.1016/0191-2615(89)90034-9
   Sun LJ, 2017, TRANSPORT RES A-POL, V100, P135, DOI 10.1016/j.tra.2017.04.011
   Sun LJ, 2017, TRANSPORT RES C-EMER, V77, P49, DOI 10.1016/j.trc.2017.01.013
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   van Eck NJ, 2009, J AM SOC INF SCI TEC, V60, P1635, DOI 10.1002/asi.21075
   van Nunen K, 2018, SAFETY SCI, V108, P248, DOI 10.1016/j.ssci.2017.08.011
   VICKREY WS, 1969, AM ECON REV, V59, P251
   Waltman L, 2010, J INFORMETR, V4, P629, DOI 10.1016/j.joi.2010.07.002
   Wardrop J.G., 1952, P I CIVIL ENG, VVol. 1, P325, DOI [10.1680/ipeds.1952.11362, DOI 10.1680/IPEDS.1952.11259]
   WILLIAMS HCWL, 1977, ENVIRON PLANN A, V9, P285, DOI 10.1068/a090285
   Yu DJ, 2017, INFORM SCIENCES, V418, P619, DOI 10.1016/j.ins.2017.08.031
   Zou X, 2018, ACCIDENT ANAL PREV, V118, P131, DOI 10.1016/j.aap.2018.06.010
NR 63
TC 17
Z9 17
U1 15
U2 52
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0191-2615
EI 1879-2367
J9 TRANSPORT RES B-METH
JI Transp. Res. Pt. B-Methodol.
PD AUG
PY 2020
VL 138
BP 268
EP 291
DI 10.1016/j.trb.2020.05.016
PG 24
WC Economics; Engineering, Civil; Operations Research & Management Science;
   Transportation; Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering; Operations Research & Management
   Science; Transportation
GA MF7QF
UT WOS:000545532200011
DA 2022-08-23
ER

PT J
AU Lyu, PH
   Ngai, EWT
   Wu, PY
AF Lyu, Peng-hui
   Ngai, Eric W. T.
   Wu, Pei-yi
TI Scientific data-driven evaluation on academic articles of low-carbon
   economy
SO ENERGY POLICY
LA English
DT Article
DE Scientific data driven evaluation; Low-carbon economy research;
   Quantitative assessment; Academic impact; Citation influence
ID BIBLIOMETRIC ANALYSIS; ENERGY RESEARCH; SUSTAINABILITY; TRANSITION;
   EMISSION; BEHAVIOR; TRENDS; CHINA
AB Low-carbon economy has played an important role in green energy economics in last two decades. The number of studies conducted on low-carbon economy has increased steadily since 1990 and now in a fast-growing stage of its academic articles' production life cycle. An increasing number of scholars have paid their attentions to low carbon economy research. Hence, the low-carbon economy has been an important and popular topic in the past 30 years as evidenced by numerous papers in journals listed in several subject categories in the Web of Science (WoS) online database. This research reviewed more than 14,000 bibliographic records from the Science Citation Index and Social Science Citation Index of WoS. In this work, paper citations, subject categories, cited journals, top countries, productive institutes, authors, cited references and publications' citations or research collaborations are analysed from published literature. We have found that the top three productive journals in low-carbon economy research are Energy Policy, Renewable and Sustainable Energy Reviews and Journal of Cleaner Production. The U.S. has an apparent advantage in the quantity and quality of low-carbon economy research. In addition, the U.S. is in the centre of international collaboration networks, including two top productive institutes, which summarized as University of California Berkeley and Carnegie Mellon University accordingly. An overall trend of the developing analysis of low-carbon economy research can provide a clear knowledge map and deep understanding of academic research on low-carbon economy.
C1 [Lyu, Peng-hui] Hefei Univ Technol, Sch Management, Hefei 230009, Anhui, Peoples R China.
   [Lyu, Peng-hui; Ngai, Eric W. T.] Hong Kong Polytech Univ, Dept Management & Mkt, Hong Kong, Peoples R China.
   [Wu, Pei-yi] Univ Chinese Acad Sci, Sch Humanities, Beijing 100099, Peoples R China.
   [Wu, Pei-yi] City Univ Hong Kong, Dept Publ Policy, Hong Kong, Peoples R China.
RP Ngai, EWT (通讯作者)，Hong Kong Polytech Univ, Dept Management & Mkt, Hong Kong, Peoples R China.
EM Eric.ngai@polyu.edu.hk
RI Ngai, Eric/ABC-2167-2020; Ngai, Eric W.T./L-8152-2015
OI Ngai, Eric W.T./0000-0002-7278-7434
FU Natural Science Foundation of China [71731005, 71690230]; Hong Kong
   Scholars Program [XJ-2016064]
FX This research was supported in part by grants from the Natural Science
   Foundation of China (71731005, 71690230) and the Hong Kong Scholars
   Program (XJ-2016064). The authors are grateful for the constructive
   comment of the three referees on the earlier version of this paper and
   Chang-dong Ye and Bing Hu for their helpful discussion.
CR Albino V, 2014, APPL ENERG, V135, P836, DOI 10.1016/j.apenergy.2014.08.012
   Aleman-Nava GS, 2014, RENEW SUST ENERG REV, V32, P140, DOI 10.1016/j.rser.2014.01.004
   Ashina S, 2012, ENERG POLICY, V41, P584, DOI 10.1016/j.enpol.2011.11.020
   Bai Y, 2013, ENERG POLICY, V61, P1261, DOI 10.1016/j.enpol.2013.06.014
   Batel S, 2013, ENERG POLICY, V58, P1, DOI 10.1016/j.enpol.2013.03.018
   Cadez S, 2013, EXPERT SYST APPL, V40, P6885, DOI 10.1016/j.eswa.2013.06.036
   Chappin EJL, 2014, RENEW SUST ENERG REV, V30, P715, DOI 10.1016/j.rser.2013.11.013
   Chen HQ, 2016, RENEW SUST ENERG REV, V58, P966, DOI 10.1016/j.rser.2015.12.239
   Chen LJ, 2017, INT J PROD ECON, V194, P73, DOI 10.1016/j.ijpe.2017.04.005
   Dagoumas AS, 2010, ENERG POLICY, V38, P3067, DOI 10.1016/j.enpol.2010.01.047
   Department of Energy (DoE, 2003, S AFR DEP EN WHIT PA
   Ekholm T, 2010, ENERG POLICY, V38, P1797, DOI 10.1016/j.enpol.2009.11.055
   Fahimnia B, 2015, INT J PROD ECON, V162, P101, DOI 10.1016/j.ijpe.2015.01.003
   Foxon TJ, 2011, ECOL ECON, V70, P2258, DOI 10.1016/j.ecolecon.2011.07.014
   Glaeser EL, 2010, J URBAN ECON, V67, P404, DOI 10.1016/j.jue.2009.11.006
   Harto C, 2010, ENERG POLICY, V38, P4933, DOI 10.1016/j.enpol.2010.03.074
   Hohne N, 2007, ENVIRON SCI POLICY, V10, P353, DOI 10.1016/j.envsci.2007.02.001
   Holland SP, 2009, AM ECON J-ECON POLIC, V1, P106, DOI 10.1257/pol.1.1.106
   Hood WW, 2001, SCIENTOMETRICS, V52, P291, DOI 10.1023/A:1017919924342
   Hsieh PN, 2009, INT J PROD ECON, V120, P540, DOI 10.1016/j.ijpe.2009.03.015
   Hu FL, 2008, INT J SUST DEV WORLD, V15, P132, DOI 10.1080/13504500809469778
   Jiang P, 2013, ENERG POLICY, V60, P611, DOI 10.1016/j.enpol.2013.05.017
   Kannan R, 2009, APPL ENERG, V86, P1873, DOI 10.1016/j.apenergy.2009.02.014
   Kawase R, 2006, ENERG POLICY, V34, P2113, DOI 10.1016/j.enpol.2005.02.005
   Konur O, 2011, APPL ENERG, V88, P3532, DOI 10.1016/j.apenergy.2010.12.059
   Leiby PN, 2013, ENERG POLICY, V56, P29, DOI 10.1016/j.enpol.2012.06.058
   Li HQ, 2012, ENERG POLICY, V41, P393, DOI 10.1016/j.enpol.2011.10.061
   Lyu PH, 2015, INFORM HEALTH SOC CA, V40, P210, DOI 10.3109/17538157.2014.892490
   Mao GZ, 2015, RENEW SUST ENERG REV, V48, P276, DOI 10.1016/j.rser.2015.03.094
   Moloney S, 2010, ENERG POLICY, V38, P7614, DOI 10.1016/j.enpol.2009.06.058
   Montoya FG, 2014, RENEW SUST ENERG REV, V29, P173, DOI 10.1016/j.rser.2013.08.094
   Mulugetta Y, 2010, ENERG POLICY, V38, P7546, DOI 10.1016/j.enpol.2010.05.049
   Olawumi TO, 2018, J CLEAN PROD, V183, P231, DOI 10.1016/j.jclepro.2018.02.162
   Phdungsilp A, 2010, ENERG POLICY, V38, P4808, DOI 10.1016/j.enpol.2009.10.026
   Protocol K., 1998, KYOT PROT UN FRAM CO
   Richerzhagen C, 2008, WORLD DEV, V36, P308, DOI 10.1016/j.worlddev.2007.06.010
   Scott D, 2010, J SUSTAIN TOUR, V18, P393, DOI 10.1080/09669581003653542
   Shealy M, 2010, ENERG POLICY, V38, P2116, DOI 10.1016/j.enpol.2009.06.051
   Shimada K, 2007, ENERG POLICY, V35, P4688, DOI 10.1016/j.enpol.2007.03.025
   Shukla PR, 2008, CLIM POLICY, V8, pS156, DOI 10.3763/cpol.2007.0498
   Siegmeier T, 2013, RENEW SUST ENERG REV, V25, P197, DOI 10.1016/j.rser.2013.04.025
   Wang MW, 2011, ENERG POLICY, V39, P6981, DOI 10.1016/j.enpol.2011.07.041
   Wang XF, 2011, ENRGY PROCED, V5, P2178, DOI 10.1016/j.egypro.2011.03.376
   Yan ZM, 2017, ENERG POLICY, V109, P499, DOI 10.1016/j.enpol.2017.07.024
   Yin MS, 2013, EXPERT SYST APPL, V40, P2767, DOI 10.1016/j.eswa.2012.11.002
   Zhang K, 2016, RENEW SUST ENERG REV, V58, P297, DOI 10.1016/j.rser.2015.12.089
   Zhou P, 2012, APPL ENERG, V90, P196, DOI 10.1016/j.apenergy.2011.02.025
   Zhou P, 2010, ENERG ECON, V32, P194, DOI 10.1016/j.eneco.2009.10.003
NR 48
TC 9
Z9 9
U1 10
U2 65
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0301-4215
EI 1873-6777
J9 ENERG POLICY
JI Energy Policy
PD FEB
PY 2019
VL 125
BP 358
EP 367
DI 10.1016/j.enpol.2018.11.004
PG 10
WC Economics; Energy & Fuels; Environmental Sciences; Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Energy & Fuels; Environmental Sciences & Ecology
GA HI9ER
UT WOS:000456758600035
DA 2022-08-23
ER

PT J
AU Jeelani, S
   Sabesan, S
   Subramanian, S
AF Jeelani, S.
   Sabesan, S.
   Subramanian, S.
TI Community knowledge, awareness and preventive practices regarding dengue
   fever in Puducherry - South India
SO PUBLIC HEALTH
LA English
DT Article
DE Community knowledge; Dengue fever; Puducherry
ID HEMORRHAGIC-FEVER; EPIDEMIC; POPULATION; ATTITUDES; MALARIA
AB Objectives: To ascertain the knowledge, attitudes and practices of adult population in Puducherry, India, regarding Dengue Fever (DF).
   Study design: A cross-sectional survey was carried out to assess the Knowledge, Awareness and Preventive practices (KAP) among a selected community in Puducherry on DF, following a major dengue outbreak in 2012.
   Methods: Between October 2012 & February 2013, an epidemiological survey was conducted among 400 households (HHs) from the most affected areas during dengue outbreak, which came under eight health delivery jurisdictions i.e. Primary Health Centres (PHCs) in Puducherry. Knowledge of dengue and the use of preventive measures were assessed by means of a pre-tested structured questionnaire. Logistic regression analysis was done to examine the association between different levels of education and income status with the knowledge of DF. The DF knowledge map was created based on the results achieved through the questionnaire using Arc GIS 10.2 software.
   Results: Although about 86% of the participants had heard of dengue, although there was no adequate knowledge on dengue vector breeding habitat as 68% of the respondents thought drains & garbage as breeding places of dengue vectors. Only 25% of participants were aware of clean water as a breeding habitat. Insufficient knowledge of disease symptoms was found, with fever (59%) being the most common symptom.
   Conclusions: There is a lack of in-depth knowledge on dengue epidemiology in the Puducherry community and observation revealed that more needs to be done by the Government as well as community members to prevent vector mosquito breeding. (C) 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
C1 [Jeelani, S.; Sabesan, S.; Subramanian, S.] Indian Council Med Res ICMR, Vector Control Res Ctr, Gorimedu 605006, Puducherry, India.
RP Jeelani, S (通讯作者)，Vector Control Res Ctr ICMR, Div Vector Ecol & Surveillance, Gorimedu 605006, Puducherry, India.
EM suhailjeelani@gmail.com
RI Sankarasubbu, Kandasami/K-8191-2017; Swaminathan,
   Subramanian/AAF-3264-2020
FU University Grants Commission (UGC), New Delhi, India
   [F.1e17.1/2011MANF-MUS-JAM-5414]
FX The author Mr. Suhail Jeelani Shah received financial support from
   University Grants Commission (UGC), New Delhi, India under Moulana Azad
   National Fellowship for Minority Students (No.
   F.1e17.1/2011MANF-MUS-JAM-5414).
CR Acharya Anita, 2005, Journal of Vector Borne Diseases, V42, P122
   Barreto FR, 2008, BMC PUBLIC HEALTH, V8, DOI 10.1186/1471-2458-8-51
   Beatty M, 2007, ABSTR ANN M AM SOC T
   Brunkard JM, 2008, SALUD PUBLICA MEXICO, V50, P227
   Chaturvedi UC, 2008, J BIOSCIENCES, V33, P429, DOI 10.1007/s12038-008-0062-3
   Degallier N, 2000, J AM MOSQUITO CONTR, V16, P114
   Dhimal M, 2014, PLOS ONE, V9
   Gubler DJ, 2004, NAT MED, V10, P129, DOI 10.1038/nm0204-129
   Gubler DJ, 1998, CLIN MICROBIOL REV, V11, P480, DOI 10.1128/CMR.11.3.480
   Gupta Pushpa, 1998, Journal of Communicable Diseases, V30, P107
   Guzman MG, 2002, LANCET INFECT DIS, V2, P33, DOI 10.1016/S1473-3099(01)00171-2
   Hales S, 2002, LANCET, V360, P830, DOI 10.1016/S0140-6736(02)09964-6
   Ibrahim Nahla Khamis Ragab, 2009, J Infect Public Health, V2, P30, DOI 10.1016/j.jiph.2009.01.004
   Itrat A, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0002620
   Kittigul Leera, 2003, Southeast Asian Journal of Tropical Medicine and Public Health, V34, P385
   Kumar Avdhesh, 2001, Journal of Communicable Diseases, V33, P274
   Kumar Neeta, 2012, Indian J Public Health, V56, P103, DOI 10.4103/0019-557X.96988
   Lall R, 1996, NATL MED J INDIA, V9, P20
   Mayxay M, 2013, BMC PUBLIC HEALTH, V13, DOI 10.1186/1471-2458-13-434
   Nalongsack S, 2009, NAGOYA J MED SCI, V71, P29
   Patz JA, 1998, ENVIRON HEALTH PERSP, V106, P147, DOI 10.1289/ehp.98106147
   RAO C V R M, 1987, Indian Journal of Pediatrics, V54, P11, DOI 10.1007/BF02751227
   Sharma Arun Kumar, 2007, Journal of Vector Borne Diseases, V44, P189
   Sharma B, 2012, INT J PHARM BIOL ARC, V3, P1383
   Shuaib F, 2010, W INDIAN MED J, V59, P139
   Swaddiwudhipong Witaya, 1992, Southeast Asian Journal of Tropical Medicine and Public Health, V23, P207
   van Benthem BHB, 2002, TROP MED INT HEALTH, V7, P993, DOI 10.1046/j.1365-3156.2002.00950.x
   WHO/ TDR Dengue, 2009, GUID DIAGN TREATM PR
   Yadav S. P., 2007, Journal of Vector Borne Diseases, V44, P205
   Yboa BC., 2013, AM J PUBLIC HLTH RES, V1, P47, DOI [10.12691/ajphr-1-2-2, DOI 10.12691/AJPHR-1-2-2]
NR 30
TC 20
Z9 22
U1 0
U2 10
PU W B SAUNDERS CO LTD
PI LONDON
PA 32 JAMESTOWN RD, LONDON NW1 7BY, ENGLAND
SN 0033-3506
EI 1476-5616
J9 PUBLIC HEALTH
JI Public Health
PD JUN
PY 2015
VL 129
IS 6
BP 790
EP 796
DI 10.1016/j.puhe.2015.02.026
PG 7
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA CK6WA
UT WOS:000356368100024
PM 25863688
DA 2022-08-23
ER

PT J
AU Gao, L
   Dai, K
   Gao, LP
   Jin, T
AF Gao, Li
   Dai, Kun
   Gao, Liping
   Jin, Tao
TI Expert knowledge recommendation systems based on conceptual similarity
   and space mapping
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Conceptual similarity; Space mapping; Core resource database (CRD);
   Institutional repository (IR); Expert Knowledge Recommendation System
   (EKRS)
AB The semantic analysis method of structured big data generated based on human knowledge is important in expert recommendation systems and scientific and technological information analysis. In these fields, the most important problem is the calculation of concept similarity. The study aims to explore the spatial mapping relationship between the general knowledge base and the professional knowledge base for the application of the general knowledge map in professional fields. With the core resource database (CRD) as the main body of the general knowledge and the institutional repository (IR) as the main body of the professional knowledge, the conceptual features of institutional expert knowledge were firstly abstracted from IR and inferred from small-scale datasets and the mathematical model was established based on the similarity of text concepts and related ranking results. Then, a two-set concept space mapping algorithm between CRD and IR was designed. In the algorithm, the more granular concept nodes were extracted from the information on the shortest paths among concepts to obtain a new knowledge set, the Expert Knowledge Recommendation System (EKRS). Finally, the simulation experiment was carried out with open datasets to verify the algorithm. The simulation results showed that the algorithm reduced the structural complexity in the calculation of large datasets. The proposed system model had a clear knowledge structure and the recommended accuracy of the text similarity was high. For small-scale knowledge base datasets with different sparsity, the system showed the stable performance, indicating the better convergence and robustness of the algorithm. (C) 2019 Published by Elsevier Ltd.
C1 [Gao, Li] Univ Shanghai Sci & Technol, Lib Dept, 516 Jungong Rd, Shanghai, Peoples R China.
   [Dai, Kun; Gao, Liping; Jin, Tao] Univ Shanghai Sci & Technol, Sch Opt Elect & Comp Engn, 516 Jungong Rd, Shanghai, Peoples R China.
RP Gao, L (通讯作者)，Univ Shanghai Sci & Technol, Lib Dept, 516 Jungong Rd, Shanghai, Peoples R China.
EM dolly2005@163.com; 2012701442@qq.com; lipinggao@usst.edu.cn;
   jintao422516@163.com
FU Humanities and Social Science Foundation of University of Shanghai for
   Science Technology [1F-18-201-001]; Natural Science Foundation of
   Shanghai [17ZR1429100]
FX The project was supported by the Humanities and Social Science
   Foundation of University of Shanghai for Science & Technology (No.
   1F-18-201-001), and Natural Science Foundation of Shanghai
   (17ZR1429100).
CR Al-Hassan M, 2015, DECIS SUPPORT SYST, V72, P97, DOI 10.1016/j.dss.2015.02.001
   Becker J, 2017, J OCCUP MED TOXICOL, V12, DOI 10.1186/s12995-017-0149-6
   Calvo H, 2016, COMPUT SPEECH LANG, V40, P79, DOI 10.1016/j.csl.2016.01.004
   [曹雪智 Cao Xuezhi], 2018, [上海交通大学学报, Journal of Shanghai Jiaotong University], V52, P1348
   De Angelis L, 2014, EUR J OPER RES, V234, P720, DOI 10.1016/j.ejor.2013.11.002
   Dias JG, 2014, EXPERT SYST APPL, V41, P7722, DOI 10.1016/j.eswa.2014.05.030
   Elliott RJ, 2014, EXPERT SYST APPL, V41, P1553, DOI 10.1016/j.eswa.2013.08.052
   Fiorelli M, 2014, IBM J RES DEV, V58, DOI 10.1147/JRD.2014.2307518
   France SL, 2012, INFORM SCIENCES, V184, P92, DOI 10.1016/j.ins.2011.07.048
   [黄晓雯 Huang Xiaowen], 2016, [计算机科学, Computer Science], V43, P51
   Jie Z., 2015, INFORM INFORM WORK, V30, P65
   Kaliraj S, 2019, MEASUREMENT, V144, P20, DOI 10.1016/j.measurement.2018.11.086
   Lei Gao, 2015, RES KNOWLEDGE ACQUIS
   Liu QD, 2019, KNOWL-BASED SYST, V163, P69, DOI 10.1016/j.knosys.2018.08.012
   Ma S, 2017, LIB SCI RES, V31, P58
   Masuda N., 2016, PHYS REP, P716
   Pham TN., 2016, INFORM SCI APPL ICIS
   Tan SL, 2014, AAAI CONF ARTIF INTE, P159
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Vyas O. P, 2014, SEMMOVIEREC EXTRACTI
   Wan Q., 2017, COMPUTER RES DEV, V54, P1655
   Wang H H., 2013, 2013 9 INT C SEM KNO
   [王俊华 Wang Junhua], 2015, [计算机研究与发展, Journal of Computer Research and Development], V52, P1499
   Wei C., 2014, J U LIB, V32, P69
   Yanes N, 2015, INNOVATION MANAGEMENT AND SUSTAINABLE ECONOMIC COMPETITIVE ADVANTAGE: FROM REGIONAL DEVELOPMENT TO GLOBAL GROWTH, VOLS I - VI, 2015, P2286
   [张立波 Zhang Libo], 2017, [计算机研究与发展, Journal of Computer Research and Development], V54, P2576
NR 26
TC 8
Z9 8
U1 7
U2 83
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD DEC 1
PY 2019
VL 136
BP 242
EP 251
DI 10.1016/j.eswa.2019.06.013
PG 10
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA IW3HO
UT WOS:000484871300020
DA 2022-08-23
ER

PT J
AU Bouarroudj, W
   Boufaida, Z
   Bellatreche, L
AF Bouarroudj, Wissem
   Boufaida, Zizette
   Bellatreche, Ladjel
TI Named entity disambiguation in short texts over knowledge graphs
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Named entity disambiguation; Entity linking; Short texts; Queries;
   Semantic and syntactic features; Linked open data
ID LINKING
AB The ever-growing usage of knowledge graphs (KGs) positions named entity disambiguation (NED) at the heart of designing accurate KG-driven systems such as query answering systems (QAS). According to the current research, most studies dealing with NED on KGs involve long texts, which is not the case of short text fragments, identified by their limited contexts. The accuracy of QASs strongly depends on the management of such short text. This limitation motivates this paper, which studies the NED problem on KGs, involving only short texts. First, we propose a NED approach including the following steps: (i) context expansion using WordNet to measure its similarity to the resource context. (ii) Exploiting coherence between entities in queries that contain more than one entity, such as "Is Michelle Obama the wife of Barack Obama?". (iii) Taking into account the relations between words to calculate their similarity with the properties of a resource. (iv) the use of syntactic features. The NED solution approach is compared to state-of-the-art approaches using five datasets. The experimental results show that our approach outperforms these systems by 27% in the F-measure. A system called Welink, implementing our proposal, is available on GitHub, and it is also accessible via a REST API.
C1 [Bouarroudj, Wissem; Boufaida, Zizette] Abdelhamid Mehri Constantine 2 Univ, LIRE Lab, Constantine, Algeria.
   [Bellatreche, Ladjel] Poitiers Univ, LIAS ISAE ENSMA, Poitiers, France.
RP Bellatreche, L (通讯作者)，Poitiers Univ, LIAS ISAE ENSMA, Poitiers, France.
EM wissem.bouarroudj@univ-constantine2.dz;
   zizette.boufaida@univ-constantine2.dz; bellatreche@ensma.fr
CR Al-Moslmi T, 2020, IEEE ACCESS, V8, P32862, DOI 10.1109/ACCESS.2020.2973928
   Alokaili A, 2020, COMPUTING, V102, P1051, DOI 10.1007/s00607-019-00748-x
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Azad HK, 2019, INFORM SCIENCES, V492, P147, DOI 10.1016/j.ins.2019.04.019
   Bellatreche L., 2021, IEEE T KNOWL DATA EN, DOI [10.1109/TKDE.2021.3059506, DOI 10.1109/TKDE.2021.3059506]
   Bellomarini L, 2018, LECT NOTES COMPUT SC, V10706, P3, DOI 10.1007/978-3-319-73117-9_1
   Bentounsi Imene, 2015, International Journal of Metadata, Semantics and Ontologies, V10, P112
   Berry Daniel M, 2007, Innovations for Requirements Analysis. From Stakeholders' Needs to Formal Designs.14th Monterey Workshop 2007. Revised Selected Papers, P1
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bouarroudj Wissem, 2018, Trends and Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing (745), P712, DOI 10.1007/978-3-319-77703-0_71
   Bouarroudj W, 2019, LECT NOTES ARTIF INT, V11529, P85, DOI 10.1007/978-3-030-27629-4_11
   Burel E, 2017, LECT NOTES COMPUT SC, V10587, P138, DOI 10.1007/978-3-319-68288-4_9
   Dai WB, 2015, IEEE T IND INFORM, V11, P771, DOI 10.1109/TII.2015.2423495
   Diefenbach Dennis, 2020, Semantic Web 17th International Conference, ESWC 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12123), P429, DOI 10.1007/978-3-030-49461-2_25
   Diefenbach D, 2018, KNOWL INF SYST, V55, P529, DOI 10.1007/s10115-017-1100-y
   Dredze M, 2010, P 23 INT C COMP LING, P277
   Dubey M, 2018, LECT NOTES COMPUT SC, V11136, P108, DOI 10.1007/978-3-030-00671-6_7
   Dubey M, 2016, LECT NOTES COMPUT SC, V9678, P300, DOI 10.1007/978-3-319-34129-3_19
   Fang Z, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P438, DOI 10.1145/3308558.3313517
   Ferragina P, 2012, IEEE SOFTWARE, V29, P70, DOI 10.1109/MS.2011.122
   Green B. F., 1961, MAY 9 11 1961 W JOIN, V19, P219, DOI DOI 10.1145/1460690.1460714
   Guo S., 2013, HLT NAACL, V2013, P1020
   Gutierrez C, 2021, COMMUN ACM, V64, P96, DOI 10.1145/3418294
   Hachey B, 2013, ARTIF INTELL, V194, P130, DOI 10.1016/j.artint.2012.04.005
   Hasibi F, 2017, LECT NOTES COMPUT SC, V10193, P40, DOI 10.1007/978-3-319-56608-5_4
   He Q, PEOPLE ACM
   He Q, 2020, WWW'20: COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2020, P307, DOI 10.1145/3366424.3383112
   Hoffner K, 2017, SEMANT WEB, V8, P895, DOI 10.3233/SW-160247
   Hoffart J., 2011, P EMNLP, DOI DOI 10.3115/V1/D11-1072
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   Hu X, 2021, KNOWL INF SYST, V63, P819, DOI 10.1007/s10115-020-01534-4
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Karlgren J., 2014, SIGIR FORUM, V48, P13, DOI [10.1145/2641383.2641387, DOI 10.1145/2641383.2641387]
   Klie J.-C., 2020, ACL, P6982
   Kolitsas Nikolaos, 2018, P 22 C COMP NAT LANG, P519
   Ling X., 2015, T ASS COMPUT LING, V3, P315, DOI DOI 10.1162/TACL_A_00141
   Logeswaran L, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3449
   Marginean A, 2017, SEMANT WEB, V8, P565, DOI 10.3233/SW-160223
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   Michel Franck, 2020, The Semantic Web - ISWC 2020. 19th International Semantic Web Conference. Lecture Notes in Computer Science (LNCS 12507), P294, DOI 10.1007/978-3-030-62466-8_19
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mishra A, 2016, J KING SAUD UNIV-COM, V28, P345, DOI 10.1016/j.jksuci.2014.10.007
   Moro A., 2014, TACL, V2, P231, DOI DOI 10.1162/TACL_A_00179
   Ngomo N., 2018, LANGUAGE, V7, P58
   Oliveira IL, 2021, INFORM SYST, V95, DOI 10.1016/j.is.2020.101624
   Parravicini A., 2019, P 2 JOINT INT WORKSH, DOI [10.1145/ 3327964.3328499, DOI 10.1145/3327964.3328499]
   Pohl K., 2005, SOFTWARE PRODUCT LIN
   Rama-Maneiro E, 2020, KNOWL-BASED SYST, V199, DOI 10.1016/j.knosys.2020.105967
   Sakor A, 2019, P 2019 C N AM CHAPT, P2336, DOI DOI 10.18653/V1/N19-1243
   Sanderson M, 2010, NAT LANG ENG, V16, P100, DOI 10.1017/S1351324909005129
   Seaborne, 2013, SPARQL 1 1 OVERVIEW
   Sevgili O., 2020, ARXIV PREPRINT ARXIV
   Sevgili O, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019:): STUDENT RESEARCH WORKSHOP, P315
   Shekarpour S, 2015, J WEB SEMANT, V30, P39, DOI 10.1016/j.websem.2014.06.002
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Singh K, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1247, DOI 10.1145/3178876.3186023
   Singhal Amit, 2012, OFFICIAL GOOGLE BLOG
   Usbeck R., 2018, LANGUAGE, V7, P1
   Usbeck R., 2017, SEMWEBEVAL 2017 SEMA, DOI [10.1007/978-3-319-69146-6_6, DOI 10.1007/978-3-319-69146-6_6]
   Usbeck R, 2014, LECT NOTES COMPUT SC, V8796, P457, DOI 10.1007/978-3-319-11964-9_29
NR 60
TC 1
Z9 1
U1 3
U2 4
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0219-1377
EI 0219-3116
J9 KNOWL INF SYST
JI Knowl. Inf. Syst.
PD FEB
PY 2022
VL 64
IS 2
BP 325
EP 351
DI 10.1007/s10115-021-01642-9
EA JAN 2022
PG 27
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZE4TI
UT WOS:000737773000002
PM 35001999
OA Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Zeng, XX
   Song, X
   Ma, TF
   Pan, XQ
   Zhou, YD
   Hou, Y
   Zhang, Z
   Li, KL
   Karypis, G
   Cheng, FX
AF Zeng, Xiangxiang
   Song, Xiang
   Ma, Tengfei
   Pan, Xiaoqin
   Zhou, Yadi
   Hou, Yuan
   Zhang, Zheng
   Li, Kenli
   Karypis, George
   Cheng, Feixiong
TI Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep
   Learning
SO JOURNAL OF PROTEOME RESEARCH
LA English
DT Article
DE COVID-19; deep learning; drug repurposing; knowledge graph;
   representation learning; SARS-CoV-2
ID TOREMIFENE; SARS-COV-2; PREDICTION; INFECTION; CANCER; GENES; DRUGS
AB There have been more than 2.2 million confirmed cases and over 120 000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there is currently a lack of proven effective medications against COVID-19. Drug repurposing offers a promising route for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep-learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from a large scientific corpus of 24 million PubMed publications. Using Amazon's AWS computing resources and a network-based, deep-learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID-19 were validated by transcriptomic and proteomics data in SARS-CoV-2-infected human cells and data from ongoing clinical trials. Whereas this study by no means recommends specific drugs, it demonstrates a powerful deep-learning methodology to prioritize existing drugs for further investigation, which holds the potential to accelerate therapeutic development for COVID-19.
C1 [Zeng, Xiangxiang; Ma, Tengfei; Pan, Xiaoqin; Li, Kenli] Hunan Univ, Sch Comp Sci & Engn, Changsha 410012, Peoples R China.
   [Song, Xiang; Zhang, Zheng] AWS Shanghai Lab, Shanghai 200335, Peoples R China.
   [Zhou, Yadi; Hou, Yuan; Cheng, Feixiong] Cleveland Clin, Lerner Res Inst, Genom Med Inst, Cleveland, OH 44106 USA.
   [Zhang, Zheng] New York Univ Shanghai, Shanghai 200122, Peoples R China.
   [Karypis, George] AWS AI, East Palo Alto, CA 94303 USA.
   [Karypis, George] Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA.
   [Cheng, Feixiong] Case Western Reserve Univ, Cleveland Clin, Dept Mol Med, Lerner Coll Med, Cleveland, OH 44195 USA.
   [Cheng, Feixiong] Case Western Reserve Univ, Case Comprehens Canc Ctr, Sch Med, Cleveland, OH 44106 USA.
RP Cheng, FX (通讯作者)，Cleveland Clin, Lerner Res Inst, Genom Med Inst, Cleveland, OH 44106 USA.; Karypis, G (通讯作者)，AWS AI, East Palo Alto, CA 94303 USA.; Karypis, G (通讯作者)，Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA.; Cheng, FX (通讯作者)，Case Western Reserve Univ, Cleveland Clin, Dept Mol Med, Lerner Coll Med, Cleveland, OH 44195 USA.; Cheng, FX (通讯作者)，Case Western Reserve Univ, Case Comprehens Canc Ctr, Sch Med, Cleveland, OH 44106 USA.
EM karypis@umn.edu; chengf@ccf.org
OI Xiao, Guoqing/0000-0001-5008-4829; Cheng, Feixiong/0000-0002-1736-2847
CR Amici C, 2006, ANTIVIR THER, V11, P1021
   [Anonymous], 2020, JAMA-J AM MED ASSOC, DOI DOI 10.1016/j.jinf.2020.03.021
   Avorn J, 2015, NEW ENGL J MED, V372, P1877, DOI 10.1056/NEJMp1500848
   Boga JA, 2012, REV MED VIROL, V22, P323, DOI 10.1002/rmv.1714
   Bojkova D, 2020, NATURE, V583, P469, DOI 10.1038/s41586-020-2332-7
   Cao B, 2020, NEW ENGL J MED, V382, P1787, DOI 10.1056/NEJMoa2001282
   Castillo R.R., 2020, SLEEP MED REV, V3, P297, DOI [10.32794/mr11250063, DOI 10.1016/j.smrv.2004.05.002]
   Chen FL, 2020, NAT PROD RES, V34, P2816, DOI 10.1080/14786419.2019.1596092
   Chen G, 2020, J CLIN INVEST, V130, P2620, DOI 10.1172/JCI137244
   Chen Z., 2020, ARXIV200204025, DOI 10.1101/2020.03.22.20040758
   Cheng FX, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-018-05116-5
   Cheng FX, 2016, PLOS COMPUT BIOL, V12, DOI 10.1371/journal.pcbi.1005074
   Chorin E, 2020, QT INTERVAL PATIENTS, DOI [DOI 10.1101/2020.04.02.20047050, 10.1101/2020.04.02.20047050]
   Cong Y, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0194868
   Dong ES, 2020, LANCET INFECT DIS, V20, P533, DOI 10.1016/S1473-3099(20)30120-1
   Dyall J, 2014, ANTIMICROB AGENTS CH, V58, P4885, DOI 10.1128/AAC.03036-14
   Gassen NC, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-13659-4
   Gordon David E, 2020, bioRxiv, DOI 10.1101/2020.03.22.002386
   Harrison C, 2020, NAT BIOTECHNOL, V38, P379, DOI 10.1038/d41587-020-00003-1
   Hoffmann M, 2020, CELL, V181, P271, DOI 10.1016/j.cell.2020.02.052
   Horby P, 2020, NEW ENGL J MED, DOI 10.1101/2020.06.22.20137273
   Johansen LM, 2013, SCI TRANSL MED, V5, DOI 10.1126/scitranslmed.3005471
   Josset L, 2013, MBIO, V4, DOI 10.1128/mBio.00165-13
   Kim YI, 2020, CELL HOST MICROBE, V27, P704, DOI 10.1016/j.chom.2020.03.023
   KIVINEN S, 1990, J STEROID BIOCHEM, V36, P217, DOI 10.1016/0022-4731(90)90008-G
   Lamb J, 2006, SCIENCE, V313, P1929, DOI 10.1126/science.1132939
   Lasso G, 2019, CELL, V178, P1526, DOI 10.1016/j.cell.2019.08.005
   Li H, 2020, LEUKEMIA, V34, P1503, DOI 10.1038/s41375-020-0848-3
   Liu J, 2020, CELL DISCOV, V6, DOI 10.1038/s41421-020-0156-0
   Percha B, 2018, BIOINFORMATICS, V34, P2614, DOI 10.1093/bioinformatics/bty114
   Powers D.M., 2011, J MACH LEARN TECHNOL, V2, P37
   Rajeshkumar N.V., 2020, FATAL TOXICITY CHLOR, DOI 10.1101/2020.03.31.018556
   Ramamoorthy S, 2016, RHEUM DIS CLIN N AM, V42, P15, DOI 10.1016/j.rdc.2015.08.002
   Reghunathan R, 2005, BMC IMMUNOL, V6, DOI 10.1186/1471-2172-6-2
   Sanders JM, 2020, JAMA-J AM MED ASSOC, V323, P1824, DOI 10.1001/jama.2020.6019
   Shi JZ, 2020, SCIENCE, V368, P1016, DOI 10.1126/science.abb7015
   Sirota M, 2011, SCI TRANSL MED, V3, DOI 10.1126/scitranslmed.3001318
   Stokes JM, 2020, CELL, V180, P688, DOI 10.1016/j.cell.2020.01.021
   Sun Z., 2019, ARXIV190210197CS
   Thompson IM, 2013, J CLIN ONCOL, V31, P515, DOI 10.1200/JCO.2012.46.6151
   VALAVAARA R, 1988, EUR J CANCER CLIN ON, V24, P785, DOI 10.1016/0277-5379(88)90316-1
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Wang XL, 2020, CELL MOL IMMUNOL, V17, P894, DOI 10.1038/s41423-020-0498-4
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Xu T, 2020, BIORXIV, DOI [10.1101/2020.04.01.017624, DOI 10.1101/2020.04.01.017624]
   Yuan SF, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-018-08015-x
   Zeng XX, 2020, CHEM SCI, V11, P1775, DOI 10.1039/c9sc04336e
   Zeng XX, 2019, BIOINFORMATICS, V35, P5191, DOI 10.1093/bioinformatics/btz418
   Zhang Z, 2019, ARXIV190901315CS
   Zhao YG, 2016, NATURE, V535, P169, DOI 10.1038/nature18615
   Zheng SJ, 2020, NAT MACH INTELL, V2, P134, DOI 10.1038/s42256-020-0152-y
   Zhou YD, 2020, CELL DISCOV, V6, DOI 10.1038/s41421-020-0153-3
NR 52
TC 84
Z9 84
U1 18
U2 48
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 1535-3893
EI 1535-3907
J9 J PROTEOME RES
JI J. Proteome Res.
PD NOV 6
PY 2020
VL 19
IS 11
BP 4624
EP 4636
DI 10.1021/acs.jproteome.0c00316
PG 13
WC Biochemical Research Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology
GA OS1RZ
UT WOS:000589944200035
PM 32654489
OA Bronze, Green Published, Green Submitted
HC Y
HP N
DA 2022-08-23
ER

PT J
AU He, X
   Zhang, R
   Rizvi, R
   Vasilakes, J
   Yang, X
   Guo, Y
   He, Z
   Prosperi, M
   Huo, JH
   Alpert, J
   Bian, J
AF He, Xing
   Zhang, Rui
   Rizvi, Rubina
   Vasilakes, Jake
   Yang, Xi
   Guo, Yi
   He, Zhe
   Prosperi, Mattia
   Huo, Jinhai
   Alpert, Jordan
   Bian, Jiang
TI ALOHA: developing an interactive graph-based visualization for dietary
   supplement knowledge graph through user-centered design
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article; Proceedings Paper
CT 3rd International Workshop on Semantics-Powered Data Analytics (SEPDA)
   held in conjunction with the IEEE International Conference on
   Bioinformatics and Biomedicine (BIBM)
CY DEC 03, 2018
CL Madrid, SPAIN
SP IEEE
DE Knowledge base; Knowledge graph; User-centered design; Usability;
   Dietary supplement; Online health information
ID HEALTH INFORMATION; INTERNET; TRENDS; QUALITY; SEARCH
AB Background Dietary supplements (DSs) are widely used. However, consumers know little about the safety and efficacy of DSs. There is a growing interest in accessing health information online; however, health information, especially online information on DSs, is scattered with varying levels of quality. In our previous work, we prototyped a web application, ALOHA, with interactive graph-based visualization to facilitate consumers' browsing of the integrated DIetary Supplement Knowledge base (iDISK) curated from scientific resources, following an iterative user-centered design (UCD) process. Methods Following UCD principles, we carried out two design iterations to enrich the functionalities of ALOHA and enhance its usability. For each iteration, we conducted a usability assessment and design session with a focus group of 8-10 participants and evaluated the usability with a modified System Usability Scale (SUS). Through thematic analysis, we summarized the identified usability issues and conducted a heuristic evaluation to map them to the Gerhardt-Powals' cognitive engineering principles. We derived suggested improvements from each of the usability assessment session and enhanced ALOHA accordingly in the next design iteration. Results The SUS score in the second design iteration decreased to 52.2 +/- 11.0 from 63.75 +/- 7.2 in our original work, possibly due to the high number of new functionalities we introduced. By refining existing functionalities to make the user interface simpler, the SUS score increased to 64.4 +/- 7.2 in the third design iteration. All participants agreed that such an application is urgently needed to address the gaps in how DS information is currently organized and consumed online. Moreover, most participants thought that the graph-based visualization in ALOHA is a creative and visually appealing format to obtain health information. Conclusions In this study, we improved a novel interactive visualization platform, ALOHA, for the general public to obtain DS-related information through two UCD design iterations. The lessons learned from the two design iterations could serve as a guide to further enhance ALOHA and the development of other knowledge graph-based applications. Our study also showed that graph-based interactive visualization is a novel and acceptable approach to end-users who are interested in seeking online health information of various domains.
C1 [He, Xing; Yang, Xi; Guo, Yi; Bian, Jiang] Univ Florida, Coll Med, Dept Hlth Outcomes & Biomed Informat, Gainesville, FL 32611 USA.
   [Zhang, Rui; Rizvi, Rubina; Vasilakes, Jake] Univ Minnesota, Inst Hlth Informat, Minneapolis, MN USA.
   [Zhang, Rui; Rizvi, Rubina; Vasilakes, Jake] Univ Minnesota, Coll Pharm, Minneapolis, MN 55455 USA.
   [Guo, Yi; Huo, Jinhai; Alpert, Jordan; Bian, Jiang] Univ Florida, Hlth Canc Ctr, Canc Informat & eHlth Core, Gainesville, FL 32611 USA.
   [He, Zhe] Florida State Univ, Sch Informat, Tallahassee, FL 32306 USA.
   [Prosperi, Mattia] Univ Florida, Epidemiol, Gainesville, FL USA.
   [Huo, Jinhai] Univ Florida, Dept Hlth Serv Res Management & Policy, Gainesville, FL USA.
   [Alpert, Jordan] Univ Florida, Coll Journalism & Commun, Dept Advertising, Gainesville, FL USA.
RP Bian, J (通讯作者)，Univ Florida, Coll Med, Dept Hlth Outcomes & Biomed Informat, Gainesville, FL 32611 USA.; Bian, J (通讯作者)，Univ Florida, Hlth Canc Ctr, Canc Informat & eHlth Core, Gainesville, FL 32611 USA.
EM bianjiang@ufl.edu
RI He, Zhe/J-2336-2014
OI He, Zhe/0000-0003-3608-0244; He, Xing/0000-0003-0290-8058
FU Cancer Informatics and eHealth Core program at the University of Florida
   Health Cancer Center [UL1TR001427, R01AT009457]; NATIONAL CENTER FOR
   ADVANCING TRANSLATIONAL SCIENCES [UL1TR001427] Funding Source: NIH
   RePORTER; National Center for Complementary & Integrative Health
   [R01AT009457] Funding Source: NIH RePORTER
FX This work was supported in part by grants UL1TR001427, R01AT009457 and
   the Cancer Informatics and eHealth Core program at the University of
   Florida Health Cancer Center. The content is solely the responsibility
   of the authors and does not necessarily represent the official views of
   the NIH. This article did not receive sponsorship for publication costs.
CR Albert Karen M., 2007, Medical Reference Services Quarterly, V26, P1, DOI 10.1300/J115v26n03_01
   [Anonymous], 2010, HDB TRANSLATION STUD, DOI DOI 10.1075/HTS.1.THI1
   Baudischova L, 2018, INT J CLIN PHARM-NET, V40, P183, DOI 10.1007/s11096-017-0564-x
   Benigeri M, 2003, HEALTH PROMOT INT, V18, P381, DOI 10.1093/heapro/dag409
   Berland GK, 2001, JAMA-J AM MED ASSOC, V285, P2612, DOI 10.1001/jama.285.20.2612
   Bian J, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0111928
   BoneID, 2019, ASTR CALC REC
   Bostock M, 2019, D3 JS DATA DRIVEN DO
   Braun V., 2006, QUAL RES PSYCHOL, V3, P77, DOI [https://doi.org/10.1191/1478088706qp063oa, 10.1191/1478088706qp063oa]
   Brooke J, 2013, J USABILITY STUD, V8, P29
   Cardel MI, 2016, OBESITY, V24, P2422, DOI 10.1002/oby.21646
   Cline RJW, 2001, HEALTH EDUC RES, V16, P671, DOI 10.1093/her/16.6.671
   Committee DGA HHS O of DP and HP ( U. S. USDA C for NPP) U. S. ( U. S.) H and HSD, 2016, DIET GUID AM 2015 20
   DeSalvo KB, 2016, JAMA-J AM MED ASSOC, V315, P457, DOI 10.1001/jama.2015.18396
   Dipko S, 2007, HLTH INFORM NATL TRE, P103
   Eysenbach G, 2002, BMJ-BRIT MED J, V324, P573, DOI 10.1136/bmj.324.7337.573
   Gahche J, 1988, NCHS DATA BRIEF, V2011, P1
   GerhardtPowals J, 1996, INT J HUM-COMPUT INT, V8, P189, DOI 10.1080/10447319609526147
   Glanz K., 2015, HLTH BEHAV THEORY RE, V5
   Government of Canada, 2019, LIC NAT HLTH PROD DA
   He X, 2018, IEEE INT C BIOINFORM, P1649, DOI 10.1109/BIBM.2018.8621340
   HelpHero, 2019, HELPHERO ADD INT PRO
   Hesse BW, 2006, J HEALTH COMMUN, V11, pVII, DOI 10.1080/10810730600692553
   Hesse BW, 2005, ARCH INTERN MED, V165, P2618, DOI 10.1001/archinte.165.22.2618
   Jacomy A, 2019, SIGMA JS
   Janvrin DianeJ., 2014, J ACCOUNTING ED, V32, P31, DOI [10.1016/j.jaccedu.2014.09.003, DOI 10.1016/J.JACCEDU.2014.09.003]
   Kantor ED, 2016, JAMA-J AM MED ASSOC, V316, P1464, DOI 10.1001/jama.2016.14403
   Keselman A, 2019, J MED INTERNET RES, V21, DOI 10.2196/11129
   Kontos Emily, 2014, J Med Internet Res, V16, pe172, DOI 10.2196/jmir.3117
   Lossio-Ventura JA, 2018, BMC MED INFORM DECIS, V18, DOI 10.1186/s12911-018-0635-5
   Manson JE, 2016, NEW ENGL J MED, V375, P1817, DOI 10.1056/NEJMp1608005
   Marik PE, 2012, JPEN-PARENTER ENTER, V36, P159, DOI 10.1177/0148607111416485
   Memorial Sloan Kettering Cancer Center, 2019, HERBS BOT OTH PROD
   Meyer J, 2010, SCHLOSS DAGSTUHL LEI, P227, DOI 10.4230/dfu.sciviz.2010
   Microsoft, 2019, TYPESCRIPT JAVASCRIP
   National Institutes of Health, 2019, DIET SUPPL LAB DAT
   Neo4j Inc, 2019, NEO4J GRAPH PLATF LE
   Neo4j Inc, 2019, PYTH GRAPH DAT
   Neo4j Inc, 2019, NEO4JS GRAPH QUER LA
   Ontotext, 2019, ONT GRAPHDB
   Palmour N, 2013, HEC FORUM, V25, P361, DOI 10.1007/s10730-013-9217-1
   Pew Research Center, 2014, SOCIAL LIFE HLTH INF
   Rizvi Rubina F, 2018, AMIA Jt Summits Transl Sci Proc, V2017, P207
   Ronacher A, 2019, FLASK PYTHON MICROFR
   Sadovsky R, 2008, CURR MED RES OPIN, V24, P1209, DOI [10.1185/030079908X280743, 10.1185/030079908X280743 ]
   Therapeutic Research Centre, 2019, NATURAL MED
NR 46
TC 10
Z9 11
U1 2
U2 20
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6947
J9 BMC MED INFORM DECIS
JI BMC Med. Inform. Decis. Mak.
PD AUG 8
PY 2019
VL 19
SU 4
SI SI
AR 150
DI 10.1186/s12911-019-0857-1
PG 18
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Medical Informatics
GA IO2EX
UT WOS:000479196800004
PM 31391091
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhang, F
   Xia, Q
   Zhang, LL
   Wang, H
   Bai, Y
   Wu, WY
AF Zhang, Fan
   Xia, Qian
   Zhang, Lianlian
   Wang, Hui
   Bai, Yan
   Wu, Wenyan
TI A bibliometric and visualized analysis of early mobilization in
   intensive care unit from 2000 to 2021
SO FRONTIERS IN NEUROLOGY
LA English
DT Article
DE early mobilization; intensive care unit; ICU-AW; bibliometric analysis;
   Citespace; VOSviewer
ID ICU-ACQUIRED WEAKNESS; CRITICAL ILLNESS; CRITICALLY-ILL; COVID-19;
   RECOMMENDATIONS; PHYSIOTHERAPY; LIBERATION; MANAGEMENT; GUIDELINE;
   DELIRIUM
AB BackgroundEarly mobilization in the intensive care unit (ICU) is a hotspot. This study aims to provide a bibliometric perspective of the progress in this field. MethodsWe extracted publications on ICU early mobilization published in the Web of Science Core Collection database from 2000 to 2021. VOSviewer was used to construct co-occurrence and co-citation relationships for authors, references, and keywords; Citespace was used to visualize knowledge mapping of subject categories, countries, and keywords with the strongest citation bursts. ResultsA total of 4,570 publications were analyzed, with a steady increase in publications in the field of ICU early mobilization. From a macro perspective, research on ICU early mobilization involves multidisciplinary involvement, including critical care medicine, neurology, and nursing; as for the meso perspective, the United States is the major contributor. Needham DM and Schweickert WD are the key researchers in this field. Moreover, the core journal is Critical Care Medicine, with the most publications and citations. The microscopic level, dominated by references and keywords, illustrates that the hotspot and frontier of research on ICU early mobilization focus on ICU-acquired weakness, delirium, the prognosis of critical illness, and severe COVID-19. ConclusionThis study presents a research landscape of ICU early mobilization from different perspectives. These findings will contribute to a better understanding of the current state of research in critical care medicine and provide the available information for future research ideas.
C1 [Zhang, Fan] Shanghai Univ Tradit Chinese Med, Dept Nephrol, Longhua Hosp, Shanghai, Peoples R China.
   [Xia, Qian; Zhang, Lianlian; Wu, Wenyan] Shanghai Univ Tradit Chinese Med, Intens Care Unit, Longhua Hosp, Shanghai, Peoples R China.
   [Wang, Hui] Shanghai Univ Tradit Chinese Med, Dept Anorectal, Longhua Hosp, Shanghai, Peoples R China.
   [Bai, Yan] Shanghai Univ Tradit Chinese Med, Dept Cardiol, Longhua Hosp, Shanghai, Peoples R China.
RP Wu, WY (通讯作者)，Shanghai Univ Tradit Chinese Med, Intens Care Unit, Longhua Hosp, Shanghai, Peoples R China.
EM wenyan1014@sohu.com
FU Longhua Hospital Shanghai University of Traditional Chinese Medicine
   Emergency and Critical Care Group; Longhua Hospital of Shanghai
   University of Traditional Chinese Medicine [YW.006.009]
FX This study was supported by Longhua Hospital Shanghai University of
   Traditional Chinese Medicine Emergency and Critical Care Group and
   Longhua Hospital of Shanghai University of Traditional Chinese Medicine
   (Grant Number: YW.006.009).
CR Adhikari NKJ, 2010, LANCET, V376, P1339, DOI 10.1016/S0140-6736(10)60446-1
   [Anonymous], SHORT HIST CITATION
   [Anonymous], DALE M NEEDHAM
   [Anonymous], EFFECT PROGRESSIVE E
   [Anonymous], EARLY REHABILITATION
   Aquim Esperidião Elias, 2019, Rev. bras. ter. intensiva, V31, P434, DOI [10.5935/0103-507x.20190084, 10.5935/0103-507X.20190084]
   Bagnato S, 2021, BRAIN SCI, V11, DOI 10.3390/brainsci11121611
   Barker-Davies RM, 2020, BRIT J SPORT MED, V54, P949, DOI 10.1136/bjsports-2020-102596
   Bein T, 2015, ANAESTHESIST, V64, pS1, DOI 10.1007/s00101-015-0071-1
   Bo Wenyan, 2021, Sports Med Health Sci, V3, P194, DOI 10.1016/j.smhs.2021.09.001
   Boehm LM, 2021, HEART LUNG, V50, P214, DOI 10.1016/j.hrtlng.2020.09.021
   Bornmann L, 2014, EMBO REP, V15, P1228, DOI 10.15252/embr.201439608
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Choong K, 2018, J PEDIATR INTENSIVE, V7, P14, DOI 10.1055/s-0037-1601424
   Cooper ID, 2015, J MED LIBR ASSOC, V103, P217, DOI 10.3163/1536-5050.103.4.013
   CYCLE, RANDOMIZED CLIN TRIA
   De Jonghe B, 2004, INTENS CARE MED, V30, P1117, DOI 10.1007/s00134-004-2174-z
   de Jonghe Bernard, 2009, Crit Care Med, V37, pS309, DOI 10.1097/CCM.0b013e3181b6e64c
   Denehy L, 2017, INTENS CARE MED, V43, P86, DOI 10.1007/s00134-016-4513-2
   Ely EW, 2017, CRIT CARE MED, V45, P321, DOI 10.1097/CCM.0000000000002175
   Evans L, 2021, INTENS CARE MED, V47, P1181, DOI 10.1007/s00134-021-06506-y
   Girard TD, 2017, AM J RESP CRIT CARE, V195, P120, DOI 10.1164/rccm.201610-2075ST
   Hanekom S, 2011, CLIN REHABIL, V25, P771, DOI 10.1177/0269215510397677
   Hermans G, 2014, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD006832.pub3
   Hodgson CL, 2021, CRIT CARE, V25, DOI 10.1186/s13054-021-03741-z
   Hodgson CL, 2018, CRIT CARE, V22, DOI 10.1186/s13054-018-1998-9
   Hodgson CL, 2014, CRIT CARE, V18, DOI 10.1186/s13054-014-0658-y
   Hodgson CL, 2013, CRIT CARE, V17, DOI 10.1186/cc11820
   Huang CL, 2021, LANCET, V397, P220, DOI 10.1016/S0140-6736(20)32656-8
   Huang CL, 2020, LANCET, V395, P497, DOI 10.1016/S0140-6736(20)30183-5
   Huang LX, 2021, LANCET, V398, P747, DOI 10.1016/S0140-6736(21)01755-4
   Intiso D, 2018, CRIT CARE, V22, DOI 10.1186/s13054-017-1923-7
   Jiandani MP, 2020, INDIAN J CRIT CARE M, V24, P905, DOI 10.5005/jp-journals-10071-23564
   Kayambu G, 2013, CRIT CARE MED, V41, P1543, DOI 10.1097/CCM.0b013e31827ca637
   Kohn R, 2017, ANN AM THORAC SOC, V14, P254, DOI 10.1513/AnnalsATS.201607-579OC
   Kress JP, 2014, NEW ENGL J MED, V370, P1626, DOI 10.1056/NEJMra1209390
   Kuehn BM, 2019, JAMA-J AM MED ASSOC, V321, P1036, DOI 10.1001/jama.2019.0420
   Lang JK, 2020, CRIT CARE MED, V48, pE1121, DOI 10.1097/CCM.0000000000004574
   Latronico N, 2011, LANCET NEUROL, V10, P931, DOI 10.1016/S1474-4422(11)70178-8
   Lilly CM, 2017, CHEST, V152, P723, DOI 10.1016/j.chest.2017.06.050
   Morandi A, 2017, CRIT CARE MED, V45, pE1111, DOI 10.1097/CCM.0000000000002640
   Needham DM, 2012, CRIT CARE MED, V40, P502, DOI 10.1097/CCM.0b013e318232da75
   Okada Y, 2019, J INTENSIVE CARE, V7, DOI 10.1186/s40560-019-0413-1
   Parry SM, 2015, J CRIT CARE, V30, DOI 10.1016/j.jcrc.2015.05.024
   Powers SK, 2013, AM J PHYSIOL-REG I, V305, pR464, DOI 10.1152/ajpregu.00231.2013
   Powers SK, 2011, ANTIOXID REDOX SIGN, V15, P2519, DOI 10.1089/ars.2011.3973
   Puthucheary ZA, 2013, JAMA-J AM MED ASSOC, V310, P1591, DOI 10.1001/jama.2013.278481
   Righy C, 2019, CRIT CARE, V23, DOI 10.1186/s13054-019-2489-3
   Sanchez D, 2020, CRIT CARE, V24, DOI 10.1186/s13054-020-03318-2
   Schweickert WD, 2009, LANCET, V373, P1874, DOI 10.1016/S0140-6736(09)60658-9
   Sommers J, 2015, CLIN REHABIL, V29, P1051, DOI 10.1177/0269215514567156
   Stevens RD, 2007, INTENS CARE MED, V33, P1876, DOI 10.1007/s00134-007-0772-2
   Truong AD, 2009, CRIT CARE, V13, DOI 10.1186/cc7885
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vanhorebeek I, 2020, INTENS CARE MED, V46, P637, DOI 10.1007/s00134-020-05944-4
   Vasilevskis EE, 2010, CRIT CARE MED, V38, pS683, DOI 10.1097/CCM.0b013e3181f245d3
   Vasilevskis EE, 2010, CHEST, V138, P1224, DOI 10.1378/chest.10-0466
   Wieske L, 2015, CRIT CARE, V19, DOI 10.1186/s13054-015-0937-2
   Wischmeyer PE, 2015, CRIT CARE, V19, DOI 10.1186/cc14724
   Yanagi N, 2021, NUTRIENTS, V13, DOI 10.3390/nu13082726
   Zhang L, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0223185
   Zorowitz RD, 2016, CHEST, V150, P966, DOI 10.1016/j.chest.2016.06.006
NR 62
TC 0
Z9 0
U1 0
U2 0
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-2295
J9 FRONT NEUROL
JI Front. Neurol.
PD JUL 18
PY 2022
VL 13
AR 848545
DI 10.3389/fneur.2022.848545
PG 12
WC Clinical Neurology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA 3P9QX
UT WOS:000837869700001
PM 35923825
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Giorgi, I
   Cangelosi, A
   Masala, GL
AF Giorgi, Ioanna
   Cangelosi, Angelo
   Masala, Giovanni L.
TI Learning Actions From Natural Language Instructions Using an ON-World
   Embodied Cognitive Architecture
SO FRONTIERS IN NEUROROBOTICS
LA English
DT Article
DE cognitive architecture; natural language learning; language to action;
   semantic mapping; abstract words; action grounding; robot action;
   developmental cognitive robotics
ID HUMAN-ROBOT INTERACTION; WORDS; CONSTRUCTIONS; ACQUISITION; MEANINGS;
   MODELS
AB Endowing robots with the ability to view the world the way humans do, to understand natural language and to learn novel semantic meanings when they are deployed in the physical world, is a compelling problem. Another significant aspect is linking language to action, in particular, utterances involving abstract words, in artificial agents. In this work, we propose a novel methodology, using a brain-inspired architecture, to model an appropriate mapping of language with the percept and internal motor representation in humanoid robots. This research presents the first robotic instantiation of a complex architecture based on the Baddeley's Working Memory (WM) model. Our proposed method grants a scalable knowledge representation of verbal and non-verbal signals in the cognitive architecture, which supports incremental open-ended learning. Human spoken utterances about the workspace and the task are combined with the internal knowledge map of the robot to achieve task accomplishment goals. We train the robot to understand instructions involving higher-order (abstract) linguistic concepts of developmental complexity, which cannot be directly hooked in the physical world and are not pre-defined in the robot's static self-representation. Our proposed interactive learning method grants flexible run-time acquisition of novel linguistic forms and real-world information, without training the cognitive model anew. Hence, the robot can adapt to new workspaces that include novel objects and task outcomes. We assess the potential of the proposed methodology in verification experiments with a humanoid robot. The obtained results suggest robust capabilities of the model to link language bi-directionally with the physical environment and solve a variety of manipulation tasks, starting with limited knowledge and gradually learning from the run-time interaction with the tutor, past the pre-trained stage.
C1 [Giorgi, Ioanna; Cangelosi, Angelo] Univ Manchester, Dept Comp Sci, Manchester, Lancs, England.
   [Masala, Giovanni L.] Manchester Metropolitan Univ, Dept Comp & Math, Manchester, Lancs, England.
RP Giorgi, I (通讯作者)，Univ Manchester, Dept Comp Sci, Manchester, Lancs, England.
EM ioanna.giorgi@manchester.ac.uk
OI Cangelosi, Angelo/0000-0002-4709-2243; Giorgi,
   Ioanna/0000-0001-9583-6959
FU Interreg 2 Seas Mers Zeeen AGE In project [2S05-014]; H2020 projects
   [857897, 952324]; UKRI Trustworthy Autonomous Systems Node on Trust
   [EP/V026682/1]
FX The authors of this paper acknowledge the partial funding provided by
   the Interreg 2 Seas Mers Zeeen AGE In project (2S05-014) to support the
   work with the NAO robot. Cangelosi's involvement in this work was
   partially supported by the H2020 projects of eLADDA (857897) and
   TRAINCREASE (952324), and the UKRI Trustworthy Autonomous Systems Node
   on Trust (EP/V026682/1).
CR Alomari M., 2017, P AAAI C ART INT, V31
   [Anonymous], 2016, P NAACL
   Araki T, 2012, IEEE INT C INT ROBOT, P1623, DOI 10.1109/IROS.2012.6385812
   Arandjelovic R, 2017, IEEE I CONF COMP VIS, P609, DOI 10.1109/ICCV.2017.73
   Armstrong B.C., 2020, P 42 ANN C COGN SCI
   Baddeley A, 2012, ANNU REV PSYCHOL, V63, P1, DOI 10.1146/annurev-psych-120710-100422
   Barsalou L., 2005, GROUNDING COGNITION, P129, DOI [10.1017/cbo9780511499968.007, DOI 10.1017/CBO9780511499968.007, 10.1017/CBO9780511499968, DOI 10.1017/CBO9780511499968]
   Beetz Michael, 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011), P529, DOI 10.1109/Humanoids.2011.6100855
   Borghi AM, 2011, FRONT PSYCHOL, V2, DOI 10.3389/fpsyg.2011.00015
   Cakmak M, 2010, IEEE T AUTON MENT DE, V2, P108, DOI 10.1109/TAMD.2010.2051030
   Cangelosi A, 2018, CHILD DEV PERSPECT, V12, P183, DOI 10.1111/cdep.12282
   Cangelosi A, 2018, PHILOS T R SOC B, V373, DOI 10.1098/rstb.2017.0131
   Cangelosi A, 2010, PHYS LIFE REV, V7, P139, DOI 10.1016/j.plrev.2010.02.001
   Cantrell R., 2011, 2011 RO-MAN: The 20th IEEE International Symposium on Robot and Human Interactive Communication, P125, DOI 10.1109/ROMAN.2011.6005199
   Cowan N., 1998, ATTENTION MEMORY INT
   Cowan N, 2012, PSYCHOL REV, V119, P480, DOI 10.1037/a0027791
   Demiris Y, 2006, ROBOT AUTON SYST, V54, P361, DOI 10.1016/j.robot.2006.02.003
   Dubba K.S., 2014, QUALITATIVE REPRESEN, P26
   Duffy B.R., 2000, P BRAIN MACH WORKSH
   ELMAN JL, 1991, MACH LEARN, V7, P195, DOI 10.1007/BF00114844
   Feldman J, 2004, BRAIN LANG, V89, P385, DOI 10.1016/S0093-934X(03)00355-9
   Giorgi I, 2021, IEEE T COGN DEV SYST, V13, P922, DOI 10.1109/TCDS.2020.3033963
   Glenberg AM, 2002, PSYCHON B REV, V9, P558, DOI 10.3758/BF03196313
   Golosio B, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0140866
   Heinrich S, 2020, FRONT NEUROROBOTICS, V14, DOI 10.3389/fnbot.2020.00052
   Heinrich S, 2018, CONNECT SCI, V30, P99, DOI 10.1080/09540091.2017.1318357
   Hinaut X., 2014, LECT NOTES COMPUTER, V8681, DOI [10.1007/978-3-319-11179-7_5, DOI 10.1007/978-3-319-11179-7_5]
   Hinaut X, 2020, IEEE T COGN DEV SYST, V12, P179, DOI 10.1109/TCDS.2019.2957006
   Hinaut X, 2014, FRONT NEUROROBOTICS, V8, P1, DOI 10.3389/fnbot.2014.00016
   Hussein A, 2017, ACM COMPUT SURV, V50, DOI 10.1145/3054912
   Iwahashi N, 2008, IEICE T INF SYST, VE91D, P312, DOI 10.1093/ietisy/e91-d.2.312
   Jamone L, 2018, IEEE T COGN DEV SYST, V10, P4, DOI 10.1109/TCDS.2016.2594134
   Kanda H, 2007, 2007 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-9, P1852
   Krizhevsky Alex, 2017, Communications of the ACM, V60, P84, DOI 10.1145/3065386
   Kurup U, 2012, BIOL INSPIR COGN ARC, V2, P88, DOI 10.1016/j.bica.2012.07.004
   Levine S, 2016, J MACH LEARN RES, V17
   Matuszek C, 2012, P INT C MACH LEARN I
   McClelland J., 1986, EXPLORATIONS MICROST, V2, P272
   Mealier AL, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.01331
   Miikkulainen R., 1993, SUBSYMBOLIC NATURAL
   Miyazawa K, 2019, FRONT ROBOT AI, V6, DOI 10.3389/frobt.2019.00131
   Morse AF, 2017, COGNITIVE SCI, V41, P32, DOI 10.1111/cogs.12390
   Moulin-Frier C, 2018, IEEE T COGN DEV SYST, V10, P1005, DOI 10.1109/TCDS.2017.2754143
   Nyga D., 2018, P 2 C ROB LEARN SER, V87, P714
   Ogata T, 2005, ADV ROBOTICS, V19, P651, DOI 10.1163/1568553054255655
   Palm G., 1990, CONCEPTS NEUROSCI, V1, P133
   Perlovsky L, 2009, NEURAL NETWORKS, V22, P247, DOI 10.1016/j.neunet.2009.03.007
   Pulvermuller F, 2014, CORTEX, V57, P1, DOI 10.1016/j.cortex.2014.02.015
   Recupero DR, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2019.102094
   Redmon J, 2016, PROC CVPR IEEE, P779, DOI 10.1109/CVPR.2016.91
   Roy D., 2003, 2003 IEEE WORKSH AUT
   Roy DK, 2002, COGNITIVE SCI, V26, P113, DOI 10.1016/S0364-0213(01)00061-1
   Roy DK, 2002, COMPUT SPEECH LANG, V16, P353, DOI 10.1016/S0885-2308(02)00024-4
   Sharkey N., 2000, SYMBOLIC NEURAL NET, DOI [10.1007/10719871_22, DOI 10.1007/10719871_22]
   She LB, 2014, IEEE ROMAN, P868, DOI 10.1109/ROMAN.2014.6926362
   Shixiang Gu, 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA), P3389, DOI 10.1109/ICRA.2017.7989385
   SoftBank Robotics, 2021, NAO HUM PROGR ROB
   Steels L, 2003, TRENDS COGN SCI, V7, P308, DOI 10.1016/S1364-6613(03)00129-3
   Stepanova K, 2018, IEEE T COGN DEV SYST, V10, P784, DOI 10.1109/TCDS.2018.2819359
   Stoytchev A, 2008, LECT NOTES ARTIF INT, V4760, P140, DOI 10.1007/978-3-540-77915-5_10
   Stramandinoli F, 2012, NEURAL NETWORKS, V32, P165, DOI 10.1016/j.neunet.2012.02.012
   Sugita Y, 2005, ADAPT BEHAV, V13, P33, DOI 10.1177/105971230501300102
   Tani J., 2016, EXPLORING ROBOTIC MI, DOI [10.1093/acprof:oso/9780190281069.001.0001, DOI 10.1093/ACPROF:OSO/9780190281069.001.0001]
   Tellex S, 2014, MACH LEARN, V94, P151, DOI 10.1007/s10994-013-5383-2
   Thomaz A. L., 2009, 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI), P15
   Twiefel J, 2016, IEEE ROMAN, P52, DOI 10.1109/ROMAN.2016.7745090
   Wilson M, 2002, PSYCHON B REV, V9, P625, DOI 10.3758/BF03196322
   Yamada T, 2017, FRONT NEUROROBOTICS, V11, DOI 10.3389/fnbot.2017.00070
   Yamada T, 2016, FRONT NEUROROBOTICS, V10, DOI 10.3389/fnbot.2016.00005
   Yamada T, 2015, IEEE INT C INT ROBOT, P4179, DOI 10.1109/IROS.2015.7353968
   Yu H., 2013, 51 ANN M ASS COMP LI, P53
NR 71
TC 0
Z9 0
U1 3
U2 12
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1662-5218
J9 FRONT NEUROROBOTICS
JI Front. Neurorobotics
PD MAY 13
PY 2021
VL 15
AR 626380
DI 10.3389/fnbot.2021.626380
PG 22
WC Computer Science, Artificial Intelligence; Robotics; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Robotics; Neurosciences & Neurology
GA SI7DB
UT WOS:000654985600001
PM 34054452
OA Green Published, gold, Green Accepted
DA 2022-08-23
ER

PT J
AU Semenza, JC
   Hoser, C
   Herbst, S
   Rechenburg, A
   Suk, JE
   Frechen, T
   Kistemann, T
AF Semenza, Jan C.
   Hoeser, Christoph
   Herbst, Susanne
   Rechenburg, Andrea
   Suk, Jonathan E.
   Frechen, Tobias
   Kistemann, Thomas
TI Knowledge Mapping for Climate Change and Food- and Waterborne Diseases
SO CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY
LA English
DT Review
DE Campylobacter sp.; climate change; climate variability; Cryptosporidium
   sp.; environment; food; food- and waterborne diseases; knowledge base;
   Listeria sp.; Norovirus; ontology; precipitation; rain; reservoir;
   Salmonella sp.; Vibrio sp.
ID 1995 HEAT-WAVE; SIMULATIONS; EUROPE; HEALTH
AB The authors extracted from the PubMed and ScienceDirect bibliographic databases all articles published between 1998 and 2009 that were relevant to climate change and food- and waterborne diseases. Any material within each article that provided information about a relevant pathogen and its relationship with climate and climate change was summarized as a key fact, entered into a relational knowledge base, and tagged with the terminology (predefined terms) used in the field. These terms were organized, quantified, and mapped according to predefined hierarchical categories. For noncholera Vibrio sp. and Cryptosporidium sp., data on climatic and environmental influences (52% and 49% of the total number of key facts, respectively) pertained to specific weather phenomena (as opposed to climate change phenomena) and environmental determinants, whereas information on the potential effects of food-related determinants that might be related to climate or climate change were virtually absent. This proportion was lower for the other pathogens studied (Campylobacter sp. 40%, Salmonella sp. 27%, Norovirus 25%, Listeria sp. 8%), but they all displayed a distinct concentration of information on general food- and water-related determinants or effects, albeit with little detail. Almost no information was available concerning the potential effects of changes in climatic variables on the pathogens evaluated, such as changes in air or water temperature, precipitation, humidity, UV radiation, wind, cloud coverage, sunshine hours, or seasonality. Frequency profiles revealed an abundance of data on weather and food-specific determinants, but also exposed extensive data deficiencies, particularly with regard to the potential effects of climate change on the pathogens evaluated. A reprioritization of public health research is warranted to ensure that funding is dedicated to explicitly studying the effects of changes in climate variables on food- and waterborne diseases.
C1 [Semenza, Jan C.; Suk, Jonathan E.] European Ctr Dis Prevent & Control ECDC, Future Threats & Determinants Sect, Sci Advice Unit, S-17183 Stockholm, Sweden.
   [Hoeser, Christoph; Herbst, Susanne; Rechenburg, Andrea; Frechen, Tobias; Kistemann, Thomas] Univ Bonn, Inst Hyg & Publ Hlth, Dept Med Geog & Publ Hlth, Bonn, Germany.
RP Semenza, JC (通讯作者)，European Ctr Dis Prevent & Control ECDC, Future Threats & Determinants Sect, Sci Advice Unit, Tomtebodavagen 11A, S-17183 Stockholm, Sweden.
EM Jan.Semenza@ecdc.europa.eu
RI Suk, Jonathan/L-2300-2017
OI Suk, Jonathan/0000-0003-4689-4583; Hoser, Christoph/0000-0002-8947-256X;
   Kistemann, Thomas/0000-0002-3306-7100
CR [Anonymous], 2004, IMPACTS WARMING ARCT
   [Anonymous], 2007, FAO SPEC EV AGR PEST, P1
   Atkins D, 2004, BMJ-BRIT MED J, V328, P1490
   Barbeau DN, 2010, ANNU REV PUBL HEALTH, V31, P165, DOI 10.1146/annurev.publhealth.012809.103643
   Beniston M, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2003GL018857
   Climate Research Unit, 2006, INF SHEETS
   Ebi KL, 2009, ENVIRON HEALTH PERSP, V117, P857, DOI 10.1289/ehp.0800088
   European Center for Disease Prevention and Control, 2009, ANN EP REP COMM DIS
   European Centre for Disease Prevention and Control, 2010, HDB NAT VULN IMP AD
   Giannakopoulos C., 2005, CLIMATE CHANGE IMPAC, VVolume 1
   Giorgi F, 2004, CLIM DYNAM, V22, P733, DOI 10.1007/s00382-004-0409-x
   Good P, 2006, CLIM RES, V31, P19, DOI 10.3354/cr031019
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Guyatt GH, 2008, BRIT MED J, V336, P924, DOI 10.1136/bmj.39489.470347.AD
   Hajat S., 2003, APPL ENV SCI PUBLIC, V1, P13
   Heer J., 2005, P SIGCHI C HUM FACT, P421
   Intergovernmental Panel on Climate Change, 2007, 4 ASS REP
   Kazoun C., 2008, PROGRAMMING FLEX
   Kennedy Heather, 2009, INTRO 3D DATA MODELI
   Kistemann T, 2002, APPL ENVIRON MICROB, V68, P2188, DOI 10.1128/AEM.68.5.2188-2197.2002
   Lake Iain R., 2005, Journal of Water and Health, V3, P469
   Matulla C, 2008, CLIM DYNAM, V31, P125, DOI 10.1007/s00382-007-0333-y
   MCMICHAEL AJ, 1993, INT J EPIDEMIOL, V22, P1, DOI 10.1093/ije/22.1.1
   Menzel A, 2006, GLOBAL CHANGE BIOL, V12, P1969, DOI 10.1111/j.1365-2486.2006.01193.x
   Munn K., 2008, APPL ONTOLOGY INTRO, P7
   Palmer TN, 2002, NATURE, V415, P512, DOI 10.1038/415512a
   Palutikof JP, 2004, NATO SCI S SS IV EAR, V37, P61
   PETITTI DB, 1994, META ANAL DECISION A
   Raisanen J, 2004, CLIM DYNAM, V22, P13, DOI 10.1007/s00382-003-0365-x
   Root TL, 2003, NATURE, V421, P57, DOI 10.1038/nature01333
   Schroter D, 2005, SCIENCE, V310, P1333, DOI 10.1126/science.1115233
   Semenza J C, 2007, Euro Surveill, V12, pE13
   Semenza J. C., 2011, CRIT REV EN IN PRESS, DOI 10/1080/10643389.2010.534706
   Semenza JC, 2008, AM J PUBLIC HEALTH, V98, P787, DOI 10.2105/AJPH.2007.120329
   Semenza JC, 2009, LANCET INFECT DIS, V9, P365, DOI 10.1016/S1473-3099(09)70104-5
   Semenza JC, 1999, AM J PREV MED, V16, P269, DOI 10.1016/S0749-3797(99)00025-2
   Semenza JC, 1996, NEW ENGL J MED, V335, P84, DOI 10.1056/NEJM199607113350203
   Tait A, 2003, J APPL METEOROL, V42, P193, DOI 10.1175/1520-0450(2003)042<0193:MFOUSD>2.0.CO;2
   UC Berkeley Visualization Lab, 2010, FLAR DAT VIS WEB FLA
   World Meteorological Organization, 2008, 1039 WMO
NR 40
TC 25
Z9 25
U1 0
U2 53
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 1064-3389
EI 1547-6537
J9 CRIT REV ENV SCI TEC
JI Crit. Rev. Environ. Sci. Technol.
PY 2012
VL 42
IS 4
BP 378
EP 411
DI 10.1080/10643389.2010.518520
PG 34
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 877CL
UT WOS:000299154100002
PM 24771989
OA Green Published
DA 2022-08-23
ER

PT J
AU Chen, JY
   Yu, J
   Lu, WJ
   Qian, YR
   Li, P
AF Chen, Jiaying
   Yu, Jiong
   Lu, Wenjie
   Qian, Yurong
   Li, Ping
TI IR-Rec: An interpretive rules-guided recommendation over knowledge graph
SO INFORMATION SCIENCES
LA English
DT Article
DE Recommender system; Knowledge graph; Rules learning; Explainable
ID INFORMATION
AB Most existing recommendation methods focus on the improvement of recommender accu-racy while ignoring the influence of recommended explanation. Recommender explainabil-ity is an efficient way to help consumers make much more suitable decisions and enhance their acceptance and trustfulness in recommender systems (RSs). Incorporating a knowl-edge graph (KG) into RS is a promising way to improve recommender results while enhanc-ing the strength of explanation. In this paper, an interactive rules-guided recommender (IR-Rec) framework based on KG is proposed. First, an existing KG is enriched by introduc-ing it to facts about e-commerce. Then, a number of paths are extracted from the enhanced KG for user-item interactions that are able to ascertain the underlying reason for recom-mendations according to the semantic strength of the KG. These paths are summarized into some common behavior rules that have the ability to explain the underlying motivations of users. According to the characteristics of users, items, and rules, different neural networks are designed, such as a graph convolutional network, to learn more accurate embeddings. Furthermore, recommendations that meet users' personalized interests are made by com-bining the public behavior rules and their individual features. Extensive experiments are carried out on four Amazon datasets for top -K recommendation. All the results show that the proposed method performs better than other respective baselines and demonstrate the effectiveness of rule extraction for making recommendations. (c) 2021 Elsevier Inc. All rights reserved.
C1 [Chen, Jiaying; Yu, Jiong; Lu, Wenjie; Li, Ping] Xinjiang Univ, Sch Informat Sci & Engn, Urumqi 830046, Peoples R China.
   [Qian, Yurong] Xinjiang Univ, Sch Software, Urumqi 830046, Peoples R China.
RP Chen, JY (通讯作者)，Xinjiang Univ, Sch Informat Sci & Engn, Urumqi 830046, Peoples R China.
EM chenjy@stu.xju.edu.cn
FU National Natural Science Foundation of China [61862060, 61462079,
   61562086, 61562078]
FX The authors are grateful to the anonymous referees for their insightful
   suggestions and comments. This research was supported in part by the
   National Natural Science Foundation of China, under Grants Nos.
   61862060, 61462079, 61562086 and 61562078.
CR [Anonymous], 2019, P 11 INT C MACH LEAR
   Betru B.T., 2017, INT J COMPUT APPL, V162, P17
   Cai MH, 2019, 2019 15TH INTERNATIONAL CONFERENCE ON MOBILE AD-HOC AND SENSOR NETWORKS (MSN 2019), P7, DOI 10.1109/MSN48538.2019.00016
   Chen SY, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1187, DOI 10.1145/3219819.3220122
   Cheng H.T., 2016, P 1 WORKSHOP DEEP LE, P7, DOI DOI 10.1145/2988450.2988454
   Choo J, 2019, P IEEE INT C CLUST C, P1
   Covington P, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P191, DOI 10.1145/2959100.2959190
   Diez J, 2020, INFORM SCIENCES, V520, P416, DOI 10.1016/j.ins.2020.02.018
   Dong M, 2020, INFORM SCIENCES, V540, P469, DOI 10.1016/j.ins.2020.05.094
   Gao L, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3378
   Guo XB, 2020, INFORM SCIENCES, V540, P263, DOI 10.1016/j.ins.2020.06.041
   Han JY, 2019, INFORM SCIENCES, V503, P521, DOI 10.1016/j.ins.2019.07.024
   He X., 2017, P 26 INT JOINT C ART, P1725, DOI [10.24963/ijcai.2017/239, DOI 10.24963/IJCAI.2017/239]
   He X., 2015, P 24 ACM INT C INF K, P1661
   He XN, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2227
   He XN, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P355, DOI 10.1145/3077136.3080777
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Huang J, 2018, ACM/SIGIR PROCEEDINGS 2018, P505, DOI 10.1145/3209978.3210017
   Jin YH, 2020, MILITARY MED RES, V7, DOI 10.1186/s40779-020-0233-6
   Khoshahval S, 2018, LECT NOTES GEOINF CA, P271, DOI 10.1007/978-3-319-78208-9_14
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Lumbantoruan R, 2019, IEEE DATA MINING, P1240, DOI 10.1109/ICDM.2019.00154
   Ma WZ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1210, DOI 10.1145/3308558.3313607
   Pujahari A, 2019, INFORM SCIENCES, V490, P126, DOI 10.1016/j.ins.2019.03.064
   Salakhutdinov R., 2007, P 24 INT C MACH LEAR, V24, P791, DOI DOI 10.1145/1273496.1273596
   Sedhain S, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P111, DOI 10.1145/2740908.2742726
   Shi C., 2015, P 24 ACM INT C INF K, P453
   Shi C, 2019, WORLD WIDE WEB, V22, P153, DOI 10.1007/s11280-018-0553-6
   Shi C, 2019, IEEE T KNOWL DATA EN, V31, P357, DOI 10.1109/TKDE.2018.2833443
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang HZ, 2018, INT GEOL REV, V60, P1684, DOI 10.1080/00206814.2018.1428829
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang SF, 2020, INFORM SCIENCES, V513, P614, DOI 10.1016/j.ins.2019.11.028
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wu S, 2016, PROC INT CONF DATA, P1218, DOI 10.1109/ICDE.2016.7498326
   Xian YK, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P285, DOI 10.1145/3331184.3331203
   Yadav P., 2019, P IEEE INT C BIG DAT, DOI [10.13140/RG.2.2.31674.24009, DOI 10.13140/RG.2.2.31674.24009]
   Yu J, 2020, PATH BASED REASONING
   Yu X, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P283, DOI 10.1145/2556195.2556259
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhang SA, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3285029
   Zhang YF, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1449, DOI 10.1145/3132847.3132892
   Zhao WNX, 2016, IEEE T KNOWL DATA EN, V28, P1147, DOI 10.1109/TKDE.2015.2508816
   Zheng XL, 2019, INFORM SCIENCES, V474, P170, DOI 10.1016/j.ins.2018.09.054
NR 46
TC 6
Z9 6
U1 9
U2 30
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD JUL
PY 2021
VL 563
BP 326
EP 341
DI 10.1016/j.ins.2021.03.004
EA APR 2021
PG 16
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SL0GL
UT WOS:000656595000001
DA 2022-08-23
ER

PT J
AU Qassimi, S
   Abdelwahed, E
   Hafidi, M
   Qazdar, A
AF Qassimi, Sara
   Abdelwahed, El Hassan
   Hafidi, Meriem
   Qazdar, Aimad
TI Towards a folksonomy graph-based context-aware recommender system of
   annotated books
SO JOURNAL OF BIG DATA
LA English
DT Article
DE Collaborative Tagging; Folksonomy; Knowledge Graph; Emergent Semantic;
   Spectral Clustering; Recommender System; Context-awareness
AB The emergence of collaborative interactions has empowered users by enabling their interactions through tagging practices that create a folksonomy, also called, classification of the shared resources, any identifiable thing or item on the system. In education, tagging is considered a powerful meta-cognitive strategy that successfully engages learners in the learning process. Besides, the collaborative tagging gathers learners' opinions, thus, provides more comprehensible recommendations. Still, the abundant shared contents are mostly unorganized which makes it hard for users to select and discover the appropriate items of their interests. Thus, the use of recommender systems overcomes the distressing search problem by assisting users in their searching and exploring experience, and suggesting relevant items matching their preferences. In this regard, this article presents a folksonomy graphs based context-aware recommender system (CARS) of annotated books. The generated graphs express the semantic relatedness between these resources, i.e. books, by effectively modeling the folksonomy relationship between user-resource-tag and integrating contextual information within a multi-layer graph referring to a Knowledge Graph (KG). To put our proposal into shape, we model a real-world application of Goodbooks-10k dataset to recommend books. The proposed approach incorporates spectral clustering to deal with the graph partitioning problem. The experimental evaluation shows relevant performance results of graph-based book recommendations.
C1 [Qassimi, Sara] Univ Cady Ayyad UCA, Fac Sci & Tech Guiliz FSTG, Dept Comp Sci, Lab L2IS, Marrakech, Morocco.
   [Abdelwahed, El Hassan; Hafidi, Meriem; Qazdar, Aimad] Univ Cady Ayyad UCA, Fac Sci Semlalia FSSM, Dept Comp Sci, Lab LISI, Marrakech, Morocco.
RP Qassimi, S (通讯作者)，Univ Cady Ayyad UCA, Fac Sci & Tech Guiliz FSTG, Dept Comp Sci, Lab L2IS, Marrakech, Morocco.
EM sara.ciassimi@uca.ac.ma
RI QASSIMI, Sara/ABG-6886-2021
OI QASSIMI, Sara/0000-0002-9441-986X
CR Abbas A, 2015, COMPUTING, V97, P667, DOI 10.1007/s00607-015-0448-7
   Angles R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1322432.1322433
   Bathla G, 2020, J SUPERCOMPUT, V76, P4057, DOI 10.1007/s11227-017-2196-2
   Cao YW, 2015, J COMPUT EDUC, V2, P93, DOI 10.1007/s40692-015-0027-0
   Rivera AC, 2018, ADV INTELL SYST, V721, P937, DOI 10.1007/978-3-319-73450-7_89
   Castells P, 2015, ACM T INTEL SYST TEC, V5, DOI 10.1145/2668113
   Catherine R, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P325, DOI 10.1145/2959100.2959131
   Codd E. F., 1981, SIGART Newsletter, P112
   Drachsler H., 2015, RECOMMENDER SYSTEMS, P421, DOI [10.1007/9781-4899-7637-6_12, DOI 10.1007/978-1-4899-7637-6_12]
   Hafidi M, 2021, CONCURR COMP-PRACT E, V33, DOI 10.1002/cpe.5624
   Jeon-Hyung Kang, 2011, Proceedings of the 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and IEEE Third International Conference on Social Computing (PASSAT/SocialCom 2011), P215, DOI 10.1109/PASSAT/SocialCom.2011.106
   Khalid O, 2014, IEEE T SERV COMPUT, V7, P401, DOI 10.1109/TSC.2013.53
   Klanja-Milievi A, 2017, FOLKSONOMY TAG BASED, P77, DOI [10.1007/978-3-319-41163-7_7, DOI 10.1007/978-3-319-41163-7_7]
   Klasnja-Milicevic A, 2018, APPL INTELL, V48, P1519, DOI 10.1007/s10489-017-1051-8
   Musto C, 2017, KNOWL-BASED SYST, V136, P1, DOI 10.1016/j.knosys.2017.08.015
   Nassar N, 2020, J BIG DATA-GER, V7, DOI 10.1186/s40537-020-00309-6
   Park Y, 2014, 2014 IEEE 30TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOPS (ICDEW), P12, DOI 10.1109/ICDEW.2014.6818295
   Pokorny J, 2015, LECT NOTES COMPUT SC, V9339, P58, DOI 10.1007/978-3-319-24369-6_5
   Qassimi S, 2019, COMPUTING, V101, P1489, DOI 10.1007/s00607-019-00704-9
   Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3
   Shalaby W, 2017, IEEE INT CONF BIG DA, P1544, DOI 10.1109/BigData.2017.8258088
   Srifi M, 2021, J BIG DATA-GER, V8, DOI 10.1186/s40537-021-00420-2
   Tilly R, 2017, BUS INFORM SYST ENG+, V59, P3, DOI 10.1007/s12599-016-0459-8
   Unal Y, 2018, ICIST '18: PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES, DOI 10.1145/3200842.3200852
   Wang RR, 2020, COMPUT COMMUN, V150, P429, DOI 10.1016/j.comcom.2019.12.002
   Yang K, 2018, IEEE GLOB CONF SIG, P798, DOI 10.1109/GlobalSIP.2018.8646359
   Zajac Z., 2017, GOODBOOKS 10K
NR 27
TC 2
Z9 2
U1 3
U2 6
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
EI 2196-1115
J9 J BIG DATA-GER
JI J. Big Data
PD MAY 13
PY 2021
VL 8
IS 1
AR 67
DI 10.1186/s40537-021-00457-3
PG 17
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SF2MU
UT WOS:000652596200001
OA gold
DA 2022-08-23
ER

PT J
AU Guo, J
   Zhou, Y
   Zhang, P
   Song, B
   Chen, C
AF Guo, Jie
   Zhou, Yan
   Zhang, Peng
   Song, Bin
   Chen, Chen
TI Trust-aware recommendation based on heterogeneous multi-relational
   graphs fusion
SO INFORMATION FUSION
LA English
DT Article
DE Trust-aware; Knowledge graph; Heterogeneous data fusion; Graph neural
   networks
AB Users' trust relations have a significant influence on their choice towards different products. However, few recommendation or prediction algorithms both consider users' social trust relations and item-related knowledge, which makes them difficult to cope with cold start and the data sparsity problems. In this paper, we propose a novel trust-ware recommendation method based on heterogeneous multi-relational graphs fusion, termed as T-MRGF. In contrast with other traditional methods, it fuses the user-related and item-related graphs with the user-item interaction graph and fully utilizes the high-level connections existing in heterogeneous graphs. Specifically, we first establish the user-user trust relation graph, user-item interaction graph and item-item knowledge graph, and the user feature and item feature, which have been obtained from the user-item graph, are used as the input of the user-related graph and the item-related graph respectively. The fusion is achieved through the cascade of feature vectors before and after feature propagation. In this way, the heterogeneous multi-relational graphs are fused for the feature propagation, which largely refines the user and item representation for model prediction. Simulation results show that the proposed method significantly improve the recommendation performance compared to the state-of-the-art KG-based algorithms both in accuracy and training efficiency.
C1 [Guo, Jie; Zhou, Yan; Zhang, Peng; Song, Bin; Chen, Chen] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China.
RP Song, B (通讯作者)，Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China.
EM bsong@mail.xidian.edu.cn
OI Song, Bin/0000-0002-8096-3370
FU National Natural Science Foundation of China [61802296, 61772387,
   61906156]; Fundamental Research Funds for the Central Universities,
   China [XJS200102]; China Postdoctoral Science Foundation [2017M620438];
   National Natural Science Foundation of Shaanxi Province, China
   [2019ZDLGY03-03, 2019JQ-375]; ISN State Key Laboratory, China
FX This work was supported by the National Natural Science Foundation of
   China under Grant (Nos. 61802296, 61772387 and 61906156), the
   Fundamental Research Funds for the Central Universities, China (No.
   XJS200102), China Postdoctoral Science Foundation Grant (No.
   2017M620438), the National Natural Science Foundation of Shaanxi
   Province, China (Nos. 2019ZDLGY03-03, 2019JQ-375) and also supported by
   the ISN State Key Laboratory, China.
CR Ahmed A., 2020, IEEE ACCESS, V8
   Ai Q., 2018, Algorithms, V11, P1
   Fout A., 2017, ADV NEURAL INF PROCE, V30, P6530
   Hongwei Wang, 2019, ACM Transactions on Information Systems, V37, DOI 10.1145/3312738
   Kipf T. N., 2016, ARXIV161107308, P1
   Kirk W.A., 2011, INTRO METRIC SPACES
   Li Y., 2018, ICLR
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Ma T., 2018, ARXIV180902630, V31, P7113
   Maas A., 2013, P 30 INT C MACH LEAR, P1152
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
   Sun Z, 2019, ELECTRON COMMER R A, V37, DOI 10.1016/j.elerap.2019.100879
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Wang H., 2019, ARXIV190504413
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang HW, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P592, DOI 10.1145/3159652.3159666
   Wang X, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P165, DOI 10.1145/3331184.3331267
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Wu ZH, 2021, IEEE T NEUR NET LEAR, V32, P4, DOI 10.1109/TNNLS.2020.2978386
   Yan SJ, 2018, AAAI CONF ARTIF INTE, P7444
   Yao L, 2019, AAAI CONF ARTIF INTE, P7370
   Yu X., 2013, RECSYS, P347
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhao J, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2347, DOI 10.1145/3292500.3330686
   Ziegler CN, 2007, DECIS SUPPORT SYST, V43, P460, DOI 10.1016/j.dss.2006.11.003
NR 28
TC 7
Z9 7
U1 8
U2 22
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1566-2535
EI 1872-6305
J9 INFORM FUSION
JI Inf. Fusion
PD OCT
PY 2021
VL 74
BP 87
EP 95
DI 10.1016/j.inffus.2021.04.001
EA APR 2021
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SO7EE
UT WOS:000659136200007
DA 2022-08-23
ER

PT J
AU Manian, V
   Orozco-Sandoval, J
   Diaz-Martinez, V
AF Manian, Vidya
   Orozco-Sandoval, Jairo
   Diaz-Martinez, Victor
TI An Integrative Network Science and Artificial Intelligence Drug
   Repurposing Approach for Muscle Atrophy in Spaceflight Microgravity
SO FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
LA English
DT Article
DE muscle atrophy; network measures; random walk; graph neural network;
   random forest; gradient boosting; preferential attachment; link
   prediction
ID STRESS
AB Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight. An integrative graph-theoretic network-based drug repurposing methodology quantifying the interplay of key gene regulations and protein-protein interactions in muscle atrophy conditions is presented. Transcriptomic datasets from mice in spaceflight from GeneLab have been extensively mined to extract the key genes that cause muscle atrophy in organ muscle tissues such as the thymus, liver, and spleen. Top muscle atrophy gene regulators are selected by Bayesian Markov blanket method and gene-disease knowledge graph is constructed using the scalable precision medicine knowledge engine. A deep graph neural network is trained for predicting links in the network. The top ranked diseases are identified and drugs are selected for repurposing using drug bank resource. A disease drug knowledge graph is constructed and the graph neural network is trained for predicting new drugs. The results are compared with machine learning methods such as random forest, and gradient boosting classifiers. Network measure based methods shows that preferential attachment has good performance for link prediction in both the gene-disease and disease-drug graphs. The receiver operating characteristic curves, and prediction accuracies for each method show that the random walk similarity measure and deep graph neural network outperforms the other methods. Several key target genes identified by the graph neural network are associated with diseases such as cancer, diabetes, and neural disorders. The novel link prediction approach applied to the disease drug knowledge graph identifies the Monoclonal Antibodies drug therapy as suitable candidate for drug repurposing for spaceflight induced microgravity. There are a total of 21 drugs identified as possible candidates for treating muscle atrophy. Graph neural network is a promising deep learning architecture for link prediction from gene-disease, and disease-drug networks.</p>
C1 [Manian, Vidya; Orozco-Sandoval, Jairo; Diaz-Martinez, Victor] Univ Puerto Rico, Dept Elect & Comp Engn, Lab Appl Remote Sensing Imaging & Photon, Mayaguez, PR 00682 USA.
RP Manian, V (通讯作者)，Univ Puerto Rico, Dept Elect & Comp Engn, Lab Appl Remote Sensing Imaging & Photon, Mayaguez, PR 00682 USA.
EM vidya.manian@upr.edu
FU NASA EPSCoR [80NSSC20M0132]
FX This research was funded by NASA EPSCoR (Grant Number 80NSSC20M0132).
   Opinions, findings, conclusions, or recommendations expressed in this
   material are those of the authors and do not necessarily reflect the
   views of NASA.
CR Abbas K, 2021, BMC BIOINFORMATICS, V22, DOI 10.1186/s12859-021-04082-y
   Ahmed SS, 2020, IEEE ACM T COMPUT BI, V17, P56, DOI 10.1109/TCBB.2018.2853728
   Ainsworth HF, 2017, GENET EPIDEMIOL, V41, P577, DOI 10.1002/gepi.22061
   Anand G.K., 2009, THESIS WAGENINGEN U
   Bui AT, 2012, PATTERN RECOGN LETT, V33, P2134, DOI 10.1016/j.patrec.2012.06.013
   Ballerini A, 2020, ADV THER-GERMANY, V3, DOI 10.1002/adtp.202000014
   Beheshti A, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0199621
   Bei YH, 2017, NAT REV CARDIOL, V14, DOI 10.1038/nrcardio.2017.122
   Bhatnagar S, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0013262
   Biggs N., 1993, ALGEBRAIC GRAPH THEO
   Burckart K, 2010, CURR OPIN CLIN NUTR, V13, P410, DOI 10.1097/MCO.0b013e328339fdd2
   Cadena SM, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-45821-9
   Campins L, 2017, PHARMACOLOGY, V99, P1, DOI 10.1159/000448247
   Chen BL, 2018, IEEE ACCESS, V6, P28122, DOI 10.1109/ACCESS.2018.2838259
   Chen ZY, 2021, DRUG DISCOV TODAY, V26, P1256, DOI 10.1016/j.drudis.2020.12.013
   Chowdhury HA, 2020, IEEE ACM T COMPUT BI, V17, P1154, DOI 10.1109/TCBB.2019.2893170
   Costa L, 2007, PHYS REV STAT NONLIN, V75, P1
   Dapp C, 2004, PHYSIOL GENOMICS, V20, P97, DOI 10.1152/physiolgenomics.00100.2004
   Doig J, 2013, FEBS J, V280, P6528, DOI 10.1111/febs.12554
   Dostert C, 2019, PHYSIOL REV, V99, P115, DOI 10.1152/physrev.00045.2017
   Dudgeon WD, 2006, HIV MED, V7, P299, DOI 10.1111/j.1468-1293.2006.00380.x
   Fire M., 2011, Proceedings of the 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and IEEE Third International Conference on Social Computing (PASSAT/SocialCom 2011), P73, DOI 10.1109/PASSAT/SocialCom.2011.20
   GeneCards, 2021, DRUGBANK ONL DAT DRU
   Goropashnaya AV, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-66030-9
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Gysi D.M., 2020, ARXIV200407229CSQBIO
   Horie K, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-56432-9
   Horie K, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-44067-9
   Hu ZY, 2007, DIABETES, V56, P2449, DOI 10.2337/db06-1731
   Janwa H, 2019, FRONT GENET, V10, DOI 10.3389/fgene.2019.00240
   Jarada TN, 2020, J CHEMINFORMATICS, V12, DOI 10.1186/s13321-020-00450-7
   Kalyani RR, 2014, LANCET DIABETES ENDO, V2, P819, DOI 10.1016/S2213-8587(14)70034-8
   Koromina M, 2019, OMICS, V23, P539, DOI 10.1089/omi.2019.0151
   Lebsack TW, 2010, J CELL BIOCHEM, V110, P372, DOI 10.1002/jcb.22547
   Li TS, 2016, LECT NOTES COMPUT SC, V9772, P81, DOI 10.1007/978-3-319-42294-7_7
   Malkani S, 2020, CELL REP, V33, DOI 10.1016/j.celrep.2020.108448
   Mingcong Wu, 2018, 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). Proceedings, P251, DOI 10.1109/IHMSC.2018.10164
   Mutlu EC, 2020, MACH LEARN KNOW EXTR, V2, P672, DOI 10.3390/make2040036
   NASA GeneLab, NASA GENELAB OP SCI
   Nelson CA, 2021, LIFE-BASEL, V11, DOI 10.3390/life11010042
   Neutelings T, 2015, NPJ MICROGRAVITY, V1, DOI 10.1038/npjmgrav.2015.2
   Opsahl T, 2010, SOC NETWORKS, V32, P245, DOI 10.1016/j.socnet.2010.03.006
   Pellet JP, 2008, J MACH LEARN RES, V9, P1295
   Ram R, 2011, IEEE ACM T COMPUT BI, V8, P353, DOI 10.1109/TCBB.2009.70
   Rausch V, 2021, ONCOGENESIS, V10, DOI 10.1038/s41389-020-00288-6
   Reda C, 2020, COMPUT STRUCT BIOTEC, V18, P241, DOI 10.1016/j.csbj.2019.12.006
   Rullman E, 2016, PHYSIOL REP, V4, DOI 10.14814/phy2.12753
   Smith RC, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0230818
   Tsamardinos I., 2003, FLAIRS C, V2, P376
   Versari S, 2013, FASEB J, V27, P4466, DOI 10.1096/fj.13-229195
   Wang YB, 2020, BMC MED INFORM DECIS, V20, DOI 10.1186/s12911-020-1052-0
   Wu WR, 2020, THERANOSTICS, V10, P707, DOI 10.7150/thno.39018
   Wyart E, 2020, INT J MOL SCI, V21, DOI 10.3390/ijms21228592
   Xuan P, 2019, MOLECULES, V24, DOI 10.3390/molecules24152712
   Yang J, 2018, ADV EXP MED BIOL, V1088, P329, DOI 10.1007/978-981-13-1435-3_15
   Yang Y, 2015, KNOWL INF SYST, V45, P751, DOI 10.1007/s10115-014-0789-0
   Zea L, 2015, P INT ASTRONAUT C, V1, P273
   Zhao K, 2019, IEEE J BIOMED HEALTH, V23, P1304, DOI 10.1109/JBHI.2018.2856535
NR 58
TC 2
Z9 2
U1 3
U2 9
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-634X
J9 FRONT CELL DEV BIOL
JI Front. Cell. Dev. Biol.
PD SEP 16
PY 2021
VL 9
AR 732370
DI 10.3389/fcell.2021.732370
PG 18
WC Cell Biology; Developmental Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Cell Biology; Developmental Biology
GA UZ2AH
UT WOS:000702012200001
PM 34604234
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Xue, YY
   Jin, JH
   Song, AB
   Zhang, YX
   Liu, YY
   Wang, KX
AF Xue, Yingying
   Jin, Jiahui
   Song, Aibo
   Zhang, Yingxue
   Liu, Yangyang
   Wang, Kaixuan
TI Relation-based multi-type aware knowledge graph embedding q
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph embedding; Graph attention network; Ontology; Taxonomy
   tree; Multi-type
AB Knowledge graph (KG) embedding projects the graph into a low-dimensional space and preserves the graph information. An essential part of a KG is the ontology, which always is organized as a taxonomy tree, depicting the type (or multiple types) of each entity and the hierarchical relationships among these types. The importance of considering the ontology during KG embedding lies in its ability to provide side information, improving the downstream applications' accuracy (e.g., link prediction, entity alignment or recommendation). However, the ontology has yet to receive adequate attention during the KG embedding, especially for instances where each entity may belong to multiple types. This ontology-enhanced KG embedding's main challenges are twofold: determining how to discover the relationships among these types and how to integrate them with the entities' relationship network. Although it is common to see attention-based models used in KG embedding, they cannot settle the issues raised simultaneously. Only a single type is assigned to each entity and the correlation among types are ignored in those models, leading to information loss and encumbered downstream tasks. To overcome these challenges, we propose a composite multi-type aware KG embedding model, whose main components are a multi-type layer and entity embedding layer. We model it as a natural language processing task at the multi-type layer to discover each entity's multi-type feature and automatically capture their correlations. Additionally, a relation-based attention mechanism is conducted at the entity embedding layer, which aggregates neighborhoods' information and integrates the multi-type layer's information through common entities of these two layers. Through extensive experiments on two real KGs, we demonstrate that, compared to several state-of-the-art baselines, our Multi-Type aware Embedding (MTE) model achieves substantial gain in both Mean Rank and Hit@N for the link prediction task and accuracy for multi-type classification. (c) 2021 Published by Elsevier B.V.
C1 [Xue, Yingying; Jin, Jiahui; Song, Aibo; Zhang, Yingxue; Liu, Yangyang] Southeast Univ, Dept Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China.
   [Wang, Kaixuan] Southeast Univ, Dept Cyber Sci & Engn, Nanjing, Jiangsu, Peoples R China.
   [Xue, Yingying; Jin, Jiahui; Song, Aibo; Zhang, Yingxue; Liu, Yangyang; Wang, Kaixuan] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Jiangsu, Peoples R China.
RP Song, AB (通讯作者)，Southeast Univ, Dept Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China.
EM absong@seu.edu.cn
FU National Key R&D Program of China [2018AAA0101200]; National Natural
   Science Foundation of China [62061146001, 62072099]; SGCC Science and
   Technology Program "Artificial intelligence support technology of new
   generation automation system"; Collabora-tive Innovation Center of Novel
   Software Technology and Industrialization
FX This work is supported by the National Key R&D Program of China under
   grant 2018AAA0101200, the National Natural Science Foundation of China
   [Grant Nos. 62061146001, 62072099] , SGCC Science and Technology Program
   "Artificial intelligence support technology of new generation automation
   system", and Collabora-tive Innovation Center of Novel Software
   Technology and Industrialization.
CR Balazevic I., 2019, P 2019 C EMP METH NA, P5185
   Balazevic I., 2019, ADV NEURAL INFORM PR, V32, P4463
   Cai HM, 2019, COMPUT MATH METHOD M, V2019, DOI 10.1155/2019/2717454
   Chen J., 2020, ARXIV200713533
   Chen JZ, 2020, IEEE T BIO-MED ENG, V67, P987, DOI 10.1109/TBME.2019.2927157
   Cui P, 2019, IEEE T KNOWL DATA EN, V31, P833, DOI 10.1109/TKDE.2018.2849727
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dong YX, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P135, DOI 10.1145/3097983.3098036
   Dutta A, 2018, PATTERN RECOGN, V76, P596, DOI 10.1016/j.patcog.2017.12.003
   Feng J, 2016, FIFTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P557
   Fischer A, 2015, PATTERN RECOGN, V48, P331, DOI 10.1016/j.patcog.2014.07.015
   Fu TY, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1797, DOI 10.1145/3132847.3132953
   Gibert J, 2013, INT J PATTERN RECOGN, V27, DOI 10.1142/S0218001413600021
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Guangyuan Piao, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P496, DOI 10.1007/978-3-319-93417-4_32
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Ji S., 2020, ABS200200388 ARXIV
   Kou XY, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P2089, DOI 10.1145/3340531.3412104
   kovic P. Velic., 2017, ARXIV PREPRINT ARXIV
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Ma SH, 2017, LECT NOTES ARTIF INT, V10534, P717, DOI 10.1007/978-3-319-71249-9_43
   Mikolov T, 2013, P NAACL HLT 2013, P746
   Moon C, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2215, DOI 10.1145/3132847.3133095
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Neelakantan Arvind, 2015, P 2015 C N AM CHAPT, P515
   Nguyen D. Q, 2017, ARXIV171202121
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
   Rahman Md Mostafizur, 2018, Neural Information Processing. 25th International Conference, ICONIP 2018. Proceedings: Lecture Notes in Computer Science (LNCS 11303), P114, DOI 10.1007/978-3-030-04182-3_11
   Riesen K., 2014, COMPUTING UPPER LOWE
   Riesen K., 2009, INT WORKSH MULT CLAS
   Riesen K, 2020, IEEE ACM T COMPUT BI, V17, P483, DOI 10.1109/TCBB.2015.2478463
   Riesen K, 2018, NEURAL PROCESS LETT, V48, P691, DOI 10.1007/s11063-017-9739-7
   Riesen K, 2009, IMAGE VISION COMPUT, V27, P950, DOI 10.1016/j.imavis.2008.04.004
   Riesen K, 2008, LECT NOTES COMPUT SC, V5342, P287
   Ristoski P, 2019, SEMANT WEB, V10, P721, DOI 10.3233/SW-180317
   Sala F, 2018, PR MACH LEARN RES, V80
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Sun Z., 2019, 7 INT C LEARN REPR
   Toutanova K., 2015, P 2015 C EMP METH NA, P1499, DOI DOI 10.18653/V1/D15-1174
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang DX, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1225, DOI 10.1145/2939672.2939753
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weng WL, 2020, NEUROCOMPUTING, V378, P375, DOI 10.1016/j.neucom.2019.10.014
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Yang B., 2014, ABS14126575 CORR
   Yang J., 2020, INTERDIPLINARY ENCES, P12
   Zhang S., 2019, NEURIPS, P2731
   Zhou B, 2019, LECT NOTES ARTIF INT, V11856, P258, DOI 10.1007/978-3-030-32381-3_21
   Zhou J., 2020, ARXIV PREPRINT ARXIV, V1, P57, DOI 10.1016/j.aiopen.2021.01.001
   Zhu Q, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P2241, DOI 10.1145/3366423.3380289
NR 59
TC 1
Z9 1
U1 2
U2 9
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD OCT 7
PY 2021
VL 456
BP 11
EP 22
DI 10.1016/j.neucom.2021.05.021
EA JUN 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UD8SE
UT WOS:000687471900002
DA 2022-08-23
ER

PT J
AU Bi, ZQ
   Wang, SY
   Chen, Y
   Li, YB
   Kim, JY
AF Bi, Zhongqin
   Wang, Shiyang
   Chen, Yan
   Li, Yongbin
   Kim, Jung Yoon
TI A Knowledge-Enhanced Dialogue Model Based on Multi-Hop Information with
   Graph Attention
SO CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
LA English
DT Article
DE E-commerce ecosystem; conversation generation; knowledge graph; graph
   neural network; graph attention
AB With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, in the context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day. In order to improve the timeliness of customer service responses, many systems have begun to use customer service robots to respond to consumer questions, but the current customer service robots tend to respond to specific questions. For many questions that lack background knowledge, they can generate only responses that are biased towards generality and repetitiveness. To better promote the understanding of dialogue and generate more meaningful responses, this paper introduces knowledge information into the research of question answering system by using a knowledge graph. The unique structured knowledge base of the knowledge graph is convenient for knowledge query, can acquire knowledge faster, and improves the background information needed for answering questions. To avoid the lack of information in the dialogue process, this paper proposes the Multi-hop Knowledge Information Enhanced Dialogue-Graph Attention (MKIED-GA) model. The model first retrieves the problem subgraph directly related to the input information from the entire knowledge base and then uses the graph neural network as the knowledge inference module on the subgraph to encode the subgraph. The graph attention mechanism is used to determine the one-hop and two-hop entities that are more relevant to the problem to achieve the aggregation of highly relevant neighbor information. This further enriches the semantic information to provide a better understanding of the meaning of the input question and generate appropriate response information. In the process of generating a response, a multi-attention flow mechanism is used to focus on different information to promote the generation of better responses. Experiments have proved that the model presented in this article can generate more meaningful responses than other models.
C1 [Bi, Zhongqin; Wang, Shiyang; Li, Yongbin] Shanghai Univ Elect Power, Coll Comp Sci & Technol, Shanghai 201306, Peoples R China.
   [Chen, Yan] Guilin Univ Aerosp Technol, Dept Comp Sci & Engn, Guilin 541004, Peoples R China.
   [Kim, Jung Yoon] Gachon Univ, Coll Future Ind, Dept Game Media, Seongnam Si 13120, Gyeonggi Do, South Korea.
RP Chen, Y (通讯作者)，Guilin Univ Aerosp Technol, Dept Comp Sci & Engn, Guilin 541004, Peoples R China.; Kim, JY (通讯作者)，Gachon Univ, Coll Future Ind, Dept Game Media, Seongnam Si 13120, Gyeonggi Do, South Korea.
EM a18616387167@163.com; kjyoon@gachon.ac.kr
FU National Nature Science Foundation of China [61672337, 61972357];
   Guangxi Colleges and Universities Basic Ability Improvement Project of
   Young and Middle-Aged Teachers [2018KY0651]
FX Funder One, National Nature Science Foundation of China, Grant/Award No.
   61972357; Funder Two, National Nature Science Foundation of China,
   Grant/Award No. 61672337; Funder Three, Guangxi Colleges and
   Universities Basic Ability Improvement Project of Young and Middle-Aged
   Teachers, Grant/Award No. 2018KY0651.
CR Bahdanau D., 2014, ARXIV
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cai Hengyi, 2020, P 58 ANN M ASS COMP, P6334
   Chen, 2020, INFUSING MULTISOURCE
   Cho Kyunghyun, 2014, ARXIV14061078, P1724, DOI DOI 10.3115/V1/D14-1179
   Devlin J., 2019, BERT PRETRAINING DEE, DOI [10.18653/v1/N19-1423, DOI 10.18653/V1/N19-1423]
   Doddington, 2002, P 2 INT C HUM LANG T, P138, DOI [DOI 10.3115/1289189.1289273, 10.5555/1289189.1289273]
   Dolan B., 2016, P 2016 C N AM CHAPT, P110, DOI 10.18653/v1/N16-1014
   Dong L, 2019, ADV NEUR IN, V32
   Gao HH, 2022, IEEE TETCI, V6, P66, DOI 10.1109/TETCI.2020.3023155
   Gao HH, 2021, IEEE T INTELL TRANSP, V22, P3533, DOI 10.1109/TITS.2020.2983835
   Gao HH, 2021, ACM T INTERNET TECHN, V21, DOI 10.1145/3391198
   Gao J., 2018, END TO END CONVERSAT
   GHAZVININEJAD M, 2018, P AAAI C ARTIFICIAL, V32
   Guan J, 2019, AAAI CONF ARTIF INTE, P6473
   Hochreiter S, 1997, Neural Comput, V9, P1735
   Hu, 2020, P 58 ANN M ASS COMP, P7087, DOI [10.18653/v1/2020.acl-main.634, DOI 10.18653/V1/2020.ACL-MAIN.634]
   Lavie Alon, 2007, P ANN M ASS COMP LIN
   Lewis M., 2019, P 58 ANN M ASS COMP, DOI 10.18653/v1/2020.acl
   Lin C.Y., 2004, TEXT SUMMARIZATION B, P74, DOI DOI 10.2307/3105454
   Papineni K, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P311, DOI 10.3115/1073083.1073135
   Pennington J., 2014, EMNLP, P1532
   Peters M., 2018, IMPROVING LANGUAGE U
   SPEER R, 2017, P AAAI C ARTIFICIAL, V31, DOI DOI 10.5555/3298023.3298212
   Srivastava R.K., 2015, ADV NEURAL INFORM PR
   Sun H., 2018, P 2018 C EMP METH NA, P4231, DOI DOI 10.18653/V1/D18-1455
   Sutskever I, 2014, ADV NEUR IN, V27
   Tang JH, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P5624
   Thang Luong, 2015, P 2015 C EMP METH NA, P1412, DOI 10.18653/v1/D15- 1166
   Tian ZL, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P650
   Trischler A, 2020, P C EMP METH NAT LAN, P8980, DOI [10.18653/v1/2020.emnlp-main.722, DOI 10.18653/V1/2020.EMNLP-MAIN.722]
   Wang J, 2020, AAAI CONF ARTIF INTE, V34, P9169
   Wang W. Y, 2021, ANN C N AM CHAPT ASS, P5866
   Wang XZ, 2021, T ASSOC COMPUT LING, V9, P176, DOI 10.1162/tacl_a_00360
   Wu S., 2020, P ANN M ASS COMP LIN, P5811
   XING C, 2017, P AAAI C ARTIFICIAL, V31, DOI DOI 10.5555/3298023.3298055
   Xiong WH, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4258
   Yang PC, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P2002
   Yang XX, 2020, MOBILE NETW APPL, V25, P376, DOI 10.1007/s11036-019-01246-2
   Yin YY, 2021, IEEE T IND INFORM, V17, P6153, DOI 10.1109/TII.2020.3039500
   YOUNG T, 2018, P AAAI C ARTIFICIAL, V32
   Zhang HY, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P2031
   Zhang Y, 2018, ADV NEURAL INFORM PR, V31, P1810
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
   Zhou H, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4623
   Zhou P, 2020, NATURE, V588, pE6, DOI 10.1038/s41586-020-2951-z
NR 46
TC 1
Z9 1
U1 4
U2 8
PU TECH SCIENCE PRESS
PI HENDERSON
PA 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA
SN 1526-1492
EI 1526-1506
J9 CMES-COMP MODEL ENG
JI CMES-Comp. Model. Eng. Sci.
PY 2021
VL 128
IS 2
BP 403
EP 426
DI 10.32604/cmes.2021.016729
PG 24
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA UA1NG
UT WOS:000684932000002
OA gold
DA 2022-08-23
ER

PT J
AU Li, Z
   Liu, X
   Wang, X
   Liu, PK
   Shen, YX
AF Li, Zhao
   Liu, Xin
   Wang, Xin
   Liu, Pengkai
   Shen, Yuxin
TI TransO: a knowledge-driven representation learning method with ontology
   information constraints
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE Knowledge graph; Representation learning; Ontology information
AB Representation learning techniques for knowledge graphs (KGs) are crucial for constructing knowledge-driven decisions in complex network data application scenarios. Most existing methods focus mainly on structured information, ignoring the important value of rich ontology information constraints and complements, however, ontology information is the key for building knowledge-driven decision-making processes. In this paper, we propose a novel ontology information constrained knowledge representation learning model, TransO, which can efficiently model relations explicitly and seamlessly incorporate rich ontology information to improve model performance and maintain low model complexity. Moreover, specific constraint strategies are proposed for entity types, relations, and hierarchical information to effectively implement reasoning and completion of KGs and construct knowledge-driven decisions that are more consistent with the logic of human knowledge in complex network applications. The experimental tasks of link prediction and triple classification are performed on two public datasets. The experimental results demonstrate the effectiveness of our proposed method with better performance than state-of-the-art methods.
C1 [Li, Zhao; Liu, Xin; Wang, Xin; Liu, Pengkai; Shen, Yuxin] Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China.
   [Li, Zhao; Liu, Xin; Wang, Xin; Liu, Pengkai; Shen, Yuxin] Tianjin Key Lab Cognit Comp & Applicat, Tianjin, Peoples R China.
   [Li, Zhao] Bank Shizuishan, Financial Big Data Lab, Yinchuan, Ningxia, Peoples R China.
   [Liu, Xin; Wang, Xin] Tianjin Univ, Tianjin Int Engn Inst, Tianjin, Peoples R China.
RP Wang, X (通讯作者)，Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China.; Wang, X (通讯作者)，Tianjin Key Lab Cognit Comp & Applicat, Tianjin, Peoples R China.; Wang, X (通讯作者)，Tianjin Univ, Tianjin Int Engn Inst, Tianjin, Peoples R China.
EM wangx@tju.edu.cn
FU National Key R&D Program of China [2020AAA0108504]; National Natural
   Science Foundation of China [61972275]
FX This work is supported by the National Key R&D Program of China
   (2020AAA0108504) and the National Natural Science Foundation of China
   (61972275).
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cai TT, 2022, IEEE T KNOWL DATA EN, V34, P1993, DOI 10.1109/TKDE.2020.3003047
   Chen J, 2021, IEEE T CYBERNETICS, DOI 10.1109/TCYB.2021.3064092
   Fan M, 2014, P 28 PAC AS C LANG I, V28, P328, DOI DOI 10.3115/V1/P14-1048.URL
   Guo S, 2017, IEEE T KNOWL DATA EN, V29, P884, DOI 10.1109/TKDE.2016.2638425
   Han X., 2018, P C EMP METH NAT LAN, P139
   Hao JH, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1709, DOI 10.1145/3292500.3330838
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Kazemi SM, 2018, ADV NEUR IN, V31
   Li Z, 2021, KNOWL-BASED SYST, V212, DOI 10.1016/j.knosys.2020.106618
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lv X, 2018, EMNLP, P1971
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Niu L, 2021, WORLD WIDE WEB, V24, P419, DOI 10.1007/s11280-020-00847-2
   Shen Y., 2021, P 30 ACM INT C INFOR, P1630
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wan GJ, 2020, WORLD WIDE WEB, V23, P471, DOI 10.1007/s11280-019-00711-y
   Wang Q, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1859
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xie Y, 2021, PATTERN RECOGN, V116, DOI 10.1016/j.patcog.2021.107936
   Xue G., 2021, ARXIVABS210315447
   Yang B., 2015, P 3 INT C LEARNING R, P809
   Yang C., 2020, IEEE T KNOWL DATA EN, P1
   Yang Y., 2021, IEEE T KNOWL DATA EN
   Zhang FX, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2987
   Zhang SX, 2013, PROCEEDINGS OF 2013 IEEE INTERNATIONAL CONFERENCE ON MEDICAL IMAGING PHYSICS AND ENGINEERING (ICMIPE), P1, DOI 10.1109/ICMIPE.2013.6864491
   Zhang Z, 2018, EMNLP, P3198, DOI DOI 10.18653/V1/D18-1358
   Zheng S., 2021, WORLD WIDE WEB, P1
NR 34
TC 1
Z9 1
U1 10
U2 10
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
DI 10.1007/s11280-022-01016-3
EA FEB 2022
PG 23
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YZ3YC
UT WOS:000755413600001
DA 2022-08-23
ER

PT J
AU Hong, Y
   Yao, Q
   Yang, Y
   Feng, JJ
   Wu, SD
   Ji, WX
   Yao, L
   Liu, ZY
AF Hong, Yang
   Yao, Qiang
   Yang, Ying
   Feng, Jun-jian
   Wu, Shu-de
   Ji, Wen-xue
   Yao, Lan
   Liu, Zhi-yong
TI Knowledge structure and theme trends analysis on general practitioner
   research: A Co-word perspective
SO BMC FAMILY PRACTICE
LA English
DT Article
DE General practitioners; Research trends; Co-word analysis; Social network
   analysis; Knowledge map
ID PRIMARY-CARE; UNITED-STATES; PALLIATIVE CARE; FAMILY MEDICINE; RESEARCH
   OUTPUT; FOLLOW-UP; HEALTH; SERVICE; SPECIALIST; PHYSICIANS
AB Background: General practitioners (GPs) are the most important providers of primary health care, as proven by related research published several decades ago. However, the knowledge structure and theme trends of such research remain unclear. Accordingly, this study aimed to provide an overview of the development of research on GPs over the period of 1999 to 2014.
   Methods: Studies on GPs conducted from 1999 to 2014 were retrieved from PubMed. In this work, co-word, social network analysis, and theme trends analyses were conducted to reveal the knowledge structures and thematic evolution of research on GPs.
   Results: The number of conducted studies on GPs increased. However, growth speed slowed down during the past 16 years. A total of 27 high-frequency keywords were identified in 1999 to 2003, and more new and specific high-frequency keywords emerged in the subsequent periods. The dynamic of this field was first divergent and then considered convergent. Specifically, network centralization is 19.77 %, 19.09 %, and 13.04 % in 1999 to 2003, 2004 to 2008 and 2009 to 2014, respectively. The major topics of research on GPs completed from 1999 to 2014 were "physician/family,""attitude of health personnel," and "primary health care," and "general practitioner" communities, and so on.
   Conclusion: The research themes on GPs are relatively stable at the beginning of the 21st century. However, the thematic evolution and research topics of research on GPs are changing dynamically in recent years. Themes related to the roles and competencies of GPs, and the relations between general practitioner and patients/others have become research foci on GPs. In addition, more substantial research especially on comprehensive approaches and holistic modeling, which have been defined in the European Definition of General Practice/Family Medicine, are expected to be accomplished.
C1 [Hong, Yang; Yang, Ying; Feng, Jun-jian; Wu, Shu-de; Ji, Wen-xue; Yao, Lan; Liu, Zhi-yong] Huazhong Univ Sci & Technol, Sch Med & Hlth Management, Tongji Med Coll, Wuhan 430030, Hubei, Peoples R China.
   [Yao, Qiang] Wuhan Univ, Sch Polit Sci & Publ Adm, Wuhan 430072, Hubei, Peoples R China.
RP Liu, ZY (通讯作者)，Huazhong Univ Sci & Technol, Sch Med & Hlth Management, Tongji Med Coll, Wuhan 430030, Hubei, Peoples R China.; Yao, Q (通讯作者)，Wuhan Univ, Sch Polit Sci & Publ Adm, Wuhan 430072, Hubei, Peoples R China.
EM yaoqianghero@126.com; zhiyongliu@hust.edu.cn
RI Yao, Qiang/Q-5758-2019; YAO, QIANG/X-9176-2019
OI Yao, Qiang/0000-0002-4408-087X; 
FU China Health Promotion Foundation [0231516031]
FX This research was sponsored by the China Health Promotion Foundation
   (no. 0231516031).
CR [Anonymous], 1994, BMJ, V309, P1144
   Atun R., 2004, WHAT ARE ADVANTAGES
   Bajwa RS, 2013, SCIENTOMETRICS, V95, P529, DOI 10.1007/s11192-012-0839-x
   Bajwa RS, 2013, SCIENTOMETRICS, V94, P333, DOI 10.1007/s11192-012-0699-4
   Bodenheimer T, 1999, JAMA-J AM MED ASSOC, V281, P2045, DOI 10.1001/jama.281.21.2045
   Boyack KW, 2005, SCIENTOMETRICS, V64, P351, DOI 10.1007/s11192-005-0255-6
   Callon M., 1986, MAPPING DYNAMICS SCI
   Coast J, 2005, BMJ-BRIT MED J, V331, P1444, DOI 10.1136/bmj.38676.446910.7C
   Cobo MJ, 2012, J AM SOC INF SCI TEC, V63, P1609, DOI 10.1002/asi.22688
   Cottrell E, 2010, BMC FAM PRACT, V11, DOI 10.1186/1471-2296-11-4
   Craven MA, 2013, CAN J PSYCHIAT, V58, P442, DOI 10.1177/070674371305800802
   Ding Y, 2001, INFORM PROCESS MANAG, V37, P817, DOI 10.1016/S0306-4573(00)00051-0
   Europe W., 2002, EUROPEAN DEFINITION
   Gask L, 2013, CAN J PSYCHIAT, V58, P449, DOI 10.1177/070674371305800803
   Gillespie U, 2009, ARCH INTERN MED, V169, P894, DOI 10.1001/archinternmed.2009.71
   Glanville J, 2011, BRIT MED J, V342, DOI 10.1136/bmj.d1028
   GRAY DP, 1992, BRIT J GEN PRACT, V42, P29
   Grumbach K, 1999, JAMA-J AM MED ASSOC, V282, P261, DOI 10.1001/jama.282.3.261
   Hinrichs Timo, 2012, Curr Aging Sci, V5, P41
   Jackson CL, 2006, MED J AUSTRALIA, V185, P125, DOI 10.5694/j.1326-5377.2006.tb00491.x
   Kurtz JE, 2010, CRIT REV ONCOL HEMAT, V75, P47, DOI 10.1016/j.critrevonc.2009.03.002
   Lee B, 2011, J INF SCI, V37, P67, DOI 10.1177/0165551510392147
   Lei CLLWY, 2008, NEW TECHNOLOGY LIB I, V8, P015
   Li F, 2015, J MED INTERNET RES, V17, DOI 10.2196/jmir.3326
   Mahmood-Yousuf K, 2008, BRIT J GEN PRACT, V58, P256, DOI 10.3399/bjgp08X279760
   Marshall MN, 1998, BRIT J GEN PRACT, V48, P1379
   McColl A, 1998, BRIT MED J, V316, P361, DOI 10.1136/bmj.316.7128.361
   McCormick A, 1995, MORBIDITY STAT GEN P
   McGeoch GRB, 2006, RESPIROLOGY, V11, P611, DOI 10.1111/j.1440-1843.2006.00892.x
   McLean Rick, 2007, Rural Remote Health, V7, P767
   Mendis K, 2005, FAM PRACT, V22, P570, DOI 10.1093/fampra/cmi085
   Mitchell GK, 2002, PALLIATIVE MED, V16, P457, DOI 10.1191/0269216302pm573oa
   Moffat MA, 2006, BMC HEALTH SERV RES, V6, DOI 10.1186/1472-6963-6-62
   Nocon A, 2004, BRIT J GEN PRACT, V54, P50
   Peat G, 2001, ANN RHEUM DIS, V60, P91, DOI 10.1136/ard.60.2.91
   Phillips RL, 2014, ANN FAM MED, V12, P250, DOI 10.1370/afm.1651
   Piraveenan M, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0053095
   Piterman L, 2005, INTERN MED J, V35, P430, DOI 10.1111/j.1445-5994.2005.00855.x
   Pouris A, 2007, SCIENTOMETRICS, V70, P541, DOI 10.1007/s11192-007-0301-7
   Pullon S., 2008, NZ FAMILY PHYS, V35, P301
   Qinglan S, 1992, CHINA SOC LIB SCI, V18, P79
   Rosser Walter W, 2004, Ann Fam Med, V2 Suppl 2, pS2, DOI 10.1370/afm.145
   Rosvall M, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0008694
   RUSSELL MAH, 1979, BRIT MED J, V2, P231, DOI 10.1136/bmj.2.6184.231
   Schiebel E, 2010, SCIENTOMETRICS, V83, P765, DOI 10.1007/s11192-009-0137-4
   Schoenmakers B, 2009, SCAND J PRIM HEALTH, V27, P31, DOI 10.1080/02813430802588907
   Singer S, 2013, PEDIATR BLOOD CANCER, V60, P1565, DOI 10.1002/pbc.24586
   Soler Jean Karl, 2007, Eur J Gen Pract, V13, P248, DOI 10.1080/13814780701814986
   Stange KC, 2010, J GEN INTERN MED, V25, P601, DOI 10.1007/s11606-010-1291-3
   Starfield B, 2005, MILBANK Q, V83, P457, DOI 10.1111/j.1468-0009.2005.00409.x
   Uddin S, 2012, SCIENTOMETRICS, V90, P687, DOI 10.1007/s11192-011-0511-x
   van Hezewijk M, 2012, BREAST, V21, P693, DOI 10.1016/j.breast.2012.09.009
   van WC, 2004, ANN FAM MED, V2, pS5, DOI DOI 10.1370/AFM.194
   van Weel C, 2011, FAM PRACT, V28, P351, DOI 10.1093/fampra/cmr048
   van Weel C, 2011, FAM PRACT, V28, P239, DOI 10.1093/fampra/cmr021
   VanDormael M, 1995, ANN SOC BELG MED TR, V75, P79
   VanRaan AFJ, 1997, SCIENTOMETRICS, V38, P205, DOI 10.1007/BF02461131
   Wagner EH, 2004, BMJ-BRIT MED J, V328, P177, DOI 10.1136/bmj.328.7433.177
   Wang JL, 2007, LECT NOTES COMPUT SC, V4426, P320
   Wang XG, 2014, SCIENTOMETRICS, V101, P1253, DOI 10.1007/s11192-014-1347-y
   Watson EK, 1999, FAM PRACT, V16, P420, DOI 10.1093/fampra/16.4.420
   Yao Q, 2014, HEALTH RES POLICY SY, V12, DOI 10.1186/1478-4505-12-26
   Yao Q, 2013, BMC MED INFORM DECIS, V13, DOI 10.1186/1472-6947-13-77
   Young AF, 2000, AUST NZ J PUBL HEAL, V24, P474, DOI 10.1111/j.1467-842X.2000.tb00496.x
   Zhang CC, 2013, BMC MED INFORM DECIS, V13, DOI 10.1186/1472-6947-13-52
   Zhang J, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0034497
NR 66
TC 23
Z9 23
U1 2
U2 76
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1471-2296
J9 BMC FAM PRACT
JI BMC Fam. Pract.
PD JAN 29
PY 2016
VL 17
AR 10
DI 10.1186/s12875-016-0403-5
PG 15
WC Primary Health Care; Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA DC4CI
UT WOS:000369167200001
PM 26831329
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Koopman, R
   Wang, SH
   Scharnhorst, A
AF Koopman, Rob
   Wang, Shenghui
   Scharnhorst, Andrea
TI Contextualization of topics: browsing through the universe of
   bibliographic information
SO SCIENTOMETRICS
LA English
DT Article
DE Random projection; Clustering; Visualization; Topical modelling;
   Interactive search interface; Semantic map; Knowledge map
AB This paper describes how semantic indexing can help to generate a contextual overview of topics and visually compare clusters of articles. The method was originally developed for an innovative information exploration tool, called Ariadne, which operates on bibliographic databases with tens of millions of records (Koopman et al. in Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems. doi:10.1145/2702613.2732781, 2015b). In this paper, the method behind Ariadne is further developed and applied to the research question of the special issue "Same data, different results"-the better understanding of topic (re-)construction by different bibliometric approaches. For the case of the Astro dataset of 111,616 articles in astronomy and astrophysics, a new instantiation of the interactive exploring tool, LittleAriadne, has been created. This paper contributes to the overall challenge to delineate and define topics in two different ways. First, we produce two clustering solutions based on vector representations of articles in a lexical space. These vectors are built on semantic indexing of entities associated with those articles. Second, we discuss how LittleAriadne can be used to browse through the network of topical terms, authors, journals, citations and various cluster solutions of the Astro dataset. More specifically, we treat the assignment of an article to the different clustering solutions as an additional element of its bibliographic record. Keeping the principle of semantic indexing on the level of such an extended list of entities of the bibliographic record, LittleAriadne in turn provides a visualization of the context of a specific clustering solution. It also conveys the similarity of article clusters produced by different algorithms, hence representing a complementary approach to other possible means of comparison.
C1 [Koopman, Rob; Wang, Shenghui] OCLC Res, Schipholweg 99, Leiden, Netherlands.
   [Scharnhorst, Andrea] KNAW, DANS, Anna van Saksenlaan 51, The Hague, Netherlands.
RP Koopman, R (通讯作者)，OCLC Res, Schipholweg 99, Leiden, Netherlands.
EM rob.koopman@oclc.org; shenghui.wang@oclc.org;
   andrea.scharnhorst@dans.knaw.nl
RI Scharnhorst, Andrea/A-7802-2010
OI Scharnhorst, Andrea/0000-0001-8879-8798; Wang,
   Shenghui/0000-0003-0583-6969
FU COST Action TD1210 Knowescape; FP7 Project ImpactEV
FX Part of this work has been funded by the COST Action TD1210 Knowescape,
   and the FP7 Project ImpactEV. We would like to thank the internal
   reviewers Frank Havemann, Bart Thijs as well as the anonymous external
   referees for their valuable comments and suggestions. We would also like
   to thank Jochen Glaser, William Harvey and Jean Godby for comments on
   the text.
CR Achlioptas D, 2003, J COMPUT SYST SCI, V66, P671, DOI 10.1016/S0022-0000(03)00025-4
   [Anonymous], 2015, P 33 ANN ACM C EXT A, DOI [DOI 10.1145/2702613.2732781, 10.1145/2702613.2732781]
   Bingham E., 2001, KDD-2001. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, P245
   Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008
   Borner K, 2011, COMMUN ACM, V54, P60, DOI 10.1145/1897852.1897871
   Boyack K., 2010, SCI MAPS SCHOLARS PL
   Boyack K. W., 2017, SAME DATA DIFFERENT
   Galison Peter, 1997, IMAGE LOGIC MAT CULT
   Glanzel W, 2004, HANDBOOK OF QUANTITATIVE SCIENCE AND TECHNOLOGY RESEARCH: THE USE OF PUBLICATION AND PATENT STATISTICS IN STUDIES OF S&T SYSTEMS, P257
   Glanzel W., 2017, SAME DATA DIFFERENT
   Glaser J., 2017, SAME DATA DIFFERENT
   Havemann F., 2012, ARXIV12125211 CORR
   Havemann F., 2017, SAME DATA DIFFERENT
   Janssens F, 2009, INFORM PROCESS MANAG, V45, P683, DOI 10.1016/j.ipm.2009.06.003
   Johnson W. B., 1984, EXTENSIONS LIPSCHITZ
   Koopman R., 2015, P ISSI 2015 IST 15 I
   Koopman R., 2017, SAME DATA DIFFERENT
   Kouw M, 2013, VIRTUAL KNOWLEDGE: EXPERIMENTING IN THE HUMANITIES AND THE SOCIAL SCIENCES, P89
   Leydesdorff L, 2011, J INFORMETR, V5, P469, DOI 10.1016/j.joi.2011.01.008
   Lu K, 2012, J AM SOC INF SCI TEC, V63, P1973, DOI 10.1002/asi.22628
   Mahalanobis P., 1936, PROC NATL I SCI INDI, V2, P49
   Mali F, 2012, UNDERST COMPLEX SYST, P195, DOI 10.1007/978-3-642-23068-4_6
   Mayr P, 2015, SCIENTOMETRICS, V102, P2193, DOI 10.1007/s11192-014-1484-3
   Mutschke P, 2015, SCIENTOMETRICS, V102, P2323, DOI 10.1007/s11192-014-1485-2
   Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711
   Petersen A. C., 2006, SIMULATING NATURE PH
   PRICE DJD, 1965, SCIENCE, V149, P510
   Radicchi F, 2012, UNDERST COMPLEX SYST, P233, DOI 10.1007/978-3-642-23068-4_7
   Salton G., 1986, INTRO MODERN INFORM
   Van Eck N.J, 2017, SAME DATA DIFFERENT
   van Heur B, 2013, SOC STUD SCI, V43, P341, DOI 10.1177/0306312712458144
   Velden T., 2017, SAME DATA DIFFERENT
   Vinh NX, 2010, J MACH LEARN RES, V11, P2837
   Wang S., 2017, SAME DATA DIFFERENT, P234
   Zitt M, 2006, INFORM PROCESS MANAG, V42, P1513, DOI 10.1016/j.ipm.2006.03.016
   Zitt M, 2011, J AM SOC INF SCI TEC, V62, P19, DOI 10.1002/asi.21440
NR 36
TC 17
Z9 17
U1 0
U2 25
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAY
PY 2017
VL 111
IS 2
BP 1119
EP 1139
DI 10.1007/s11192-017-2303-4
PG 21
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA ES9JC
UT WOS:000399871500026
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Ma, T
   Liu, Y
   Han, M
AF Ma, Teng
   Liu, Ya
   Han, Min
TI Visualization Analysis of Organizational Resilience Research Based on
   CiteSpace From 1990-2022
SO IEEE ACCESS
LA English
DT Article
DE Resilience; Organizations; Bibliometrics; Databases; Indexes;
   Psychology; Market research; Organizational resilience; visualization;
   knowledge map; general review; CiteSpace
ID EMPLOYEE PERFORMANCE; COCITATION; ENGAGEMENT; MANAGEMENT; CLIMATE;
   CONTEXT; IMPACT; SUSTAINABILITY; GOVERNANCE; LEADERSHIP
AB The world is currently dealing with the aftermath of the coronavirus disease 2019 (COVID-19) pandemic, which has resulted in momentous changes, the likes of which had not been witnessed within the previous century. These adverse events and the resulting uncertainty have posed enormous challenges to organizations, and many are on the verge of collapse. Organizations urgently need to enhance their risk management abilities and capacity to cope with crises, and organizational resilience, as such a tool, has attracted widespread attention in China and abroad. The purpose of this study is to understand the research status and development trend of organizational resilience. In this study, we applied CiteSpace to perform a visual analysis. Searching for topics related to organizational resilience, we retrieved papers published from 1990-2022 in the Web of Science Core database. Second, we constructed an author, institution, and country/region collaboration network to identify the most prolific authors, institutions and countries, respectively. The distribution of core journals determined by journal co-citations, the document co-citation network, and a clustering analysis revealed the research topics and knowledge structure, the author co-citation network revealed which authors were influential, the keyword co-citation network indicated popular research topics, and the keyword bursts highlighted the research fields. This paper analyzes the main contributions of organizational resilience research at the author, institution, and country levels; knowledge sources; interdisciplinary characteristics and research areas in organizational resilience; and direction of future research.
C1 [Ma, Teng; Liu, Ya; Han, Min] Hebei Univ Sci & Technol, Sch Econ & Management, Shijiazhuang 050018, Hebei, Peoples R China.
RP Liu, Y (通讯作者)，Hebei Univ Sci & Technol, Sch Econ & Management, Shijiazhuang 050018, Hebei, Peoples R China.
EM liuyakf@163.com
OI ma, teng/0000-0002-6115-683X
FU Hebei Education Department [QN2020407]; Science and Technology (S&T)
   Program of Hebei [G2021208004]
FX This work was supported in part by the Hebei Education Department under
   Grant QN2020407, and in part by the Science and Technology (S&T) Program
   of Hebei under Grant G2021208004.
CR Khan SA, 2021, IEEE T ENG MANAGE, DOI 10.1109/TEM.2021.3052239
   Albott CS, 2020, ANESTH ANALG, V131, P43, DOI 10.1213/ANE.0000000000004912
   Ambulkar S, 2015, J OPER MANAG, V33-34, P111, DOI 10.1016/j.jom.2014.11.002
   Andersson E, 2015, ECOSYST SERV, V12, P157, DOI 10.1016/j.ecoser.2014.08.001
   Annarelli A, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12030958
   Avey JB, 2011, HUM RESOUR DEV Q, V22, P127, DOI 10.1002/hrdq.20070
   Azadeh A, 2014, SAFETY SCI, V68, P99, DOI 10.1016/j.ssci.2014.03.004
   Bakker YW, 2019, MAR POLICY, V99, P132, DOI 10.1016/j.marpol.2018.09.032
   Balaei B, 2019, INT J DISAST RISK RE, V37, DOI 10.1016/j.ijdrr.2019.101187
   Beaver D. deB., 1978, Scientometrics, V1, P65, DOI 10.1007/BF02016840
   Bento F, 2021, SAFETY SCI, V133, DOI 10.1016/j.ssci.2020.105036
   Bethune E, 2022, J DESTIN MARK MANAGE, V23, DOI 10.1016/j.jdmm.2021.100687
   Branicki LJ, 2018, INT J ENTREP BEHAV R, V24, P1244, DOI 10.1108/IJEBR-11-2016-0396
   Bustinza OF, 2019, INT J HUM RESOUR MAN, V30, P1370, DOI 10.1080/09585192.2016.1216878
   Carmeli A, 2020, BUS STRATEG ENVIRON, V29, P154, DOI 10.1002/bse.2355
   Carmeli A, 2011, STRATEGIC MANAGE J, V32, P322, DOI 10.1002/smj.880
   Chen C, 2016, CITESPACE PRACTICAL
   Chen C., 2017, TEXT MINING VISUALIZ, V2
   Chen Chaomei, 2020, Front Res Metr Anal, V5, P607286, DOI 10.3389/frma.2020.607286
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen CM, 2009, J INFORMETR, V3, P191, DOI 10.1016/j.joi.2009.03.004
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen CM, 2003, INFOVIS 2002: IEEE SYMPOSIUM ON INFORMATION VISUALIZATION 2003, PROCEEDINGS, P67, DOI 10.1109/INFVIS.2003.1249010
   Chen RJ, 2021, IEEE ACCESS, V9, P48899, DOI 10.1109/ACCESS.2021.3069301
   Chen XY, 2020, TRANSPORT POLICY, V85, P1, DOI 10.1016/j.tranpol.2019.10.004
   Cimellaro GP, 2016, J STRUCT ENG, V142, DOI 10.1061/(ASCE)ST.1943-541X.0001514
   Cooke FL, 2019, INT J HUM RESOUR MAN, V30, P1239, DOI 10.1080/09585192.2015.1137618
   Cooper B, 2019, HUM RESOUR MANAGE-US, V58, P85, DOI 10.1002/hrm.21934
   Cruickshank N, 2020, TRANSNATL CORP REV, V12, P94, DOI 10.1080/19186444.2020.1764326
   Dawkins S, 2013, J OCCUP ORGAN PSYCH, V86, P348, DOI 10.1111/joop.12007
   DesJardine M, 2019, J MANAGE, V45, P1434, DOI 10.1177/0149206317708854
   Dewald J, 2010, ENTREP THEORY PRACT, V34, P197, DOI 10.1111/j.1540-6520.2009.00312.x
   Do H, 2022, J BUS RES, V141, P808, DOI 10.1016/j.jbusres.2021.11.090
   Dubey R, 2020, INT J PROD RES, V58, P3381, DOI 10.1080/00207543.2020.1722860
   Dubey R, 2021, INT J PROD RES, V59, P110, DOI 10.1080/00207543.2019.1582820
   Duchek S., 2020, BUSINESS RES, V13, P215, DOI DOI 10.1007/S40685-019-0085-7
   Dutta S, 2017, ADMIN SCI QUART, V62, P443, DOI 10.1177/0001839216668172
   El Baz J, 2021, INT J PROD ECON, V233, DOI 10.1016/j.ijpe.2020.107972
   Falagas ME, 2008, FASEB J, V22, P338, DOI 10.1096/fj.07-9492LSF
   Fastenrath S, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11030693
   Feng YB, 2019, J CONSTR ENG M, V145, DOI 10.1061/(ASCE)CO.1943-7862.0001720
   Folke C, 2005, ANNU REV ENV RESOUR, V30, P441, DOI 10.1146/annurev.energy.30.050504.144511
   Francis R, 2014, RELIAB ENG SYST SAFE, V121, P90, DOI 10.1016/j.ress.2013.07.004
   Gilson L, 2017, BMJ GLOB HEALTH, V2, DOI 10.1136/bmjgh-2016-000224
   Hahn T, 2006, HUM ECOL, V34, P573, DOI 10.1007/s10745-006-9035-z
   Hobfoll SE, 2018, ANNU REV ORGAN PSYCH, V5, P103, DOI 10.1146/annurev-orgpsych-032117-104640
   Hu CP, 2011, SCIENTOMETRICS, V86, P657, DOI 10.1007/s11192-010-0313-6
   Iborra M, 2022, J SMALL BUS MANAGE, DOI 10.1080/00472778.2021.2014508
   Iborra M, 2020, LONG RANGE PLANN, V53, DOI 10.1016/j.lrp.2019.101947
   Ivanov D, 2021, IEEE T ENG MANAGE, DOI 10.1109/TEM.2021.3095193
   Jain V, 2017, INT J PROD RES, V55, P6779, DOI 10.1080/00207543.2017.1349947
   Jiang C., 2017, SCI MANAGE, V37, P22
   Kantur D., 2015, J BUSINESS EC FINANC, V4, P456
   Karatepe OM, 2015, INT J CONTEMP HOSP M, V27, P1254, DOI 10.1108/IJCHM-01-2014-0028
   Khlystova O, 2022, J BUS RES, V139, P1192, DOI 10.1016/j.jbusres.2021.09.062
   Li QW, 2020, J CLEAN PROD, V245, DOI 10.1016/j.jclepro.2019.118775
   Liao HC, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10010166
   Lin BQ, 2020, INT REV ECON FINANC, V67, P133, DOI 10.1016/j.iref.2020.01.007
   Linnenluecke MK, 2017, INT J MANAG REV, V19, P4, DOI 10.1111/ijmr.12076
   Liu ZG, 2015, SCIENTOMETRICS, V103, P135, DOI 10.1007/s11192-014-1517-y
   Luthans F., 2006, HUMAN RESOURCE DEV R, V5, P25, DOI DOI 10.1177/1534484305285335
   Luthans F, 2008, J ORGAN BEHAV, V29, P219, DOI 10.1002/job.507
   Luthans F, 2010, HUM RESOUR DEV Q, V21, P41, DOI 10.1002/hrdq.20034
   Lv WD, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11061561
   Ma T., 2018, SCI TECHNOL PROG POL, V35, P22, DOI [10.6049/kjjbydc.2017060644, DOI 10.6049/KJJBYDC.2017060644]
   Mahmoudi A, 2022, EXPERT SYST APPL, V188, DOI 10.1016/j.eswa.2021.116067
   MCCAIN KW, 1991, J AM SOC INFORM SCI, V42, P290, DOI 10.1002/(SICI)1097-4571(199105)42:4<290::AID-ASI5>3.0.CO;2-9
   Mitsakis FV, 2020, HUM RESOUR DEV INT, V23, P321, DOI 10.1080/13678868.2019.1669385
   Moran KA, 2016, WORK, V54, P267, DOI 10.3233/WOR-162299
   Newman A, 2014, J ORGAN BEHAV, V35, pS120, DOI 10.1002/job.1916
   Olawumi TO, 2018, J CLEAN PROD, V183, P231, DOI 10.1016/j.jclepro.2018.02.162
   Orchiston C, 2016, ANN TOURISM RES, V56, P145, DOI 10.1016/j.annals.2015.11.002
   Orsetti E, 2022, INT J ENV RES PUB HE, V19, DOI 10.3390/ijerph19031355
   Ortiz-de-Mandojana N, 2016, STRATEGIC MANAGE J, V37, P1615, DOI 10.1002/smj.2410
   Osareh F, 1996, LIBRI, V46, P149, DOI 10.1515/libr.1996.46.3.149
   Pan XL, 2018, J INFORMETR, V12, P481, DOI 10.1016/j.joi.2018.03.005
   Rai SS, 2021, ENVIRON DEV SUSTAIN, V23, P12006, DOI 10.1007/s10668-020-01154-6
   Robertson IT, 2015, J OCCUP ORGAN PSYCH, V88, P533, DOI 10.1111/joop.12120
   Saier MH, 2007, WATER AIR SOIL POLL, V181, P1, DOI 10.1007/s11270-007-9372-6
   Salehi V, 2020, J LOSS PREVENT PROC, V63, DOI 10.1016/j.jlp.2019.104001
   Sawalha IHS, 2015, MANAG RES REV, V38, P346, DOI 10.1108/MRR-01-2014-0010
   Senbeto DL, 2020, SERV IND J, V40, P1119, DOI 10.1080/02642069.2020.1734573
   Shanafelt TD, 2017, MAYO CLIN PROC, V92, P129, DOI 10.1016/j.mayocp.2016.10.004
   Shaw D, 2014, GLOBAL ENVIRON CHANG, V25, P194, DOI 10.1016/j.gloenvcha.2014.01.006
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Soni U, 2014, COMPUT IND ENG, V74, P11, DOI 10.1016/j.cie.2014.04.019
   Steen R, 2011, SAFETY SCI, V49, P292, DOI 10.1016/j.ssci.2010.09.003
   Sweya L.N., 2020, GLOBAL BUSINESS ORG, V39, P6, DOI [10.1002/joe.21985, DOI 10.1002/JOE.21985]
   Wang T, 2021, J MANUF SYST, V58, P261, DOI 10.1016/j.jmsy.2020.07.011
   Wang XH, 2016, ANN OPER RES, V240, P471, DOI 10.1007/s10479-014-1646-y
   Wang XX, 2021, INFORM SCIENCES, V547, P328, DOI 10.1016/j.ins.2020.08.036
   Wang Y., 2019, STAT DECIS, V35, P178
   West CP, 2016, LANCET, V388, P2272, DOI 10.1016/S0140-6736(16)31279-X
   Williams TA, 2017, ACAD MANAG ANN, V11, P733, DOI 10.5465/annals.2015.0134
   Xue XL, 2018, NAT HAZARDS, V90, P477, DOI 10.1007/s11069-017-3040-y
   Youssef CM, 2007, J MANAGE, V33, P774, DOI 10.1177/0149206307305562
   Zandi S, 2019, J ENVIRON MANAGE, V247, P462, DOI 10.1016/j.jenvman.2019.06.066
   Zhang CY, 2020, TOUR MANAG PERSPECT, V35, DOI 10.1016/j.tmp.2020.100715
   Zhang X, 2015, COMPUT HUM BEHAV, V51, P803, DOI 10.1016/j.chb.2014.11.084
   Zhang XL, 2018, CITIES, V72, P141, DOI 10.1016/j.cities.2017.08.009
   Zhong BT, 2019, AUTOMAT CONSTR, V101, P17, DOI 10.1016/j.autcon.2018.12.013
   Zhu JL, 2019, LEADERSHIP QUART, V30, P215, DOI 10.1016/j.leaqua.2018.06.003
NR 102
TC 0
Z9 0
U1 11
U2 11
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 65854
EP 65872
DI 10.1109/ACCESS.2022.3184686
PG 19
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 2J2SP
UT WOS:000815513800001
OA gold
DA 2022-08-23
ER

PT J
AU Gurbuz, O
   Alanis-Lobato, G
   Picart-Armada, S
   Sun, M
   Haslinger, C
   Lawless, N
   Fernandez-Albert, F
AF Gurbuz, Ozge
   Alanis-Lobato, Gregorio
   Picart-Armada, Sergio
   Sun, Miao
   Haslinger, Christian
   Lawless, Nathan
   Fernandez-Albert, Francesc
TI Knowledge Graphs for Indication Expansion: An Explainable Target-Disease
   Prediction Method
SO FRONTIERS IN GENETICS
LA English
DT Article
DE knowledge graphs; ontologies; drug discovery; target repurposing; target
   repositioning
ID DRUG; ONTOLOGIES
AB Indication expansion aims to find new indications for existing targets in order to accelerate the process of launching a new drug for a disease on the market. The rapid increase in data types and data sources for computational drug discovery has fostered the use of semantic knowledge graphs (KGs) for indication expansion through target centric approaches, or in other words, target repositioning. Previously, we developed a novel method to construct a KG for indication expansion studies, with the aim of finding and justifying alternative indications for a target gene of interest. In contrast to other KGs, ours combines human-curated full-text literature and gene expression data from biomedical databases to encode relationships between genes, diseases, and tissues. Here, we assessed the suitability of our KG for explainable target-disease link prediction using a glass-box approach. To evaluate the predictive power of our KG, we applied shortest path with tissue information- and embedding-based prediction methods to a graph constructed with information published before or during 2010. We also obtained random baselines by applying the shortest path predictive methods to KGs with randomly shuffled node labels. Then, we evaluated the accuracy of the top predictions using gene-disease links reported after 2010. In addition, we investigated the contribution of the KG's tissue expression entity to the prediction performance. Our experiments showed that shortest path-based methods significantly outperform the random baselines and embedding-based methods outperform the shortest path predictions. Importantly, removing the tissue expression entity from the KG severely impacts the quality of the predictions, especially those produced by the embedding approaches. Finally, since the interpretability of the predictions is crucial in indication expansion, we highlight the advantages of our glass-box model through the examination of example candidate target-disease predictions.
C1 [Gurbuz, Ozge] Boehringer Ingelheim Pharm GmbH & Co KG, Discovery Res Coordinat Germany, Biberach An Der Riss, Germany.
   [Alanis-Lobato, Gregorio; Picart-Armada, Sergio; Sun, Miao; Haslinger, Christian; Lawless, Nathan; Fernandez-Albert, Francesc] Boehringer Ingelheim Pharm GmbH & Co KG, Global Computat Biol & Data Sci, Biberach An Der Riss, Germany.
RP Gurbuz, O (通讯作者)，Boehringer Ingelheim Pharm GmbH & Co KG, Discovery Res Coordinat Germany, Biberach An Der Riss, Germany.; Fernandez-Albert, F (通讯作者)，Boehringer Ingelheim Pharm GmbH & Co KG, Global Computat Biol & Data Sci, Biberach An Der Riss, Germany.
EM ozge.gurbuz@boehringer-ingelheim.com;
   francesc.fernandez-albert@boehringer-ingelheim.com
CR Andronis C, 2011, BRIEF BIOINFORM, V12, P357, DOI 10.1093/bib/bbr005
   Tran AN, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-3059-z
   Bialek K, 2020, PEERJ, V8, DOI 10.7717/peerj.8676
   Brown AS, 2017, SCI DATA, V4, DOI 10.1038/sdata.2017.29
   Cannistraci CV, 2013, SCI REP-UK, V3, DOI 10.1038/srep01613
   Celebi R, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-3284-5
   Chen HJ, 2010, EXPERT OPIN DRUG DIS, V5, P413, DOI 10.1517/17460441003762709
   Dudley JT, 2011, BRIEF BIOINFORM, V12, P303, DOI 10.1093/bib/bbr013
   Ergun S, 2018, PSYCHIAT CLIN PSYCH, V28, P66, DOI 10.1080/24750573.2017.1380920
   Frydecka D, 2013, MOL BIOL REP, V40, P5607, DOI 10.1007/s11033-013-2662-8
   Fu G, 2016, BMC BIOINFORMATICS, V17, DOI 10.1186/s12859-016-1005-x
   Geleta D., 2021, BIOL INSIGHTS KNOWLE, DOI [10.28.46626210.1101/2021.10.28.466262, DOI 10.1101/2021.10.28.466262]
   Gurbuz O, 2020, IEEE INT C BIOINFORM, P1720, DOI 10.1109/BIBM49941.2020.9313179
   Han ZJ, 2018, CNS NEUROSCI THER, V24, P1253, DOI 10.1111/cns.13051
   Hartner Franziska, 2018, Appl Netw Sci, V3, P10, DOI 10.1007/s41109-018-0066-3
   Hasegawa Masato, 2007, Brain and Nerve (Tokyo), V59, P1171
   Herrero-Zazo M, 2015, J CHEM INF MODEL, V55, P1698, DOI 10.1021/acs.jcim.5b00119
   Holzinger Andreas, 2018, 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA). Proceedings, P55, DOI 10.1109/DISA.2018.8490530
   Holzinger Andreas, 2017, ARXIV171209923
   Kanza S, 2019, EXPERT OPIN DRUG DIS, V14, P433, DOI 10.1080/17460441.2019.1586880
   Katsila T, 2018, EXPERT OPIN DRUG DIS, V13, P791, DOI 10.1080/17460441.2018.1504767
   Keller BA, 2012, ACTA NEUROPATHOL, V124, P733, DOI 10.1007/s00401-012-1035-z
   Lecue F, 2020, SEMANT WEB, V11, P41, DOI 10.3233/SW-190374
   Lekka E., 2011, DRUG DISCOV TODAY TH, V8, P103, DOI [DOI 10.1016/J.DDSTR.2011.06.005, 10.1016/j.ddstr.2011.06.005]
   Lin Y, 2017, J BIOMED SEMANT, V8, DOI 10.1186/s13326-017-0161-x
   Lopez-Gonzalez I, 2019, EUR NEUROPSYCHOPHARM, V29, P384, DOI 10.1016/j.euroneuro.2018.12.011
   Nunes S., 2020, PREDICTING GENE DIS
   Ochoa D, 2021, NUCLEIC ACIDS RES, V49, pD1302, DOI 10.1093/nar/gkaa1027
   Page L., 1999, 199966 STANF INFOLAB
   Paliwal S, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-74922-z
   Parisi D, 2020, COMPUT STRUCT BIOTEC, V18, P1043, DOI 10.1016/j.csbj.2020.04.004
   Picart-Armada S, 2019, PLOS COMPUT BIOL, V15, DOI 10.1371/journal.pcbi.1007276
   Qu XA, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-S5-S4
   Sang ST, 2019, IEEE ACCESS, V7, P8404, DOI 10.1109/ACCESS.2018.2886311
   Sang ST, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2167-5
   Sebastian Y, 2017, KNOWL-BASED SYST, V115, P66, DOI 10.1016/j.knosys.2016.10.015
   Smalheiser NR, 2012, J AM SOC INF SCI TEC, V63, P218, DOI 10.1002/asi.21599
   Sosa DN, 2020, PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, P463
   Suzuki H, 2013, MOL CELL BIOCHEM, V372, P241, DOI 10.1007/s11010-012-1465-x
   Uhlen M, 2010, NAT BIOTECHNOL, V28, P1248, DOI 10.1038/nbt1210-1248
   Watanabe T, 2010, J NEUROL SCI, V298, P52, DOI 10.1016/j.jns.2010.08.026
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Williams AJ, 2012, DRUG DISCOV TODAY, V17, P1188, DOI 10.1016/j.drudis.2012.05.016
   Zhu YJ, 2020, HEALTH INFORM J, V26, P2737, DOI 10.1177/1460458220937101
NR 44
TC 0
Z9 0
U1 7
U2 7
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1664-8021
J9 FRONT GENET
JI Front. Genet.
PD MAR 14
PY 2022
VL 13
AR 814093
DI 10.3389/fgene.2022.814093
PG 14
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA 0F2LU
UT WOS:000777197700001
PM 35360842
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Novacek, V
   McGauran, G
   Matallanas, D
   Blanco, AV
   Conca, P
   Munoz, E
   Costabello, L
   Kanakaraj, K
   Nawaz, Z
   Walsh, B
   Mohamed, SK
   Vandenbussche, PY
   Ryan, C
   Kolch, W
   Fey, D
AF Novacek, Vit
   McGauran, Gavin
   Matallanas, David
   Blanco, Adrian Vallejo
   Conca, Piero
   Munoz, Emir
   Costabello, Luca
   Kanakaraj, Kamalesh
   Nawaz, Zeeshan
   Walsh, Brian
   Mohamed, Sameh K.
   Vandenbussche, Pierre-Yves
   Ryan, Colm
   Kolch, Walter
   Fey, Dirk
TI Accurate prediction of kinase-substrate networks using knowledge graphs
SO PLOS COMPUTATIONAL BIOLOGY
LA English
DT Article
ID PHOSPHORYLATION; DISCOVERY; PATHWAY; CANCER; PHOSPHOSITEPLUS; SEQUENCE
AB Author summary
   LinkPhinder is a new approach to prediction of protein signalling networks based on kinase-substrate relationships that outperforms existing approaches. Phosphorylation networks govern virtually all fundamental biochemical processes in cells, and thus have moved into the centre of interest in biology, medicine and drug development. Fundamentally different from current approaches, LinkPhinder is inherently network-based and makes use of the most recent AI developments. We represent existing phosphorylation data as knowledge graphs, a format for large-scale and robust knowledge representation. Training a link prediction model on such a structure leads to novel, biologically valid phosphorylation network predictions that cannot be made with competing tools. Thus our new conceptual approach can lead to establishing a new niche of AI applications in computational biology.
   Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder).
C1 [Novacek, Vit; Munoz, Emir; Kanakaraj, Kamalesh; Nawaz, Zeeshan; Walsh, Brian; Mohamed, Sameh K.] Natl Univ Ireland Galway, Data Sci Inst, Galway, Ireland.
   [Conca, Piero; Munoz, Emir; Costabello, Luca; Vandenbussche, Pierre-Yves] Fujitsu Ireland Ltd, Dublin, Ireland.
   [McGauran, Gavin; Matallanas, David; Blanco, Adrian Vallejo; Ryan, Colm; Kolch, Walter; Fey, Dirk] Univ Coll Dublin, Syst Biol Ireland, Dublin 4, Ireland.
   [Blanco, Adrian Vallejo] Univ Navarra, Dept Oncol, Pamplona, Spain.
   [Kolch, Walter] Univ Coll Dublin, Conway Inst Biomol & Biomed Res, Dublin, Ireland.
   [Kolch, Walter; Fey, Dirk] Univ Coll Dublin, Sch Med, Dublin 4, Ireland.
   [Novacek, Vit] Masaryk Univ, Fac Informat, Brno, Czech Republic.
RP Novacek, V (通讯作者)，Natl Univ Ireland Galway, Data Sci Inst, Galway, Ireland.; Fey, D (通讯作者)，Univ Coll Dublin, Syst Biol Ireland, Dublin 4, Ireland.; Fey, D (通讯作者)，Univ Coll Dublin, Sch Med, Dublin 4, Ireland.; Novacek, V (通讯作者)，Masaryk Univ, Fac Informat, Brno, Czech Republic.
EM novacek@fi.muni.cz; dirk.fey@ucd.ie
RI Novacek, Vit/ABD-3387-2020; Kolch, Walter/ABF-2102-2021; Munoz,
   Emir/J-5126-2017
OI Munoz, Emir/0000-0002-0089-8135; Novacek, Vit/0000-0003-4687-6043;
   Matallanas, David/0000-0002-2360-3141; Blanco,
   Alfonso/0000-0003-0439-0838
FU CLARIFY project - European Commission [875160]; Fujitsu Laboratories
   Ltd., Japan; Insight Centre for Data Analytics at National University of
   Ireland Galway (Science Foundation Ireland) [12/RC/2289]; Science
   Foundation Ireland [14/IA/2395, 15/CDA/3495]
FX This work was supported by the CLARIFY project funded by European
   Commission under the grant number 875160, the TOMOE project funded by
   Fujitsu Laboratories Ltd., Japan and Insight Centre for Data Analytics
   at National University of Ireland Galway (supported by the Science
   Foundation Ireland grant 12/RC/2289) and Science Foundation Ireland
   grants 14/IA/2395 and 15/CDA/3495 to Walter Kolch and David Matallanas,
   respectively. The funders had no role in study design, data collection
   and analysis, decision to publish or preparation of the manuscript.
CR Bergstra J, 2012, J MACH LEARN RES, V13, P281
   Blom N, 2004, PROTEOMICS, V4, P1633, DOI 10.1002/pmic.200300771
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Boudewijn MT, 1995, NATURE, V376, P599
   Cohen P, 2013, ACS CHEM BIOL, V8, P96, DOI 10.1021/cb300610s
   Davis J., 2006, P 23 INT C MACH LEAR, P233
   Dinkel H, 2011, NUCLEIC ACIDS RES, V39, pD261, DOI 10.1093/nar/gkq1104
   Embogama DM, 2017, CHEMBIOCHEM, V18, P136, DOI 10.1002/cbic.201600511
   Fallahi E, 2016, GENES-BASEL, V7, DOI 10.3390/genes7060028
   Ferguson FM, 2018, NAT REV DRUG DISCOV, V17, P353, DOI 10.1038/nrd.2018.21
   Gomez M, 2014, CLIN TRANSL MED, V3, DOI 10.1186/2001-1326-3-22
   Hernandez-Armenta C, 2017, BIOINFORMATICS, V33, P1845, DOI 10.1093/bioinformatics/btx082
   Hijazi M, 2020, NAT BIOTECHNOL, P1
   Horn H, 2014, NAT METHODS, V11, P603, DOI 10.1038/nmeth.2968
   Hornbeck PV, 2015, NUCLEIC ACIDS RES, V43, pD512, DOI 10.1093/nar/gku1267
   Hornbeck PV, 2012, NUCLEIC ACIDS RES, V40, pD261, DOI 10.1093/nar/gkr1122
   Kolch W, 2015, NAT REV CANCER, V15, P515, DOI 10.1038/nrc3983
   Linding R, 2007, CELL, V129, P1415, DOI 10.1016/j.cell.2007.05.052
   Manning G, 2002, SCIENCE, V298, P1912, DOI 10.1126/science.1075762
   Mantamadiotis T, 2012, BIOESSAYS, V34, P293, DOI 10.1002/bies.201100133
   Martini M, 2014, ANN MED, V46, P372, DOI 10.3109/07853890.2014.912836
   Matallanas D, 2007, MOL CELL, V27, P962, DOI 10.1016/j.molcel.2007.08.008
   Mayer IA, 2016, ANNU REV MED, V67, P11, DOI 10.1146/annurev-med-062913-051343
   Needham EJ, 2019, SCI SIGNAL, V12, DOI 10.1126/scisignal.aau8645
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Obenauer JC, 2003, NUCLEIC ACIDS RES, V31, P3635, DOI 10.1093/nar/gkg584
   Romano D, 2010, CANCER RES
   Song JN, 2017, SCI REP-UK, V7, DOI [10.1038/srep42521, 10.1038/s41598-017-07199-4]
   Technology CS, PI3K AKT SUBSTR TABL
   Trouillon T, 2016, ARXIV160606357
   Turriziani Benedetta, 2014, Biology (Basel), V3, P320, DOI 10.3390/biology3020320
   Venter JC, 2001, SCIENCE, V291, P1304, DOI 10.1126/science.1058040
   von Kriegsheim A, 2009, NAT CELL BIOL, V11, P1458, DOI 10.1038/ncb1994
   Wang JY, 2013, HEPATOLOGY, V58, P1011, DOI 10.1002/hep.26420
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wu P, 2015, TRENDS PHARMACOL SCI, V36, P422, DOI 10.1016/j.tips.2015.04.005
   Xue Y, 2008, MOL CELL PROTEOMICS, V7, P1598, DOI 10.1074/mcp.M700574-MCP200
   Yang B., 2014, ABS14126575 CORR
NR 38
TC 1
Z9 1
U1 5
U2 9
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1553-734X
EI 1553-7358
J9 PLOS COMPUT BIOL
JI PLoS Comput. Biol.
PD DEC
PY 2020
VL 16
IS 12
AR e1007578
DI 10.1371/journal.pcbi.1007578
PG 30
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA PC7BC
UT WOS:000597151200003
PM 33270624
OA Green Submitted, Green Published, gold
DA 2022-08-23
ER

PT J
AU He, K
   Yao, LX
   Zhang, JW
   Li, YF
   Li, C
AF He, Kai
   Yao, Lixia
   Zhang, JiaWei
   Li, Yufei
   Li, Chen
TI Construction of Genealogical Knowledge Graphs From Obituaries: Multitask
   Neural Network Extraction System
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE genealogical knowledge graph; EHR; information extraction; genealogy;
   neural network
AB Background: Genealogical information, such as that found in family trees, is imperative for biomedical research such as disease heritability and risk prediction. Researchers have used policyholder and their dependent information in medical claims data and emergency contacts in electronic health records (EHRs) to infer family relationships at a large scale. We have previously demonstrated that online obituaries can be a novel data source for building more complete and accurate family trees.
   Objective: Aiming at supplementing EHR data with family relationships for biomedical research, we built an end-to-end information extraction system using a multitask-based artificial neural network model to construct genealogical knowledge graphs (GKGs) from online obituaries. GKGs are enriched family trees with detailed information including age, gender, death and birth dates, and residence.
   Methods: Built on a predefined family relationship map consisting of 4 types of entities (eg, people's name, residence, birth date, and death date) and 71 types of relationships, we curated a corpus containing 1700 online obituaries from the metropolitan area of Minneapolis and St Paul in Minnesota. We also adopted data augmentation technology to generate additional synthetic data to alleviate the issue of data scarcity for rare family relationships. A multitask-based artificial neural network model was then built to simultaneously detect names, extract relationships between them, and assign attributes (eg, birth dates and death dates, residence, age, and gender) to each individual. In the end, we assemble related GKGs into larger ones by identifying people appearing in multiple obituaries.
   Results: Our system achieved satisfying precision (94.79%), recall (91.45%), and F-1 measures (93.09%) on 10-fold cross-validation. We also constructed 12,407 GKGs, with the largest one made up of 4 generations and 30 people.
   Conclusions: In this work, we discussed the meaning of GKGs for biomedical research, presented a new version of a corpus with a predefined family relationship map and augmented training data, and proposed a multitask deep neural system to construct and assemble GKGs. The results show our system can extract and demonstrate the potential of enriching EHR data for more genetic research. We share the source codes and system with the entire scientific community on GitHub without the corpus for privacy protection.
C1 [He, Kai; Zhang, JiaWei; Li, Yufei; Li, Chen] Xi An Jiao Tong Univ, Sch Comp Sci & Technol, Xianning West Rd,27th, Xian 0086710049, Peoples R China.
   [He, Kai; Zhang, JiaWei; Li, Yufei; Li, Chen] Xi An Jiao Tong Univ, Natl Engn Lab Big Data Analyt, Xian, Peoples R China.
   [He, Kai; Zhang, JiaWei; Li, Yufei; Li, Chen] Shanxi Prov Key Lab Satellite & Terr Network Tech, Xian, Peoples R China.
   [Yao, Lixia] Mayo Clin, Dept Hlth Sci Res, Rochester, MN USA.
RP Li, C (通讯作者)，Xi An Jiao Tong Univ, Sch Comp Sci & Technol, Xianning West Rd,27th, Xian 0086710049, Peoples R China.
EM cli@xjtu.edu.cn
RI Li, Chen/P-9915-2015
OI Li, Chen/0000-0002-0079-3106; , Yufei/0000-0001-5207-5336
FU National Key Research and Development Program of China [2018YFC0910404];
   National Natural Science Foundation of China [61772409]; Innovative
   Research Group of the National Natural Science Foundation of China
   [61721002]; Innovation Research Team of the Ministry of Education,
   Project of China Knowledge Centre for Engineering Science and Technology
   [IRT_17R86]
FX This work has been supported by grant 2018YFC0910404 from the National
   Key Research and Development Program of China, grant 61772409 from the
   National Natural Science Foundation of China; grant 61721002 from the
   Innovative Research Group of the National Natural Science Foundation of
   China, and grant IRT_17R86 from the Innovation Research Team of the
   Ministry of Education, Project of China Knowledge Centre for Engineering
   Science and Technology.
CR Bender JL, 2017, J MED INTERNET RES, V19, DOI 10.2196/jmir.7029
   Bhatia P, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P954
   Blumenthal D, 2010, NEW ENGL J MED, V363, P501, DOI 10.1056/NEJMp1006114
   Caruana R, 1997, MACH LEARN, V28, P41, DOI 10.1023/A:1007379606734
   Casalino LP, 2017, HEALTH AFFAIR, V36, P865, DOI 10.1377/hlthaff.2016.1536
   Clark K, 2018, P 2018 C EMP METH NA, P1914, DOI DOI 10.18653/V1/D18-1217
   Collobert R., 2008, ICML, V10, P160, DOI [DOI 10.1145/1390156.1390177, 10.1145/1390156.1390177]
   Denecke K, 2015, Yearb Med Inform, V10, P137, DOI 10.15265/IY-2015-001
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dixit K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P5308
   Gao Tianyu, 2019, P 2019 C EMP METH NA, P6250
   He K, 2019, SOCIAL MEDIA MINING FOR HEALTH APPLICATIONS (#SMM4H) WORKSHOP & SHARED TASK, P1
   Jamal MA, 2019, PROC CVPR IEEE, P11711, DOI 10.1109/CVPR.2019.01199
   Lee K., 2017, P 2017 C EMP METH NA, P188, DOI [10.18653/v1/D17-1018, DOI 10.18653/V1/D17-1018]
   Li F, 2017, BMC BIOINFORMATICS, V18, DOI 10.1186/s12859-017-1609-9
   Lin YH, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3125
   Liu S, 2019, P AAAI C ARTIFIC INT, V33, P9977, DOI [10.1609/aaai.v33i01.33019977, DOI 10.1609/AAAI.V33I01.33019977]
   Mayer J, 2014, GENET EPIDEMIOL, V38, P692, DOI 10.1002/gepi.21855
   Pan SJ, 2010, IEEE T KNOWL DATA EN, V22, P1345, DOI 10.1109/TKDE.2009.191
   Polubriaginof FCG, 2018, CELL, V173, P1692, DOI 10.1016/j.cell.2018.04.032
   Ruder S., 2019, P 57 ANN M ASS COMP, P31
   St Sauver JL, 2011, AM J EPIDEMIOL, V173, P1059, DOI 10.1093/aje/kwq482
   Sun CZ, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1361
   Tourassi G, 2016, J BIOMED INFORM, V61, P110, DOI 10.1016/j.jbi.2016.03.027
   Van Schalkwyk GJ, 2006, CHINA PERSPECT, P1, DOI [10.4000/chinaperspectives.603, DOI 10.4000/CHINAPERSPECTIVES.603]
   Verga Patrick, 2018, P NAACL HLT, V1, P872
   Wang K, 2017, NAT GENET, V49, P1319, DOI 10.1038/ng.3931
   Wang ZR, 2019, PROC CVPR IEEE, P11285, DOI 10.1109/CVPR.2019.01155
   Wei J., 2019, ARXIV PREPRINT ARXIV, P6381, DOI 10.18653/v1/D19-1670
   Yoon Hong-Jun, 2015, Soc Comput Behav Cult Model Predict (2015), V9021, P464, DOI 10.1007/978-3-319-16268-3_60
   Zheng SC, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1227, DOI 10.18653/v1/P17-1113
   Zheng SC, 2017, NEUROCOMPUTING, V257, P59, DOI 10.1016/j.neucom.2016.12.075
NR 32
TC 0
Z9 0
U1 1
U2 5
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD AUG 4
PY 2021
VL 23
IS 8
AR e25670
DI 10.2196/25670
PG 15
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA TU5CN
UT WOS:000681054500001
PM 34346903
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Zheng, KH
   Yang, JF
   Zeng, LK
   Gong, QH
   Li, S
   Zhou, SL
AF Zheng, Kaihong
   Yang, Jingfeng
   Zeng, Lukun
   Gong, Qihang
   Li, Sheng
   Zhou, Shangli
TI Constructing Bi-Order-Transformer-CRF With Neural Cosine Similarity
   Function for Power Metering Entity Recognition
SO IEEE ACCESS
LA English
DT Article
DE Feature extraction; Task analysis; Transformers; Data models; Hidden
   Markov models; Current; Meters; Power metering; entity recognition;
   neural network; pattern recognition
AB In recent years, knowledge graphs are applied to provide knowledge support and data support for power grid monitoring and decision-making. To construct a power metering knowledge graph, the power metering entities should be effectively recognized and extracted. However, the existing machine learning models do not fully consider the situation that some power metering entities' names are partially overlapping and boundaries of some power metering entities are fuzzy. In this paper, we propose a Bi-order-Transformer-CRF to recognize power metering entities. Specifically, to alleviate the problem of fuzzy entity boundaries, we train our power metering word-vectors, and then we design Neural Cosine Similarity Function for distinguishing similar entities and Bi-order Feature Extracting Mechanism for recognizing overlapping entity names in the proposed Bi-order-Transformer-CRF. Moreover, we analyze the complexity of the proposed methods and verify that Bi-order-Transformer-CRF achieves better power metering entity recognition results compared with the commonly used machine learning methods in experiments.
C1 [Zheng, Kaihong; Zeng, Lukun; Gong, Qihang; Li, Sheng; Zhou, Shangli] China Southern Power Grid, Digital Grid Res Inst, Guangzhou 510663, Peoples R China.
   [Yang, Jingfeng] China Southern Power Grid Co Ltd, Guangzhou 510663, Peoples R China.
RP Zheng, KH (通讯作者)，China Southern Power Grid, Digital Grid Res Inst, Guangzhou 510663, Peoples R China.
EM zhengkh@csg.cn
FU China Southern Power Grid Company Ltd.; Digital Grid Research Institute,
   China Southern Power Grid
FX This work was supported in part by China Southern Power Grid Company
   Ltd., and in part by the Digital Grid Research Institute, China Southern
   Power Grid.
CR Adewumi T. P., ARXIV200716007
   Chiu Jason PC, 2016, T ASS COMPUT LINGUIS, V4, P357, DOI 10.1162/tacl_a_00104
   Goyal A, 2018, COMPUT SCI REV, V29, P21, DOI 10.1016/j.cosrev.2018.06.001
   Guimaraes V, 2015, PROC 5 NAMED ENTITY, DOI DOI 10.18653/V1/W15-3904
   Ji Z., 2020, GLOBAL ENERGY INTERC, V3, P92
   [蒋晨 Jiang Chen], 2021, [电网技术, Power System Technology], V45, P2141
   Li J, 2018, P 2 SMART GRID C BEJ, P77
   Luoma J., ARXIV200601563
   Morwal S., 2012, IJNLC, V1, P15, DOI [DOI 10.5121/IJNLC.2012.1402, 10.5121/ijnlc.2012.1402, DOI 10.5121/ijnlc.2012.1402]
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Tan YH, 2017, IEEE INT CONF CON AU, P7, DOI 10.1109/ICCA.2017.8003026
   Thattinaphanich S, 2019, PROCEEDINGS OF THE 2019 4TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY (INCIT), P149, DOI 10.1109/INCIT.2019.8912091
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Xiao Yong, 2021, Journal of Zhejiang University (Science Edition), V48, P321, DOI 10.3785/j.issn.1008-9497.2021.03.008
   Yan H., ARXIV191104474
   Zhuang H., 2021, P J PHYS C, V1848
NR 16
TC 0
Z9 0
U1 5
U2 7
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2021
VL 9
BP 133491
EP 133499
DI 10.1109/ACCESS.2021.3112541
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA WC2PU
UT WOS:000704104600001
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, ZH
AF Zhang, Zhihao
TI A Method of Recommending Physical Education Network Course Resources
   Based on Collaborative Filtering Technology
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB Through the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the collaborative filtering recommendation technology is applied, in this paper, to the process of personalized recommendation of learning resources. We obtain user content and functional interest and predict the comprehensive interest of web and big data through an infinite deep neural network. Based on the collaborative knowledge graph and the collaborative filtering algorithm, the semantic information of teaching network resources is extracted from the collaborative knowledge graph. According to the principles of the nearest neighbor recommendation, the course attribute value preference matrix (APM) is obtained first. Next, the course-predicted values are sorted in descending order, and the top T courses with the highest predicted values are selected as the final recommended course set for the target learners. Each course has its own online classroom; the teacher will publish online class details ahead of time, and students can purchase online access to the classroom number and password. The experimental results show that the optimal number of clusters k is 9. Furthermore, for extremely sparse matrices, the collaborative filtering technique method is more suitable for clustering in the transformed low-dimensional space. The average recommendation satisfaction degree of collaborative filtering technology method is approximately 43.6%, which demonstrates high recommendation quality.
C1 [Zhang, Zhihao] Xinyang Normal Univ, Coll Phys Educ, Xinyang 464000, Henan, Peoples R China.
RP Zhang, ZH (通讯作者)，Xinyang Normal Univ, Coll Phys Educ, Xinyang 464000, Henan, Peoples R China.
EM 2011010115@st.btbu.edu.cn
CR Brubacher MS, 2019, MED EDUC, V53, P1135, DOI 10.1111/medu.13981
   Butko D., 2020, J ACOUST SOC AM, V148, P2526, DOI [10.1121/1.5147018, DOI 10.1121/1.5147018]
   Campbell CD, 2020, J CHEM EDUC, V97, P3023, DOI 10.1021/acs.jchemed.0c00884
   Darici D, 2021, ANN ANAT, V236, DOI 10.1016/j.aanat.2021.151718
   Green CA, 2019, ACAD MED, V94, P1532, DOI 10.1097/ACM.0000000000002751
   Huang-Saad A, 2020, ANN BIOMED ENG, V48, P881, DOI 10.1007/s10439-019-02427-6
   Jones Andrew, 2018, Artificial Intelligence in Education. 19th International Conference, AIED 2018. Proceedings: LNAI 10948, P433, DOI 10.1007/978-3-319-93846-2_81
   Ma L, 2020, FASEB J, V34, DOI 10.1096/fasebj.2020.34.s1.02127
   Mohindra ASVK, 2021, BRIT DENT J, V230, P32, DOI 10.1038/s41415-020-2506-6
   Pasternak JD, 2020, WORLD J SURG, V44, P2667, DOI 10.1007/s00268-020-05583-2
   Ranauta A, 2020, ORAL DIS, V26, P123, DOI 10.1111/odi.13401
   Sheshasaayee A, 2017, 2017 INTERNATIONAL CONFERENCE ON INNOVATIVE MECHANISMS FOR INDUSTRY APPLICATIONS (ICIMIA), P735, DOI 10.1109/ICIMIA.2017.7975562
   Taj M, 2020, J CLIN ONCOL, V38
   Zhang XQ, 2021, SCI PROGRAMMING-NETH, V2021, DOI 10.1155/2021/1773561
   Zhu H, 2021, SCI PROGRAMMING-NETH, V2021, DOI 10.1155/2021/8387277
NR 15
TC 1
Z9 1
U1 4
U2 9
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD OCT 28
PY 2021
VL 2021
AR 9531111
DI 10.1155/2021/9531111
PG 9
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WW1DK
UT WOS:000717666000002
OA gold
DA 2022-08-23
ER

PT J
AU Wang, SK
   Xu, F
   Li, YY
   Wang, J
   Zhang, K
   Liu, Y
   Wu, M
   Zheng, J
AF Wang, Shike
   Xu, Fan
   Li, Yunyang
   Wang, Jie
   Zhang, Ke
   Liu, Yong
   Wu, Min
   Zheng, Jie
TI KG4SL: knowledge graph neural network for synthetic lethality prediction
   in human cancers
SO BIOINFORMATICS
LA English
DT Article; Proceedings Paper
CT Biennial Joint Meeting of 29th Annual Conference on Intelligent Systems
   for Molecular Biology (ISMB) / 20th European Conference on Computational
   biology (ECCB)
CY JUL 25-30, 2021
CL ELECTR NETWORK
ID GENETIC INTERACTIONS; SCREEN
AB Motivation: Synthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer drug targets. Wet-lab screening of SL pairs is afflicted with high cost, batch-effect, and off-target problems. Current computational methods for SL prediction include gene knock-out simulation, knowledge-based data mining and machine learning methods. Most of the existing methods tend to assume that SL pairs are independent of each other, without taking into account the shared biological mechanisms underlying the SL pairs. Although several methods have incorporated genomic and proteomic data to aid SL prediction, these methods involve manual feature engineering that heavily relies on domain knowledge.
   Results: Here, we propose a novel graph neural network (GNN)-based model, named KG4SL, by incorporating knowledge graph (KG) message-passing into SL prediction. The KG was constructed using 11 kinds of entities including genes, compounds, diseases, biological processes and 24 kinds of relationships that could be pertinent to SL. The integration of KG can help harness the independence issue and circumvent manual feature engineering by conducting message-passing on the KG. Our model outperformed all the state-of-the-art baselines in area under the curve, area under precision-recall curve and F1. Extensive experiments, including the comparison of our model with an unsupervised TransE model, a vanilla graph convolutional network model, and their combination, demonstrated the significant impact of incorporating KG into GNN for SL prediction.
C1 [Wang, Shike; Xu, Fan; Wang, Jie; Zhang, Ke; Zheng, Jie] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China.
   [Li, Yunyang] ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China.
   [Zhang, Ke] Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 200050, Peoples R China.
   [Liu, Yong] Nanyang Technol Univ, Joint NTU UBC Res Ctr Excellence Act Living Elder, Singapore 639798, Singapore.
   [Wu, Min] ASTAR, Inst Infocomm Res, Singapore 138632, Singapore.
   [Zheng, Jie] Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai 201210, Peoples R China.
RP Zheng, J (通讯作者)，ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China.; Wu, M (通讯作者)，ASTAR, Inst Infocomm Res, Singapore 138632, Singapore.; Zheng, J (通讯作者)，Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai 201210, Peoples R China.
EM wumin@i2r.a-star.edu.sg; zhengjie@shanghaitech.edu.cn
OI Liu, Yong/0000-0001-9031-9696; Wu, Min/0000-0003-0977-3600
CR Ashworth A, 2011, CELL, V145, P30, DOI 10.1016/j.cell.2011.03.020
   Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Breiman L., 2001, RANDOM FORESTS, V45, P5, DOI 10.1023/A:1010933404324
   Cai RC, 2020, BIOINFORMATICS, V36, P4458, DOI 10.1093/bioinformatics/btaa211
   Das S, 2019, BIOINFORMATICS, V35, P701, DOI 10.1093/bioinformatics/bty673
   Dhabhar FS, 2009, NEUROIMMUNOMODULAT, V16, P300, DOI 10.1159/000216188
   DOBZHANSKY T, 1946, GENETICS, V31, P269
   Du D, 2017, NAT METHODS, V14, P577, DOI [10.1038/nmeth.4286, 10.1038/NMETH.4286]
   Folger O, 2011, MOL SYST BIOL, V7, DOI 10.1038/msb.2011.35
   Glorot X., 2010, JMLR WRKSHOP C P, P249
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Guo J, 2016, NUCLEIC ACIDS RES, V44, pD1011, DOI 10.1093/nar/gkv1108
   Hamilton W.L., 2020, SYNTH LECT ARTIF INT, V14, P1, DOI 10.2200/S01045ED1V01Y202009AIM046
   Hamilton Will, 2017, NEURIPS
   Hartwell LH, 1997, SCIENCE, V278, P1064, DOI 10.1126/science.278.5340.1064
   Huang JL, 2019, BMC FAM PRACT, V20, DOI 10.1186/s12875-019-0949-0
   Jacunski A, 2015, PLOS COMPUT BIOL, V11, DOI 10.1371/journal.pcbi.1004506
   Jerby-Arnon L, 2014, CELL, V158, P1199, DOI 10.1016/j.cell.2014.07.027
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Kranthi T, 2013, MOL BIOSYST, V9, P2163, DOI 10.1039/c3mb25589a
   Kwok M, 2016, BLOOD, V127, P582, DOI 10.1182/blood-2015-05-644872
   Liany H, 2020, BIOINFORMATICS, V36, P2209, DOI 10.1093/bioinformatics/btz893
   Lin X, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2739
   Liu Y, 2020, IEEE ACM T COMPUT BI, V17, P748, DOI 10.1109/TCBB.2019.2909908
   Lord CJ, 2017, SCIENCE, V355, P1152, DOI 10.1126/science.aam7344
   Luo J, 2009, CELL, V137, P835, DOI 10.1016/j.cell.2009.05.006
   O'Neil NJ, 2017, NAT REV GENET, V18, P613, DOI 10.1038/nrg.2017.47
   Ou MD, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1105, DOI 10.1145/2939672.2939751
   Paladugu SR, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-426
   Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
   Sanders JT, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-20047-w
   Simons A, 2001, GENOME RES, V11, P266, DOI 10.1101/gr.154201
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Tsherniak A, 2017, CELL, V170, P564, DOI 10.1016/j.cell.2017.06.010
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Velickovic Petar, 2017, P INT C LEARN REPR
   Wan FP, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.00112
   Wang HW, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P968, DOI 10.1145/3292500.3330836
   Wang M., 2019, DEEP GRAPH LIB GRAPH
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wu M, 2014, CANCER INFORM, V13, P71, DOI 10.4137/CIN.S14026
   Zhang F, 2015, J BIOINF COMPUT BIOL, V13, DOI 10.1142/S0219720015410024
NR 43
TC 2
Z9 2
U1 5
U2 15
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD JUL
PY 2021
VL 37
SU 1
BP I418
EP I425
DI 10.1093/bioinformatics/btab271
PG 8
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA US8VF
UT WOS:000697703700050
PM 34252965
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Lin, GX
   Nan, JN
   Chen, KT
   Sun, LW
   Tai, CT
   Jhang, SW
   Chen, CM
   Rui, G
   Hu, BS
AF Lin, Guang-Xun
   Nan, Jin-Niang
   Chen, Kuo-Tai
   Sun, Li-Wei
   Tai, Ching-Ting
   Jhang, Shang-Wun
   Chen, Chien-Min
   Rui, Gang
   Hu, Bao-Shan
TI Bibliometric analysis and visualization of research trends on oblique
   lumbar interbody fusion surgery
SO INTERNATIONAL ORTHOPAEDICS
LA English
DT Review
DE Oblique lumbar interbody fusion; Research productivity; Bibliometric
   analysis; Visualization; Research trends
ID INDIRECT NEURAL DECOMPRESSION; COMPLICATIONS
AB Purpose To determine trends and hot subjects in the field of oblique lumbar interbody fusion (OLIF) research during the last decade using bibliometric analysis and visualization tools, in order to assist researchers in exploring new directions for future research in that field.
   Methods Articles published from January 1, 2012, to August 15, 2021, were screened in the Web of Science database. The data were analyzed with CiteSpace software, which generated visualization knowledge maps. All literature was assessed for the following parameters: the number of total publications, distribution, h-index, institutions, journals, authors, co-occurrence state, and research hotspots.
   Results A total of 173 articles were identified. The country with the largest number of articles was China (41.04%), followed by South Korea (20.81%), the USA (15.61%), Japan (9.83%), and Thailand (2.89%). South Korea and the USA had the highest h-index (9), followed by China (8), Japan (7), and Thailand (2). Catholic University of Korea was the organization that produced the most literature. World Neurosurgery published the most papers about OLIF (12.50%), but articles in Spine were most frequently cited (151). Kim JS was the most productive author, whereas Silvestre C was the most cited author. The main research hotspots are anatomy, discectomy, approach, injure, and diseases.
   Conclusions The number of publications in the field of OLIF has increased considerably in recent years. The USA, China, South Korea, and Japan have made substantial contributions to this field. Anatomy, complications, decompression surgery, and application in various degenerative lumbar diseases have been the research hotspots in recent years.
C1 [Lin, Guang-Xun; Rui, Gang; Hu, Bao-Shan] Xiamen Univ, Affiliated Hosp 1, Sch Med, Dept Orthoped, Xiamen, Peoples R China.
   [Nan, Jin-Niang] Nanchang Med Coll, Dept Clin Med, Nanchang, Jiangxi, Peoples R China.
   [Chen, Kuo-Tai] Chang Gung Mem Hosp, Dept Neurosurg, Chiayi, Taiwan.
   [Sun, Li-Wei; Jhang, Shang-Wun; Chen, Chien-Min] Changhua Christian Hosp, Dept Surg, Div Neurosurg, Changhua, Taiwan.
   [Tai, Ching-Ting] Lukang Christian Hosp, Dept Family Med, Lukang, Taiwan.
   [Chen, Chien-Min] Dayeh Univ, Coll Nursing & Hlth Sci, Changhua, Taiwan.
   [Chen, Chien-Min] Kaohsiung Med Univ, Sch Med, Kaohsiung, Taiwan.
RP Rui, G; Hu, BS (通讯作者)，Xiamen Univ, Affiliated Hosp 1, Sch Med, Dept Orthoped, Xiamen, Peoples R China.; Chen, CM (通讯作者)，Changhua Christian Hosp, Dept Surg, Div Neurosurg, Changhua, Taiwan.; Chen, CM (通讯作者)，Dayeh Univ, Coll Nursing & Hlth Sci, Changhua, Taiwan.; Chen, CM (通讯作者)，Kaohsiung Med Univ, Sch Med, Kaohsiung, Taiwan.
EM linguangxun@hotmail.com; njn0433@hotmail.com; tad91116@gmail.com;
   182034@cch.org.tw; tct1130@gamil.com; 133393@cch.org.tw; xmhbs@21cn.com;
   reigang@163.com; 96015@cch.org.tw
FU Natural Science Foundation of Fujian Province [2021J05282]; "Xiamen
   Health High-Level Talent Training Program
FX This research was funded by the Natural Science Foundation of Fujian
   Province, grant number 2021J05282; and funded by the "Xiamen Health
   High-Level Talent Training Program."
CR Abe K, 2017, SPINE, V42, P55, DOI 10.1097/BRS.0000000000001650
   Aldawsari K, 2020, GLOB SPINE J, V10, P353, DOI 10.1177/2192568219868194
   Badhiwala JH, 2018, SPINE, V43, P1746, DOI 10.1097/BRS.0000000000002735
   Du X, 2020, INT J SURG, V83, P39, DOI 10.1016/j.ijsu.2020.09.012
   Fujibayashi S, 2015, SPINE, V40, pE175, DOI 10.1097/BRS.0000000000000703
   Guo SM, 2019, WORLD NEUROSURG, V132, pE246, DOI 10.1016/j.wneu.2019.08.191
   Hah R, 2019, CURR REV MUSCULOSKE, V12, P305, DOI 10.1007/s12178-019-09562-6
   He W, 2020, BMC MUSCULOSKEL DIS, V21, DOI 10.1186/s12891-020-03192-7
   Li JXJ, 2017, WORLD NEUROSURG, V98, P113, DOI 10.1016/j.wneu.2016.10.074
   Li RJ, 2020, ORTHOP SURG, V12, P355, DOI 10.1111/os.12625
   Li YZ, 2019, J ORTHOP SURG RES, V14, DOI 10.1186/s13018-019-1247-1
   Liang YD, 2017, J PAIN RES, V10, P951, DOI 10.2147/JPR.S132808
   Lin GX, 2021, EUR SPINE J, V30, P151, DOI 10.1007/s00586-020-06604-3
   Lin GX, 2019, CLIN NEUROL NEUROSUR, V184, DOI 10.1016/j.clineuro.2019.105407
   Liu JP, 2020, WORLD NEUROSURG, V134, pE944, DOI 10.1016/j.wneu.2019.11.046
   Lykissas MG, 2014, SPINE J, V14, P749, DOI 10.1016/j.spinee.2013.06.066
   Ma Y, 2021, ANN PALLIAT MED, V10, P9057, DOI 10.21037/apm-21-1836
   Mayer HM, 1997, SPINE, V22, P691, DOI 10.1097/00007632-199703150-00023
   Park SJ, 2017, CLIN SPINE SURG, V30, pE784, DOI 10.1097/BSD.0000000000000406
   Quillo-Olvera J, 2018, ANN TRANSL MED, V6, DOI 10.21037/atm.2018.01.22
   Regev GJ, 2009, SPINE, V34, P1330, DOI 10.1097/BRS.0b013e3181a029e1
   Silvestre C, 2012, ASIAN SPINE J, V6, P89, DOI 10.4184/asj.2012.6.2.89
   Woods KRM, 2017, SPINE J, V17, P545, DOI 10.1016/j.spinee.2016.10.026
   Yin Mengchen, 2022, Clin Spine Surg, V35, pE259, DOI 10.1097/BSD.0000000000001160
   Yin MC, 2021, GLOB SPINE J, V11, P988, DOI 10.1177/2192568220948832
NR 25
TC 1
Z9 1
U1 15
U2 18
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0341-2695
EI 1432-5195
J9 INT ORTHOP
JI Int. Orthop.
PD JUL
PY 2022
VL 46
IS 7
BP 1597
EP 1608
DI 10.1007/s00264-022-05316-1
EA JAN 2022
PG 12
WC Orthopedics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Orthopedics
GA 1V0LB
UT WOS:000750485100001
PM 35099577
DA 2022-08-23
ER

PT J
AU Colli, M
   Stingl, V
   Waehrens, BV
AF Colli, Michele
   Stingl, Verena
   Waehrens, Brian V.
TI Making or breaking the business case of digital transformation
   initiatives: the key role of learnings
SO JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT
LA English
DT Article
DE Industry 4; 0; Innovation; Knowledge mapping; Learning; Dynamic
   capabilities
ID INDUSTRY 4.0; DYNAMIC CAPABILITIES; RADICAL INNOVATION; FUTURE;
   ADOPTION; MODEL; SMES
AB Purpose The research aims to investigate how firms can develop their sensing capabilities for Industry 4.0 (I4.0) technology adoption through reframing their opportunity perceptions related to learnings from I4.0 initiatives. Design/methodology/approach The research follows a design science research approach. Following the case of I4.0 technology introduction at a large food manufacturer, the paper develops a theoretical framework (artefact) and validates the applicability and efficacy of the framework within the case study. Findings The theoretical framework highlights the different temporal (short-term/long-term) and locational (direct/indirect) value dimensions of I4.0 opportunities. The findings show that the use of the framework can shift managers' perception regarding the business value of an I4.0 technology implementation. Specifically, the framework reversed initially negative perceptions around a narrowly scoped business case towards an opportunity-oriented attitude exploring further potentials of the technology. Research limitations/implications The research adds to the debate when and why firms engage in, and sustain their I4.0 initiatives by providing a novel perspective on firms' sensing capabilities. As a single-case study, the framework requires further validation in practice. Practical implications The proposed framework provides practitioners with an extended view concerning the potential value of digital transformation projects and serves as a conversational tool. Originality/value The presented wider frame for evaluating digital transformation projects, taking into account the more "intangible" value of their learnings, tackles the fundamental issue of translating explorative innovation efforts into exploitative value - a key challenge when dealing with innovation and one of the main barriers for the digital transformation.
C1 [Colli, Michele; Stingl, Verena; Waehrens, Brian V.] Aalborg Univ, Mat & Prod, Aalborg, Denmark.
RP Stingl, V (通讯作者)，Aalborg Univ, Mat & Prod, Aalborg, Denmark.
EM colli@mp.aau.dk; vstingl@mp.aau.dk; bvw@business.aau.dk
OI Waehrens, Brian Vejrum/0000-0001-6140-5587; Stingl,
   Verena/0000-0001-7128-4467
FU Manufacturing Academy of Denmark
FX This research has been funded by the Manufacturing Academy of Denmark
   and has been part of its MADE Digital program.
CR Arnold C, 2016, INT J INNOV MANAG, V20, DOI 10.1142/S1363919616400156
   Bharadwaj A., 2014, 15080 FOX SCH BUS, DOI [10.2139/ssrn.2510111, DOI 10.2139/SSRN.2510111]
   Boer H, 2015, INT J OPER PROD MAN, V35, P1231, DOI 10.1108/IJOPM-03-2015-0119
   Bordeleau F, 2019, 27 EUR C INF SYST EC
   Bosman L, 2020, J MANUF TECHNOL MANA, V31, P1117, DOI 10.1108/JMTM-09-2018-0283
   BOWMAN EH, 1993, ACAD MANAGE REV, V18, P760, DOI 10.2307/258597
   Boyer K, 2008, J OPER MANAG, V26, P337, DOI 10.1016/j.jom.2008.03.001
   Buer SV, 2021, J MANUF TECHNOL MANA, V32, P621, DOI 10.1108/JMTM-05-2019-0174
   Cimini C, 2021, J MANUF TECHNOL MANA, V32, P695, DOI 10.1108/JMTM-04-2019-0135
   Colli M, 2019, ANNU REV CONTROL, V48, P165, DOI 10.1016/j.arcontrol.2019.06.001
   Cooper RG, 2011, J PROD INNOVAT MANAG, V28, P2, DOI 10.1111/j.1540-5885.2011.00858.x
   Gomes LAD, 2018, TECHNOL FORECAST SOC, V136, P30, DOI 10.1016/j.techfore.2016.11.009
   Demeter K, 2021, J MANUF TECHNOL MANA, V32, P820, DOI 10.1108/JMTM-06-2019-0226
   Dregger J, 2018, PROC CIRP, V67, P140, DOI 10.1016/j.procir.2017.12.190
   Erol S., 2016, INT C COMP MAN, P495
   Fetterman D.M., 2010, ETHNOGRAPHY STEP BY
   Frank AG, 2019, TECHNOL FORECAST SOC, V141, P341, DOI 10.1016/j.techfore.2019.01.014
   Ganzarain J, 2016, J IND ENG MANAG-JIEM, V9, P1119, DOI 10.3926/jiem.2073
   Garms F., 2019, IND 4 0 CAPTURING VA
   Geissbauer R., 2018, GLOBAL DIGITAL OPERA
   Ghadge A, 2020, J MANUF TECHNOL MANA, V31, P669, DOI 10.1108/JMTM-10-2019-0368
   Ghobakhloo M, 2020, J MANUF TECHNOL MANA, V31, P1, DOI 10.1108/JMTM-11-2018-0417
   Ghobakhloo M, 2018, J MANUF TECHNOL MANA, V29, P910, DOI 10.1108/JMTM-02-2018-0057
   Ghobakhloo M, 2018, INF RESOUR MANAG J, V31, P47, DOI 10.4018/IRMJ.2018040103
   Hermann M, 2020, J MANUF TECHNOL MANA, V31, P935, DOI 10.1108/JMTM-08-2018-0274
   Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625
   Holmstrom J, 2009, DECISION SCI, V40, P65, DOI 10.1111/j.1540-5915.2008.00221.x
   Horvath D, 2019, TECHNOL FORECAST SOC, V146, P119, DOI 10.1016/j.techfore.2019.05.021
   J?rvi K, 2018, ISPIM INN C STOCKH S
   Kagermann H., 2013, SECURING FUTURE GERM
   Kagermann H., 2015, MANAGEMENT PERMANENT, P23, DOI DOI 10.1007/978-3-658-05014-6_2
   Kapoor R, 2013, STRATEGIC MANAGE J, V34, P274, DOI 10.1002/smj.2010
   Kiel D, 2017, INT J INNOV MANAG, V21, DOI 10.1142/S1363919617400151
   Lasi H, 2014, BUS INFORM SYST ENG+, V6, P239, DOI 10.1007/s12599-014-0334-4
   Liao YX, 2017, INT J PROD RES, V55, P3609, DOI 10.1080/00207543.2017.1308576
   March JG, 1991, ORGAN SCI, V2, P71, DOI 10.1287/orsc.2.1.71
   McGrath RG, 1997, ACAD MANAGE REV, V22, P974, DOI 10.2307/259251
   Moeuf A, 2018, INT J PROD RES, V56, P1118, DOI 10.1080/00207543.2017.1372647
   Muller JM, 2018, INT J PR ENG MAN-GT, V5, P659, DOI 10.1007/s40684-018-0056-z
   Muller JM, 2019, J MANUF TECHNOL MANA, V30, P1127, DOI 10.1108/JMTM-01-2018-0008
   Muller JM, 2018, TECHNOL FORECAST SOC, V132, P2, DOI 10.1016/j.techfore.2017.12.019
   O'Reilly CA, 2013, ACAD MANAGE PERSPECT, V27, P324, DOI 10.5465/amp.2013.0025
   Oks SJ, 2017, SP SER WIRELESS TECH, P21, DOI 10.1007/978-3-319-42559-7_2
   Ortt R, 2020, J MANUF TECHNOL MANA, V31, P825, DOI 10.1108/JMTM-07-2020-0284
   Paulson AS, 2007, RES TECHNOL MANAGE, V50, P17, DOI 10.1080/08956308.2007.11657458
   Pessot E, 2021, J MANUF TECHNOL MANA, V32, P795, DOI 10.1108/JMTM-05-2019-0169
   Pirola F, 2020, J MANUF TECHNOL MANA, V31, P1045, DOI 10.1108/JMTM-09-2018-0305
   Raffaelli R, 2019, STRATEGIC MANAGE J, V40, P1013, DOI 10.1002/smj.3011
   Rice MP, 2001, R&D MANAGE, V31, P409, DOI 10.1111/1467-9310.00228
   Szasz L, 2021, J MANUF TECHNOL MANA, V32, P667, DOI 10.1108/JMTM-10-2019-0371
   Teece DJ, 2007, STRATEGIC MANAGE J, V28, P1319, DOI 10.1002/smj.640
   Teece DJ, 2017, J ORGAN DES, V6, DOI 10.1186/s41469-017-0018-x
   Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
   Tortorella GL, 2020, INT J PROD ECON, V219, P284, DOI 10.1016/j.ijpe.2019.06.023
   TUSHMAN ML, 1986, ADMIN SCI QUART, V31, P439, DOI 10.2307/2392832
   van Aken J, 2016, J OPER MANAG, V47-48, P1, DOI 10.1016/j.jom.2016.06.004
   Veile JW, 2020, J MANUF TECHNOL MANA, V31, P977, DOI 10.1108/JMTM-08-2018-0270
   Wessel L, 2021, J ASSOC INF SYST, V22, P102, DOI 10.17705/1jais.00655
   Yin RK, 2009, CASE STUDY RES DESIG
NR 59
TC 1
Z9 1
U1 12
U2 45
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1741-038X
EI 1758-7786
J9 J MANUF TECHNOL MANA
JI J. Manuf. Technol. Manag.
PD JAN 3
PY 2022
VL 33
IS 1
BP 41
EP 60
DI 10.1108/JMTM-08-2020-0330
EA JUN 2021
PG 20
WC Engineering, Industrial; Engineering, Manufacturing; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics
GA XX0RK
UT WOS:000664456800001
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Li, YN
   Fang, RY
   Liu, ZH
   Jiang, LP
   Zhang, JD
   Li, HH
   Liu, CY
   Li, F
AF Li, Yanan
   Fang, Ruying
   Liu, Zehua
   Jiang, Luping
   Zhang, Jingdong
   Li, Honghu
   Liu, Chaoyang
   Li, Fei
TI The association between toxic pesticide environmental exposure and
   Alzheimer's disease: A scientometric and visualization analysis
SO CHEMOSPHERE
LA English
DT Review
DE Environmental toxins; Pesticides; Alzheimer's disease; Bibliometric
   analysis; Knowledge mapping; Citespace
ID ANTI-PARKINSON DRUG; PROTEIN-KINASE-C; PP SWEDISH MICE;
   CHOLINESTERASE-INHIBITORS; BIOLOGICAL EVALUATION; RISK; HEALTH;
   DERIVATIVES; BRAIN; PARAQUAT
AB Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. The association between environmental factors (e.g., pesticide) and AD has attracted considerable attention. However, no systematic analysis has been performed and make it difficult to provide deeper insights of AD correlated with pesticide exposure. Hence, this study utilized a bibliometric and visual approach that included map collaborations, co-citations, and keywords, to identifying the knowledge structure, hot topics and the research trends based on 372 publications from the Web of Science Core Collection and PubMed databases. The results showed that 116 institutions from 52 countries published articles in this field. The United States and Israel played a leading role with numerous publications in related journals, as well as prolific institutions and authors, respectively. Three hot topics in pesticide-induced AD were recognized based on co-occurrence keywords detection, including acetylcholinesterase (AChE) inhibitor, oxidative stress, and AChE. Moreover, analysis of keywords burst suggests that some potential molecular mechanisms and therapy targets of pesticide-induced AD, especially for mitochondrial dysfunction and monoamine oxidase-B (MAO-B) that catalyzes the oxidative deamination and causes oxidative stress, are emerging trends. In addition, the study of various pesticides and the assessment method of pesticide exposure will step forward as well. To the best of our knowledge, this study is the first to specifically visualize the relationship between AD and pesticide exposure and to predict potential future research directions. (C) 2020 Elsevier Ltd. All rights reserved.
C1 [Li, Yanan; Fang, Ruying; Liu, Zehua; Jiang, Luping; Zhang, Jingdong; Li, Honghu; Liu, Chaoyang; Li, Fei] Zhongnan Univ Econ & Law, Res Ctr Environm & Hlth, Wuhan 430073, Peoples R China.
   [Liu, Chaoyang] Wuhan Univ, Dept Neurol, Renmin Hosp, Wuhan 430073, Peoples R China.
   [Li, Fei] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Peoples R China.
RP Liu, CY; Li, F (通讯作者)，Zhongnan Univ Econ & Law, Res Ctr Environm & Hlth, Wuhan 430073, Peoples R China.
EM lcy@zuel.edu.cn; lifei@zuel.edu.cn
RI Li, Honghu/AAT-6349-2020
OI Li, Honghu/0000-0001-8788-6577; Zhang, Jingdong/0000-0002-8370-9730
FU National Social Science Foundation of China [19FJYY002]; Social Science
   Foundation of Hubei Province [2017161]; Hubei Special Technology
   Innovation (Soft Science Research) [2018ADC074]; Fundamental Research
   Funds for the Central Universities; Zhongnan University of Economics and
   Law [2722020JCG067]
FX This study was financially supported by National Social Science
   Foundation of China (19FJYY002), Social Science Foundation of Hubei
   Province (2017161), Hubei Special Technology Innovation (Soft Science
   Research) (2018ADC074), the Fundamental Research Funds for the Central
   Universities, Zhongnan University of Economics and Law (2722020JCG067).
CR Abadi B, 2018, J CLEAN PROD, V205, P1069, DOI 10.1016/j.jclepro.2018.09.147
   Alzheimer's Association, 2016, Alzheimers Dement, V12, P459
   Andreeva TV, 2017, BIOCHEMISTRY-MOSCOW+, V82, P122, DOI 10.1134/S0006297917020043
   Andrisano V, 2018, EXPERT OPIN THER PAT, V28, P455, DOI 10.1080/13543776.2018.1476494
   Balazs HE, 2020, APPL SOIL ECOL, V150, DOI 10.1016/j.apsoil.2019.103467
   Baldi I, 2003, AM J EPIDEMIOL, V157, P409, DOI 10.1093/aje/kwf216
   Bar-Am O, 2004, J NEUROCHEM, V89, P1119, DOI 10.1111/j.1471-4159.2004.02425.x
   Bassil KL, 2007, CAN FAM PHYSICIAN, V53, P1705
   Benedetti D, 2018, MUTAGENESIS, V33, P87, DOI 10.1093/mutage/gex035
   Braun AB, 2019, ENVIRON POLLUT, V255, DOI 10.1016/j.envpol.2019.113221
   Canevari L, 2007, NEUROCHEM RES, V32, P739, DOI 10.1007/s11064-006-9200-1
   Cannon JR, 2009, NEUROBIOL DIS, V34, P279, DOI 10.1016/j.nbd.2009.01.016
   Chen C., 2015, THE CITESPACE MANUAL
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Chen Chaomei, 2005, AMIA Annu Symp Proc, P121
   Chen CW, 2014, ALZHEIMERS DEMENT, V10, P196, DOI 10.1016/j.jalz.2013.05.1766
   Chen HB, 2014, INT J LIFE CYCLE ASS, V19, P1674, DOI 10.1007/s11367-014-0777-3
   Chen LJ, 2012, NEUROBIOL AGING, V33, DOI 10.1016/j.neurobiolaging.2011.01.008
   Chen XP, 2017, J ENVIRON SCI HEAL B, V52, P77, DOI 10.1080/03601234.2016.1239973
   Cheung J, 2012, J MED CHEM, V55, P10282, DOI 10.1021/jm300871x
   Costello S, 2009, AM J EPIDEMIOL, V169, P919, DOI 10.1093/aje/kwp006
   Czerniczyniec A, 2015, J BIOENERG BIOMEMBR, V47, P395, DOI 10.1007/s10863-015-9624-x
   Daviglus ML, 2010, ANN INTERN MED, V153, P176, DOI 10.7326/0003-4819-153-3-201008030-00260
   Assis CRD, 2012, SCI TOTAL ENVIRON, V441, P141, DOI 10.1016/j.scitotenv.2012.09.058
   Dong GZ, 2014, CHINESE J ELECTRON, V23, P695
   Eid A, 2019, PHARMACOL THERAPEUT, V199, P173, DOI 10.1016/j.pharmthera.2019.03.005
   Elfikrie N, 2020, SCI TOTAL ENVIRON, V712, DOI 10.1016/j.scitotenv.2020.136540
   Fan J.J., 2016, PLOS ONE, V11
   Fan YS, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15081590
   Fang L, 2008, J MED CHEM, V51, P713, DOI 10.1021/jm701491k
   Gauthier E, 2001, ENVIRON RES, V86, P37, DOI 10.1006/enrs.2001.4254
   Gauthier S, 2006, LANCET, V367, P1262, DOI 10.1016/S0140-6736(06)68542-5
   Godfray HCJ, 2010, SCIENCE, V327, P812, DOI 10.1126/science.1185383
   Goldman SM, 2014, ANNU REV PHARMACOL, V54, P141, DOI 10.1146/annurev-pharmtox-011613-135937
   Gongora-Echeverria V.R., 2020, ECOTOX ENVIRON SAFE, V200, P8
   Greig NH, 2001, CURR MED RES OPIN, V17, P159
   Hayden KM, 2010, NEUROLOGY, V74, P1524, DOI 10.1212/WNL.0b013e3181dd4423
   He K, 2019, SCI TOTAL ENVIRON, V670, P236, DOI 10.1016/j.scitotenv.2019.03.184
   He Q, 2018, BIOORG CHEM, V81, P512, DOI 10.1016/j.bioorg.2018.09.010
   Hu CY, 2020, CHEMOSPHERE, V253, DOI 10.1016/j.chemosphere.2020.126668
   Joubert J, 2017, EUR J MED CHEM, V125, P853, DOI 10.1016/j.ejmech.2016.09.041
   Jurewicz J, 2008, INT J OCCUP MED ENV, V21, P121, DOI 10.2478/v10001-008-0014-z
   Kayyali A., 2014, AM J NURS, V114, P56
   Khalid S, 2020, SCI TOTAL ENVIRON, V711, DOI 10.1016/j.scitotenv.2019.134645
   Khokhar JY, 2012, TOXICOL SCI, V126, P325, DOI 10.1093/toxsci/kfs029
   Kolpin DW, 2013, SCI TOTAL ENVIRON, V443, P700, DOI 10.1016/j.scitotenv.2012.09.063
   Kosak U, 2020, EUR J MED CHEM, V197, DOI 10.1016/j.ejmech.2020.112282
   Kuehn BM, 2011, JAMA-J AM MED ASSOC, V305, P1188, DOI 10.1001/jama.2011.318
   Lentola A, 2017, ENVIRON POLLUT, V228, P297, DOI 10.1016/j.envpol.2017.03.084
   Li KK, 2017, FOOD CHEM TOXICOL, V106, P356, DOI 10.1016/j.fct.2017.05.067
   Li QW, 2020, J CLEAN PROD, V245, DOI 10.1016/j.jclepro.2019.118775
   Li X.J., 2017, BIOSCI REP, V37
   Li XT, 2018, IEEE ACCESS, V6, P63243, DOI 10.1109/ACCESS.2018.2873696
   Liang YD, 2017, J PAIN RES, V10, P951, DOI 10.2147/JPR.S132808
   Liu CY, 2020, FRONT PUBLIC HEALTH, V8, DOI 10.3389/fpubh.2020.00091
   Liu CY, 2016, TRANSL NEURODEGENER, V5, DOI 10.1186/s40035-015-0048-7
   Liu S, 2019, NEURAL REGEN RES, V14, P1643, DOI 10.4103/1673-5374.255995
   Mao GZ, 2018, SCI TOTAL ENVIRON, V635, P1081, DOI 10.1016/j.scitotenv.2018.04.173
   Marcelino A.F., 2019, INT J ENV RES PUB HE, V16, P11
   Maruszak A, 2011, PROG NEURO-PSYCHOPH, V35, P320, DOI 10.1016/j.pnpbp.2010.07.004
   Maruyama W, 2003, NEUROSCI LETT, V341, P233, DOI 10.1016/S0304-3940(03)00211-8
   Md Meftaul I., 2020, SCI TOTAL ENVIRON, V711
   Mink PJ, 2011, REGUL TOXICOL PHARM, V61, P172, DOI 10.1016/j.yrtph.2011.07.006
   Mohammadzadeh L, 2020, NUTR NEUROSCI, V23, P221, DOI 10.1080/1028415X.2018.1492772
   Niu LL, 2016, ENVIRON POLLUT, V219, P315, DOI 10.1016/j.envpol.2016.10.060
   Okada E, 2018, ENVIRON SCI POLLUT R, V25, P15120, DOI 10.1007/s11356-018-1734-7
   Olayinka O, 2019, AGING MED HEALTHCARE, V10, P4, DOI 10.33879/AMH.2019.1727
   Ono M, 2005, NUCL MED BIOL, V32, P329, DOI 10.1016/j.nucmedbio.2005.01.006
   Martinho VJPD, 2019, SCI TOTAL ENVIRON, V688, P346, DOI 10.1016/j.scitotenv.2019.06.199
   Peris-Sampedro F, 2014, FOOD CHEM TOXICOL, V72, P289, DOI 10.1016/j.fct.2014.07.036
   Puglielli L, 2003, NAT NEUROSCI, V6, P345, DOI 10.1038/nn0403-345
   Quaghebeur M, 2013, J CLEAN PROD, V55, P72, DOI 10.1016/j.jclepro.2012.06.012
   Richardson JR, 2014, JAMA NEUROL, V71, P284, DOI 10.1001/jamaneurol.2013.6030
   Rohlman DS, 2011, NEUROTOXICOLOGY, V32, P268, DOI 10.1016/j.neuro.2010.12.008
   Salazar JG, 2011, CURR ALZHEIMER RES, V8, P732
   Sandy M S, 1988, Basic Life Sci, V49, P795
   Sarkar B, 2017, METAB BRAIN DIS, V32, P2045, DOI 10.1007/s11011-017-0093-2
   Secinaro S, 2020, J CLEAN PROD, V264, DOI 10.1016/j.jclepro.2020.121503
   Selkoe DJ, 2001, PHYSIOL REV, V81, P741, DOI 10.1152/physrev.2001.81.2.741
   Singh NK, 2013, HUM EXP TOXICOL, V32, P24, DOI 10.1177/0960327112456315
   Steenland K, 2013, ENVIRON RES, V120, P96, DOI 10.1016/j.envres.2012.08.014
   Sterling J, 2003, J MED CHEM, V46, P3180, DOI 10.1021/jm030124x
   Su YA, 2020, SCI TOTAL ENVIRON, V733, DOI 10.1016/j.scitotenv.2020.138984
   Tartagline A.M., 2015, CURRENT TOPICS BEHAV, V416
   Tripathi VK, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0214164
   Valcke M, 2017, ENVIRON INT, V108, P63, DOI 10.1016/j.envint.2017.07.023
   Vanzetto GV, 2019, ENVIRON POLLUT, V252, P74, DOI 10.1016/j.envpol.2019.05.092
   Wang L., 2018, INT J ENV RES PUBL H, V15
   Weinreb O, 2004, FASEB J, V18, P1471, DOI 10.1096/fj.04-1916fje
   Weinstock M, 2003, PROG NEURO-PSYCHOPH, V27, P555, DOI 10.1016/S0278-5846(03)00053-8
   Wu CK, 2005, EXP NEUROL, V195, P484, DOI 10.1016/j.expneurol.2005.06.020
   Xia N, 2015, SENSORS-BASEL, V15, P499, DOI 10.3390/s150100499
   Xu YX, 2019, ACS CHEM NEUROSCI, V10, P482, DOI 10.1021/acschemneuro.8b00357
   Yadav AS, 2011, INT J HUM GENET, V11, P105, DOI 10.1080/09723757.2011.11886131
   Yakushiji Y, 2020, INT J MOL SCI, V21, DOI 10.3390/ijms21072295
   Yan D.D., 2016, SCI REP, V6, P9
   Yegambaram M, 2015, CURR ALZHEIMER RES, V12, P116, DOI 10.2174/1567205012666150204121719
   Yogev-Falach M, 2002, FASEB J, V16, P1674, DOI 10.1096/fj.02-0198fje
   Youdim MBH, 2006, NAT REV NEUROSCI, V7, P295, DOI 10.1038/nrn1883
   Youdim MBH, 2001, ANN NY ACAD SCI, V939, P450
   Yuan CC, 2017, ENVIRON SCI POLLUT R, V24, P12899, DOI 10.1007/s11356-017-8812-0
   Zhou XQ, 2018, ENVIRON INT, V120, P246, DOI 10.1016/j.envint.2018.07.047
   Zhu QY, 2020, CHEMOSPHERE, V254, DOI 10.1016/j.chemosphere.2020.126779
   Zuanazzi NR, 2020, CHEMOSPHERE, V241, DOI 10.1016/j.chemosphere.2019.125016
NR 104
TC 18
Z9 19
U1 18
U2 89
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0045-6535
EI 1879-1298
J9 CHEMOSPHERE
JI Chemosphere
PD JAN
PY 2021
VL 263
AR 128238
DI 10.1016/j.chemosphere.2020.128238
PG 13
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA PA7HQ
UT WOS:000595802200291
PM 33297185
DA 2022-08-23
ER

PT J
AU Kleinknecht-Dolf, M
   Spichiger, E
   Muller, M
   Bartholomeyczik, S
   Spirig, R
AF Kleinknecht-Dolf, Michael
   Spichiger, Elisabeth
   Muller, Marianne
   Bartholomeyczik, Sabine
   Spirig, Rebecca
TI Advancement of the German version of the moral distress scale for acute
   care nurses-A mixed methods study
SO NURSING OPEN
LA English
DT Article
DE ethics; hospital; instrument development; mixed methods design;
   monitoring; moral distress; nurses; nursing; psychometrics;
   questionnaire
ID HEALTH; RESILIENCE; VALIDITY; ETHICS; DETERMINANTS; RELIABILITY;
   ENVIRONMENT; EDUCATION; INTERNET; TYPOLOGY
AB Aim: Moral distress experienced by nurses in acute care hospitals can adversely impact the affected nurses, their patients and their hospitals; therefore, it is advisable for organizations to establish internal monitoring of moral distress. However, until now, no suitable questionnaire has been available for use in German-speaking contexts. Hence, the aim of this study was to develop and psychometrically test a German-language version of the Moral Distress Scale.
   Design: We chose a sequential explanatory mixed methods design, followed by a second quantitative cross-sectional survey.
   Methods: An American moral distress scale was chosen, translated, culturally adapted, tested in a pilot study and subsequently used in 2011 to conduct an initial web-based quantitative cross-sectional survey of nurses in all inpatient units at five hospitals in Switzerland's German-speaking region. Data were analysed descriptively and via a Rasch analysis. In 2012, four focus group interviews were conducted with 26 nurses and then evaluated using knowledge maps. The results were used to improve the questionnaire. In 2015, using the revised German-language instrument, a second survey and Rasch analysis were conducted.
   Results: The descriptive results of the first survey's participants (n = 2153; response rate: 44%) indicated that moral distress is a salient phenomenon in Switzerland. The data from the focus group interviews and the Rasch analysis produced information valuable for the questionnaire's further development. Alongside the data from the second survey's participants (n = 1965; response rate: 40%), the Rasch analysis confirmed the elimination of previous deficiencies on its psychometrics. A Rasch-scaled German version of the Moral Distress Scale is now available for use.
C1 [Kleinknecht-Dolf, Michael; Spirig, Rebecca] Univ Hosp Zurich, Dept Nursing & Allied Hlth Care Profess, Ramistr, Zurich, Switzerland.
   [Kleinknecht-Dolf, Michael; Bartholomeyczik, Sabine; Spirig, Rebecca] Univ Witten Herdecke, Sch Nursing Sci, Fac Hlth, Witten, Germany.
   [Spichiger, Elisabeth] Bern Univ Hosp, Directorate Nursing Med Tech & Med Therapeut Area, Inselspital, Bern, Switzerland.
   [Spichiger, Elisabeth; Spirig, Rebecca] Univ Basel, Dept Publ Hlth, Nursing Sci, Fac Med, Basel, Switzerland.
   [Muller, Marianne] Zurich Univ Appl Sci, Inst Data Anal & Proc Design, Sch Engn, Winterthur, Switzerland.
RP Spirig, R (通讯作者)，Univ Hosp Zurich, Dept Nursing & Allied Hlth Care Profess, Ramistr, Zurich, Switzerland.
EM rebecca.spirig@usz.ch
FU Swiss National Science Foundation [CRSII3_132/86/1];
   Kathe-Zingg-Schwichtenberg Fond; Gottfried und Julia
   Bangerter-Rhyner-Stiftung; Olga Mayenfisch Stiftung; Swiss Nurses
   Association; Stiftung Pflegewissenschaft Schweiz
FX This research was funded in part by the Swiss National Science
   Foundation (CRSII3_132/86/1), the Kathe-Zingg-Schwichtenberg Fond, the
   Gottfried und Julia Bangerter-Rhyner-Stiftung, the Olga Mayenfisch
   Stiftung, the Swiss Nurses Association, the Stiftung Pflegewissenschaft
   Schweiz, and by another Foundation which does not want to be mentioned
   with its name
CR American Association of Critical-Care Nurses (AACN), 2008, MOR DISTR POS STAT
   Andrich D., 2010, RUMM 2030
   Bannigan K, 2009, J CLIN NURS, V18, P3237, DOI 10.1111/j.1365-2702.2009.02939.x
   Bentzen G., 2013, NURSING RES PRACTICE, V2013, P8
   BORHANI F, 2014, J MED ETHICS HIST ME, V7
   Burston AS, 2013, NURS ETHICS, V20, P312, DOI 10.1177/0969733012462049
   Clark PG, 1997, GERONTOLOGIST, V37, P441, DOI 10.1093/geront/37.4.441
   Cook C, 2000, EDUC PSYCHOL MEAS, V60, P821, DOI 10.1177/00131640021970934
   Corley M C, 1995, Am J Crit Care, V4, P280
   Corley MC, 2001, J ADV NURS, V33, P250, DOI 10.1046/j.1365-2648.2001.01658.x
   Corley MC, 2002, NURS ETHICS, V9, P636, DOI 10.1191/0969733002ne557oa
   Creswell J.W., 2011, BEST PRACTICES MIXED, P2094, DOI [10.1037/e566732013-001, DOI 10.1037/E566732013-001]
   Creswell J.W, 2006, DESIGNING CONDUCTING
   Creswell JW., 2003, HDB MIXED METHODS SO, DOI DOI 10.4135/9781506335193
   de Veer AJE, 2013, INT J NURS STUD, V50, P100, DOI 10.1016/j.ijnurstu.2012.08.017
   De Villers MJ, 2013, NURS ETHICS, V20, P589, DOI 10.1177/0969733012452882
   DeVellis R.F., 2012, SCALE DEV THEORY APP
   DeVon HA, 2007, J NURS SCHOLARSHIP, V39, P155, DOI 10.1111/j.1547-5069.2007.00161.x
   Ebener S, 2006, B WORLD HEALTH ORGAN, V84, P636, DOI 10.2471/BLT.06.029736
   Eizenberg MM, 2009, J ADV NURS, V65, P885, DOI 10.1111/j.1365-2648.2008.04945.x
   Epstein EG, 2009, J CLIN ETHIC, V20, P330
   European Medicines Agency, 2002, ICH TOP E 6 R1 GUID
   Farmer T, 2006, QUAL HEALTH RES, V16, P377, DOI 10.1177/1049732305285708
   Fenton M, 1988, Can J Nurs Adm, V1, P8
   Fernandez-Parsons R, 2013, J EMERG NURS, V39, P547, DOI 10.1016/j.jen.2012.12.009
   Goethals S, 2010, INT J NURS STUD, V47, P635, DOI 10.1016/j.ijnurstu.2009.12.010
   Greene J. C., 1989, EDUC EVAL POLICY AN, V11, P255, DOI [10.3102/01623737011003255, DOI 10.3102/01623737011003255]
   Gutierrez KM, 2005, DIMENS CRIT CARE NUR, V24, P229, DOI 10.1097/00003465-200509000-00011
   Hagquist C, 2009, INT J NURS STUD, V46, P380, DOI 10.1016/j.ijnurstu.2008.10.007
   Hamric A.B., 2012, AJOB PRIMARY RES, V3, P1, DOI [10.1080/21507716.2011.652337, DOI 10.1080/21507716.2011.652337]
   Hamric AB, 2007, CRIT CARE MED, V35, P422, DOI 10.1097/01.CCM.0000254722.50608.2D
   Hamric Ann B, 2006, Pharos Alpha Omega Alpha Honor Med Soc, V69, P16
   Hardingham Lorraine B, 2004, Nurs Philos, V5, P127, DOI 10.1111/j.1466-769X.2004.00160.x
   Holmes S, 2009, INT J NURS STUD, V46, P394, DOI 10.1016/j.ijnurstu.2008.08.004
   Horton K, 2007, NURS ETHICS, V14, P716, DOI 10.1177/0969733007082112
   Houser J, 2008, J SPEC PEDIATR NURS, V13, P297, DOI 10.1111/j.1744-6155.2008.00171.x
   Huffman DM, 2012, CRIT CARE NURS CLIN, V24, P91, DOI 10.1016/j.ccell.2012.01.004
   IBM Corp, 2019, IBM SPSS STAT WINDOW
   Ivankova NV, 2006, FIELD METHOD, V18, P3, DOI 10.1177/1525822X05282260
   Jager R, 2004, DIAGNOSTICA, V50, P31, DOI 10.1026/0012-1924.50.1.31
   Jager R., 2001, 70 ANN M INT SOC PSY
   Jones PS, 2001, NURS RES, V50, P300, DOI 10.1097/00006199-200109000-00008
   Kangasniemi M, 2015, J ADV NURS, V71, P1744, DOI 10.1111/jan.12619
   Kleinknecht-Dolf M, 2015, NURS ETHICS, V22, P77, DOI 10.1177/0969733014534875
   Kleinknecht-Dolfz M, 2015, PFLEGE, V28, P93, DOI 10.1024/1012-5302/a000411
   Kramer M, 2009, RES NURS HEALTH, V32, P229, DOI 10.1002/nur.20315
   Lazzarin M, 2012, NURS ETHICS, V19, P183, DOI 10.1177/0969733011416840
   Lutzen K, 2013, J BIOETHIC INQ, V10, P317, DOI 10.1007/s11673-013-9469-0
   Martin Jacqueline S, 2007, Pflege, V20, P157
   Mayring P., 2008, QUALITATIVE INHALTSA
   McCarthy J, 2015, NURS ETHICS, V22, P131, DOI 10.1177/0969733014557139
   Monteverde S, 2016, NURS ETHICS, V23, P104, DOI 10.1177/0969733014557140
   Monteverde S, 2014, NURS ETHICS, V21, P385, DOI 10.1177/0969733013505308
   O'Cathain Alicia, 2010, BMJ, V341, pc4587, DOI 10.1136/bmj.c4587
   Onwuegbuzie AJ, 2007, QUAL REP, V12, P281
   Pelz C., 2004, KNOWLEDGE MAPPING AL
   Pendry PS, 2007, NURS ECON, V25, P217
   Piers RD, 2012, J AM MED DIR ASSOC, V13, DOI 10.1016/j.jamda.2010.12.014
   Rice EM, 2008, J NURS MANAGE, V16, P360, DOI 10.1111/j.1365-2834.2007.00798.x
   Rushton CH, 2013, J PALLIAT MED, V16, P1080, DOI 10.1089/jpm.2013.0105
   Rushton CH, 2016, AACN ADV CRIT CARE, V27, P111, DOI 10.4037/aacnacc2016275
   Schreuder JAH, 2012, NURS OUTLOOK, V60, P37, DOI 10.1016/j.outlook.2011.05.005
   Schweizer Berufsverband der Pflegefachfrauen und Pflegefachmanner (SBK), 2013, ETH PFLEG
   Spirig B., 2015, PFLEGE GESELLSCHAFT, V20, P115
   Spirig R, 2014, GERMAN MED SCI, V12, P1
   SPSS INC, 2009, PASW STAT WIND VERS, V2009
   SwissDRG AG, 2010, FALLP SCHWEIZ SPIT B
   Teddlie C, 2007, J MIX METHOD RES, V1, P77, DOI 10.1177/2345678906292430
   Ulrich BT, 2014, CRIT CARE NURSE, V34, P64, DOI 10.4037/ccn2014731
   VANALPHEN A, 1994, J ADV NURS, V20, P196
   Varcoe C, 2012, HEC FORUM, V24, P51, DOI 10.1007/s10730-012-9180-2
   WAMPOLD BE, 1990, J CONSULT CLIN PSYCH, V58, P360, DOI 10.1037/0022-006X.58.3.360
   Webster G. C., 2000, MARGIN ERROR ETHICS, P217
   Whitehead PB, 2015, J NURS SCHOLARSHIP, V47, P117, DOI 10.1111/jnu.12115
   Wiggleton C, 2010, ACAD MED, V85, P111, DOI 10.1097/ACM.0b013e3181c4782b
   Wilkinson Judith M, 1988, Nurs Forum, V23, P16, DOI 10.1111/j.1744-6198.1987.tb00794.x
   Wilson MA, 2013, J CLIN NURS, V22, P1455, DOI 10.1111/jocn.12128
   Wlodarczyk D, 2011, NURS ETHICS, V18, P847, DOI 10.1177/0969733011408053
   Wong DL, 2001, PAIN, V89, P295, DOI 10.1016/S0304-3959(00)00375-4
   Woods M, 2015, NURS ETHICS, V22, P117, DOI 10.1177/0969733014542679
   Zavotsky KE, 2016, ADV EMERG NURS J, V38, P133, DOI 10.1097/TME.0000000000000100
   Zhang WQ, 2013, MED CARE, V51, pe51, DOI 10.1097/MLR.0b013e31824642fd
   Zuzelo PR, 2007, NURS ETHICS, V14, P344, DOI 10.1177/0969733007075870
NR 83
TC 9
Z9 9
U1 0
U2 8
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN, NJ 07030 USA
SN 2054-1058
J9 NURS OPEN
JI NURS. OPEN
PD OCT
PY 2017
VL 4
IS 4
BP 251
EP 266
DI 10.1002/nop2.91
PG 16
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA FQ7PT
UT WOS:000418555300008
PM 29085651
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Han, XT
   Xie, RX
   Li, XT
   Li, JY
AF Han, Xueting
   Xie, Ruixia
   Li, Xutao
   Li, Junyi
TI SmileGNN: Drug-Drug Interaction Prediction Based on the SMILES and Graph
   Neural Network
SO LIFE-BASEL
LA English
DT Article
DE drug-drug interaction prediction; graph neural network; knowledge graph;
   structural features; topological features
ID MEDETOMIDINE
AB Concurrent use of multiple drugs can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. However, it is difficult to test the drug-drug interactions widely and effectively before the drugs enter the market. Therefore, the prediction of drug-drug interactions has become one of the research priorities in the biomedical field. In recent years, researchers have been using deep learning to predict drug-drug interactions by exploiting drug structural features and graph theory, and have achieved a series of achievements. A drug-drug interaction prediction model SmileGNN is proposed in this paper, which can be characterized by aggregating the structural features of drugs constructed by SMILES data and the topological features of drugs in knowledge graphs obtained by graph neural networks. The experimental results show that the model proposed in this paper combines a variety of data sources and has a better prediction performance compared with existing prediction models of drug-drug interactions. Five out of the top ten predicted new drug-drug interactions are verified from the latest database, which proves the credibility of SmileGNN.
C1 [Han, Xueting; Li, Xutao; Li, Junyi] Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen 518055, Peoples R China.
   [Xie, Ruixia] Shenzhen Polytech, Sch Med Technol & Nursing, Shenzhen 518055, Peoples R China.
RP Li, JY (通讯作者)，Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen 518055, Peoples R China.
EM 21s051051@stu.hit.edu.cn; xieruixia07@szpt.edu.cn; lixutao@hit.edu.cn;
   lijunyi@hit.edu.cn
OI Li, Junyi/0000-0001-8045-5264
FU National Key Research Program [2021YFA0910700]; Shenzhen Science and
   Technology University Stable Support Program
   [GXWD20201230155427003-20200821222112001]; Guangdong Key Area Research
   Program [2020B0101380001]; Shenzhen Science and Technology Program
   [JCYJ20200109113201726]; National Natural Science Foundation of China
   [61972111]
FX This research study was funded by the National Key Research Program
   (2021YFA0910700), Shenzhen Science and Technology University Stable
   Support Program (GXWD20201230155427003-20200821222112001), Guangdong Key
   Area Research Program (2020B0101380001), Shenzhen Science and Technology
   Program (JCYJ20200109113201726), and National Natural Science Foundation
   of China (61972111).
CR Avsaroglu H, 2008, J VET PHARMACOL THER, V31, P368, DOI 10.1111/j.1365-2885.2008.00969.x
   Bansal M, 2014, NAT BIOTECHNOL, V32, P1213, DOI 10.1038/nbt.3052
   Belleau F, 2008, J BIOMED INFORM, V41, P706, DOI 10.1016/j.jbi.2008.03.004
   Bougiatiotis Konstantinos, 2020, Artificial Intelligence in Medicine. 18th International Conference on Artificial Intelligence in Medicine, AIME 2020. Proceedings. Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science (LNAI 12299), P122, DOI 10.1007/978-3-030-59137-3_12
   Dai YF, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbaa256
   Defferrard M., 2016, 30 INT C NEURAL INFO, P3844
   Duhamel MC, 2010, BIOMED CHROMATOGR, V24, P868, DOI 10.1002/bmc.1379
   Edwards IR, 2000, LANCET, V356, P1255, DOI 10.1016/S0140-6736(00)02799-9
   Goh G. B., 2017, ARXIV171202034
   Hamilton W.L., 2017, ARXIV170905584
   Johnson AEW, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.35
   Kanehisa M, 2000, NUCLEIC ACIDS RES, V28, P27, DOI 10.1093/nar/28.1.27
   Karim MR, 2019, ACM-BCB'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND HEALTH INFORMATICS, P113, DOI 10.1145/3307339.3342161
   Lassila O., 1998, RESOURCE DESCRIPTION
   Lee G, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-3013-0
   Lin X., 2020, P 29 INT JOINT C ART
   Pan RQ, 2017, CANCER CELL, V32, P748, DOI 10.1016/j.ccell.2017.11.003
   Pujara J., 2013, PROC 12 INT SEMANTIC, P542
   Ryu JY, 2018, P NATL ACAD SCI USA, V115, pE4304, DOI 10.1073/pnas.1803294115
   VANGINNEKEN CAM, 1989, CLIN PHARMACOKINET, V16, P38, DOI 10.2165/00003088-198916010-00003
   Wang B, 2019, IEEE INT C BIOINFORM, P1253, DOI 10.1109/BIBM47256.2019.8983257
   Wang M, 2017, LECT NOTES COMPUT SC, V10588, P219, DOI 10.1007/978-3-319-68204-4_23
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Xu Z, 2017, ACM-BCB' 2017: PROCEEDINGS OF THE 8TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY,AND HEALTH INFORMATICS, P285, DOI 10.1145/3107411.3107424
   Yifan D., 2020, BIOINFORMATICS, V15, P15
   Yu Y, 2021, BIOINFORMATICS, V37, P2988, DOI 10.1093/bioinformatics/btab207
   Yu ZX, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbaa243
   Zitnik M, 2018, BIOINFORMATICS, V34, P457, DOI 10.1093/bioinformatics/bty294
NR 28
TC 0
Z9 0
U1 7
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-1729
J9 LIFE-BASEL
JI Life-Basel
PD FEB
PY 2022
VL 12
IS 2
AR 319
DI 10.3390/life12020319
PG 14
WC Biology; Microbiology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Life Sciences & Biomedicine - Other Topics; Microbiology
GA ZN5UT
UT WOS:000765099800001
PM 35207606
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Zhao, MX
   Li, D
   Long, YS
AF Zhao, Mengxi
   Li, Dan
   Long, Yongshen
TI Forestry big data platform by Knowledge Graph
SO JOURNAL OF FORESTRY RESEARCH
LA English
DT Article
DE Intelligent forestry; Co-word analysis; Knowledge Graph; Big data
ID MANAGEMENT
AB Using the advantages of web crawlers in data collection and distributed storage technologies, we accessed to a wealth of forestry-related data. Combined with the mature big data technology at its present stage, Hadoop's distributed system was selected to solve the storage problem of massive forestry big data and the memory-based Spark computing framework to realize real-time and fast processing of data. The forestry data contains a wealth of information, and mining this information is of great significance for guiding the development of forestry. We conducts co-word and cluster analyses on the keywords of forestry data, extracts the rules hidden in the data, analyzes the research hotspots more accurately, grasps the evolution trend of subject topics, and plays an important role in promoting the research and development of subject areas. The co-word analysis and clustering algorithm have important practical significance for the topic structure, research hotspot or development trend in the field of forestry research. Distributed storage framework and parallel computing have greatly improved the performance of data mining algorithms. Therefore, the forestry big data mining system by big data technology has important practical significance for promoting the development of intelligent forestry.
C1 [Zhao, Mengxi; Li, Dan; Long, Yongshen] Northeast Forestry Univ, Harbin 150040, Peoples R China.
RP Li, D (通讯作者)，Northeast Forestry Univ, Harbin 150040, Peoples R China.
EM 2393901357@qq.com
FU Fundamental Research Funds for the Central Universities [2572018BH02];
   Special Funds for Scientific Research in the Forestry Public Welfare
   Industry [201504307-03]
FX We acknowledge grants from the Fundamental Research Funds for the
   Central Universities (Grant No. 2572018BH02) and Special Funds for
   Scientific Research in the Forestry Public Welfare Industry (Grant Nos.
   201504307-03).
CR Boettcher SEP, 2015, ATTEN PERCEPT PSYCHO, V77, P1132, DOI 10.3758/s13414-015-0858-9
   Bovenzi M, 2009, INT ARCH OCC ENV HEA, V82, P893, DOI 10.1007/s00420-008-0376-3
   CALLON M, 1991, SCIENTOMETRICS, V22, P155, DOI 10.1007/BF02019280
   de Groc C., 2011, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, P497, DOI 10.1109/WI-IAT.2011.253
   Dong X.Y, 2015, BIORESOURCES, V10, P7, DOI [10.15376/biores.10.4.7854-7864, DOI 10.15376/BIORES.10.4.7854-7864]
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Fisher D. H., 1987, Machine Learning, V2, P139, DOI 10.1007/BF00114265
   Frank S, 2015, FORESTS, V6, P533, DOI 10.3390/f6030533
   Gong Xia-yi, 2014, Journal of System Simulation, V26, P489
   Gu YQ, 2012, INT CONF DAT MIN WOR, P835, DOI 10.1109/ICDMW.2012.115
   Hou Jian, 2009, Kezaisheng Nengyuan / Renewable Energy Resources, V27, P113
   Jiang LG, 2011, PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON NUCLEAR ENGINEERING 2010, VOL 4 PTS A AND B, P915
   Jie W, 2016, INT C FUZZ SYST KNOW, DOI [10.1109/FSKD.2015.7382112, DOI 10.1109/FSKD.2015.7382112]
   Li L, 2017, J CLEAN PROD, V142, P513, DOI 10.1016/j.jclepro.2016.02.078
   Li Z, 2012, M ASS COMP LING HUM
   Liu GY, 2012, SCIENTOMETRICS, V91, P203, DOI 10.1007/s11192-011-0586-4
   MOHAI P, 1995, POLICY STUD J, V23, P247, DOI 10.1111/j.1541-0072.1995.tb01740.x
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Pujara J, 2013, KNOWLEDGE GRAPH IDEN
   Qi KL, 2016, J APPL REMOTE SENS, V10, DOI 10.1117/1.JRS.10.042005
   Ruslandi, 2014, INT FOREST REV, V16, P497, DOI 10.1505/146554814813484112
   Shvachko K.V, 2010, LOGIN, V35, P6
   Stevanovic D, 2012, EXPERT SYST APPL, V39, P8707, DOI 10.1016/j.eswa.2012.01.210
   Sun W, 2012, M ASS COMP LING HUM, DOI [10.2514/6.2006-1683, DOI 10.2514/6.2006-1683]
   Tang L, 2009, INT J SUST DEV WORLD, V16, P94, DOI 10.1080/13504500902794000
   [张梅山 Zhang Meishan], 2012, [中文信息学报, Journal of Chinese Information Processing], V26, P8
   Zhao Y, 2011, INT C GEOINF, DOI [10.1109/CTS.2014.6867550, DOI 10.1109/CTS.2014.6867550]
   [郑力明 ZHENG Li-ming], 2009, [微计算机信息, Microcomputer Information], V25, P123
NR 28
TC 2
Z9 3
U1 10
U2 35
PU NORTHEAST FORESTRY UNIV
PI HARBIN
PA NO 26 HEXING RD, XIANGFANG DISTRICT, HARBIN, 150040, PEOPLES R CHINA
SN 1007-662X
EI 1993-0607
J9 J FORESTRY RES
JI J. For. Res.
PD JUN
PY 2021
VL 32
IS 3
BP 1305
EP 1314
DI 10.1007/s11676-020-01130-w
EA APR 2020
PG 10
WC Forestry
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Forestry
GA RO1FW
UT WOS:000529460600001
DA 2022-08-23
ER

PT J
AU Ren, J
   Xia, F
   Chen, XT
   Liu, JY
   Hou, ML
   Shehzad, A
   Sultanova, N
   Kong, XJ
AF Ren, Jing
   Xia, Feng
   Chen, Xiangtai
   Liu, Jiaying
   Hou, Mingliang
   Shehzad, Ahsan
   Sultanova, Nargiz
   Kong, Xiangjie
TI Matching Algorithms: Fundamentals, Applications and Challenges
SO IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
LA English
DT Article
DE Computer science; Search engines; Electronic mail; Databases;
   Computational intelligence; Biological system modeling; Recommender
   systems; Information retrieval; knowledge graph; matching theory;
   recommender system; stable matching
ID SERVICE RECOMMENDATION; STABILITY; INFORMATION; MARKETS; TIME
AB Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people's daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the question-answer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theory's basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching.
C1 [Ren, Jing; Xia, Feng; Sultanova, Nargiz] Federat Univ Australia, Sch Engn IT & Phys Sci, Ballarat, Vic 3353, Australia.
   [Chen, Xiangtai; Liu, Jiaying; Hou, Mingliang; Shehzad, Ahsan] Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China.
   [Kong, Xiangjie] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Peoples R China.
RP Xia, F (通讯作者)，Federat Univ Australia, Sch Engn IT & Phys Sci, Ballarat, Vic 3353, Australia.
EM ch.yum@outlook.com; f.xia@ieee.org; chenxiangtai@outlook.com;
   jiaying_liu@outlook.com; teemohold@outlook.com;
   ahsan.shehzad@outlook.com; n.sultanova@federation.edu.au;
   xjkong@ieee.org
RI Kong, Xiangjie/B-8809-2016; Xia, Feng/Y-2859-2019
OI Kong, Xiangjie/0000-0003-2698-3319; Sultanova,
   Nargiz/0000-0003-2373-0834; Liu, Jiaying/0000-0001-9090-6305; Xia,
   Feng/0000-0002-8324-1859; Ren, Jing/0000-0003-0169-1491; Hou,
   Mingliang/0000-0001-5225-2195; Chen, Xiangtai/0000-0002-6619-9396
FU National Natural Science Foundation of China [61872054]
FX This work was supported by National Natural Science Foundation of China
   under Grant 61872054.
CR Abdollahpouri Himan, 2020, RecSys '20: Fourteenth ACM Conference on Recommender Systems, P726, DOI 10.1145/3383313.3418487
   Abdulkadiroglu A, 2013, ECON SOC MONOGR, P3
   Abed AT, 2016, I SYMPOS TELECOM TEC, P6, DOI 10.1109/ISTT.2016.7918075
   Akbarpour M, 2020, J POLIT ECON, V128, P783, DOI 10.1086/704761
   [Anonymous], 2019, P IEEE INT C DEP AUT
   [Anonymous], 2020, COMPUT NETW, DOI DOI 10.1016/j.comnet.2020.107249
   Asabere NY, 2014, IEEE T HUM-MACH SYST, V44, P689, DOI 10.1109/THMS.2014.2325837
   Babkina T, 2014, P 2 INT WORKSH ONT I, P43
   Babri UM, 2016, INT J ADV COMPUT SC, V7, P206
   Baluja Shumeet, 2008, VIDEO SUGGESTION DIS, P895, DOI DOI 10.1145/1367497.1367618
   Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014
   Bayat S, 2016, IEEE SIGNAL PROC MAG, V33, P103, DOI 10.1109/MSP.2016.2598848
   BECKER GS, 1973, J POLIT ECON, V81, P813, DOI 10.1086/260084
   Bedru HD, 2020, COMPUT SCI REV, V37, DOI 10.1016/j.cosrev.2020.100247
   Bendersky M, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P1769, DOI 10.1145/2623330.2623344
   Bendlin T, 2019, AAAI CONF ARTIF INTE, P9917
   Bengio Y, 2009, FOUND TRENDS MACH LE, V2, P1, DOI 10.1561/2200000006
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Berger Adam, 2017, ACM SIGIR Forum, V51, P219, DOI 10.1145/3130348.3130371
   BERGSTROM TC, 1993, J POLIT ECON, V101, P185, DOI 10.1086/261871
   Betru B.T., 2017, INT J COMPUT APPL, V162, P17
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bordes Antoine, 2014, Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2014. Proceedings: LNCS 8724, P165, DOI 10.1007/978-3-662-44848-9_11
   Bordes A, 2014, P 2014 C EMP METH NA, P615, DOI DOI 10.3115/V1/D14-1067
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   [曹斌 Cao Bin], 2019, [计算机研究与发展, Journal of Computer Research and Development], V56, P866
   Celikyilmaz A., 2010, GENRE
   Che YK, 2019, ECONOMETRICA, V87, P65, DOI 10.3982/ECTA13547
   Chen HK, 2019, AAAI CONF ARTIF INTE, P3312
   Chen JY, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P335, DOI 10.1145/3077136.3080797
   Chen TW, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1172, DOI 10.1145/3394486.3403170
   Chen T, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P891, DOI 10.1145/3397271.3401042
   Cho K., 2014, ARXIV14061078, P1724
   Choi J, 2018, AAAI CONF ARTIF INTE, P5094
   Covington P, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P191, DOI 10.1145/2959100.2959190
   Davidson James, 2010, RECSYS
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Dumais ST, 2004, ANNU REV INFORM SCI, V38, P189
   Ekstrand MD, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P1403, DOI 10.1145/3331184.3331380
   Feng Xia, 2016, IEEE Transactions on Big Data, V2, P101, DOI 10.1109/TBDATA.2016.2555318
   Fu ZH, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P69, DOI 10.1145/3397271.3401051
   Gabaix X, 2008, Q J ECON, V123, P49, DOI 10.1162/qjec.2008.123.1.49
   Gai K., 2017, ARXIV170405194
   GALE D, 1962, AM MATH MON, V69, P9, DOI 10.2307/2312726
   Garcia-Duran Alberto, 2014, Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2014. Proceedings: LNCS 8724, P434, DOI 10.1007/978-3-662-44848-9_28
   Gedam S. S, 2012, INT J EMERG TECHNOL, V2, P130
   Gers FA, 2000, IEEE IJCNN, P189, DOI 10.1109/IJCNN.2000.861302
   Gomaa W. H., 2013, INT J COMPUTER APPL, V68, P13, DOI 10.5120/11638-7118
   Gruen AW., 1985, S AFR J PHOTOGRAM, V14, P175
   Gu YN, 2014, IEEE GLOB COMM CONF, P4910, DOI 10.1109/GLOCOM.2014.7037583
   Gu YA, 2015, IEEE COMMUN MAG, V53, P52, DOI 10.1109/MCOM.2015.7105641
   Guo GB, 2020, AAAI CONF ARTIF INTE, V34, P67
   Guo GD, 2019, COMPUT VIS IMAGE UND, V189, DOI 10.1016/j.cviu.2019.102805
   Harris C., 1988, P 4 ALV VIS C, P147, DOI DOI 10.5244/C.2.23
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   He X., 2017, P 26 INT JOINT C ART, P1725, DOI [10.24963/ijcai.2017/239, DOI 10.24963/IJCAI.2017/239]
   He XN, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P355, DOI 10.1145/3077136.3080777
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   He YZ, 2022, IEEE T SOFTWARE ENG, V48, P1743, DOI 10.1109/TSE.2020.3034721
   Hossler T, 2020, IEEE T COMMUN, V68, P5228, DOI 10.1109/TCOMM.2020.2995150
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Hofmann T, 1999, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, P289
   Hu B., 2014, PROC 27 INT C NEURAL, V27, P2042
   Huang JZ, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2677, DOI 10.1145/3394486.3403318
   Huang PS, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P2333
   Ishiyama R, 2018, IEEE GLOB CONF SIG, P380, DOI 10.1109/GlobalSIP.2018.8646344
   Ismaili A, 2019, J ARTIF INTELL RES, V65, P393, DOI 10.1613/jair.1.11582
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Jorswieck, 2014, ARXIV14011671
   KELSO AS, 1982, ECONOMETRICA, V50, P1483, DOI 10.2307/1913392
   Khazaen Y, 2020, UNDERSTANDING IMPROV, P267, DOI DOI 10.1007/978-3-030-38825-6_13
   Kim DH, 2020, IEEE ACCESS, V8, P73494, DOI 10.1109/ACCESS.2020.2987753
   Kim S, 2019, AAAI CONF ARTIF INTE, P6586
   Kong XJ, 2017, IEEE T IND INFORM, V13, P1202, DOI 10.1109/TII.2017.2684163
   Koren Y, 2011, RECOMMENDER SYSTEMS HANDBOOK, P145, DOI 10.1007/978-0-387-85820-3_5
   KREMER M, 1993, Q J ECON, V108, P551, DOI 10.2307/2118400
   Kubler D, 2019, SP22019205 WZB
   Kurino M, 2020, JPN ECON REV, V71, P135, DOI 10.1007/s42973-019-00004-z
   LeCun Y., 2006, PREDICTING STRUCTURE, V1, P59, DOI DOI 10.1073/PNAS.0502269102
   Lei WQ, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2073, DOI 10.1145/3394486.3403258
   Leshem A, 2012, IEEE J SEL AREA COMM, V30, P82, DOI 10.1109/JSAC.2012.120108
   Li T, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P794, DOI 10.1145/3394486.3403122
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HF, 2017, IEEE SYST J, V11, P295, DOI 10.1109/JSYST.2015.2427193
   Liu HX, 2017, PR MACH LEARN RES, V70
   Liu JY, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P2153, DOI 10.1145/3397271.3401405
   Liu JY, 2019, SCIENTOMETRICS, V118, P617, DOI 10.1007/s11192-018-2974-5
   Liu JY, 2018, IEEE ACCESS, V6, P34403, DOI 10.1109/ACCESS.2018.2819688
   Liu Quan, 2016, ARXIV160307704V2
   Liu Y, 2020, AAAI CONF ARTIF INTE, V34, P4932
   Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94
   Ma JY, 2021, INT J COMPUT VISION, V129, DOI 10.1007/s11263-020-01359-2
   Manzo M, 2019, MACH LEARN KNOW EXTR, V1, DOI 10.3390/make1030046
   MASARANI F, 1989, THEOR DECIS, V26, P305, DOI 10.1007/BF00134111
   Mehrabi N, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3457607
   Mikolov T., 2013, ARXIV PREPRINT ARXIV, P1
   Minaee Shervin, 2020, ARXIV200403705V1CSCL
   Mitra Bhaskar, 2017, P 26 INT C WORLD, P1291, DOI [10.1145/3038912.3052579, DOI 10.1145/3038912.3052579]
   Moore S, 2019, ACR N AM ADV
   Moravec H. P., 1981, INT JOINT C ART INT, P785, DOI DOI 10.1007/S00427-011-0383-3
   Naumov M., 2019, CORR
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Niu XCA, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P3405, DOI 10.1145/3394486.3403393
   Ong YS, 2019, IEEE TETCI, V3, P411, DOI 10.1109/TETCI.2019.2928344
   Palangi H, 2014, ARXIV14126629
   Pang L, 2016, AAAI CONF ARTIF INTE, P2793
   Parikh A., 2016, P 2016 C EMP METH NA, P2249, DOI DOI 10.18653/V1/D16-1244
   Park N, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P503, DOI 10.1145/3394486.3403093
   Patro GK, 2020, AAAI CONF ARTIF INTE, V34, P181
   Qi LY, 2020, INFORM SCIENCES, V515, P91, DOI 10.1016/j.ins.2019.11.021
   Qian YJ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3841
   Qihong Yang, 2018, 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), P1166, DOI 10.1109/FSKD.2018.8686856
   Qiu MH, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P208
   Ramos Juan, 2003, P 1 INSTR C MACH LEA, V242, P133
   Reddy BS, 1996, IEEE T IMAGE PROCESS, V5, P1266, DOI 10.1109/83.506761
   Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, P333, DOI 10.1561/1500000019
   ROSEN S, 1981, AM ECON REV, V71, P845
   Roth A. E., 1992, HDB GAME THEORY EC A, V1, P485
   ROTH AE, 1984, ECONOMETRICA, V52, P47, DOI 10.2307/1911460
   Rublee E, 2011, IEEE I CONF COMP VIS, P2564, DOI 10.1109/ICCV.2011.6126544
   SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0
   Schroff F, 2015, PROC CVPR IEEE, P815, DOI 10.1109/CVPR.2015.7298682
   Sedhain S, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P111, DOI 10.1145/2740908.2742726
   Shehata, 2017, ARXIV 2017 171002726
   Shen Yelong, 2014, P 23 ACM INT C C INF, DOI DOI 10.1145/2661829.2661935
   Shimada N., 2020, J INF PROCESS, V28, P406
   Singh J, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P770, DOI 10.1145/3289600.3290620
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Sotomayor M. A. O, 1990, 2 SIDED MATCHING STU
   Su Xiaoyuan, 2009, ADV ARTIFICIAL INTEL, DOI DOI 10.1155/2009/421425
   Sutskever I., 2009, NIPS
   Taigman Y, 2014, PROC CVPR IEEE, P1701, DOI 10.1109/CVPR.2014.220
   Tay Y, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P729, DOI 10.1145/3178876.3186154
   Tervio M, 2008, AM ECON REV, V98, P642, DOI 10.1257/aer.98.3.642
   Tran T, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P687, DOI 10.1145/3269206.3271730
   Toutanova K, 2018, BERT PRETRAINING DEE, DOI DOI 10.18653/V1/N19-1423
   Ullah Irfan, 2020, Behaviour & Information Technology, V39, P443, DOI 10.1080/0144929X.2019.1599069
   Vo HV, 2019, PROC CVPR IEEE, P8279, DOI 10.1109/CVPR.2019.00848
   Wan S.X., 2016, P 25 INT JOINT C ART, P2922, DOI DOI 10.5555/3060832.3061030
   Wan SX, 2016, AAAI CONF ARTIF INTE, P2835
   Wang L, 2020, IEEE T COMPUT SOC SY, V7, P503, DOI 10.1109/TCSS.2019.2962819
   Wang LW, 2019, IEEE T PATTERN ANAL, V41, P394, DOI 10.1109/TPAMI.2018.2797921
   Wang M, 2019, IEEE I CONF COMP VIS, P692, DOI 10.1109/ICCV.2019.00078
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang W, 2021, ACM T ASIAN LOW-RESO, V20, DOI 10.1145/3404995
   Wang XH, 2019, J ADV COMPUT INTELL, V23, P25, DOI 10.20965/jaciii.2019.p0025
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wenbin Tang, 2010, Proceedings 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT), P34, DOI 10.1109/WI-IAT.2010.133
   Wu P, 2019, J INTELL FUZZY SYST, V37, P4431, DOI 10.3233/JIFS-179275
   Wu Y, 2016, PROCEEDINGS OF THE NINTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'16), P153, DOI 10.1145/2835776.2835837
   Wu Y, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P496, DOI 10.18653/v1/P17-1046
   Xia F, 2020, IEEE TETCI, V4, P95, DOI 10.1109/TETCI.2019.2952908
   Xia F, 2017, IEEE T BIG DATA, V3, P18, DOI 10.1109/TBDATA.2016.2641460
   Xia F, 2014, IEEE T EMERG TOP COM, V2, P364, DOI 10.1109/TETC.2014.2356505
   Xia F, 2015, IEEE T COMPUT, V64, P1857, DOI 10.1109/TC.2014.2345409
   Xia F, 2017, IEEE SYST J, V11, P2255, DOI 10.1109/JSYST.2014.2342375
   Xia F, 2014, IEEE T PARALL DISTR, V25, P3167, DOI 10.1109/TPDS.2013.2295805
   Xia F, 2013, IEEE ACCESS, V1, P606, DOI 10.1109/ACCESS.2013.2281156
   Xiao J, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3119
   Xie RB, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2732
   Xu H, 2011, IEEE INFOCOM SER, P586, DOI 10.1109/INFCOM.2011.5935232
   Xu Y, 2020, AAAI CONF ARTIF INTE, V34, P270
   Xue HJ, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3203
   Yagi, 1973, COMPUT VISION GRAPH, V2, P131
   Yang L, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P287, DOI 10.1145/2983323.2983818
   You JX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2236, DOI 10.1145/3308558.3313747
   Zhang SA, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3285029
   Zhang YF, 2020, FOUND TRENDS INF RET, V14, P1, DOI 10.1561/1500000066
   Zhang Z, 2018, ARXIV180609102
   Zhao W, 2003, ACM COMPUT SURV, V35, P399, DOI 10.1145/954339.954342
   Zhou X., 2016, P 2016 C EMP METH NA, P372, DOI [10.18653/v1/D16-1036, DOI 10.18653/V1/D16-1036]
   Zho XY, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1118
   Zhou ZL, 2020, IEEE TETCI, V4, P593, DOI 10.1109/TETCI.2019.2909936
   Zhu, 2015, ARXIV PREPRINT ARXIV
   Zimmerman S, 2020, CHIIR'20: PROCEEDINGS OF THE 2020 CONFERENCE ON HUMAN INFORMATION INTERACTION AND RETRIEVAL, P124, DOI 10.1145/3343413.3377958
   Zimmerman S, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P953, DOI 10.1145/3331184.3331280
   Zitova B, 2003, IMAGE VISION COMPUT, V21, P977, DOI 10.1016/S0262-8856(03)00137-9
NR 178
TC 6
Z9 6
U1 10
U2 20
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2471-285X
J9 IEEE TETCI
JI IEEE Trans. Emerg. Top. Comput. Intell.
PD JUN
PY 2021
VL 5
IS 3
BP 332
EP 350
DI 10.1109/TETCI.2021.3067655
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA SN5HL
UT WOS:000658320500002
OA Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Qiu, QJ
   Xie, Z
   Wu, L
   Tao, LF
AF Qiu, Qinjun
   Xie, Zhong
   Wu, Liang
   Tao, Liufeng
TI Automatic spatiotemporal and semantic information extraction from
   unstructured geoscience reports using text mining techniques
SO EARTH SCIENCE INFORMATICS
LA English
DT Article
DE Geoscience document; Knowledge graph; Geological text mining; Natural
   language processing
ID CONSTRUCTION; TIME
AB A large number of georeferenced quantitative data about rock and geoscience surveys are buried in geological documents and remain unused. Data analytics and information extraction offer opportunities to use this data for improved understanding of ore forming processes and to enhance our knowledge. Extracting spatiotemporal and semantic information from a set of geological documents enables us to develop a rich representation of the geoscience knowledge recorded in unstructured text written in Chinese. This paper presents the workflow for spatiotemporal and semantic information extraction, which is a geological document analysis approach that uses automated techniques for browsing and searching relevant geological content. The developed workflow applies spatial and temporal gazetteer matching, pattern-based rules and spatiotemporal relationship extraction to identify and label terms in geological text documents. It offers a representation of contextual information in knowledge graph form, extracts a set of relevant tables and figures, and queries a list of relevant documents by using geological topic information. Here, text mining techniques are used to facilitate the analysis of geological knowledge and to show the effectiveness of text analysis for improving the rapid assessment of a massive number of documents. Furthermore, autogenerated keyword suggestions derived from extracted keyword associations are used to reduce document search efforts. This research illustrates the usefulness and effectiveness of the developed information extraction workflow and demonstrates the potential of incorporating text mining and NLP techniques for geoscience.
C1 [Qiu, Qinjun; Xie, Zhong; Wu, Liang; Tao, Liufeng] China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China.
   [Qiu, Qinjun; Xie, Zhong; Wu, Liang; Tao, Liufeng] Natl Engn Res Ctr Geog Informat Syst, Wuhan 430074, Peoples R China.
RP Tao, LF (通讯作者)，China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China.; Tao, LF (通讯作者)，Natl Engn Res Ctr Geog Informat Syst, Wuhan 430074, Peoples R China.
EM taoliufeng@cug.edu.cn
FU National Natural Science Foundation of China [U1711267, 41671400,
   41871311, 41871305]; National Key Research and Development Program
   [2018YFB0505500, 2018YFB0505504]
FX We would like to thank the anonymous reviewers for carefully reading
   this paper and their very useful comments. This study was financially
   supported by the National Natural Science Foundation of China (U1711267,
   41671400, 41871311, 41871305), the National Key Research and Development
   Program (2018YFB0505500, 2018YFB0505504).
CR Abraham S, 2018, T GIS, V22, P677, DOI 10.1111/tgis.12448
   Clark C, 2016, ACM-IEEE J CONF DIG, P143, DOI 10.1145/2910896.2910904
   Cox SJD, 2015, EARTH SCI INFORM, V8, P5, DOI 10.1007/s12145-014-0170-6
   De Sa C, 2016, SIGMOD REC, V45, P60, DOI 10.1145/2949741.2949756
   Du SH, 2016, T GIS, V20, P824, DOI 10.1111/tgis.12179
   Du SH, 2017, INT J GEOGR INF SCI, V31, P542, DOI 10.1080/13658816.2016.1212356
   Du SH, 2015, INT J GEOGR INF SCI, V29, P980, DOI 10.1080/13658816.2015.1004333
   Enkhsaikhan M, 2018, LECT NOTES ARTIF INT, V11323, P224, DOI 10.1007/978-3-030-05090-0_20
   Harisinghaney A, 2014, PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON RELIABILTY, OPTIMIZATION, & INFORMATION TECHNOLOGY (ICROIT 2014), P153, DOI 10.1109/ICROIT.2014.6798302
   Holden EJ, 2019, ORE GEOL REV, V111, DOI 10.1016/j.oregeorev.2019.05.005
   Hovy E., 1998, P WORKSH HELD BALT M, P197, DOI DOI 10.3115/1119089.1119121
   Hwang J, 2012, COMPUT GEOSCI-UK, V48, P173, DOI 10.1016/j.cageo.2012.05.005
   Ireson N, 2005, INT C MACH LEARN
   Konkol M, 2015, EXPERT SYST APPL, V42, P3470, DOI 10.1016/j.eswa.2014.12.015
   Li DK, 2017, DESTECH TRANS SOC, P103
   Li S, 2018, IEEE ACCESS, V6, P52286, DOI 10.1109/ACCESS.2018.2870203
   Lima LA, 2017, COMPUT GEOSCI-UK, V106, P33, DOI 10.1016/j.cageo.2017.05.004
   Liu W, 2015, HEALTH INF SCI SYST, V3, DOI 10.1186/s13755-015-0013-y
   Luo X, 2018, IEEE ACCESS, V6, P5705, DOI 10.1109/ACCESS.2017.2785229
   Ma K, 2018, IEEE ACCESS, V6, P28064, DOI 10.1109/ACCESS.2018.2837666
   Ma XG, 2011, COMPUT GEOSCI-UK, V37, P1602, DOI 10.1016/j.cageo.2011.02.011
   Manning C., 1999, FDN STAT NATURAL LAN
   Moens M.-F., 2006, INFORM EXTRACTION AL, V21
   Nadeau D, 2007, LINGUIST INVESTIG, V30, P3
   Paulus R., 2018, INT C LEARN REPR
   Peters SE, 2017, GEOLOGY, V45, P487, DOI 10.1130/G38931.1
   Peters SE, 2016, PALEOBIOLOGY, V42, P1, DOI 10.1017/pab.2015.39
   Peters SE, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0113523
   Qiu Q, 2019, EARTH SPACE SCI
   Qiu Q., 2018, GEOMATICA, V72, P16, DOI DOI 10.1139/GEOMAT-2018-0007
   Qiu QJ, 2020, EARTH SPACE SCI, V7, DOI 10.1029/2019EA000993
   Qiu QJ, 2019, EXPERT SYST APPL, V125, P157, DOI 10.1016/j.eswa.2019.02.001
   Qiu QJ, 2018, COMPUT GEOSCI-UK, V121, P1, DOI 10.1016/j.cageo.2018.08.006
   Rafiei-Asl J, 2017, APPL SOFT COMPUT, V58, P620, DOI 10.1016/j.asoc.2017.05.014
   Schuhmacher M, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P543, DOI 10.1145/2556195.2556250
   SPARCKJONES K, 1972, J DOC, V28, P11, DOI 10.1108/eb026526
   Toutanova K, 2000, PROCEEDINGS OF THE 2000 JOINT SIGDAT CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND VERY LARGE CORPORA, P63, DOI 10.3115/1117794.1117802
   Wang CB, 2018, COMPUT GEOSCI-UK, V115, P12, DOI 10.1016/j.cageo.2018.03.004
   Wang CB, 2018, COMPUT GEOSCI-UK, V112, P112, DOI 10.1016/j.cageo.2017.12.007
   Wang R, 2015, LECT NOTES COMPUT SC, V9093, P257, DOI 10.1007/978-3-319-19548-3_21
   Wong W, 2012, ACM COMPUT SURV, V44, DOI 10.1145/2333112.2333115
   Wu L, 2017, ISPRS INT J GEO-INF, V6, DOI 10.3390/ijgi6110373
   Yang DD, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4532
   Yang SS, 2017, NEUROCOMPUTING, V224, P58, DOI 10.1016/j.neucom.2016.10.052
   Young T, 2018, IEEE COMPUT INTELL M, V13, P55, DOI 10.1109/MCI.2018.2840738
   Zhang F, 2019, AUTOMAT CONSTR, V99, P238, DOI 10.1016/j.autcon.2018.12.016
   Zhang JS, 2016, J COMPUT CIVIL ENG, V30, DOI 10.1061/(ASCE)CP.1943-5487.0000346
   Zhang Y, 2015, INT CONF SOFTW ENG, P681, DOI 10.1109/ICSESS.2015.7339149
   Zhou P, 2018, NEURAL NETWORKS, V108, P240, DOI 10.1016/j.neunet.2018.08.016
   Zhu CB, 2017, IEEE TRANSP EL ASIA, P311
NR 50
TC 13
Z9 13
U1 11
U2 47
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1865-0473
EI 1865-0481
J9 EARTH SCI INFORM
JI Earth Sci. Inform.
PD DEC
PY 2020
VL 13
IS 4
BP 1393
EP 1410
DI 10.1007/s12145-020-00527-9
EA SEP 2020
PG 18
WC Computer Science, Interdisciplinary Applications; Geosciences,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Geology
GA OT0JM
UT WOS:000571030400001
DA 2022-08-23
ER

PT J
AU He, LL
   Jiang, PY
AF He, Longlong
   Jiang, Pingyu
TI Manufacturing Knowledge Graph: A Connectivism to Answer Production
   Problems Query With Knowledge Reuse
SO IEEE ACCESS
LA English
DT Article
DE Production problems (PPs); manufacturing knowledge (MK); manufacturing
   knowledge graph (MKG)
ID DESIGN; MANAGEMENT; SYSTEMS; BASE
AB Manufacturing knowledge (MK) is enjoying a "new golden age" in the academic domain, marked by vast reuse to support product-related production problems (PPs) solving decision making for manufacturing enterprises in the industry sector. However, the practice of MK reuse and research is fragmented and insufficient, which cannot be mature to provide a systemic solution for that a decision-maker has to consider the involving issues: how MK can be used earlier and rightly; what kind of practical problems can be solved? In order to answer those interconnecting issues, this paper firstly proposes a connectivism framework to clarify the compressive relationship of problem-to-problem, knowledge-to-knowledge and problem-to-knowledge with knowledge integration, knowledge matching, and problem-solving layers. Then, based on the framework, an ontology-based MK graph (MKG) is constructed with a unified MK-filter to collect and integrate multifactor and multilevel MK, and a graph-oriented meta-knowledge model (MKM) is proposed to represent the details between the knowledge entities (i.e., concept and instance), which also shows the contribution to knowledge reasoning. After that, driven by a structure temporal query (i.e., 5W2H), a semantics-based knowledge computation is developed to compute the intrinsic term similarity (IS) and relational term similarity (RS) between two knowledge entities in the MKG. Finally, a case study is taken to demonstrate the effectiveness and performance of the proposed methods.
C1 [He, Longlong; Jiang, Pingyu] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Shaanxi, Peoples R China.
RP Jiang, PY (通讯作者)，Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Shaanxi, Peoples R China.
EM pjiang@mail.xjtu.edu.cn
FU Ministry of Science and Technologies through the Innovation Method Fund
   of China [2016IM010100]; National Natural Science Foundation of China
   [71571142]
FX This work was supported in part by the Ministry of Science and
   Technologies through the Innovation Method Fund of China under Grant
   2016IM010100, and in part by the National Natural Science Foundation of
   China under Grant 71571142.
CR Alizon F, 2006, J COMPUT INF SCI ENG, V6, P170, DOI 10.1115/1.2202135
   ALLEN JF, 1983, COMMUN ACM, V26, P832, DOI 10.1145/182.358434
   Amit S., INTRO KNOWLEDGE GRAP
   [Anonymous], 2010, DOD LIF CYCL MAN LCM
   [Anonymous], 1999, 5252013 ISO
   Arenas M., 2012, DIRECT MAPPING RELAT
   Arnaout H, 2018, J WEB SEMANT, V48, P66, DOI 10.1016/j.websem.2017.12.001
   Arp R, 2015, BUILDING ONTOLOGIES WITH BASIC FORMAL ONTOLOGY, P1, DOI 10.7551/mitpress/9780262527811.001.0001
   Barrasa J., IMPORTING RDF DATA N
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Camarillo A, 2018, J MANUF SYST, V47, P115, DOI 10.1016/j.jmsy.2018.04.002
   Chang XM, 2010, J MECH DESIGN, V132, DOI 10.1115/1.4000697
   Cheng Y, 2018, J IND INF INTEGR, V9, P1, DOI 10.1016/j.jii.2017.08.001
   Das S., 2019, R2RML RDB RDF MAPPIN
   DAVIS R, 1993, AI MAG, V14, P17
   Ding K, 2017, CHIN J MECH ENG-EN, V30, P1150, DOI 10.1007/s10033-017-0167-4
   Dong B, 2019, AAAI CONF ARTIF INTE, P3478, DOI 10.1609/aaai.v33i01.33013478
   Dong B, 2017, IEEE INT CONF BIG DA, P596, DOI 10.1109/BigData.2017.8257975
   Du YW, 2018, INT J INFORM MANAGE, V39, P136, DOI 10.1016/j.ijinfomgt.2017.11.006
   Dubey R, 2017, J CLEAN PROD, V141, P56, DOI 10.1016/j.jclepro.2016.09.035
   Ettlie JE, 2008, J PROD INNOVAT MANAG, V25, P457, DOI 10.1111/j.1540-5885.2008.00314.x
   Guan NN, 2019, KNOWL-BASED SYST, V164, P38, DOI 10.1016/j.knosys.2018.10.008
   Guerra-Zubiaga DA, 2008, INT J COMPUT INTEG M, V21, P526, DOI 10.1080/09511920701258040
   He L., 2019, SMILE ISMI HANGZH CH
   Hedberg TD, 2017, INT J PROD RES, V55, P819, DOI 10.1080/00207543.2016.1213453
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Jean-Mary YR, 2009, J WEB SEMANT, V7, P235, DOI 10.1016/j.websem.2009.04.001
   Joksimovic S, 2018, INTERNET HIGH EDUC, V36, P54, DOI 10.1016/j.iheduc.2017.09.004
   Kamis NH, 2018, KNOWL-BASED SYST, V162, P103, DOI 10.1016/j.knosys.2018.05.039
   Kamvar S, 2004, LINEAR ALGEBRA APPL, V386, P51, DOI 10.1016/j.laa.2003.12.008
   Karwasz A., 2016, J MACHINE ENG, V16, P61
   Klyne G., RDF 1 1 CONCEPTS ABS
   Kulvatunyou B, 2014, INT J COMPUT INTEG M, V27, P803, DOI 10.1080/0951192X.2013.834477
   Lee D, 2010, STUD COMPUT INTELL, V317, P121
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li X., 2010, INT J ADV MANUF TECH, V96, P4173
   McGuinness D.L., 2004, OWL WEB ONTOLOGY LAN
   Navigli R, 2012, ARTIF INTELL, V193, P217, DOI 10.1016/j.artint.2012.07.001
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   PATELSCHNEIDER PF, OWL WEB ONTOLOGY LAN
   Powers D.M., 2011, J MACH LEARN TECHNOL, V2, P37
   Ruiz PAP, 2013, KNOWL-BASED SYST, V50, P171, DOI 10.1016/j.knosys.2013.06.005
   Siemens G., 2005, INT J INSTR TECHNOL, V2, P2005
   Song DZ, 2019, IEEE T SERV COMPUT, V12, P356, DOI 10.1109/TSC.2017.2711600
   Sozo V, 2019, INT J IND ERGONOM, V70, P38, DOI 10.1016/j.ergon.2019.01.003
   Suyeon Kim, 2003, Journal of Knowledge Management, V7, P34, DOI 10.1108/13673270310477270
   Venkatachalam S, 2016, INT J PROD RES, V54, P4087, DOI 10.1080/00207543.2015.1088666
   Wang C, 2018, J INTELL MANUF, V29, P1485, DOI 10.1007/s10845-016-1194-1
   Warren P, 2019, INT J HUM-COMPUT ST, V122, P145, DOI 10.1016/j.ijhcs.2018.08.009
   Xie R., 2016, P 13 AAAI C ART INT, P2660
   Yang CH, 2018, ADV MECH ENG, V10, DOI 10.1177/1687814018795785
   Yang CH, 2018, ADV MECH ENG, V10, DOI 10.1177/1687814018783625
   Zhang C, 2017, INT J PROD RES, V55, P7187, DOI 10.1080/00207543.2017.1351643
   Zhou GH, 2020, INT J PROD RES, V58, P1034, DOI 10.1080/00207543.2019.1607978
NR 55
TC 23
Z9 24
U1 16
U2 45
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 101231
EP 101244
DI 10.1109/ACCESS.2019.2931361
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA IR8JN
UT WOS:000481688500059
OA gold
DA 2022-08-23
ER

PT J
AU Nam, D
   Kejriwal, M
AF Nam, Daye
   Kejriwal, Mayank
TI How Do Organizations Publish Semantic Markup? Three Case Studies Using
   Public Schema. org Crawls
SO COMPUTER
LA English
DT Article
AB Jointly launched in mid-2011 by major search engines like Google and Bing, Schema.org is designed to facilitate structured and knowledge graph-centric search applications on the Web. The Web Data Commons project has crawled increasing amounts of Schema.org data in recent years, providing a golden opportunity for socio-technological data studies that consider the semantics of content. The authors present empirical studies of organizations in three economic sectors that expose semantically linked Schema.org annotations.
C1 [Nam, Daye; Kejriwal, Mayank] Univ Southern Calif, Los Angeles, CA 90089 USA.
RP Nam, D (通讯作者)，Univ Southern Calif, Los Angeles, CA 90089 USA.
EM dayenam@usc.edu; kejriwal@isi.edu
RI Kejriwal, Mayank/W-1331-2019
OI Kejriwal, Mayank/0000-0001-5988-8305
CR Barabasi AL, 2000, PHYSICA A, V281, P69, DOI 10.1016/S0378-4371(00)00018-2
   Bizer C, 2009, INT J SEMANT WEB INF, V5, P1, DOI 10.4018/jswis.2009081901
   Bizer C, 2013, LECT NOTES COMPUT SC, V8219, P17, DOI 10.1007/978-3-642-41338-4_2
   Christen P, 2012, DATA MATCHING CONCEP, DOI DOI 10.1007/978-3-642-31164-2
   Guha RV, 2016, COMMUN ACM, V59, P44, DOI 10.1145/2844544
   Kejriwa M, 2015, LECT NOTES COMPUT SC, V9088, P388, DOI 10.1007/978-3-319-18818-8_24
   Kejriwal M, 2014, LECT NOTES COMPUT SC, V8797, P521, DOI 10.1007/978-3-319-11915-1_34
   Meusel R., 2016, THESIS
   Muhleisen H., 2012, P WWW 2012 WORKSH LI
   Patel-Schneider PF, 2014, LECT NOTES COMPUT SC, V8796, P261, DOI 10.1007/978-3-319-11964-9_17
   Schmachtenberg M, 2014, LECT NOTES COMPUT SC, V8796, P245, DOI 10.1007/978-3-319-11964-9_16
   Volz J, 2009, P WWW 2009 WORKSH LI
NR 12
TC 3
Z9 3
U1 1
U2 10
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0018-9162
EI 1558-0814
J9 COMPUTER
JI Computer
PD JUN
PY 2018
VL 51
IS 6
BP 42
EP 51
DI 10.1109/MC.2018.2701635
PG 10
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GK7QW
UT WOS:000436404600006
DA 2022-08-23
ER

PT J
AU Hao, F
   Zheng, K
AF Hao, Feng
   Zheng, Kai
TI Online Disease Identification and Diagnosis and Treatment Based on
   Machine Learning Technology
SO JOURNAL OF HEALTHCARE ENGINEERING
LA English
DT Article
AB The article uses machine learning algorithms to extract disease symptom keyword vectors. At the same time, we used deep learning technology to design a disease symptom classification model. We apply this model to an online disease consultation recommendation system. The system integrates machine learning algorithms and knowledge graph technology to help patients conduct online consultations. The system analyses the misclassification data of different departments through high-frequency word analysis. The study found that the accuracy rate of our machine learning algorithm model to identify entities in electronic medical records reached 96.29%. This type of model can effectively screen out the most important pathogenic features.
C1 [Hao, Feng; Zheng, Kai] Jilin Med Univ, Jilin 132013, Jilin, Peoples R China.
RP Zheng, K (通讯作者)，Jilin Med Univ, Jilin 132013, Jilin, Peoples R China.
EM zhengkai@jlmu.edu.cn
OI Hao, Feng/0000-0002-6843-1566; Zheng, Kai/0000-0002-4004-8610
CR Ahn JC, 2021, HEPATOLOGY, V73, P2546, DOI 10.1002/hep.31603
   Alimadadi A, 2020, PHYSIOL GENOMICS, V52, P200, DOI 10.1152/physiolgenomics.00029.2020
   Balyen L, 2019, ASIA-PAC J OPHTHALMO, V8, P264, DOI 10.22608/APO.2018479
   Goecks J, 2020, CELL, V181, P92, DOI 10.1016/j.cell.2020.03.022
   Jamshidi A, 2019, NAT REV RHEUMATOL, V15, P49, DOI 10.1038/s41584-018-0130-5
   Khourdifi Y., 2019, INT J INTELLIGENT EN, V12, P242, DOI [10.22266/ijies2019.0228.24, DOI 10.22266/IJIES2019.0228.24]
   Minchole A, 2019, NAT MED, V25, P22, DOI 10.1038/s41591-018-0306-1
   Samie F, 2019, IEEE INTERNET THINGS, V6, P4921, DOI 10.1109/JIOT.2019.2893866
   Shatte ABR, 2019, PSYCHOL MED, V49, P1426, DOI 10.1017/S0033291719000151
   Vafea MT, 2020, CELL MOL BIOENG, V13, P249, DOI 10.1007/s12195-020-00629-w
   Yang Q, 2019, ACM T INTEL SYST TEC, V10, DOI 10.1145/3298981
   Yin ZJ, 2019, J AM MED INFORM ASSN, V26, P561, DOI 10.1093/jamia/ocz009
NR 12
TC 0
Z9 0
U1 4
U2 4
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 2040-2295
EI 2040-2309
J9 J HEALTHC ENG
JI J. Healthc. Eng.
PD APR 12
PY 2022
VL 2022
AR 6736249
DI 10.1155/2022/6736249
PG 7
WC Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services
GA 1Z1GW
UT WOS:000808582500013
PM 35449857
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Bangerter, ML
   Fenza, G
   Gallo, M
   Loia, V
   Petrone, A
   Volpe, A
AF Bangerter, Micaela Lucia
   Fenza, Giuseppe
   Gallo, Mariacristina
   Loia, Vincenzo
   Petrone, Alessandra
   Volpe, Alberto
TI Terrorist Organization Identification Using Link Prediction over
   Heterogeneous GNN
SO HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
LA English
DT Article
DE Counter-Terrorism; Graph Neural Network; Link Prediction; HinSAGE;
   Knowledge Graph
AB Terrorist attacks are threats to undermine state security and citizens' confidence. In the last year, the Member States of European Union reported a total of 57 completed, failed, and foiled terrorist attacks, with 21 people killed. Counter-terrorism activities, through intelligence analysis experts, attempts to face, tackle, and prevent new attacks. In this sense, Artificial Intelligence demonstrates promising support for data analysis and patterns identification in security concerns. This work treats the application of a deep learning approach for the association between attacks and perpetrator groups, which is often unknown. Identifying the most involved actors helps extract inherited features and better study attacks to implement suitable countermeasures and predict feature events. Starting from the well-known resource related to anti-terrorism operations, Global Terrorism Database (GTD), we build a knowledge graph (KG) representing entities and relationships involved in terrorist attacks. Subsequently, we adopted the KG to train a graph neural network (GNN) to identify terrorist organizations from events using the inductive link prediction technique. The experimentation, conducted by adopting the HinSAGE framework, demonstrates promising performance in terms of accuracy with a discrete improvement to state-of-the-art.
C1 [Bangerter, Micaela Lucia; Fenza, Giuseppe; Gallo, Mariacristina; Loia, Vincenzo; Petrone, Alessandra; Volpe, Alberto] Univ Salerno, Dept Management & Innovat Syst, Salerno, Italy.
RP Loia, V (通讯作者)，Univ Salerno, Dept Management & Innovat Syst, Salerno, Italy.
EM loia@unisa.it
CR Agarwal P., 2019, 2019 12 INT C CONT C, DOI [10.1109/IC3.2019.8844904, DOI 10.1109/IC3.2019.8844904, 10.1109/ic3.2019.8844904]
   ALfatih M., 2019, P 2019 INT C ART INT, P320
   Europol, 2021, EQF LEV
   Fenza G, 2022, NEURAL COMPUT APPL, V34, P10279, DOI 10.1007/s00521-021-06617-z
   Ganesan B., 2020, EXPLAINABLE LINK PRE
   Hamilton WL, 2017, ADV NEUR IN, V30
   Huang L., 2019, TEXT LEVEL GRAPH NEU
   Ionescu B, 2020, IEEE MULTIMEDIA, V27, P55, DOI 10.1109/MMUL.2020.2994403
   Kou HZ, 2021, APPL SOFT COMPUT, V100, DOI 10.1016/j.asoc.2020.106942
   Liu HW, 2020, COMPLEXITY, V2020, DOI 10.1155/2020/2085638
   Mason R., 2012, Proceedings of the 2012 IEEE International Conference on Intelligence and Security Informatics. Cyberspace, Border, and Immigration Securities (ISI 2012), P84, DOI 10.1109/ISI.2012.6284096
   Oh J., 2019, ADV GRAPHSAGE DATA D
   Samizadeh M, 2020, INT J ARTIF INTELL T, V29, DOI 10.1142/S0218213020500013
   Saracco F, 2017, NEW J PHYS, V19, DOI 10.1088/1367-2630/aa6b38
   Sinka D, 2013, ASSESSING ATTRACTIVE
   Sudhakaran S., 2017, 2017 14 IEEE INT C A, P1, DOI DOI 10.1109/AVSS.2017.8078468
   Sun YZ, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P797
   Talreja D, 2017, 2017 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), P1723, DOI 10.1109/ICACCI.2017.8126092
   Uddin MI, 2020, COMPLEXITY, V2020, DOI 10.1155/2020/1373087
   Zhou XK, 2021, IEEE T EMERG TOP COM, V9, P246, DOI 10.1109/TETC.2018.2860051
NR 20
TC 0
Z9 0
U1 8
U2 8
PU KOREA INFORMATION PROCESSING SOC
PI SEOUL
PA 1002HO YONGSUNGBIZTEL 314-1 2GA HANKANGRO YONGSAN-GU, SEOUL, 140-750,
   SOUTH KOREA
EI 2192-1962
J9 HUM-CENT COMPUT INFO
JI Human-centric Comput. Inf. Sci.
PD MAR 15
PY 2022
VL 12
AR 12
DI 10.22967/HCIS.2022.12.012
PG 13
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZU3SA
UT WOS:000769761500001
DA 2022-08-23
ER

PT J
AU Yang, ML
   Xue, LF
   Ran, XJ
   Sang, XJ
   Yan, Q
   Dai, JH
AF Yang Mingli
   Xue Linfu
   Ran Xiangjin
   Sang Xuejia
   Yan Qun
   Dai Junhao
TI Intelligent mineral geological survey method: Daqiao-Yawan area in Gansu
   Province as an example
SO ACTA PETROLOGICA SINICA
LA Chinese
DT Article
DE Mineral geological survey; Intelligent prospecting prediction; Deep
   Learning; Knowledge Graph; Cloud platform; Ecosystem
ID EXPLORATION
AB Based on the theory and practice of intelligent mineral geological survey, an intelligent mineral geological survey method is proposed. It is interpreted that the new type of intelligent mineral geological survey ecosystem is a complete system composed of intelligent data acquisition equipment, application, user, standard, specification, intelligent geological survey cloud platform and other components related to intelligent mineral geological survey. The main steps of intelligent mineral geological survey include: intelligent data analysis, key workspace delineation, mineral geological data collection, key area field work, intelligent prospecting prediction and so on. The prospecting prediction method driven by data and knowledge is proposed, and the method of feature matching prospecting prediction using Deep Learning and the prospecting prediction method based on Knowledge Graph are integrated. The architecture and functions of the intelligent mineral geological survey cloud platform are designed and implemented. The feature matching method is applied to delineate five prospecting areas in Daqiao-Yawan area of Gansu Province. High precision magnetic survey is deployed in a prediction area, and the results show that the prediction area delineated by intelligent mineral geological survey method is effective.
C1 [Yang Mingli; Xue Linfu; Ran Xiangjin; Yan Qun; Dai Junhao] Jilin Univ, Coll Earth Sci, Changchun 130061, Peoples R China.
   [Sang Xuejia] China Univ Min & Technol, Xuzhou 221116, Jiangsu, Peoples R China.
RP Xue, LF (通讯作者)，Jilin Univ, Coll Earth Sci, Changchun 130061, Peoples R China.
EM 3464471@qq.com; xuelf@jlu.edu.cn
CR Akin S, 2010, COMPUT GEOSCI-UK, V36, P776, DOI 10.1016/j.cageo.2009.11.006
   Al-Garni MA, 2010, ACTA GEOPHYS, V58, P143, DOI 10.2478/s11600-009-0029-2
   APPLETON JD, 1993, APPLIED GEOCHEMISTRY, SUPPLEMENTARY ISSUE NO 2, JANUARY 1993, P103
   Barik RK, 2019, ARAB J GEOSCI, V12, DOI 10.1007/s12517-018-4104-3
   BONHAMCARTER GF, 1988, PHOTOGRAMM ENG REM S, V54, P1585
   BUGAETS AN, 1991, MATH GEOL, V23, P9, DOI 10.1007/BF02065961
   Butt CRM, 2016, ORE GEOL REV, V73, P380, DOI 10.1016/j.oregeorev.2015.08.018
   [蔡惠慧 Cai Huihui], 2019, [地质通报, Geological Bulletin of China], V38, P1999
   Casana J, 2014, J ARCHAEOL SCI, V45, P207, DOI 10.1016/j.jas.2014.02.015
   [陈建平 Chen Jianping], 2005, [矿床地质, Mineral Deposits], V24, P15
   Chen YL, 2015, ORE GEOL REV, V71, P749, DOI 10.1016/j.oregeorev.2014.08.012
   Filho CRD, 2007, SURV GEOPHYS, V28, P377, DOI 10.1007/s10712-008-9031-5
   Eberle D, 2015, J AFR EARTH SCI, V106, P60, DOI 10.1016/j.jafrearsci.2015.03.011
   Friedel MJ, 2016, HYDROGEOL J, V24, P211, DOI 10.1007/s10040-015-1318-2
   Funaki M, 2014, POLAR SCI, V8, P342, DOI 10.1016/j.polar.2014.07.001
   Gholami R, 2011, APPL WATER SCI, V1, P125, DOI 10.1007/s13201-011-0016-z
   Holden EJ, 2019, ORE GEOL REV, V111, DOI 10.1016/j.oregeorev.2019.05.005
   Hu J, 2016, Multichannelairbornemagneticmeasurement dataacquisitionsystem basedonFPGA, Patent No. [201610157898.2, 2016101578982]
   [季斌 Ji Bin], 2018, [地质科学, Chinese Journal of Geology], V53, P1347
   Lee S, 2018, EARTH SCI INFORM, V11, P463, DOI 10.1007/s12145-018-0343-9
   [李超岭 Li Chaoling], 2015, [地质通报, Geological Bulletin of China], V34, P1288
   [李丰丹 Li Fengdan], 2015, [地质通报, Geological Bulletin of China], V34, P1300
   Li S, 2020, NEURAL COMPUT APPL, V32, P2037, DOI 10.1007/s00521-019-04341-3
   [刘艳鹏 Liu Yanpeng], 2020, [大地构造与成矿学, Geotectonica et Metallogenia], V44, P192
   Ma XG, 2007, GEO-SPAT INF SCI, V10, P223, DOI 10.1007/s11806-007-0093-1
   Markwitz V, 2016, PRECAMBRIAN RES, V274, P61, DOI 10.1016/j.precamres.2015.05.028
   Pereira B, 2015, J GEOCHEM EXPLOR, V158, P34, DOI 10.1016/j.gexplo.2015.06.016
   Qiu QJ, 2019, EARTH SCI INFORM, V12, P565, DOI 10.1007/s12145-019-00390-3
   Rodriguez-Galiano V, 2015, ORE GEOL REV, V71, P804, DOI 10.1016/j.oregeorev.2015.01.001
   Sang XJ, 2020, ISPRS INT J GEO-INF, V9, DOI 10.3390/ijgi9020099
   [陕亮 Shan Liang], 2016, [地质通报, Geological Bulletin of China], V35, P2045
   Wang CB, 2018, COMPUT GEOSCI-UK, V112, P112, DOI 10.1016/j.cageo.2017.12.007
   Wang HT, 2018, ACTA PETROL SIN, V34, P3195
   Wu C, 2020, FRONT INFORM TECH EL, V21, P649, DOI 10.1631/FITEE.2030000
   Wu Y.L., 2017, CHINA MIN MAG, V26, P79
   [向杰 Xiang Jie], 2019, [地质通报, Geological Bulletin of China], V38, P2010
   Xiong YH, 2021, COMPUT GEOSCI-UK, V147, DOI 10.1016/j.cageo.2020.104667
   [薛林福 Xue Linfu], 2014, [吉林大学学报. 地球科学版, Journal of Jilin University. Earth Science Edition], V44, P2051
   [于萍萍 Yu Pingping], 2015, [地质通报, Geological Bulletin of China], V34, P1333
   Yuan M, J JILIN U INFORM SCI, V39, P215
   [翟明国 Zhai Mingguo], 2018, [中国科学院院刊, Bulletin of the Chinese Academy of Sciences], V33, P825
   [张亚光 Zhang Yaguang], 2019, [地质通报, Geological Bulletin of China], V38, P2033
   [赵鹏大 Zhao Pengda], 2015, [地质通报, Geological Bulletin of China], V34, P1255
   [周永章 Zhou Yongzhang], 2021, [地学前缘, Earth Science Frontiers], V28, P67
   [周永章 Zhou Yongzhang], 2020, [大地构造与成矿学, Geotectonica et Metallogenia], V44, P173
   Zhou YZ, 2018, ACTA PETROL SIN, V34, P255
   [周永章 Zhou Yongzhang], 2017, [矿物岩石地球化学通报, Bulletin of Mineralogy Petrology and Geochemistry], V36, P327
   Zuo RG, 2019, EARTH-SCI REV, V192, P1, DOI 10.1016/j.earscirev.2019.02.023
   [左仁广 Zuo Renguang], 2019, [矿物岩石地球化学通报, Bulletin of Mineralogy Petrology and Geochemistry], V38, P53
   Zuo RG, 2018, NAT RESOUR RES, V27, P5, DOI 10.1007/s11053-017-9357-0
NR 50
TC 0
Z9 0
U1 25
U2 25
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1000-0569
EI 2095-8927
J9 ACTA PETROL SIN
JI Acta Petrol. Sin.
PD DEC
PY 2021
VL 37
IS 12
BP 3880
EP 3892
DI 10.18654/1000-0569/2021.12.16
PG 13
WC Geology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geology
GA YN8JZ
UT WOS:000747500200016
DA 2022-08-23
ER

PT J
AU Yuan, Z
   Zhao, ZY
   Sun, HX
   Li, J
   Wang, F
   Yu, S
AF Yuan, Zheng
   Zhao, Zhengyun
   Sun, Haixia
   Li, Jiao
   Wang, Fei
   Yu, Sheng
TI CODER: Knowledge-infused cross-lingual medical term embedding for term
   normalization
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Medical term normalization; Cross-lingual; Medical term representation;
   Knowledge graph embedding; Contrastive learning
ID SYSTEM; CORPUS
AB Objective: This paper aims to propose knowledge-aware embedding, a critical tool for medical term normalization. Methods: We develop CODER (Cross-lingual knowledge-infused medical term embedding) via contrastive learning based on a medical knowledge graph (KG) named the Unified Medical Language System, and similarities are calculated utilizing both terms and relation triplets from the KG. Training with relations injects medical knowledge into embeddings and can potentially improve their performance as machine learning features. Results: We evaluate CODER based on zero-shot term normalization, semantic similarity, and relation classifi-cation benchmarks, and the results show that CODER outperforms various state-of-the-art biomedical word embeddings, concept embeddings, and contextual embeddings. Conclusion: CODER embeddings excellently reflect semantic similarity and relatedness of medical concepts. One can use CODER for embedding-based medical term normalization or to provide features for machine learning. Similar to other pretrained language models, CODER can also be fine-tuned for specific tasks. Codes and models are available at https://github.com/GanjinZero/CODER.
C1 [Yuan, Zheng; Zhao, Zhengyun; Yu, Sheng] Tsinghua Univ, Ctr Stat Sci, Weiqinglou Rm 209, Beijing 100084, Peoples R China.
   [Yuan, Zheng; Zhao, Zhengyun; Yu, Sheng] Tsinghua Univ, Dept Ind Engn, Beijing, Peoples R China.
   [Sun, Haixia; Li, Jiao] Chinese Acad Med Sci, Inst Med Informat, Peking Union Med Coll, Beijing, Peoples R China.
   [Wang, Fei] Cornell Univ, Weill Cornell Med Coll, Dept Populat Hlth Sci, Ithaca, NY 14853 USA.
RP Yu, S (通讯作者)，Tsinghua Univ, Ctr Stat Sci, Weiqinglou Rm 209, Beijing 100084, Peoples R China.
EM syu@tsinhua.edu.cn
RI Yuan, Zheng/GOP-2223-2022
OI Yuan, Zheng/0000-0001-7179-2437; Li, Jiao/0000-0001-6391-8343; Zhao,
   Zhengyun/0000-0001-9425-7752; Wang, Fei/0000-0001-9459-9461; Yu,
   Sheng/0000-0002-6347-0507
FU Natural Science Foundation of Bei-jing Municipality [Z190024]; National
   Natural Science Foundation of China [11801301]; National Key R&D Program
   of China [2016YFC0901901]; Tsinghua University Initiative Scientific
   Research Program
FX This work was supported by the Natural Science Foundation of Bei-jing
   Municipality (Grant No. Z190024) , the National Natural Science
   Foundation of China (Grant No. 11801301) , the National Key R&D Program
   of China (Grant No. 2016YFC0901901) , and the Tsinghua University
   Initiative Scientific Research Program.
CR Afzal Z, 2016, LECT NOTES COMPUT SC, V9822, P162, DOI 10.1007/978-3-319-44564-9_13
   Alsentzer E., 2019, P 2 CLIN NAT LANG PR
   Beam AL, 2020, PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, P295
   Beltagy I., 2019, P 2019 C EMPIRICAL M, P3615
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Cai X., 2018, ARXIV PREPR ARXIV180
   Caron M., 2020, ADV NEURAL INFORM PR, VVolume 33, P9912
   Chen T., 2020, ADV NEUR IN, VVolume 33, P22243
   Chiu B, 2016, P 15 WORKSH BIOM NAT, P166, DOI DOI 10.18653/V1/W16-2922
   Choi E, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1495, DOI 10.1145/2939672.2939823
   Choi Youngduck, 2016, AMIA Jt Summits Transl Sci Proc, V2016, P41
   De Vine L., 2014, CIKM 2014 PROC 2014, P1819, DOI [10.1145/2661829.2661974, DOI 10.1145/2661829.2661974]
   Devlin J., 2019, BERT PRETRAINING DEE, DOI [10.18653/v1/N19-1423, DOI 10.18653/V1/N19-1423]
   Donnelly K, 2006, STUD HEALTH TECHNOL, V121, P279
   Fakhraei S, 2020, LECT NOTES ARTIF INT, V11907, P665, DOI 10.1007/978-3-030-46147-8_40
   Hermans Alexander, 2017, ARXIV170307737
   Huang K., 2019, ARXIV190405342
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Jin Q, 2019, P 3 WORKSHOP EVALUAT, DOI DOI 10.18653/V1/W19-2011
   Kaiming He, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P9726, DOI 10.1109/CVPR42600.2020.00975
   Karimi S, 2015, J BIOMED INFORM, V55, P73, DOI 10.1016/j.jbi.2015.03.010
   Kors JA, 2015, J AM MED INFORM ASSN, V22, P948, DOI 10.1093/jamia/ocv037
   Le-Khac PH, 2020, IEEE ACCESS, V8, P193907, DOI 10.1109/ACCESS.2020.3031549
   Leaman R, 2013, BIOINFORMATICS, V29, P2909, DOI 10.1093/bioinformatics/btt474
   Li HD, 2017, BMC BIOINFORMATICS, V18, DOI 10.1186/s12859-017-1805-7
   Limsopatham N, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1014
   Lin C, 2019, JMIR MED INF, V7, DOI 10.2196/14499
   Liu F., P 2021 C N AM CHAPTE, V2021, P4228, DOI [10.18653/v1/2021.naacl-main.334, DOI 10.18653/V1/2021.NAACL-MAIN.334]
   Liu H., 2017, 34 INT C MACH LEARN, V5, P3422
   Loshkarev IY, 2019, J PHYS CONF SER, V1333, DOI 10.1088/1742-6596/1333/4/042019
   Miftahutdinov Z, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019:): STUDENT RESEARCH WORKSHOP, P393
   Mikolov T., 2013, ARXIV, P746, DOI DOI 10.3109/10826089109058901
   Miotto R, 2016, SCI REP-UK, V6, DOI 10.1038/srep26094
   Neumann M, 2019, SIGBIOMED WORKSHOP ON BIOMEDICAL NATURAL LANGUAGE PROCESSING (BIONLP 2019), P319
   Niu JH, 2019, NEURAL PROCESS LETT, V49, P1239, DOI 10.1007/s11063-018-9873-x
   Palshikar G., 2019, PROC MACH LEARN RES, VXX, P1
   Peng YF, 2019, SIGBIOMED WORKSHOP ON BIOMEDICAL NATURAL LANGUAGE PROCESSING (BIONLP 2019), P58
   Perez N, 2020, BIOINFORMATICS, V36, P1872, DOI 10.1093/bioinformatics/btz853
   Peters M. E., 2018, P ANN C N AM CHAPT A, DOI DOI 10.18653/V1/N18-1202
   Phan M.C., 2020, ACL 2019 57 ANN MEET, P3275, DOI [10.18653/v1/p19-1317, DOI 10.18653/V1/P19-1317]
   Pyysalo S., 2013, P LBM, P39
   Reimers N., 2019, PROC C EMPIRICAL MET, DOI DOI 10.18653/V1/D19-1410
   Roller R., CROSS LINGUAL CANDID
   Shen KL, 2020, WORLD J PEDIATR, V16, P219, DOI 10.1007/s12519-020-00344-6
   Si JL, 2018, PROC CVPR IEEE, P5363, DOI 10.1109/CVPR.2018.00562
   Soldaini L., 2016, MEDIR WORK SIGIR
   Sung M, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3641
   Tutubalina E, 2018, J BIOMED INFORM, V84, P93, DOI 10.1016/j.jbi.2018.06.006
   van den Oord A., 2019, ARXIV
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Wang X, 2019, PROC CVPR IEEE, P5017, DOI 10.1109/CVPR.2019.00516
   WHO, 2012, GLOBAL TUBERCULOSIS REPORT 2012, P1
   Yang B., 2015, ICLR
   Yu Gu, 2022, ACM Transactions on Computing and Healthcare, V3, DOI 10.1145/3458754
   Yu S, 2018, J AM MED INFORM ASSN, V25, P54, DOI 10.1093/jamia/ocx111
   Zhang X, 2020, AAAI CONF ARTIF INTE, V34, P9579
   Zolnoori M, 2019, DATA BRIEF, V24, DOI 10.1016/j.dib.2019.103838
NR 57
TC 0
Z9 0
U1 5
U2 5
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD FEB
PY 2022
VL 126
AR 103983
DI 10.1016/j.jbi.2021.103983
PG 10
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA YF9RN
UT WOS:000742136900001
PM 34990838
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Segler, MHS
   Waller, MP
AF Segler, Marwin H. S.
   Waller, Mark P.
TI Modelling Chemical Reasoning to Predict and Invent Reactions
SO CHEMISTRY-A EUROPEAN JOURNAL
LA English
DT Article; Proceedings Paper
CT 8th Munster Symposium on Cooperative Effects in Chemistry
CY MAY 12, 2017
CL Munster, GERMANY
DE artificial intelligence; augmented scientific discovery; computational
   chemistry; graph theory; organic chemistry
ID ORGANIC-CHEMISTRY; CLASSIFICATION; DESIGN; VALIDATION; REACTIVITY;
   DISCOVERY; NETWORKS; DATABASE; TOOL
AB The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery.
C1 [Segler, Marwin H. S.; Waller, Mark P.] Westfal Wilhelms Univ Munster, Inst Organ Chem, Corrensstr 40, D-48149 Munster, Germany.
   [Segler, Marwin H. S.; Waller, Mark P.] Westfal Wilhelms Univ Munster, Ctr Multiscale Theory & Computat, Corrensstr 40, D-48149 Munster, Germany.
   [Waller, Mark P.] Shanghai Univ, Dept Phys, Shangda Rd 99, Shanghai 200444, Peoples R China.
   [Waller, Mark P.] Shanghai Univ, Int Ctr Quantum & Mol Struct, Shangda Rd 99, Shanghai 200444, Peoples R China.
RP Waller, MP (通讯作者)，Westfal Wilhelms Univ Munster, Inst Organ Chem, Corrensstr 40, D-48149 Munster, Germany.; Waller, MP (通讯作者)，Westfal Wilhelms Univ Munster, Ctr Multiscale Theory & Computat, Corrensstr 40, D-48149 Munster, Germany.; Waller, MP (通讯作者)，Shanghai Univ, Dept Phys, Shangda Rd 99, Shanghai 200444, Peoples R China.; Waller, MP (通讯作者)，Shanghai Univ, Int Ctr Quantum & Mol Struct, Shangda Rd 99, Shanghai 200444, Peoples R China.
EM m.waller@uni-muenster.de
FU Deutsche Forschungsgemeinschaft [SFB 858]
FX This work was supported by the Deutsche Forschungsgemeinschaft (SFB
   858). We thank D. Evans (RELX Intellectual Properties SA) as well as J.
   Swienty-Busch and S. Radestock (Elsevier Information Systems GmbH) for
   the reaction dataset and ChemAxon for an academic software license.
CR Andersen Jakob L., 2014, International Journal of Computational Biology and Drug Design, V7, P225, DOI 10.1504/IJCBDD.2014.061649
   [Anonymous], 1979, ANGEW CHEM, V91, P99
   [Anonymous], 2015, ANGEW CHEM, V127, P3514
   [Anonymous], 1985, ANGEW CHEM, V97, P596
   [Anonymous], 1988, ANGEW CHEM, V100, P1618
   [Anonymous], 1993, ANGEW CHEM, V105, P210
   [Anonymous], 2002, EXPLORING SCI COGNIT
   [Anonymous], 2012, ANGEW CHEM, V124, P8046
   [Anonymous], 2006, ANGEW CHEM, V118, P2120
   Benko G, 2003, J CHEM INF COMP SCI, V43, P1085, DOI 10.1021/ci0200570
   Biju AT, 2011, ACCOUNTS CHEM RES, V44, P1182, DOI 10.1021/ar2000716
   Bogevig A, 2015, ORG PROCESS RES DEV, V19, P357, DOI 10.1021/op500373e
   Bugaut X, 2012, CHEM SOC REV, V41, P3511, DOI 10.1039/c2cs15333e
   Campeau LC, 2008, J AM CHEM SOC, V130, P3266, DOI 10.1021/ja710451s
   Carrera GVSM, 2009, J COMPUT AID MOL DES, V23, P419, DOI 10.1007/s10822-009-9275-2
   Chevillard F, 2015, J CHEM INF MODEL, V55, P1824, DOI 10.1021/acs.jcim.5b00203
   Christ CD, 2012, J CHEM INF MODEL, V52, P1745, DOI 10.1021/ci300116p
   Collins KD, 2013, NAT CHEM, V5, P597, DOI [10.1038/nchem.1669, 10.1038/NCHEM.1669]
   de Luca A, 2012, J CHEM INF MODEL, V52, P2325, DOI 10.1021/ci300149n
   Dunbar K.N., 2012, OXFORD HDB THINKING, DOI [10.1093/oxfordhb/9780199734689.001.0001, DOI 10.1093/OXFORDHB/9780199734689.013.0035]
   Finkelmann AR, 2016, CHEM COMMUN, V52, P681, DOI 10.1039/c5cc07887c
   FORSTMEYER D, 1988, ANGEW CHEM INT EDIT, V27, P1558, DOI 10.1002/anie.198815581
   Gasteiger J., 2003, CHEMOINFORMATICS TXB
   Gini A, 2015, CHEM-EUR J, V21, P12053, DOI 10.1002/chem.201501314
   Gothard CM, 2012, ANGEW CHEM INT EDIT, V51, P7922, DOI 10.1002/anie.201202155
   Graulich N, 2010, CHEM SOC REV, V39, P1503, DOI 10.1039/b911536f
   Grzybowski BA, 2009, NAT CHEM, V1, P31, DOI [10.1038/NCHEM.136, 10.1038/nchem.136]
   Guo C, 2014, J AM CHEM SOC, V136, P17402, DOI 10.1021/ja510737n
   Hartenfeller M, 2011, J CHEM INF MODEL, V51, P3093, DOI 10.1021/ci200379p
   HERGES R, 1992, SCIENCE, V255, P711, DOI 10.1126/science.255.5045.711
   HERGES R, 1985, ANGEW CHEM INT EDIT, V24, P594, DOI 10.1002/anie.198505941
   HERGES R, 1990, J CHEM INF COMP SCI, V30, P377, DOI 10.1021/ci00068a006
   Holyoak K. J., 2012, OXFORD HDB THINKING, P234, DOI DOI 10.1093/OXFORDHB/9780199734689.001.0001
   Kayala MA, 2012, J CHEM INF MODEL, V52, P2526, DOI 10.1021/ci3003039
   Kayala MA, 2011, J CHEM INF MODEL, V51, P2209, DOI 10.1021/ci200207y
   Kowalczyk B, 2009, J PHYS ORG CHEM, V22, P897, DOI 10.1002/poc.1535
   Kurti L., 2005, STRATEGIC APPL NAMED
   Latino DARS, 2006, ANGEW CHEM INT EDIT, V45, P2066, DOI 10.1002/anie.200503833
   Law J, 2009, J CHEM INF MODEL, V49, P593, DOI 10.1021/ci800228y
   Ley SV, 2015, ANGEW CHEM INT EDIT, V54, P3449, DOI 10.1002/anie.201410744
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Marcou G, 2015, J CHEM INF MODEL, V55, P239, DOI 10.1021/ci500698a
   Ramakrishnan R., 2015, ARXIV151007512
   Ramakrishnan R, 2015, J CHEM THEORY COMPUT, V11, P2087, DOI 10.1021/acs.jctc.5b00099
   Rogers D, 2010, J CHEM INF MODEL, V50, P742, DOI 10.1021/ci100050t
   Sadowski P, 2016, J CHEM INF MODEL, V56, P2125, DOI 10.1021/acs.jcim.6b00351
   Schneider N, 2015, J CHEM INF MODEL, V55, P39, DOI 10.1021/ci5006614
   Sheridan RP, 2013, J CHEM INF MODEL, V53, P783, DOI 10.1021/ci400084k
   Smith S. M., 2012, OXFORD HDB THINKING, P456
   Steinbeck C, 2003, J CHEM INF COMP SCI, V43, P493, DOI 10.1021/ci025584y
   Todd MH, 2005, CHEM SOC REV, V34, P247, DOI 10.1039/b104620a
   UGI I, 1993, ANGEW CHEM INT EDIT, V32, P201, DOI 10.1002/anie.199302011
   UGI I, 1979, ANGEW CHEM INT EDIT, V18, P111, DOI 10.1002/anie.197901111
   Warr WA, 2014, MOL INFORM, V33, P469, DOI 10.1002/minf.201400052
   Wei JN, 2016, ACS CENTRAL SCI, V2, P725, DOI 10.1021/acscentsci.6b00219
   Zhang QY, 2005, J CHEM INF MODEL, V45, P1775, DOI 10.1021/ci0502707
NR 56
TC 96
Z9 97
U1 7
U2 74
PU WILEY-V C H VERLAG GMBH
PI WEINHEIM
PA POSTFACH 101161, 69451 WEINHEIM, GERMANY
SN 0947-6539
EI 1521-3765
J9 CHEM-EUR J
JI Chem.-Eur. J.
PD MAY 2
PY 2017
VL 23
IS 25
BP 6118
EP 6128
DI 10.1002/chem.201604556
PG 11
WC Chemistry, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Chemistry
GA ET8UZ
UT WOS:000400580000038
PM 27862477
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Dang, DP
   Chen, CX
   Li, HC
   Yan, RE
   Guo, ZX
   Wang, XJ
AF Dang, Depeng
   Chen, Chuangxia
   Li, Haochen
   Yan, Rongen
   Guo, Zixian
   Wang, Xingjian
TI Deep knowledge-aware framework for web service recommendation
SO JOURNAL OF SUPERCOMPUTING
LA English
DT Article
DE Web service; Recommendation; Knowledge graph; Attention module; Deep
   learning
AB Web services are products in the era of service-oriented computing and cloud computing. Considering the information overload problem arising from the task of selecting web services, a recommendation system is by far the most effective solution for performing such selections. However, users calling a limited number of services will cause severe data sparseness and a weak correlation with services. In addition, fully mining the semantic features and knowledge features of the text description is also a major problem that needs to be solved urgently. This paper proposes a deep knowledge-aware approach which introduces knowledge graph and knowledge representation into web service recommendation for the first time. We solve the data sparse problem and optimize the user's feature representation. In this approach, an attention module is introduced to model the impact of tags for the candidate services on different words of user queries, and a deep neural network is used to model the high-level features of user-service invocation behaviors. The results of experiments demonstrate that the proposed approach can achieve better recommendation performance than other state-of-the-art methods.
C1 [Dang, Depeng; Chen, Chuangxia; Yan, Rongen; Guo, Zixian; Wang, Xingjian] Beijing Normal Univ, Sch Artificial Intelligence, Beijing 100875, Peoples R China.
   [Li, Haochen] Univ Edinburgh, Business Sch, Edinburgh EH8 9JS, Midlothian, Scotland.
RP Yan, RE (通讯作者)，Beijing Normal Univ, Sch Artificial Intelligence, Beijing 100875, Peoples R China.
EM ddepeng@bnu.edu.cn; chuangxia@mail.bnu.edu.cn; lihaochen@ymail.com;
   reyan@mail.bnu.edu.cn; 201821210035@mail.bnu.eud.cn; ejingle@qq.com
OI Yan, Rongen/0000-0002-5107-4339
FU National Natural Science Foundation of China [61672102, 61073034,
   61370064, 60940032]; National Social Science Foundation of China
   [BCA150050]; Program for New Century Excellent Talents in the University
   of Ministry of Education of China [NCET-10-0239]
FX This research is supported by the National Natural Science Foundation of
   China under Grant No. 61672102, No. 61073034, No. 61370064 and No.
   60940032; the National Social Science Foundation of China under Grant
   No. BCA150050; and the Program for New Century Excellent Talents in the
   University of Ministry of Education of China under Grant No.
   NCET-10-0239.
CR [Anonymous], 2017, LSTM SEARCH SPACE OD
   Ayyadevara VK, 2018, PROMACHINE LEARNING, P167
   Aznoli F, 2017, J NETW COMPUT APPL, V77, P73, DOI 10.1016/j.jnca.2016.10.009
   Bai B, 2020, IEEE T SERV COMPUT, V13, P73, DOI 10.1109/TSC.2017.2681666
   Bai B, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), P209, DOI 10.1109/ICWS.2015.37
   Botangen KA, 2020, EXPERT SYST APPL, V151, DOI 10.1016/j.eswa.2020.113347
   Brank J., 2017, P SLOV KDD C DAT MIN
   Buqing Cao, 2020, IEEE Transactions on Services Computing, V13, P99, DOI 10.1109/TSC.2017.2686390
   Buqing Cao, 2013, 2013 IEEE 20th International Conference on Web Services (ICWS), P99, DOI 10.1109/ICWS.2013.23
   Cao BQ, 2019, J PARALLEL DISTR COM, V132, P177, DOI 10.1016/j.jpdc.2018.04.002
   Chen X, 2014, IEEE T PARALL DISTR, V25, P1913, DOI 10.1109/TPDS.2013.308
   Chune Li, 2013, 2013 IEEE International Conference on Services Computing (SCC), P49, DOI 10.1109/SCC.2013.107
   Church KW, 2017, NAT LANG ENG, V23, P155, DOI 10.1017/S1351324916000334
   Dong X, 2017, AAAI CONF ARTIF INTE, P1309
   Emami H, 2019, ACM TRANS MANAG INF, V10, DOI 10.1145/3314949
   Fellbaum C, 2010, THEORY AND APPLICATIONS OF ONTOLOGY: COMPUTER APPLICATIONS, P231, DOI 10.1007/978-90-481-8847-5_10
   Guermah H, 2018, PROCEDIA COMPUT SCI, V127, P398, DOI 10.1016/j.procs.2018.01.137
   Hao YS, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017), P285, DOI 10.1109/ICWS.2017.44
   Hidasi B., 2015, P 4 INT C LEARN REPR
   Kalai A, 2018, FUTURE GENER COMP SY, V80, P355, DOI 10.1016/j.future.2017.05.036
   Kalai A, 2016, LECT NOTES COMPUT SC, V9936, P517, DOI 10.1007/978-3-319-46295-0_32
   Kang GS, 2016, IEEE T SERV COMPUT, V9, P566, DOI 10.1109/TSC.2015.2415807
   Ketkar N, 2017, CONVOLUTIONAL NEURAL, DOI [10.1007/978-1-4842-2766-4_5, DOI 10.1007/978-1-4842-2766-4_5]
   Liang XL, 2019, IEEE T SERV COMPUT, V12, P276, DOI 10.1109/TSC.2016.2598776
   Liu XM, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), P185, DOI 10.1109/ICWS.2015.34
   Ma YT, 2021, IEEE T ENG MANAGE, V68, P105, DOI 10.1109/TEM.2019.2961376
   Moro A., 2014, TACL, V2, P231, DOI DOI 10.1162/TACL_A_00179
   Paliwal AV, 2012, IEEE T SERV COMPUT, V5, P260, DOI 10.1109/TSC.2011.19
   Patankar, 2016, P 2 INT C INF COMM T, P1
   Platzer C, 2005, Third European Conference on Web Services, Proceedings, P62
   Pradeep D, 2020, FUTURE GENER COMP SY, V108, P849, DOI 10.1016/j.future.2020.03.010
   Rodriguez-Mier P, 2016, IEEE T SERV COMPUT, V9, P537, DOI 10.1109/TSC.2015.2402679
   Roman D, 2015, J WEB SEMANT, V31, P39, DOI 10.1016/j.websem.2014.11.006
   Shi M, 2019, IEEE T PARALL DISTR, V30, P1077, DOI 10.1109/TPDS.2018.2877363
   Shi M, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017), P9, DOI 10.1109/ICWS.2017.9
   Shi M, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), P444, DOI 10.1109/ICWS.2016.64
   Tajbakhsh MS, 2016, 2016 IEEE 4TH INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD WORKSHOPS (FICLOUDW), P252, DOI 10.1109/W-FiCloud.2016.59
   Tsalgatidou A, 2002, DISTRIB PARALLEL DAT, V12, P135, DOI 10.1023/A:1016599017660
   Vincent P, 2010, J MACH LEARN RES, V11, P3371
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu XK, 2017, IEEE T SERV COMPUT, V10, P352, DOI 10.1109/TSC.2015.2479228
   Wulam A, 2019, CMC-COMPUT MATER CON, V60, P1003, DOI 10.32604/cmc.2019.07704
   Xia BF, 2015, IEEE T SERV COMPUT, V8, P674, DOI 10.1109/TSC.2014.2379251
   Xiong RB, 2018, EXPERT SYST APPL, V110, P191, DOI 10.1016/j.eswa.2018.05.039
   Yao L, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), P217, DOI 10.1109/ICWS.2015.38
   Zhang N, 2020, IEEE T SERV COMPUT, V13, P488, DOI 10.1109/TSC.2017.2693147
   Zhong Y, 2018, IEEE T AUTOM SCI ENG, V15, P468, DOI 10.1109/TASE.2016.2624310
   Zhong Y, 2015, IEEE T SERV COMPUT, V8, P356, DOI 10.1109/TSC.2014.2381496
   Zhou Y, 2019, ADV INTELL SYST, V935, P950, DOI 10.1007/978-3-030-19063-7_74
NR 49
TC 3
Z9 3
U1 7
U2 23
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0920-8542
EI 1573-0484
J9 J SUPERCOMPUT
JI J. Supercomput.
PD DEC
PY 2021
VL 77
IS 12
BP 14280
EP 14304
DI 10.1007/s11227-021-03832-2
EA MAY 2021
PG 25
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA WY6YT
UT WOS:000649221900002
DA 2022-08-23
ER

PT J
AU Lu, JZ
   Yang, ZR
   Zheng, XC
   Wang, J
   Dimitris, K
AF Lu Jinzhi
   Yang Zhaorui
   Zheng Xiaochen
   Wang Jian
   Dimitris, Kiritsis
TI Exploring the concept of Cognitive Digital Twin from model-based systems
   engineering perspective
SO INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
LA English
DT Article
DE Cognitive Digital Twin; Digital Twin; Knowledge graph; Semantic
   modelling; Model-based systems engineering; KARMA language
AB Digital Twin technology has been widely applied in various industry domains. Modern industrial systems are highly complex consisting of multiple interrelated systems, subsystems and components. During the lifecycle of an industrial system, multiple digital twin models might be created related to different domains and lifecycle phases. The integration of these relevant models is crucial for creating higher-level intelligent systems. The Cognitive Digital Twin (CDT) concept has been proposed to address this challenge by empowering digital twins with augmented semantic capabilities. It aims at identifying the dynamics and interrelationships of virtual models, thus to enhance complexity management capability and to support decision-making during the entire system lifecycle. This paper aims to explore the CDT concept and its core elements following a systems engineering approach. A conceptual architecture is designed according to the ISO 42010 standard to support CDT development; and an application framework enabled by knowledge graph is provided to guide the CDT applications. In addition, an enabling tool-chain is proposed corresponding to the framework to facilitate the implementation of CDT. Finally, a case study is conducted, based on simulation experiments as a proof-of-concept.
C1 [Lu Jinzhi; Zheng Xiaochen] Ecole Polytech Federate Lausanne EPFL, ICT Sustainable Mfg, CH-1015 Lausanne, Switzerland.
   [Yang Zhaorui; Wang Jian; Dimitris, Kiritsis] Univ Elect Sci & Technol China, Chengdu 611731, Peoples R China.
RP Zheng, XC (通讯作者)，Ecole Polytech Federate Lausanne EPFL, ICT Sustainable Mfg, CH-1015 Lausanne, Switzerland.
EM xiaochen.zheng@epfl.ch
OI Zheng, Xiaochen/0000-0003-1506-3314
FU EPFL Lausanne; EU [869951, 825030]
FX Open access funding provided by EPFL Lausanne The work presented in this
   paper has been partially funded by the EU H2020 project FACTLOG (869951)
   -Energy-aware Factory Analytics for Process Industries, and EU H2020
   project QU4LITY (825030) -Digital Reality in Zero Defect Manufacturing.
CR Abburu S, 2020, P 2020 IEEE INT C EN, P1, DOI [10.1109/ICE/ITMC49519.2020.9198403, DOI 10.1109/ICE/ITMC49519.2020.9198403]
   Adl AE, 2016, COGNITIVE DIGITAL TW
   Arp R, 2015, BUILDING ONTOLOGIES WITH BASIC FORMAL ONTOLOGY, P1
   Banerjee A, 2017, PROCEEDINGS OF THE 2017 ACM WEB SCIENCE CONFERENCE (WEBSCI '17), P425
   Berthold MR, 2008, STUD CLASS DATA ANAL, P319, DOI 10.1145/1656274.1656280
   Borgo S, 2022, APPL ONTOL, V17, P45, DOI 10.3233/AO-210259
   Botta A, 2016, FUTURE GENER COMP SY, V56, P684, DOI 10.1016/j.future.2015.09.021
   Chen J, 2019, 2019 14 ANN C SYSTEM, DOI [10.1109/SYSOSE.2019.8753883, DOI 10.1109/SYSOSE.2019.8753883]
   Cloutier R, 2015, IEEE T SYST MAN CY-S, V45, P662, DOI 10.1109/TSMC.2014.2379657
   Dai S, 2021, ROBOT CIM-INT MANUF, V72, DOI 10.1016/j.rcim.2021.102173
   Ehrlinger L, 2016, CEUR WORKSHOP PROC
   Fortino G, 2018, IEEE T SYST MAN CY-S, V48, P1939, DOI 10.1109/TSMC.2017.2780618
   Gharaei A, 2020, IFIP ADV INF COMM TE, V591, P82, DOI 10.1007/978-3-030-57993-7_10
   Grieves M., 2014, GLOBAL J ENG SCI RES, DOI [10.5281/zenodo.1493930, DOI 10.5281/ZENODO.1493930]
   Guo J, 2020, INCOSE INT S, V30, P1460, DOI [10.1002/j.2334-5837.2020.00797.x, DOI 10.1002/J.2334-5837.2020.00797.X]
   Haskins C, 2014, INSIGHT, V17, P63, DOI [10.1002/inst.201417263a, DOI 10.1002/INST.201417263A]
   He B, 2021, ADV MANUF, V9, P1, DOI 10.1007/s40436-020-00302-5
   Jinzhi Lu, 2020, INCOSE International Symposium, V30, P323, DOI 10.1002/j.2334-5837.2020.00725.x
   Jinzhi Lu, 2020, Proceedings of 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing, (AMP 2020). Lecture Notes in Mechanical Engineering (LNME), P105, DOI 10.1007/978-3-030-46212-3_7
   Kharlamov E, 2018, IEEE INT CONF BIG DA, P4189, DOI 10.1109/BigData.2018.8622503
   Kritzinger W, 2018, IFAC PAPERSONLINE, V51, P1016, DOI 10.1016/j.ifacol.2018.08.474
   Kumar P, 2018, IEEE T SYST MAN CY-S, V48, P1309, DOI 10.1109/TSMC.2017.2668065
   Li YF, 2021, INT J PROD RES, DOI 10.1080/00207543.2021.1971318
   Liu SM, 2021, ROBOT CIM-INT MANUF, V71, DOI 10.1016/j.rcim.2021.102123
   Lomov I, 2021, J IND INF INTEGR, V23, DOI 10.1016/j.jii.2021.100216
   Lu J, 2016, 8 EUR C EMB REAL TIM
   Lu J, 2021, SMART SERV SUMM, P105
   Lu JZ, 2021, IEEE SYST J, DOI 10.1109/JSYST.2021.3106195
   Lu JZ, 2018, IEEE ACCESS, V6, P50443, DOI 10.1109/ACCESS.2018.2868055
   Lu J, 2021, IEEE T NEUR NET LEAR, DOI 10.1109/TNNLS.2021.3053306
   Ochoa JL, 2013, EXPERT SYST APPL, V40, P2058, DOI 10.1016/j.eswa.2012.10.017
   Meierhofer J, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app112311418
   Gomez-Berbis JM, 2019, COMM COM INF SC, V1124, P178, DOI 10.1007/978-3-030-34989-9_14
   Minerva R, 2015, TECH REP
   Mordecai Y, 2018, IEEE T SYST MAN CY-S, V48, P637, DOI 10.1109/TSMC.2016.2602543
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   OConnor M, 2009, CEUR WORKSHOP PROC
   Qi QL, 2021, J MANUF SYST, V58, P3, DOI 10.1016/j.jmsy.2019.10.001
   Rozanec JM, 2022, INT J PROD RES, V60, P452, DOI 10.1080/00207543.2021.2002967
   Tao F, 2019, IEEE T IND INFORM, V15, P2405, DOI 10.1109/TII.2018.2873186
   Tao F, 2019, IEEE T SYST MAN CY-S, V49, P81, DOI 10.1109/TSMC.2017.2723764
   Tao Fei, 2017, Computer Integrated Manufacturing Systems, V23, P1, DOI 10.13196/j.cims.2017.01.001
   Tavcar J, 2019, IEEE T SYST MAN CY-S, V49, P145, DOI 10.1109/TSMC.2018.2814539
   Wang H, 2007, COMPUT IND, V58, P227, DOI 10.1016/j.compind.2006.05.002
   Zheng P, 2020, ROBOT CIM-INT MANUF, V64, DOI 10.1016/j.rcim.2020.101958
   Zheng XT, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2021.3116147
   Zhou GH, 2020, INT J PROD RES, V58, P1034, DOI 10.1080/00207543.2019.1607978
NR 47
TC 0
Z9 0
U1 9
U2 9
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0268-3768
EI 1433-3015
J9 INT J ADV MANUF TECH
JI Int. J. Adv. Manuf. Technol.
PD AUG
PY 2022
VL 121
IS 9-10
BP 5835
EP 5854
DI 10.1007/s00170-022-09610-5
EA JUL 2022
PG 20
WC Automation & Control Systems; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Engineering
GA 3O1RG
UT WOS:000827380900004
OA hybrid
DA 2022-08-23
ER

PT J
AU Deng, Y
   Xie, YX
   Li, YL
   Yang, M
   Lam, W
   Shen, Y
AF Deng, Yang
   Xie, Yuexiang
   Li, Yaliang
   Yang, Min
   Lam, Wai
   Shen, Ying
TI Contextualized Knowledge-aware Attentive Neural Network: Enhancing
   Answer Selection with Knowledge
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE Answer selection; knowledge graph; attention mechanism; graph
   convolutional network
ID GRAPH
AB Answer selection, which is involved in many natural language processing applications, such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge. In this article, we extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG). First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network, which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. Then, we develop two kinds of knowledge-aware attention mechanism to summarize both the context-based and knowledge-based interactions between questions and answers. To handle the diversity and complexity of KG information, we further propose a Contextualized Knowledge-aware Attentive Neural Network, which improves the knowledge representation learning with structure information via a customized Graph Convolutional Network and comprehensively learns context-based and knowledge-based sentence representation via the multi-view knowledge-aware attention mechanism. We evaluate our method on four widely used benchmark QA datasets, including WikiQA, TREC QA, InsuranceQA, and Yahoo QA. Results verify the benefits of incorporating external knowledge from KG and show the robust superiority and extensive applicability of our method.
C1 [Deng, Yang; Lam, Wai] Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, Hong Kong, Peoples R China.
   [Xie, Yuexiang] Alibaba Grp, Hangzhou, Peoples R China.
   [Li, Yaliang] Alibaba Grp, Bellevue, WA USA.
   [Yang, Min] Chinese Acad Sci, SIAT, Shenzhen, Peoples R China.
   [Shen, Ying] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou, Peoples R China.
RP Shen, Y (通讯作者)，Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou, Peoples R China.
EM ydeng@se.cuhk.edu.hk; yuexiang.xyx@alibaba-inc.com;
   yaliang.li@alibaba-inc.com; min.yang@siat.ac.cn; wlam@se.cuhk.edu.hk;
   sheny76@mail.sysu.edu.cn
RI Lam, Wai/GNW-3026-2022
OI Deng, Yang/0000-0002-8122-5943
FU Research Grant Council of the Hong Kong Special Administrative Region,
   China [14204418]; Shenzhen General Research Project
   [JCYJ20190808182805919]
FX The work described in this article is substantially supported supported
   by a grant from the Research Grant Council of the Hong Kong Special
   Administrative Region, China (Project Code: 14204418) and the Shenzhen
   General Research Project (No. JCYJ20190808182805919).
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen D, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P1677, DOI 10.1145/3397271.3401199
   Chen Q, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P993, DOI 10.1145/3077136.3080699
   Chung Y.-A., 2018, P C N AM CHAPT ASS C, V1, P1585
   Cui P, 2019, IEEE T KNOWL DATA EN, V31, P833, DOI 10.1109/TKDE.2018.2849727
   Dai ZH, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P800
   Defferrard M., 2016, 30 INT C NEURAL INFO, P3844
   Deng Y., 2018, P 27 INT C COMP LING, P3295
   Deng Y, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P1693, DOI 10.1145/3397271.3401208
   Deng Y, 2020, AAAI CONF ARTIF INTE, V34, P7651
   Deng Y, 2019, AAAI CONF ARTIF INTE, P6318
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dodge Jesse, 2020, FINE TUNING PRETRAIN
   Dong L, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P260
   Feng MW, 2015, 2015 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING (ASRU), P813, DOI 10.1109/ASRU.2015.7404872
   Garg S, 2020, AAAI CONF ARTIF INTE, V34, P7780
   Han X, 2018, AAAI CONF ARTIF INTE, P4832
   Huang HY, 2019, ACM T INFORM SYST, V37, DOI 10.1145/3233771
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kipf T. N., 2016, ARXIV160902907
   Kumar Sawan, 2019, P C EMP METH NAT LAN, P5910
   Lai Tuan Manh, 2019, P C EMP METH NAT LAN, P5952
   Li Dongfang, 2019, BERTSEL ANSWER SELEC
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Molino P, 2016, ACM T INFORM SYST, V35, DOI 10.1145/2948063
   Mueller J, 2016, AAAI CONF ARTIF INTE, P2786
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Nicosia Massimo, 2018, EMNLP, P1070, DOI 10.18653/v1/D18-1133
   Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
   Peters M., 2018, IMPROVING LANGUAGE U
   Qiu XP, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1305
   Rao JF, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1913, DOI 10.1145/2983323.2983872
   Rao Jinfeng, 2019, P C EMP METH NAT LAN, P5369
   Santos C. D., 2016, ATTENTIVE POOLING NE
   Savenkov D, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2, P299, DOI 10.18653/v1/P17-2047
   Sawant U, 2019, INFORM RETRIEVAL J, V22, P324, DOI 10.1007/s10791-018-9348-8
   Severyn A, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P373, DOI 10.1145/2766462.2767738
   Severyn Aliaksei, 2013, P 2013 C EMP METH NA, P458
   Shen Chenlin, 2018, P 2018 C EMPIRICAL M, P3654, DOI 10.18653/v1/d18-1401
   Shen Y, 2018, ACM/SIGIR PROCEEDINGS 2018, P901, DOI 10.1145/3209978.3210081
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Tan M, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P464, DOI 10.18653/v1/p16-1044
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Tay Y, 2018, AAAI CONF ARTIF INTE, P5512
   Tay Y, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P583, DOI 10.1145/3159652.3159664
   Tay Y, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P695, DOI 10.1145/3077136.3080790
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang BN, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1288, DOI 10.18653/v1/p16-1122
   Wang CL, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2061, DOI 10.1145/3097983.3098140
   Wang D, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (IJCNLP), VOL 2, P707
   Wang M., 2007, EMNLP CONLL, V7, P22
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Wu W, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1746
   Xin J, 2018, AAAI CONF ARTIF INTE, P5997
   Xu D, 2020, AAAI CONF ARTIF INTE, V34, P9330
   Yang BS, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1436, DOI 10.18653/v1/P17-1132
   Yang Y., 2015, EMNLP
   Yih W., 2013, P 51 ANN M ASS COMP, V1, P1744
   Yin W., 2016, T ASSOC COMPUT LING, V4, P259, DOI [DOI 10.1162/TACL_A_00097, 10.1162/tacl_a_00097, DOI 10.1162/TACL_A_00244]
   Yoon S, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P2093, DOI 10.1145/3357384.3358148
   Yu M, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P571, DOI 10.18653/v1/P17-1053
   Zhang Tianyi, 2020, REV FEW SAMPLE BERT
   Zhang WX, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P569, DOI 10.1145/3397271.3401166
   Zhang WX, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P2620, DOI 10.1145/3366423.3380015
   Zhang Y., 2018, P 2018 C EMP METH NA, DOI [10.18653/v1/D18-1244, DOI 10.18653/V1/D18-1244]
NR 69
TC 1
Z9 1
U1 7
U2 7
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1046-8188
EI 1558-2868
J9 ACM T INFORM SYST
JI ACM Trans. Inf. Syst.
PD JAN
PY 2022
VL 40
IS 1
AR 2
DI 10.1145/3457533
PG 33
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZV7AR
UT WOS:000770678900002
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Du, W
   Jiang, GR
   Xu, W
   Ma, J
AF Du, Wei
   Jiang, Guanran
   Xu, Wei
   Ma, Jian
TI Sequential patent trading recommendation using knowledge-aware
   attentional bidirectional long short-term memory network (KBiLSTM)
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article; Early Access
DE Deep learning; knowledge graph; patent trading recommendation;
   sequential recommendation
ID VECTOR-SPACE MODEL; TECHNOLOGY; RETRIEVAL; DIVERSITY; MARKET; SYSTEM
AB With the rapid development of the patent marketplace, patent trading recommendation is required to mitigate the technology searching cost of patent buyers. Current research focuses on the recommendation based on existing patents of a company; a few studies take into account the sequential pattern of patent acquisition activities and the possible diversity of a company's business interests. Moreover, the profiling of patents based on solely patent documents fails to capture the high-order information of patents. To bridge the gap, we propose a knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM) method for patent trading recommendation. KBiLSTM uses knowledge graph embeddings to profile patents with rich patent information. It introduces bidirectional long short-term memory network (BiLSTM) to capture the sequential pattern in a company's historical records. In addition, to address a company's diverse technology interests, we design an attention mechanism to aggregate the company's historical patents given a candidate patent. Experimental results on the United States Patent and Trademark Office (USPTO) data set show that KBiLSTM outperforms state-of-the-art baselines for patent trading recommendation in terms of F1 and normalised discounted cumulative gain (nDCG). The attention visualisation of randomly selected company intuitively demonstrates the recommendation effectiveness.
C1 [Du, Wei; Jiang, Guanran; Xu, Wei] Renmin Univ China, Sch Informat, 59 Zhongguancun St, Beijing 100872, Peoples R China.
   [Ma, Jian] City Univ Hong Kong, Dept Informat Syst, Hong Kong, Peoples R China.
RP Xu, W (通讯作者)，Renmin Univ China, Sch Informat, 59 Zhongguancun St, Beijing 100872, Peoples R China.
EM weixu@ruc.edu.cn
OI MA, Jian/0000-0003-2644-5756; DU, Wei/0000-0003-3272-3222
FU National Natural Science Foundation of China [71901208, 71771212,
   U1711262, 71801217]; Humanities and Social Sciences Foundation of the
   Ministry of Education [18YJC630025]; Key Projects of Philosophy and
   Social Sciences Research of Chinese Ministry of Education [19JZD021];
   Ministry of Education, Science and Technology Development Center
   [2019J01010]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: This work
   was supported in part by the National Natural Science Foundation of
   China (Grant Nos. 71901208, 71771212, U1711262, 71801217), Humanities
   and Social Sciences Foundation of the Ministry of Education (No.
   18YJC630025), Key Projects of Philosophy and Social Sciences Research of
   Chinese Ministry of Education (No. 19JZD021), and Ministry of Education,
   Science and Technology Development Center (No. 2019J01010).
CR Alves T, 2018, COMPUT METH PROG BIO, V159, P125, DOI 10.1016/j.cmpb.2018.03.012
   Benassi M, 2009, R&D MANAGE, V39, P68, DOI 10.1111/j.1467-9310.2008.00537.x
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen J, 2020, SCIENTOMETRICS, V125, P2091, DOI 10.1007/s11192-020-03666-4
   Chen YL, 2013, J INF SCI, V39, P737, DOI 10.1177/0165551513494641
   Chen YL, 2012, J INF SCI, V38, P222, DOI 10.1177/0165551512437635
   Chen YL, 2011, INFORM PROCESS MANAG, V47, P309, DOI 10.1016/j.ipm.2010.06.001
   Chen YS, 2010, TECHNOL FORECAST SOC, V77, P20, DOI 10.1016/j.techfore.2009.06.003
   Comins JA, 2018, J INFORMETR, V12, P1223, DOI 10.1016/j.joi.2018.10.002
   Crow VL., 2009, Patent, Patent No. 8131735
   Feng J, 2016, FIFTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P557
   Fischer Elisabeth, 2020, KI 2020: Advances in Artificial Intelligence. 43rd German Conference on AI. Proceedings. Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science (LNAI 12325), P275, DOI 10.1007/978-3-030-58285-2_23
   Fujita S, 2007, INFORM PROCESS MANAG, V43, P1154, DOI 10.1016/j.ipm.2006.11.009
   Gers FA, 2000, NEURAL COMPUT, V12, P2451, DOI 10.1162/089976600300015015
   Hagiu A, 2013, J ECON PERSPECT, V27, P45, DOI 10.1257/jep.27.1.45
   He XJ, 2019, KNOWL-BASED SYST, V184, DOI 10.1016/j.knosys.2019.104899
   Hu C., 2020, WEB INFORM SYSTEMS E, P228
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kang IS, 2007, INFORM PROCESS MANAG, V43, P1173, DOI 10.1016/j.ipm.2006.11.006
   Kang WC, 2018, IEEE DATA MINING, P197, DOI 10.1109/ICDM.2018.00035
   Kingma D., ADAM METHOD STOCHAST
   Kolahkaj M, 2020, ELECTRON COMMER R A, V42, DOI 10.1016/j.elerap.2020.100978
   Krestel R., 2013, P 7 ACM C REC SYST, P395, DOI DOI 10.1145/2507157.2507232
   Lau JH., EMPIRICAL EVALUATION
   Lee CS, 2019, SOFT COMPUT, V23, P537, DOI 10.1007/s00500-017-2859-1
   Lee J, 2021, SCIENTOMETRICS, V126, P1, DOI 10.1007/s11192-020-03731-y
   Li Y, 2007, INFORM PROCESS MANAG, V43, P1183, DOI 10.1016/j.ipm.2006.11.005
   Lin BW, 2006, IEEE T ENG MANAGE, V53, P17, DOI 10.1109/TEM.2005.861813
   Liu LF, 2020, COMPUT IND, V115, DOI 10.1016/j.compind.2019.103154
   Min, 2016, ADV COMPUTER SCI UBI, P843
   Mishra R, 2015, DECIS SUPPORT SYST, V75, P1, DOI 10.1016/j.dss.2015.04.004
   Praiss D M, 2001, Nat Biotechnol, V19 Suppl, pBE5, DOI 10.1038/89426
   Qingqin Wang, 2020, Web and Big Data. 4th International Joint Conference, APWeb-WAIM 2020. Lecture Notes in Computer Science (LNCS 12317), P493, DOI 10.1007/978-3-030-60259-8_36
   Risch J, 2017, LECT NOTES COMPUT SC, V10450, P40, DOI 10.1007/978-3-319-67008-9_4
   Serrano CJ, 2018, INT ECON REV, V59, P1877, DOI 10.1111/iere.12338
   Shi J., 2020, SECURITY PRIVACY SOC, P433
   Srinivasan R, 2008, INT J RES MARK, V25, P119, DOI 10.1016/j.ijresmar.2007.12.005
   Sun F, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P1441, DOI 10.1145/3357384.3357895
   Sun K, 2018, LECT NOTES COMPUT SC, V11305, P370, DOI 10.1007/978-3-030-04221-9_33
   Tang JX, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P565, DOI 10.1145/3159652.3159656
   Trappey AJC, 2013, J NETW COMPUT APPL, V36, P1441, DOI 10.1016/j.jnca.2013.02.035
   Trappey AJC, 2012, ADV ENG INFORM, V26, P26, DOI 10.1016/j.aei.2011.06.005
   Trappey CV, 2016, WORLD PAT INF, V46, P64, DOI 10.1016/j.wpi.2016.06.004
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang Q, 2019, INT J ELECTRON COMM, V23, P147, DOI 10.1080/10864415.2018.1564549
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang XM, 2020, LECT NOTES ARTIF INT, V12084, P168, DOI 10.1007/978-3-030-47426-3_14
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiang Ji, 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011), P1699, DOI 10.1109/FSKD.2011.6019821
   Xing Wei, 2019, Knowledge Science, Engineering and Management. 12th International Conference, KSEM 2019. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 11775), P72, DOI 10.1007/978-3-030-29551-6_7
   Xingwei Zhu, 2020, Web and Big Data. 4th International Joint Conference, APWeb-WAIM 2020. Lecture Notes in Computer Science (LNCS 12317), P181, DOI 10.1007/978-3-030-60259-8_15
   Xu CF, 2021, NEUROCOMPUTING, V423, P580, DOI 10.1016/j.neucom.2020.10.066
   Yanagisawa T., 2009, OECD SCI TECHNOLOGY, DOI [10.1787/218413152254, DOI 10.1787/218413152254]
   Yu Liu, 2020, Database Systems for Advanced Applications. DASFAA 2020 International Workshops. BDMS, SeCoP, BDQM, GDMA, and AIDE. Proceedings. Lecture Notes in Computer Science (LNCS 12115), P241, DOI 10.1007/978-3-030-59413-8_20
   Yuan FJ, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P582, DOI 10.1145/3289600.3290975
   Yuan WH, 2020, INFORM SCIENCES, V510, P122, DOI 10.1016/j.ins.2019.09.007
   Zhang S., NEXT ITEM RECOMMENDA
   Zhang Y, 2016, J INFORMETR, V10, P1108, DOI 10.1016/j.joi.2016.09.006
NR 59
TC 0
Z9 0
U1 9
U2 28
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
AR 01655515211023937
DI 10.1177/01655515211023937
EA JUN 2021
PG 17
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA TA6HM
UT WOS:000667349700001
DA 2022-08-23
ER

PT J
AU Cao, YM
   Cui, Y
   Yu, XK
   Li, T
   Chang, IS
   Wu, J
AF Cao, Yunmeng
   Cui, Yue
   Yu, Xiaokun
   Li, Tong
   Chang, I-Shin
   Wu, Jing
TI Bibliometric analysis of phosphogypsum research from 1990 to 2020 based
   on literatures and patents
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article
DE Phosphogypsum; Bibliometric; Knowledge map; CiteSpace; Technology life
   cycle; S-curve
ID RARE-EARTH-ELEMENTS; CHEMICAL LOOPING GASIFICATION; PHOSPHATE
   FERTILIZERS; ENVIRONMENTAL-IMPACT; BUILDING-MATERIALS; KNOWLEDGE DOMAIN;
   WASTE; RADIONUCLIDES; VALORISATION; HEMIHYDRATE
AB The demand together with the urgency of phosphogypsum (PG) treatment will pose significant challenges for many countries. This research aims to explore the research progress of PG, including basic status, cooperation situation, research fields, and development trends, based on the Web of Science database through bibliometric analysis of publications (articles and patents) from 1990 to 2020. The results show that academic research on PG originated early, but the number of patents grew quickly. China is a global leader in terms of the number of publications and plays a significant role in international cooperation. The knowledge of PG has remained concentrated in the fields of natural radioactivity, cement paste backfilling, soil, crystal morphology, and synthetic gas. However, academic hotspots focus on the microstructure of chemical processes and various environmental impacts; patents and hot technologies are based on the production of refractory materials, ceramics, surface materials, cement mortar, and composite materials. The academic frontiers of PG will be centered on exploiting the methods of recovering rare earth elements from PG, the conditions of ion solidification/stabilization in PG, the impact of reaction conditions on product quality, and the reaction mechanism at the micro-level. The frontiers of patents need to focus on the improvement of manufacturing equipment, new wall materials, and chemically modified polymer materials. Envisaging the number of articles and patents to be published in the future, architectural research has a large room for improvement. This paper conducts an in-depth analysis of PG and provides information on the technological development prospects and opportunities, which is helpful for researchers engaged in PG management.
C1 [Cao, Yunmeng; Cui, Yue; Yu, Xiaokun; Li, Tong; Wu, Jing] Nankai Univ, Coll Environm Sci & Engn, Tianjin 300350, Peoples R China.
   [Chang, I-Shin] Inner Mongolia Univ, Sch Ecol & Environm, Hohhot 010021, Inner Mongolia, Peoples R China.
RP Wu, J (通讯作者)，Nankai Univ, Coll Environm Sci & Engn, Tianjin 300350, Peoples R China.; Chang, IS (通讯作者)，Inner Mongolia Univ, Sch Ecol & Environm, Hohhot 010021, Inner Mongolia, Peoples R China.
EM heartchang@126.com; wujing@nankai.edu.cn
OI Wu, Jing/0000-0002-0611-7440
FU National Key R&D Program of China [2018YFC1903604]
FX This research is supported by the National Key R&D Program of China
   (2018YFC1903604).
CR Attallah MF, 2019, MICROCHEM J, V146, P789, DOI 10.1016/j.microc.2019.02.001
   BERETKA J, 1993, RESOUR CONSERV RECY, V9, P179, DOI 10.1016/0921-3449(93)90002-W
   Binnemans K, 2015, J CLEAN PROD, V99, P17, DOI 10.1016/j.jclepro.2015.02.089
   Bu X, LIGHTWEIGHT ENV FRIE
   Campos MP, 2017, J ENVIRON RADIOACTIV, V172, P232, DOI 10.1016/j.jenvrad.2017.04.002
   Canovas CR, 2019, J CLEAN PROD, V219, P225, DOI 10.1016/j.jclepro.2019.02.104
   Canovas CR, 2017, J CLEAN PROD, V143, P497, DOI 10.1016/j.jclepro.2016.12.083
   Cardenas-Escudero C, 2011, J HAZARD MATER, V196, P431, DOI 10.1016/j.jhazmat.2011.09.039
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Chen L., CHEM ADDITIVE USEFUL
   Chen QS, 2018, J CLEAN PROD, V186, P418, DOI 10.1016/j.jclepro.2018.03.131
   Chernysh Y, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11041575
   Dai QX, 2018, WATER RES, V142, P337, DOI 10.1016/j.watres.2018.06.009
   Dai SL, 2020, J CLEAN PROD, V262, DOI 10.1016/j.jclepro.2020.121168
   El Zrelli R, 2018, ENVIRON SCI POLLUT R, V25, P14690, DOI 10.1007/s11356-018-1648-4
   El Zrelli R, 2019, J HAZARD MATER, V363, P258, DOI 10.1016/j.jhazmat.2018.09.083
   Fahimnia B, 2015, INT J PROD ECON, V162, P101, DOI 10.1016/j.ijpe.2015.01.003
   FRANSES PH, 1994, TECHNOL FORECAST SOC, V46, P45, DOI 10.1016/0040-1625(94)90016-7
   Gao R, PHOSPHOGYPSUM SLURRY
   Gijbels K, 2019, J CLEAN PROD, V237, DOI 10.1016/j.jclepro.2019.117793
   Gong P, MODIFIED PHOSPHOGYPS
   Gong XQ, 2020, J TEST EVAL, V48, P2803, DOI 10.1520/JTE20180702
   Gu K, 2020, CONSTR BUILD MATER, V265, DOI 10.1016/j.conbuildmat.2020.120749
   Guerrero JL, 2021, ENVIRON POLLUT, V268, DOI 10.1016/j.envpol.2020.115843
   Haque MA, 2020, J CLEAN PROD, V261, DOI 10.1016/j.jclepro.2020.121268
   Haupt R, 2007, RES POLICY, V36, P387, DOI 10.1016/j.respol.2006.12.004
   Hentati O, 2015, J HAZARD MATER, V294, P80, DOI 10.1016/j.jhazmat.2015.03.034
   Huang T, ADDITIVE USEFUL PREP
   Huang YB, 2019, CONSTR BUILD MATER, V203, P432, DOI 10.1016/j.conbuildmat.2019.01.112
   Jalali J, 2020, J HAZARD MATER, V399, DOI 10.1016/j.jhazmat.2020.122821
   Jiang Z, HIGH WATER RESISTANT
   Jin ZH, 2020, CONSTR BUILD MATER, V242, DOI 10.1016/j.conbuildmat.2020.118198
   Kim Y, 2011, J OPER MANAG, V29, P194, DOI 10.1016/j.jom.2010.11.001
   Lambert A, 2018, ACS SUSTAIN CHEM ENG, V6, P16471, DOI 10.1021/acssuschemeng.8b03588
   Li B, 2019, J CLEAN PROD, V235, P308, DOI 10.1016/j.jclepro.2019.06.340
   LI XB, 2019, MINERALS-BASEL, V9, DOI DOI 10.3390/min9050321
   Liang YD, 2017, J PAIN RES, V10, P951, DOI 10.2147/JPR.S132808
   Liu CY, 2010, SCIENTOMETRICS, V82, P21, DOI 10.1007/s11192-009-0055-5
   Liu SH, 2020, J CLEAN PROD, V272, DOI 10.1016/j.jclepro.2020.122660
   Liu WS, 2017, INT J FUZZY SYST, V19, P1, DOI 10.1007/s40815-016-0272-z
   Liu Y, GUIZHOU HONGXIN CHUA
   Lu DH, 2021, J CHEM TECHNOL BIOT, V96, P374, DOI 10.1002/jctb.6549
   Lu WD, 2019, CONSTR BUILD MATER, V214, P399, DOI 10.1016/j.conbuildmat.2019.04.148
   Guerrero JL, 2020, ENVIRON POLLUT, V258, DOI 10.1016/j.envpol.2019.113729
   Lutke SF, 2020, CHEMOSPHERE, V256, DOI 10.1016/j.chemosphere.2020.127138
   Lv PH, 2011, SCIENTOMETRICS, V88, P399, DOI 10.1007/s11192-011-0386-x
   Masmoudi-Soussi A, 2020, HYDROMETALLURGY, V191, DOI 10.1016/j.hydromet.2020.105253
   Meyer PS, 1999, TECHNOL FORECAST SOC, V61, P247, DOI 10.1016/S0040-1625(99)00021-9
   Millan-Becerro R, 2020, J ENVIRON MANAGE, V275, DOI 10.1016/j.jenvman.2020.111251
   Mohammed F, 2018, J CLEAN PROD, V188, P497, DOI 10.1016/j.jclepro.2018.03.309
   Moreira RH, 2018, J ENVIRON CHEM ENG, V6, P6664, DOI 10.1016/j.jece.2018.10.019
   Msila X, 2016, CLEAN TECHNOL ENVIR, V18, P2709, DOI 10.1007/s10098-016-1157-4
   Nayak S, 2018, ENVIRON MONIT ASSESS, V190, DOI 10.1007/s10661-018-6933-7
   Peng BH, 2018, J CLEAN PROD, V197, P1177, DOI 10.1016/j.jclepro.2018.06.283
   Perez-Lopez R, 2016, SCI TOTAL ENVIRON, V553, P42, DOI 10.1016/j.scitotenv.2016.02.070
   Pinto SR, 2020, CEMENT CONCRETE RES, V136, DOI 10.1016/j.cemconres.2020.106172
   Qamouche K, 2020, MATER TODAY-PROC, V27, P3234, DOI 10.1016/j.matpr.2020.04.703
   Rashad AM, 2015, J CLEAN PROD, V87, P717, DOI 10.1016/j.jclepro.2014.09.080
   Ru XH, 2012, J AM CERAM SOC, V95, P3478, DOI 10.1111/j.1551-2916.2012.05429.x
   Canovas CR, 2018, J CLEAN PROD, V174, P678, DOI 10.1016/j.jclepro.2017.10.293
   Canovas CR, 2018, SCI TOTAL ENVIRON, V618, P847, DOI 10.1016/j.scitotenv.2017.08.220
   Salo M, 2020, MINER ENG, V155, DOI 10.1016/j.mineng.2020.106408
   Shen C, FOSHAN JIUMO TECHNOL
   Sheng ZM, 2018, BOL SOC ESP CERAM V, V57, P73, DOI 10.1016/j.bsecv.2017.09.005
   Sinfort C, 2019, DATA BRIEF, V27, DOI 10.1016/j.dib.2019.104661
   Sonnenwald DH, 2007, ANNU REV INFORM SCI, V41, P643, DOI 10.1002/aris.2007.1440410121
   Su Y, PREPARING PHOSPHOGYP
   Sun YW, 2018, SCIENTOMETRICS, V116, P203, DOI 10.1007/s11192-018-2748-0
   Tayibi H, 2011, MATER CONSTRUCC, V61, P503, DOI 10.3989/mc.2010.58910
   Tayibi H, 2009, J ENVIRON MANAGE, V90, P2377, DOI 10.1016/j.jenvman.2009.03.007
   Tsioka M, 2020, J CLEAN PROD, V266, DOI 10.1016/j.jclepro.2020.121386
   Vasconez-Maza MD, 2021, J ENVIRON MANAGE, V278, DOI 10.1016/j.jenvman.2020.111517
   Vital C, 2009, ETHOLOGY, V115, P347, DOI 10.1111/j.1439-0310.2009.01613.x
   Wang CQ, 2020, ENVIRON SCI POLLUT R, V27, P23364, DOI 10.1007/s11356-020-09029-z
   Wang JM, 2020, J CLEAN PROD, V276, DOI 10.1016/j.jclepro.2020.123337
   Wang XW, 2011, SCIENTOMETRICS, V88, P761, DOI 10.1007/s11192-011-0414-x
   Xu H, PREPARING HIGH STREN
   Yang JK, 2009, CONSTR BUILD MATER, V23, P687, DOI 10.1016/j.conbuildmat.2008.02.011
   Yang J, 2019, ENERGY, V168, P126, DOI 10.1016/j.energy.2018.11.106
   Yang J, 2018, ENERG FUEL, V32, P7857, DOI 10.1021/acs.energyfuels.8b01112
   Yang J, 2017, FUEL, V194, P448, DOI 10.1016/j.fuel.2016.12.077
   Yin D, MACHINE SPRAYING HEA
   Zeng LL, 2021, GEOMECH ENERGY ENVIR, V25, DOI 10.1016/j.gete.2020.100195
   Zhang GP, 2020, J CLEAN PROD, V273, DOI 10.1016/j.jclepro.2020.122907
   Zhang W, 2020, J CO2 UTIL, V36, P253, DOI 10.1016/j.jcou.2019.11.020
   Zhang W, 2018, J KNOWL MANAG, V22, P1037, DOI 10.1108/JKM-02-2017-0061
   Zhou L, PHOSPHOGYPSUM PRETRE
   Zhou L, METHOD MANUFACTURING
   Zhou P, BUILDING PUTTY POWDE
   Zhu X, PRETREATMENT SYSTEM
   Zupic I, 2015, ORGAN RES METHODS, V18, P429, DOI 10.1177/1094428114562629
NR 92
TC 3
Z9 3
U1 29
U2 59
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
PD DEC
PY 2021
VL 28
IS 47
BP 66845
EP 66857
DI 10.1007/s11356-021-15237-y
EA JUL 2021
PG 13
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA XI4WV
UT WOS:000671920700005
PM 34235698
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Wang, Y
   Sun, MC
   Wang, HJ
   Sun, YD
AF Wang, Ying
   Sun, Mingchen
   Wang, Hongji
   Sun, Yudong
TI Research on Knowledge Graph Completion Model Combining Temporal
   Convolutional Network and Monte Carlo Tree Search
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID DEEP NEURAL-NETWORKS; GAME; GO
AB In knowledge graph completion (KGC) and other applications, learning how to move from a source node to a target node with a given query is an important problem. It can be formulated as a reinforcement learning (RL) problem transition model under a given state. In order to overcome the challenges of sparse rewards and historical state encoding, we develop a deep agent network (graph-agent, GA), which combines temporal convolutional network (TCN) and Monte Carlo Tree Search (MCTS). Firstly, we combine MCTS with neural network to generate more positive reward trajectories, which can effectively solve the problem of sparse rewards. TCN is used to encode the history state, which is used for policy and Q-value respectively. Secondly, according to these trajectories, we use Q-Learning to improve the network and parameter sharing to enhance TCN strategy. We apply these steps repeatedly to learn the model. Thirdly, in the prediction stage of the model, Monte Carlo Tree Search combined with Q-value method is used to predict the target nodes. The experimental results on several graph-walking benchmarks show that GA is better than other RL methods based on-policy gradient. The performance of GA is also better than the traditional KGC baselines.
C1 [Wang, Ying; Sun, Mingchen; Sun, Yudong] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China.
   [Wang, Ying; Sun, Mingchen; Wang, Hongji; Sun, Yudong] Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China.
   [Wang, Hongji] Jilin Univ, Coll Artificial Intelligence, Changchun 130012, Peoples R China.
RP Wang, HJ (通讯作者)，Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China.; Wang, HJ (通讯作者)，Jilin Univ, Coll Artificial Intelligence, Changchun 130012, Peoples R China.
EM wanghj21877@163.com
OI Sun, Mingchen/0000-0002-7199-0836
FU Foundation of the National Natural Science Foundation of China
   [61872161]; Jilin Educational Committee [JJKH20191257KJ]; Nature Science
   Foundation of Jilin Province [20200201297JC]; Foundation of Development
   and Reform of Jilin Province [2019C053-8]; Interdisciplinary and
   integrated innovation of JLU [JLUXKJC2020207]
FX This work was supported by the Foundation of the National Natural
   Science Foundation of China (no. 61872161), the Jilin Educational
   Committee (no. JJKH20191257KJ), the Nature Science Foundation of Jilin
   Province (no. 20200201297JC), the Foundation of Development and Reform
   of Jilin Province (no. 2019C053-8), and the Interdisciplinary and
   integrated innovation of JLU (no. JLUXKJC2020207).
CR [Anonymous], 2016, REINFORCEMENT LEARNI
   Aytar Y, 2018, ADV NEUR IN, V31
   Bai S., 2018, EMPIRICAL EVALUATION
   Bertsekas DP, 1995, PROCEEDINGS OF THE 34TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4, P560, DOI 10.1109/CDC.1995.478953
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cho K., 2014, ARXIV14061078, P1724
   Daiya D, 2020, INT CONF ACOUST SPEE, P8359, DOI 10.1109/ICASSP40776.2020.9053479
   Das R., 2018, P INT C LEARN REPR I
   Dauphin YN, 2017, PR MACH LEARN RES, V70
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Fabbri M., 2018, P 7 INT C DAT SCI TE, P142, DOI [10.5220/0006922101420153, DOI 10.5220/0006922101420153]
   Guo SY, 2021, INFORM SCIENCES, V567, P185, DOI 10.1016/j.ins.2021.03.034
   Haarnoja T, 2018, PR MACH LEARN RES, V80
   Hamaguchi T, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1802
   HART PE, 1968, IEEE T SYST SCI CYB, VSSC4, P100, DOI 10.1109/TSSC.1968.300136
   Huang J, 2021, NEURAL COMPUT APPL, V33, P9751, DOI 10.1007/s00521-021-05742-z
   Ivanov S., 2019, MODERN DEEP REINFORC
   Jagvaral B, 2020, EXPERT SYST APPL, V142, DOI 10.1016/j.eswa.2019.112960
   Kipf T., 2016, 5 INT C LEARN REPR, P1
   Kipf T, 2020, PROC INT C LEARN REP
   Lao N, 2010, MACH LEARN, V81, P53, DOI 10.1007/s10994-010-5205-8
   Lea C, 2017, PROC CVPR IEEE, P1003, DOI 10.1109/CVPR.2017.113
   Lillicrap T. P., 2016, P INT C LEARN REPR
   METROPOLIS N, 1949, J AM STAT ASSOC, V44, P335, DOI 10.2307/2280232
   Mirowski P., 2019, P 1 INT WORKSH MULT, P25, DOI [10.1145/3347450.3357659, DOI 10.1145/3347450.3357659]
   Mnih V, 2015, NATURE, V518, P529, DOI 10.1038/nature14236
   Moravcik M, 2017, SCIENCE, V356, P508, DOI 10.1126/science.aam6960
   Nachum O., 2017, ARXIV170208892
   Oord A.v.d., 2016, ARXIV
   Parcollet Titouan, 2019, ICLR
   Sak H, 2014, INTERSPEECH, P338
   Segler MHS, 2018, NATURE, V555, P604, DOI 10.1038/nature25978
   Shen Y., 2018, P NEUR INF PROC SYST
   Silver D, 2017, NATURE, V550, P354, DOI 10.1038/nature24270
   Silver D, 2016, NATURE, V529, P484, DOI 10.1038/nature16961
   Sun YW, 2019, CHIN AUTOM CONGR, P4736, DOI 10.1109/CAC48633.2019.8996875
   Sutton R. S., 1998, INTRO REINFORCEMENT
   Sutton R.S., 2018, REINFOCEMENT LEARNIN
   Szepesvari C., 2010, SYNTHESIS LECT ARTIF, V4, P1, DOI 10.2200/S00268ED1V01Y201005AIM009
   Teytaud F, 2010, LECT NOTES COMPUT SC, V6048, P65
   Trouillon T., 2017, U.S. Patent Application, Patent No. [15/156 849, 15156849]
   van Hasselt H, 2016, AAAI CONF ARTIF INTE, P2094
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang ZY, 2016, PR MACH LEARN RES, V48
   Yang B., 2015, P INT C LEARN REPR
   Yang F., 2017, P C EMP METH NAT LAN, P575
   Yang F, 2017, ADV NEUR IN, V30
   Zhang YW, 2020, NEUROCOMPUTING, V387, P40, DOI 10.1016/j.neucom.2019.11.032
   Zhu HC, 2020, INT J COMPUT ASS RAD, V15, P193, DOI 10.1007/s11548-019-02082-1
   Zhu S., 2020, P INT C LEARN REPR A
   Zou LX, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P749, DOI 10.1145/3397271.3401181
   Zou LB, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1471, DOI 10.1145/3292500.3330892
NR 52
TC 0
Z9 0
U1 6
U2 6
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1024-123X
EI 1563-5147
J9 MATH PROBL ENG
JI Math. Probl. Eng.
PD MAR 16
PY 2022
VL 2022
AR 2290540
DI 10.1155/2022/2290540
PG 13
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA 0H1QL
UT WOS:000778513300010
OA gold
DA 2022-08-23
ER

PT J
AU Huang, HR
   Li, C
   Peng, XT
   He, LF
   Guo, S
   Peng, H
   Wang, LH
   Li, JX
AF Huang, Hongren
   Li, Chen
   Peng, Xutan
   He, Lifang
   Guo, Shu
   Peng, Hao
   Wang, Lihong
   Li, Jianxin
TI Cross-knowledge-graph entity alignment via relation prediction
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge alignment; Anchor relation; Self-training; Data augmentation;
   Relation prediction
AB The entity alignment task aims to align entities corresponding to the same object in different KGs. The recent work focuses on applying knowledge embedding or graph neural networks to obtain entity embedding for entity alignment. However, there are two challenges encountered by these models: one is some models need to design hyper-parameter to balance embedding loss and alignment loss, the other is the limited training data size. In this paper, we propose a novel entity alignment framework named RpAlign (Relation prediction based cross-knowledge-graph entity Alignment) to address these two issues. Specifically, RpAlign transforms the entity alignment task to the KG completion task to solve and does not need to design any extra alignment component. Unlike the existing models that predict aligned entities by using entity vector distance, the RpAlign defines a new relation called 'anchor' for aligned entities, and it predicts new aligned entities based on the relational predictions between the entities. RpAlign employs several data augmentation and improved self-training techniques to mitigate the impact of the data limitation. We conduct experiments on two datasets, and the experimental results show that the RpAlign model significantly outperforms the current state-of-the-art models.(c) 2021 Published by Elsevier B.V.
C1 [Huang, Hongren; Li, Chen; Peng, Hao; Li, Jianxin] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing, Peoples R China.
   [Huang, Hongren; Li, Chen; Peng, Hao; Li, Jianxin] Beihang Univ, State Key Lab Software Dev Environm, Beijing, Peoples R China.
   [Peng, Xutan] Univ Sheffield, Sheffield, S Yorkshire, England.
   [He, Lifang] Lehigh Univ, Bethlehem, PA 18015 USA.
   [Guo, Shu; Wang, Lihong] Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing, Peoples R China.
RP Wang, LH (通讯作者)，Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing, Peoples R China.
EM wlh@isc.org.cn
FU National Natural Science Foundation of China [61772151, U20B2053]
FX Acknowledgments We thank all the anonymous reviewers. This work is
   sup-ported by the National Natural Science Foundation of China (No.
   61772151 and No. U20B2053) .
CR Berrendorf Max, 2021, Advances in Information Retrieval. 43rd European Conference on IR Research, ECIR 2021. Proceedings. Lecture Notes in Computer Science (LNCS 12656), P48, DOI 10.1007/978-3-030-72113-8_4
   Bojanowski P., 2017, T ASSOC COMPUT LING, V5, P135, DOI [DOI 10.1162/TACL_A_00051, 10.1162/tacl_a_00051]
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cao YX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1452
   Chen JX, 2022, IEEE T IND INFORM, V18, P2000, DOI [10.1109/TII.2021.3088465, 10.1080/00206814.2020.1867914]
   Chen MH, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1511
   Conneau A., 2018, P C EMPIRICAL METHOD, P2475
   Cui Y., 2019, P 2019 C EMP METH NA, P1586
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Hu  W., 2011, P 20 INT C WORLD WID, P87
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Lample G., 2018, INT C LEARN REPR ICL, P1
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li CZ, 2019, AAAI CONF ARTIF INTE, P996
   Li Z, 2021, KNOWL-BASED SYST, V212, DOI 10.1016/j.knosys.2020.106618
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu JH, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P2358
   McClosky D., 2008, P 22 INT C COMP LING, P561, DOI DOI 10.3115/1599081.1599152
   Noy N.F., 2001, IJCAI 01
   Noy NF, 2000, SEVENTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-2001) / TWELFTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE (IAAI-2000), P450
   Peng H, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3238
   Shorten C, 2019, J BIG DATA-GER, V6, DOI 10.1186/s40537-019-0197-0
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun J., 2020, COLING, P3190, DOI [10.18653/ v1/2020.coling-main.284, DOI 10.18653/V1/2020.COLING-MAIN.284]
   Sun Z., 2019, 7 INT C LEARN REPR
   Sun ZQ, 2020, AAAI CONF ARTIF INTE, V34, P222
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Tuan Y.-L., EMNLP, P1855
   Velickovi Petar, 2018, ICLR
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang Z., 2013, PROC INT JOINT C ART, P1778
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Xiang Y., ACL IJCNLP, V2021, P1117, DOI [10.18653/v1/2021. findings-acl.96, DOI 10.18653/V1/2021.FINDINGS-ACL.96]
   Xie Y., 2020, ABS200608093 CORR
   Xu K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3156
   Yang B., 2015, P INT C LEARN REPR I
   Ye R, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4135
   Zhu H, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4258
   Zhu QN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1943
NR 47
TC 0
Z9 0
U1 6
U2 6
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD MAR 15
PY 2022
VL 240
AR 107813
DI 10.1016/j.knosys.2021.107813
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V1WM
UT WOS:000788135700009
DA 2022-08-23
ER

PT J
AU Ahmed, U
   Srivastava, G
   Djenouri, Y
   Lin, JCW
AF Ahmed, Usman
   Srivastava, Gautam
   Djenouri, Youcef
   Lin, Jerry Chun-Wei
TI Knowledge graph based trajectory outlier detection in sustainable smart
   cities
SO SUSTAINABLE CITIES AND SOCIETY
LA English
DT Article
DE Trajectory analysis; Outlier detection; Data mining; Road traffic
   management; Smart city application
AB Graph-based intelligent systems are emerging in the field of transportation systems. Knowledge graphs help to provide semantic and inter-connectivity capabilities to the intelligent transportation system. In this paper, we propose a graph-based method for detecting outliers in the trajectory. Normal and outlier graphs are constructed using directed weighted graphs. Then, comparison with source and target graphs leading to vectors is performed. The similarity measure is used, which measures common nodes and edges. The features are then used by the machine learning based algorithm to classify the trajectory. Instead of a manually tuned parameter, the tree-based pipeline optimization method selects the best classifier and its hyperparameter. Then, the tuned model is compared with the traditional algorithms, i.e., random forest, decision tree, Naive Bayes, and KNN. To evaluate the system under real conditions, an experiment is performed on a real dataset of trajectories. The results show that the graph-based method performs well and achieves an F-value of 0.81, while the optimization model achieves an F-value of 0.87. The graph-based model matched with the learning method helps to detect outliers and deviation points of the trajectory with high precision.
C1 [Ahmed, Usman; Lin, Jerry Chun-Wei] Western Norway Univ Appl Sci, Dept Comp Sci Elect Engn & Math Sci, Bergen, Norway.
   [Srivastava, Gautam] Brandon Univ, Dept Math & Comp Sci, Brandon, MB, Canada.
   [Srivastava, Gautam] China Med Univ, Res Ctr Interneural Comp, Taichung, Taiwan.
   [Djenouri, Youcef] SINTEF Digital, Math & Cybernet, Oslo, Norway.
RP Lin, JCW (通讯作者)，Western Norway Univ Appl Sci, Dept Comp Sci Elect Engn & Math Sci, Bergen, Norway.
EM Usman.Ahmed@hvl.no; srivastavag@brandonu.ca; youcef.djenouri@sintef.no;
   jerrylin@ieee.org
OI Srivastava, Gautam/0000-0001-9851-4103; Lin, Jerry
   Chun-Wei/0000-0001-8768-9709
CR Ahmed U, 2018, P 12 INT WORKSH SEM, P581, DOI DOI 10.18653/V1/S18-1095
   Ahmed U, 2021, SOFT COMPUT, V25, P407, DOI 10.1007/s00500-020-05152-8
   Celeux G, 1996, J STAT COMPUT SIM, V55, P287, DOI 10.1080/00949659608811772
   Chalapathy R, 2019, LECT NOTES ARTIF INT, V11051, P173, DOI 10.1007/978-3-030-10925-7_11
   Chen C, 2013, IEEE T INTELL TRANSP, V14, P806, DOI 10.1109/TITS.2013.2238531
   Das K, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P220
   Das Kaustav, 2008, P 14 ACM SIGKDD INT, P169
   Djenouri Y, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3425867
   Djenouri Y, 2019, IEEE ACCESS, V7, P12192, DOI 10.1109/ACCESS.2019.2893124
   Djenouri Y, 2019, IEEE ACCESS, V7, P10015, DOI 10.1109/ACCESS.2019.2891933
   Eblen JD, 2012, BMC BIOINFORMATICS, V13, DOI 10.1186/1471-2105-13-S10-S5
   GEMAN S, 1984, IEEE T PATTERN ANAL, V6, P721, DOI 10.1109/TPAMI.1984.4767596
   Jie An Liu Guanfeng Liu Zhao Lei Zhu, 2015, LECT NOTES COMPUTER, P16
   Kong XJ, 2018, WORLD WIDE WEB, V21, P825, DOI 10.1007/s11280-017-0487-4
   Laidi Roufaida, 2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), P209, DOI 10.1109/PERCOMW.2018.8480164
   Lee JG, 2008, PROC INT CONF DATA, P140, DOI 10.1109/ICDE.2008.4497422
   Li JL, 2020, IEEE T SYST MAN CY-S, V50, P4295, DOI 10.1109/TSMC.2018.2847625
   Li S, 2018, ACM T KNOWL DISCOV D, V12, DOI 10.1145/3168363
   Li WJ, 2018, IEEE INTERNET THINGS, V5, P716, DOI 10.1109/JIOT.2017.2720635
   Lv ZJ, 2017, LECT NOTES COMPUT SC, V10177, P231, DOI 10.1007/978-3-319-55753-3_15
   Mao Jiali, 2018, P SIAM INT C DAT MIN, P64
   Modani Natwar, 2008, P ACM C INF KNOWL MA, P1377
   Olson RS, 2016, GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, P485, DOI 10.1145/2908812.2908918
   Ren MF, 2022, SUSTAIN CITIES SOC, V76, DOI 10.1016/j.scs.2021.103207
   Senaratne H, 2018, IEEE T INTELL TRANSP, V19, P1537, DOI 10.1109/TITS.2017.2727281
   Soleimani H, 2016, IEEE T KNOWL DATA EN, V28, P2267, DOI 10.1109/TKDE.2016.2561288
   Sun CF, 2019, IEEE ACCESS, V7, P13589, DOI 10.1109/ACCESS.2018.2887119
   Sun YY, 2022, SUSTAIN CITIES SOC, V76, DOI 10.1016/j.scs.2021.103432
   Tang GT, 2015, INTELL DATA ANAL, V19, P1171, DOI 10.3233/IDA-150764
   Toth E, 2018, ACM COMPUT SURV, V51, DOI 10.1145/3203246
   Wang K, 2022, SUSTAIN CITIES SOC, V76, DOI 10.1016/j.scs.2021.103443
   Wu H, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P837, DOI 10.1145/3132847.3132933
   Xiong L., 2011, P 14 INT C ART INT S, P789
   Yu QY, 2018, APPL INTELL, V48, P2661, DOI 10.1007/s10489-017-1104-z
   Yu R, 2015, ACM T KNOWL DISCOV D, V10, DOI 10.1145/2811268
   Yu YW, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P422, DOI 10.1145/2623330.2623735
   Yu YW, 2017, ACM T DATABASE SYST, V42, DOI 10.1145/3013527
   Zhang Jianting, 2012, P ACM SIGKDD INT WOR, P157
   Zhou XB, 2017, IEEE INT CONF BIG DA, P530, DOI 10.1109/BigData.2017.8257968
   Zhu J, 2017, WORLD WIDE WEB, V20, P111, DOI 10.1007/s11280-016-0400-6
NR 40
TC 1
Z9 1
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2210-6707
EI 2210-6715
J9 SUSTAIN CITIES SOC
JI Sust. Cities Soc.
PD MAR
PY 2022
VL 78
AR 103580
DI 10.1016/j.scs.2021.103580
PG 8
WC Construction & Building Technology; Green & Sustainable Science &
   Technology; Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Science & Technology - Other Topics;
   Energy & Fuels
GA 2T3FO
UT WOS:000822361900007
DA 2022-08-23
ER

PT J
AU Tang, MJ
   Li, T
   Wang, W
   Zhu, R
   Ma, ZF
   Tang, YH
AF Tang, Mingjing
   Li, Tong
   Wang, Wei
   Zhu, Rui
   Ma, Zifei
   Tang, Yahui
TI Software Knowledge Entity Relation Extraction with Entity-Aware and
   Syntactic Dependency Structure Information
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB Software knowledge community contains a large scale of software knowledge entities with complex structure and rich semantic relations. Semantic relation extraction of software knowledge entities is a critical task for software knowledge graph construction, which has an important impact on knowledge graph based tasks such as software document generation and software expert recommendation. Due to the problems of entity sparsity, relation ambiguity, and the lack of annotated dataset in user-generated content of software knowledge community, it is difficult to apply existing methods of relation extraction in the software knowledge domain. To address these issues, we propose a novel software knowledge entity relation extraction model which incorporates entity-aware information with syntactic dependency information. Bidirectional Gated Recurrent Unit (Bi-GRU) and Graph Convolutional Networks (GCN) are used to learn the features of contextual semantic representation and syntactic dependency representation, respectively. To obtain more syntactic dependency information, a weight graph convolutional network based on Newton's cooling law is constructed by calculating a weight adjacency matrix. Specifically, an entity-aware attention mechanism is proposed to integrate the entity information and syntactic dependency information to improve the prediction performance of the model. Experiments are conducted on a dataset which is constructed based on texts of the StackOverflow and show that the proposed model has better performance than the benchmark models.
C1 [Tang, Mingjing; Wang, Wei; Zhu, Rui] Yunnan Univ, Sch Software, Kunming, Yunnan, Peoples R China.
   [Li, Tong] Yunnan Agr Univ, Sch Big Data, Kunming, Yunnan, Peoples R China.
   [Ma, Zifei] Yunnan Agr Univ, Sch Water Conservancy, Kunming, Yunnan, Peoples R China.
   [Tang, Yahui] Yunnan Univ, Sch Informat, Kunming, Yunnan, Peoples R China.
RP Li, T (通讯作者)，Yunnan Agr Univ, Sch Big Data, Kunming, Yunnan, Peoples R China.; Ma, ZF (通讯作者)，Yunnan Agr Univ, Sch Water Conservancy, Kunming, Yunnan, Peoples R China.
EM tmj@ynnu.edu.cn; 3727@ynnu.edu.cn; wangwei@ynu.edu.cn; rzhu@ynu.edu.cn;
   zf_ma@hotmail.com; bytoo_hui@163.com
RI R, R/AAD-2816-2022
OI Tang, MingJing/0000-0003-0435-8854; Li, Tong/0000-0002-3257-213X; Zhu,
   Rui/0000-0002-5445-963X
FU Yunnan Science and Technology Major Project [202002AE090010]; Subproject
   5 of Yunnan Science and Technology Major [202002AD080002-5]
FX This work was supported by the Yunnan Science and Technology Major
   Project (grant no. 202002AE090010), and Subproject 5 of Yunnan Science
   and Technology Major (grant no. 202002AD080002-5).
CR Chen YP, 2021, NEURAL NETWORKS, V141, P249, DOI 10.1016/j.neunet.2021.04.010
   Cho K., 2014, ARXIV14061078, P1724
   Ci Liu, 2020, ICCPR 2020: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition, P484, DOI 10.1145/3436369.3437431
   Deng Bo, 2007, Computer Engineering, V33, P212
   Deng K, 2020, IEEE IJCNN
   [甘丽新 Gan Lixin], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P284
   Hai-Hong E., 2019, Journal of Software, V30, P1793, DOI 10.13328/j.cnki.jos.005817
   Han X, 2020, ARXIV PREPRINT ARXIV, P745
   He ZQ, 2019, ARTIF INTELL, V275, P602, DOI 10.1016/j.artint.2019.07.004
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   LeCun Y, 1989, NEURAL COMPUT, V1, P541, DOI 10.1162/neco.1989.1.4.541
   [李冬梅 Li Dongmei], 2020, [计算机研究与发展, Journal of Computer Research and Development], V57, P1424
   Marko K., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P528
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Nguyen TH, 2015, P 1 WORKSH VECT SPAC, P39, DOI DOI 10.3115/V1/W15-1506
   Peng H, 2020, P 2020 C EMPIRICAL M, P3661
   Prayaga S.S, 2018, P 2018 C EMP METH NA, P1257
   Riedel S, 2010, LECT NOTES ARTIF INT, V6323, P148, DOI 10.1007/978-3-642-15939-8_10
   Song L., 2018, EMNLP, DOI DOI 10.18653/V1/D18-1246
   Vaswani A, 2017, ADV NEUR IN, V30
   [王飞 Wang Fei], 2020, [软件学报, Journal of Software], V31, P47
   Xu Y, 2015, P 2015 C EMP METH NA, P1785, DOI DOI 10.18653/V1/D15-1206
   Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205
   Zeng D., 2014, P COLING, P2335
   Zhang D, 2015, ARXIV150801006
   Zhang Y., 2018, P 2018 C EMP METH NA, DOI [10.18653/v1/D18-1244, DOI 10.18653/V1/D18-1244]
   Zhang Z., 2004, P 13 ACM INT C INF K, P581
   Zhao D, 2021, APPL SOFT COMPUT, V104, DOI 10.1016/j.asoc.2021.107230
   Zhao XJ, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER), P56, DOI 10.1109/SANER.2017.7884609
   Zhou L, 2020, FRONT ARTIF INTEL AP, V325, P2314, DOI 10.3233/FAIA200360
   Zhou P, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2, P207, DOI 10.18653/v1/p16-2034
   Zhu J., 2015, SEKE, P391
NR 32
TC 0
Z9 0
U1 11
U2 11
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD DEC 22
PY 2021
VL 2021
AR 7466114
DI 10.1155/2021/7466114
PG 13
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YB6XO
UT WOS:000739152800005
OA gold
DA 2022-08-23
ER

PT J
AU Long, J
   Wang, Y
   Wei, XX
   Ding, Z
   Qi, QQ
   Xie, F
   Qian, ZM
   Huang, WT
AF Long, Jun
   Wang, Ye
   Wei, Xiangxiang
   Ding, Zhen
   Qi, Qianqian
   Xie, Fang
   Qian, Zheman
   Huang, Wenti
TI Entity-Centric Fully Connected GCN for Relation Classification
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE graph convolutional network; relation classification; natural language
   processing
AB Relation classification is an important task in the field of natural language processing, and it is one of the important steps in constructing a knowledge graph, which can greatly reduce the cost of constructing a knowledge graph. The Graph Convolutional Network (GCN) is an effective model for accurate relation classification, which models the dependency tree of textual instances to extract the semantic features of relation mentions. Previous GCN based methods treat each node equally. However, the contribution of different words to express a certain relation is different, especially the entity mentions in the sentence. In this paper, a novel GCN based relation classifier is propose, which treats the entity nodes as two global nodes in the dependency tree. These two global nodes directly connect with other nodes, which can aggregate information from the whole tree with only one convolutional layer. In this way, the method can not only simplify the complexity of the model, but also generate expressive relation representation. Experimental results on two widely used data sets, SemEval-2010 Task 8 and TACRED, show that our model outperforms all the compared baselines in this paper, which illustrates that the model can effectively utilize the dependencies between nodes and improve the performance of relation classification.
C1 [Long, Jun; Wang, Ye; Wei, Xiangxiang; Ding, Zhen; Qi, Qianqian; Xie, Fang; Qian, Zheman; Huang, Wenti] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.
   [Long, Jun] Cent South Univ, Big Data Inst, Changsha 410083, Peoples R China.
RP Huang, WT (通讯作者)，Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.
EM junlong@csu.edu.cn; hai184612290@csu.edu.cn; xiangxaingwei@csu.edu.cn;
   dingz9608@csu.edu.cn; qi1997@csu.edu.cn; xiefang@csu.edu.cn;
   184612315@csu.edu.cn; huangwenti@csu.edu.cn
FU National Natural Science Foundation of China [U2003208]
FX This work was supported in part by the Joint Funds of the National
   Natural Science Foundation of China under Grant No. U2003208.
CR Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   Babic M, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10093037
   Banko M., 2007, P 20 INT JOINT C ART
   Cai R, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P756
   Chen ZM, 2019, PROC CVPR IEEE, P5172, DOI 10.1109/CVPR.2019.00532
   Guo ZJ, 2019, T ASSOC COMPUT LING, V7, P297, DOI 10.1162/tacl_a_00269
   Guo ZJ, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P241
   Hao YC, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P221, DOI 10.18653/v1/P17-1021
   Hashimoto K., 2013, P 2013 C EMP METH NA, P1372
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Kambhatla Nanda, 2004, P ACL 2004 INT POST, DOI 10.3115/ 1219044.1219066
   Kan M.Y., 2005, P 14 TEXT RETRIEVAL
   Kim M, 2018, KSII T INTERNET INF, V12, P5744, DOI 10.3837/tiis.2018.12.008
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791
   Li GH, 2019, IEEE I CONF COMP VIS, P9266, DOI 10.1109/ICCV.2019.00936
   Li LJ, 2019, IEEE I CONF COMP VIS, P10312, DOI 10.1109/ICCV.2019.01041
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu Y, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (IJCNLP), VOL 2, P285
   Manning CD, 2017, EMNLP, P35
   Miwa M, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1105
   Nguyen TH, 2015, P 1 WORKSH VECT SPAC, P39, DOI DOI 10.3115/V1/W15-1506
   Pennington J., 2014, EMNLP, P1532
   Santoro A., ARXIV 2017 170601427
   Socher R., 2012, P 2012 JOINT C EMP M, P1201, DOI [10.5555/2390948.2391084, DOI 10.1162/153244303322533223]
   Sorokin D., 2017, P 2017 C EMP METH NA, P1784
   Tai KS, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P1556
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Xu Kun, 2015, P 2015 C EMP METH NA, P536
   Xu Y, 2015, P 2015 C EMP METH NA, P1785, DOI DOI 10.18653/V1/D15-1206
   Yih WT, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P643
   Zeng D., 2014, P COLING, P2335
   Zhang Y., 2018, P 2018 C EMP METH NA, DOI [10.18653/v1/D18-1244, DOI 10.18653/V1/D18-1244]
   Zhou P, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2, P207, DOI 10.18653/v1/p16-2034
NR 34
TC 0
Z9 0
U1 3
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD FEB
PY 2021
VL 11
IS 4
AR 1377
DI 10.3390/app11041377
PG 12
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA RB4PR
UT WOS:000632095100001
OA Green Published
DA 2022-08-23
ER

PT J
AU Abel, M
   Silva, LAL
   De Ros, LF
   Mastella, LS
   Campbell, JA
   Novello, T
AF Abel, M
   Silva, LAL
   De Ros, LF
   Mastella, LS
   Campbell, JA
   Novello, T
TI PetroGrapher: managing petrographic data and knowledge using an
   intelligent database application
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article; Proceedings Paper
CT 1st Workshop on Intelligent Computing in the Petroleum Industry
CY NOV 24-25, 2002
CL MEXICO CITY, MEXICO
DE knowledge engineering; intelligent databases; sedimentary rock
   interpretation
AB This paper describes the PetroGrapher system, an intelligent database application to support petrographic analysis, interpretation of oil reservoir rocks, and management of relevant data using resources from both knowledge-based system technology and database technology. In this project, the visual tacit knowledge applied in petrographic analysis was rendered explicit through the collection of cases (rock descriptions), which were then used in the development of a domain ontology organized in a partonomy. Expert-level basic features, which we call 'visual chunks', were identified. The cases were further compared against the ontology to elucidate the relations between features in descriptions of rocks, visual chunks and expert interpretations. The domain knowledge was represented through a set of frames and knowledge graphs. The knowledge graphs are applied to recognize the visual chunks in the user data and retrieved the related interpretation. The system was developed as a structure tightly coupled with a relational database system, which acts as a repository for the knowledge base and the user data, and an object-oriented component, which preserves the semantics of data and develops inferences. The system was validated by three groups of users with different levels of expertise. (C) 2003 Elsevier Ltd. All rights reserved.
C1 UFRGS, Inst Informat, BR-91501970 Porto Alegre, RS, Brazil.
   URFGS, Inst Geociencias, BR-91501970 Porto Alegre, RS, Brazil.
RP Abel, M (通讯作者)，UFRGS, Inst Informat, Avenida Bento Goncalves 9500,Bloco 4,Campus Vale, BR-91501970 Porto Alegre, RS, Brazil.
RI De Lima Silva, Luis Alvaro/ABD-8123-2021; Abel, Mara/L-5392-2015; De
   Ros, Luiz F/Q-5439-2018
OI De Lima Silva, Luis Alvaro/0000-0002-6025-5270; Abel,
   Mara/0000-0002-9589-2616; De Ros, Luiz F/0000-0003-2651-8097
CR ABEL M, 2001, ESTUDO PERICIA PETRO, P239
   ANJOS SMC, 2000, AAPG BULL, V84, P1719
   BENJAMINS R, 2000, KNOWLEDGE ENG ONTOLO
   BERTINO E, 2001, INTELLIGENT DATABASE
   CONWY P, 2002, PETROGRAPHIC DATA CO
   de Groot A.D., 1978, THOUGHT CHOICE CHESS, V4th
   DICKINSO.WR, 1970, J SEDIMENT PETROL, V40, P695, DOI 10.1306/74D72018-2B21-11D7-8648000102C1865D
   FERNANDEZBREIS JT, 2002, INT J HUMAN COMPUTER
   Folk RL., 1974, PETROLOGY SEDIMENTAR
   GAINES B, 1998, P 11 WORKSH KNOWL AC
   GAPP U, 1998, P 11 WORKSH KNOWL AC
   GARCIA ACB, 2000, INT C IND ENG APPL A
   GARCIA AJV, 1998, PALAEOGREOGRAPHICAL, V26, P107
   GOMEZPEREZ A, 1999, INT JOINT C ART INT
   Gruber T, 1992, ONTOLINGUA MECH SUPP
   Guarino N, 1995, INT J HUM-COMPUT ST, V43, P625, DOI 10.1006/ijhc.1995.1066
   JANSEN MG, 1998, P 11 WORKSH KNOWL AC
   LEAO BD, 1988, POS GRADUACAO CARDIO, P230
   Mattos N.M., 1991, APPROACH KNOWLEDGE B
   McBride EF., 1963, SEPM J SEDIMENTARY R, V33, P664, DOI DOI 10.1306/74D70EE8-2B21-11D7-8648000102C1865D
   NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14
   PARSAYE K, 1989, INTELLIGENT DATABASE
   Schreiber G., 1999, KNOWLEDGE ENG MANAGE
   SILVA LAL, 2001, APLICANDO METODOS SO
   SPADAFORA E, 1998, DIAGENETIC EVOLUTION, V26, P241
   Studer R, 1998, DATA KNOWL ENG, V25, P161, DOI 10.1016/S0169-023X(97)00056-6
   VANLEHN K, 1989, FDN COGNITIVE SCI, P526
NR 27
TC 14
Z9 14
U1 0
U2 7
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD JAN
PY 2004
VL 26
IS 1
BP 9
EP 18
DI 10.1016/S0957-4174(03)00104-0
PG 10
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA 759XM
UT WOS:000187780200003
DA 2022-08-23
ER

PT J
AU Asgari-Bidhendi, M
   Janfada, B
   Minaei-Bidgoli, B
AF Asgari-Bidhendi, Majid
   Janfada, Behrooz
   Minaei-Bidgoli, Behrouz
TI FarsBase-KBP: A knowledge base population system for the Persian
   Knowledge Graph
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Knowledge extraction; Knowledge Graph; Canonicalization; Natural
   Language Processing; Persian language
AB While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information. Unlike English knowledge bases such as Wikidata, which have tremendous community support, the population of a knowledge base like FarsBase must rely on automatically extracted knowledge. Knowledge base population can let FarsBase keep growing in size, as the system continues working. In this paper, we present a knowledge base population system for the Persian language, which extracts knowledge from unlabelled raw text, crawled from the Web. The proposed system consists of a set of state-of-the-art modules such as an entity linking module as well as information and relation extraction modules designed for FarsBase. Moreover, a canonicalization system is introduced to link extracted relations to FarsBase properties. Then, the system uses knowledge fusion techniques with minimal intervention of human experts to integrate and filter the proper knowledge instances, extracted by each module. To evaluate the performance of the presented knowledge base population system, we present the first gold dataset for benchmarking knowledge base population in the Persian language, which consisting of 22015 FarsBase triples and verified by human experts. The evaluation results demonstrate the efficiency of the proposed system. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Asgari-Bidhendi, Majid; Janfada, Behrooz; Minaei-Bidgoli, Behrouz] Iran Univ Sci & Technol, Comp Engn Sch, Tehran, Iran.
RP Minaei-Bidgoli, B (通讯作者)，Iran Univ Sci & Technol, Comp Engn Sch, Tehran, Iran.
EM majid_asgari@comp.iust.ac.ir; behrooz.janfada@gmail.com;
   b_minaei@iust.ac.ir
RI Minaei-Bidgoli, Behrouz/L-2779-2018
OI Minaei-Bidgoli, Behrouz/0000-0002-9327-7345
CR Adel H., 2018, THESIS LUDWIG MAXIMI
   AlArfaj A., 2019, INT ROBOTICS AUTOM J, V5, P212
   Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   Asgari-Bidhendi M, 2019, SEMANT WEB, V10, P1169, DOI 10.3233/SW-190369
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Banko M, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2670
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Cetto Matthias, 2018, P 27 INT C COMP LING, P2300
   Chang A.X., 2014, TOKENSREGEX DEFINING, V2
   Claro DB, 2019, INFORMATION, V10, DOI 10.3390/info10070228
   Consoli S, 2015, COMM COM INF SC, V548, P40, DOI 10.1007/978-3-319-25518-7_4
   D.R. Group, 2012, PERSIAN DEPENDENCY T
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Etzioni O., 2011, IJCAI, V11, P3, DOI DOI 10.5591/978-1-57735-516-8/IJCAI11-012
   Fan J., 2010, 1 INT WORKSH FORM ME, P122
   Galarraga L, 2014, P 23 ACM INT C C INF, P1679
   Galarraga L.A., 2013, P 22 INT C WORLD WID, P413, DOI DOI 10.1145/2488388.2488425
   Gangemi A, 2017, SEMANT WEB, V8, P873, DOI 10.3233/SW-160240
   Gashteovski K., 2017, MINIE MINIMIZING FAC, DOI DOI 10.18653/V1/D17-1278
   Getman J, 2018, PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2018), P1552
   Glass Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P256, DOI 10.1007/978-3-319-93417-4_17
   Govindarajan V, 2019, T ASSOC COMPUT LING, V7, P501, DOI 10.1162/tacl_a_00285
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Lin XL, 2019, PROC INT CONF DATA, P950, DOI 10.1109/ICDE.2019.00089
   Liu Z., 2019, ABS190811007 CORR
   Min B., 2013, P 2013 C N AM CHAPT, P777, DOI DOI 10.1186/S40537-016-0043-6
   Minaei-Bidgoli B., 2020, 2020 6 INT C WEB RES
   Minaei-Bidgoli B., 2020, PARSEL 1 0 UNSUPERVI
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Mirza P., 2019, P 2019 C EMP METH NA, P749
   Mitchell T, 2018, COMMUN ACM, V61, P103, DOI 10.1145/3191513
   Momtazi S, 2019, SCI IRAN, V26, P26, DOI 10.24200/sci.2018.20198
   Moradi M., 2015, IRAN J INF PROCESS M, V31, P109
   Moro A., 2014, TACL, V2, P231, DOI DOI 10.1162/TACL_A_00179
   Myaeng S.-H, 2019, P 13 INT C COMP SEM, P142
   Navigli R, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P216
   Ndapandula N., 2011, P 4 ACM INT C WEB SE, P227
   Niu Feng, 2012, VLDS, V12, P25
   Nivre J, 2016, LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P1659
   Peng N., 2017, THESIS J HOPKINS U
   Peters M., 2018, IMPROVING LANGUAGE U
   Roy A., 2019, PROC C EMPIRICALMETH, P728
   Sabeti B, 2018, PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2018), P1174
   Saha S, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2, P317, DOI 10.18653/v1/P17-2050
   Sarhan I, 2016, PROC INT C TOOLS ART, P177, DOI [10.1109/ICTAI.2016.33, 10.1109/ICTAI.2016.0036]
   Schmitz M., 2012, P 2012 JOINT C EMP M, P523
   Seraji M, 2016, LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P2361
   Shamsfard M, 2004, INT J HUM-COMPUT ST, V60, P17, DOI 10.1016/j.ijhcs.2003.08.001
   Shi Y, 2019, LECT NOTES COMPUT SC, V11538, P293, DOI 10.1007/978-3-030-22744-9_23
   Smirnova A, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3241741
   Straka Milan, 2015, INT WORKSH TREEB LIN, P208
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun Y, 2019, IEEE ACCESS, V7, P173054, DOI 10.1109/ACCESS.2019.2955977
   Surdeanu M., 2012, P 2012 JOINT C EMP M, P455
   Taghizadeh N, 2018, PROCEDIA COMPUT SCI, V142, P190, DOI 10.1016/j.procs.2018.10.475
   Trisedya BD, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P229
   Vashishth S, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1317, DOI 10.1145/3178876.3186030
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   West R, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P515, DOI 10.1145/2566486.2568032
   White Aaron Steven, 2016, P 2016 C EMP METH NA, P1713, DOI DOI 10.18653/V1/D16-1177
   Wu F, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P118
   Wu W, 2012, ACM SIGMOD, DOI DOI 10.1145/2213836.2213891
   Yates A., 2004, P 13 INT C WORLD WID, P100, DOI DOI 10.1145/988672.988687
   Zakria G., 2019, INDIAN J SCI TECHNOL, V12, P46, DOI DOI 10.17485/ijst/2019/v12i46/147512
   ZHANG S, 2017, THESIS, P1, DOI DOI 10.1109/TPA-MI.2017.2700460
NR 66
TC 0
Z9 0
U1 0
U2 7
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1570-8268
EI 1873-7749
J9 J WEB SEMANT
JI J. Web Semant.
PD APR
PY 2021
VL 68
AR 100638
DI 10.1016/j.websem.2021.100638
EA APR 2021
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SD5CQ
UT WOS:000651390800002
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Chen, P
   Zhao, JC
   Yu, XS
AF Chen, Peng
   Zhao, Jiancheng
   Yu, Xiaosheng
TI LighterKGCN: A Recommender System Model Based on Bi-layer Graph
   Convolutional Networks
SO JOURNAL OF INTERNET TECHNOLOGY
LA English
DT Article
DE LighterKGCN; Embedding; Recommender system; Collaborative filtering;
   Knowledge graph
AB Recommender systems have been extensively utilized to meet users' personalized needs. Collaborative filtering is one of the most classic algorithms in the recommendation field. However, it has problems such as cold start and data sparsity. In that case, knowledge graphs and graph convolutional networks have been introduced by scholars into recommender systems to solve the above problems. However, the current graph convolutional networks fail to give full play to the advantages of graph convolution since they are employed either in the embedding representations of users and commodity entities, or in the embedding representations between entities of the knowledge graphs. Therefore, LighterKGCN, a recommender system model based on bilayer graph convolutional networks was proposed in accordance with the KGCN model and the LightGCN model. In the first layer of GCN, the model first learned the embedding representations of users and commodity entities on the user-commodity entity interaction graph. Then, the attained user embedding and commodity embedding were used as the data source for the second layer of GCN. In the second layer, the entity v and its neighborhoods were calculated using the hybrid aggregation function proposed in this paper. The result was taken as the new entity v. According to tests on three public datasets and comparison results with the KGCN, LighterKGCN improved by 0.52% and 51.16% in terms of AUC and F1 performances, respectively on the dataset of MovieLens-20M; LighterKGCN improved by 0.67% and 45.0% in terms of AUC and F1 performances, respectively on the dataset of Yelp2018; and the number was 0.67% and 36.35% in AUC and F1 performances, respectively on the dataset of Last.FM.
C1 [Chen, Peng; Zhao, Jiancheng; Yu, Xiaosheng] China Three Gorges Univ, Coll Comp & Informat Technol, Yichang, Peoples R China.
RP Yu, XS (通讯作者)，China Three Gorges Univ, Coll Comp & Informat Technol, Yichang, Peoples R China.
EM chenpeng@ctgu.edu.cn; 994106602@qq.com; yuxiaosheng@ctgu.edu.cn
CR Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   Angel Arul J. J., 2021, INT J PERFORMABILITY, V17, P695, DOI DOI 10.23940/IJPE.21.08.P5.695702
   Cheng ZY, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P639, DOI 10.1145/3178876.3186145
   Dai S. Z., 2018, P 2018 2 INT C ARTIF, P194
   Ebesu T, 2018, ACM/SIGIR PROCEEDINGS 2018, P515, DOI 10.1145/3209978.3209991
   Hamilton WL, 2017, ADV NEUR IN, V30
   He XN, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P639, DOI 10.1145/3397271.3401063
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   IMAN RL, 1980, COMMUN STAT A-THEOR, V9, P571, DOI 10.1080/03610928008827904
   Kipf T., 2016, 5 INT C LEARN REPR, P1
   Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Liang DW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P689, DOI 10.1145/3178876.3186150
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Su C, 2020, INT J PERFORMABILITY, V16, P118
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang X, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P165, DOI 10.1145/3331184.3331267
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wu L, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P235, DOI 10.1145/3331184.3331214
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
NR 23
TC 0
Z9 0
U1 5
U2 5
PU LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV
PI HUALIEN
PA LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV, HUALIEN, 00000, TAIWAN
SN 1607-9264
EI 2079-4029
J9 J INTERNET TECHNOL
JI J. Internet Technol.
PY 2022
VL 23
IS 3
BP 621
EP 629
DI 10.53106/160792642022052303020
PG 9
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 1V2HU
UT WOS:000805918100020
OA Bronze
DA 2022-08-23
ER

PT J
AU Song, YL
   Zhao, FK
AF Song, Yuling
   Zhao, Fangkun
TI Bibliometric analysis of metabolic surgery for type 2 diabetes: current
   status and future prospects
SO UPDATES IN SURGERY
LA English
DT Article
DE Type 2 diabetes; Treatment; Metabolic surgery; Obesity; Bibliometric
   analysis; VOSviewer
ID INTENSIVE MEDICAL THERAPY; BARIATRIC SURGERY; RETINOPATHY; STATEMENT;
   OBESITY; WEIGHT
AB Metabolic surgery has become a powerful treatment for obese type 2 diabetes (T2DM). Experts have been devoting to the research of metabolic surgery in the treatment of T2DM. The debate continues, and there is no comprehensive statistical and intuitive analysis for it. To explore the current research status, the latest hotspots and the changing trend in this field, we conducted a bibliometric analysis. This paper made a bibliometric analysis based on the data source from Science Core Collection Network (WoSCC). Vosviewer v.1.6.10 software is used to construct a knowledge map. From 2011 to 2020, 1424 peer-reviewed papers on metabolic surgery for T2DM were retrieved. The United States contributed the most publications and gained global impact with the most citations. Obesity Surgery was the most prolific journal in this field. Prof. Schauer Philip R., Prof. Buchwald H. and Prof. Sjostrom L. were the most renowned experts in this aspect. The top cited references discussed the status of metabolic surgery for the treatment of T2DM worldwide and the importance of regular evaluation. The extracted keywords mainly formed three clusters: (1) research on the selection of different metabolic surgery methods; (2) possible mechanisms; (3) improvement of T2DM complications by metabolic surgery. Our study makes a comprehensive and objective analysis of metabolic surgery in obese patients with T2DM, providing valuable information for further clinical application and related scientific research. Researchers can quickly locate research hotspots in a large number of relevant literature.
C1 [Zhao, Fangkun] China Med Univ, Dept Ophthalmol, Affiliated Hosp 4, Shenyang 110005, Peoples R China.
   [Song, Yuling] China Med Univ, Dept Endocrinol, Affiliated Hosp 4, 4 Chongshan Rd, Shenyang 110032, Liaoning, Peoples R China.
RP Zhao, FK (通讯作者)，China Med Univ, Dept Ophthalmol, Affiliated Hosp 4, Shenyang 110005, Peoples R China.
EM fkzhao@cmu.edu.cn
FU Scientific Research Funds Projects of Higher Institutions in Liaoning
   Province [LJKZ0773]
FX This study was supported by Scientific Research Funds Projects of Higher
   Institutions in Liaoning Province (Grant No. LJKZ0773).
CR Batterham RL, 2016, DIABETES CARE, V39, P893, DOI 10.2337/dc16-0145
   Buchwald H., 2004, JAMA (Journal of the American Medical Association), V292, P1724, DOI 10.1001/jama.292.14.1724
   Buchwald H, 2019, DIABETES CARE, V42, P331, DOI 10.2337/dc17-2654
   Buchwald H, 2013, OBES SURG, V23, P427, DOI 10.1007/s11695-012-0864-0
   Buchwald H, 2009, AM J MED, V122, P248, DOI 10.1016/j.amjmed.2008.09.041
   Chang YC, 2021, OBES SURG, V31, P117, DOI 10.1007/s11695-020-04859-9
   Chen ML, 2016, MBIO, V7, DOI 10.1128/mBio.02210-15
   Chen Y, 2017, EYE, V31, P1132, DOI 10.1038/eye.2017.119
   Cummings DE, 2018, DIABETOLOGIA, V61, P257, DOI 10.1007/s00125-017-4513-y
   Jackson SE, 2014, OBESITY FACTS, V7, P102, DOI 10.1159/000362196
   Kelly AS, 2013, CIRCULATION, V128, P1689, DOI 10.1161/CIR.0b013e3182a5cfb3
   Kim YJ, 2017, SURG OBES RELAT DIS, V13, P352, DOI 10.1016/j.soard.2016.10.002
   Mechanick JI, 2013, SURG OBES RELAT DIS, V9, P159, DOI 10.1016/j.soard.2012.12.010
   Merlotti C, 2017, OBES REV, V18, P309, DOI 10.1111/obr.12490
   Mingrone G, 2012, NEW ENGL J MED, V366, P1577, DOI 10.1056/NEJMoa1200111
   Narath SH, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0161425
   Rubino F, 2016, DIABETES CARE, V39, P861, DOI 10.2337/dc16-0236
   Ruderman NB, 2013, J CLIN INVEST, V123, P2764, DOI 10.1172/JCI67227
   Schauer PR, 2014, NEW ENGL J MED, V370, P2002, DOI [10.1056/NEJMoa1401329, 10.1056/NEJMc1407393]
   Schauer PR, 2012, NEW ENGL J MED, V366, P1567, DOI 10.1056/NEJMoa1200225
   Sjostrom L, 2004, NEW ENGL J MED, V351, P2683, DOI 10.1056/NEJMoa035622
   Sjostrom L, 2007, NEW ENGL J MED, V357, P741, DOI 10.1056/NEJMoa066254
   Tahrani AA, 2011, LANCET, V378, P182, DOI 10.1016/S0140-6736(11)60207-9
   Tang WHW, 2013, NEW ENGL J MED, V368, P1575, DOI 10.1056/NEJMoa1109400
   Troseid M, 2016, METAB SYNDR RELAT D, V14, P197, DOI 10.1089/met.2015.0120
   Zou X, 2018, ACCIDENT ANAL PREV, V118, P131, DOI 10.1016/j.aap.2018.06.010
NR 26
TC 0
Z9 0
U1 7
U2 10
PU SPRINGER-VERLAG ITALIA SRL
PI MILAN
PA VIA DECEMBRIO, 28, MILAN, 20137, ITALY
SN 2038-131X
EI 2038-3312
J9 UPDATES SURG
JI Updates Surg.
PD APR
PY 2022
VL 74
IS 2
BP 697
EP 707
DI 10.1007/s13304-021-01201-5
EA JAN 2022
PG 11
WC Surgery
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Surgery
GA 0O0NO
UT WOS:000749103200003
PM 35094308
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Zhai, Z
   Shan, M
   Darko, A
   Chan, APC
AF Zhai, Zhao
   Shan, Ming
   Darko, Amos
   Chan, Albert P. C.
TI Corruption in Construction Projects: Bibliometric Analysis of Global
   Research
SO SUSTAINABILITY
LA English
DT Review
DE corruption; construction projects; bibliometric analysis; knowledge map;
   CiteSpace
ID SCIENTOMETRIC ANALYSIS; DEVELOPING-COUNTRIES; PUBLIC PROCUREMENT;
   EMERGING TRENDS; BIM RESEARCH; INFRASTRUCTURE; GOVERNANCE; INDUSTRY;
   MEGAPROJECTS; PERFORMANCE
AB Corruption has been identified as a major problem in construction projects. It can jeopardize the success of these projects. Consequently, corruption has garnered significant attention in the construction industry over the past two decades, and several studies on corruption in construction projects (CICP) have been conducted. Previous efforts to analyze and review this body of knowledge have been manual, qualitative and subjective, thus prone to bias and limited in the number of reviewed studies. There remains a lack of inclusive, quantitative, objective and computational analysis of global CICP research to inform future research, policy and practice. This study aims to address this lack by providing the first inclusive bibliometric study exploring the state-of-the-art of global CICP research. To this end, a quantitative and objective technique aided by CiteSpace was used to systematically and computationally analyze a large corpus of 542 studies retrieved from the Web of Science and published from 2000 to 2020. The findings revealed major and influential CICP research journals, persons, institutions, countries, references and areas of focus, as well as revealing how these interact with each other in research networks. This study contributes to the in-depth understanding of global research on CICP. By highlighting the principal research areas, gaps, emerging trends and directions, as well as patterns in CICP research, the findings could help researchers, practitioners and policy makers position their future CICP research and/or mitigation strategies.
C1 [Zhai, Zhao] Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, 2nd Sect,South Wanjiali Rd, Changsha 410114, Peoples R China.
   [Shan, Ming] Cent South Univ, Sch Civil Engn, 68 South Shaoshan Rd, Changsha 410075, Peoples R China.
   [Darko, Amos; Chan, Albert P. C.] Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Peoples R China.
RP Shan, M (通讯作者)，Cent South Univ, Sch Civil Engn, 68 South Shaoshan Rd, Changsha 410075, Peoples R China.
EM zhao.zhai@csust.edu.cn; ming.shan@csu.edu.cn; amosdarko@polyu.edu.hk;
   albert.chan@polyu.edu.hk
RI Darko, Amos/C-4721-2018; Chan, Albert/I-4650-2012
OI Darko, Amos/0000-0002-7978-6039; Chan, Albert/0000-0002-4853-6440; Shan,
   Ming/0000-0003-0595-6987
FU National Natural Science Foundation of China [71901224]; Natural Science
   Foundation of Hunan Province, China [2020JJ5779]; Education Department
   of the Hunan Province [20C0029]; Changsha Municipal Natural Science
   Foundation [kq2014116]
FX This research was funded by the National Natural Science Foundation of
   China (grant number: 71901224), the Natural Science Foundation of Hunan
   Province, China (grant number: 2020JJ5779), the Education Department of
   the Hunan Province (grant number: 20C0029) and the Changsha Municipal
   Natural Science Foundation (grant number: kq2014116).
CR Ahola T, 2014, INT J PROJ MANAG, V32, P1321, DOI 10.1016/j.ijproman.2013.09.005
   Alutu OE, 2007, J PROF ISS ENG ED PR, V133, P84, DOI 10.1061/(ASCE)1052-3928(2007)133:2(84)
   Ameyaw EE, 2017, J MANAGE ENG, V33, DOI 10.1061/(ASCE)ME.1943-5479.0000555
   [Anonymous], 2014, MEASURING SCHOLARLY
   [Anonymous], 1997, CONSTRUCTION MANAGEM
   Armijo LE, 2017, POLICY STUD-UK, V38, P231, DOI 10.1080/01442872.2017.1290227
   Bandiera O, 2009, AM ECON REV, V99, P1278, DOI 10.1257/aer.99.4.1278
   Bildfell C, 2018, PUBLIC WORKS MANAG P, V23, P34, DOI 10.1177/1087724X17709791
   Bowen Paul, 2015, International Journal of Project Organisation and Management, V7, P72
   Bowen P, 2007, CONSTR MANAG ECON, V25, P631, DOI 10.1080/01446190701225707
   Bowen PA, 2012, CONSTR MANAG ECON, V30, P885, DOI 10.1080/01446193.2012.711909
   Brown J, 2015, ENG CONSTR ARCHIT MA, V22, P372, DOI 10.1108/ECAM-03-2015-0034
   Butler L, 2006, SCIENTOMETRICS, V66, P327, DOI 10.1007/s11192-006-0024-1
   Callegari C, 2018, ENERG POLICY, V114, P211, DOI 10.1016/j.enpol.2017.11.059
   Chan APC, 2017, J CONSTR ENG M, V143, DOI 10.1061/(ASCE)CO.1943-7862.0001353
   Chan APC, 2015, J INFRASTRUCT SYST, V21, DOI 10.1061/(ASCE)IS.1943-555X.0000214
   Chen C., 2014, THE CITESPACE MANUAL
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Chen CM, 2009, J INFORMETR, V3, P191, DOI 10.1016/j.joi.2009.03.004
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chirisa I, 2016, DEV SO AFR, V33, P113, DOI 10.1080/0376835X.2015.1113122
   Choudhry RM, 2013, J MANAGE ENG, V29, P42, DOI 10.1061/(ASCE)ME.1943-5479.0000122
   Collier P, 2016, WORLD BANK ECON REV, V30, P522, DOI 10.1093/wber/lhv037
   Cummins M, 2020, ENERG POLICY, V137, DOI 10.1016/j.enpol.2019.111188
   Darko A, 2020, SECONDARY RES METHOD
   Darko A, 2020, AUTOMAT CONSTR, V112, DOI 10.1016/j.autcon.2020.103081
   de Jong M., 2009, LEADERSHIP MANAGEMEN, V9, P105, DOI DOI 10.1061/(ASCE)1532-6748(2009)9:3(105)
   Dethier JJ, 2011, WORLD BANK RES OBSER, V26, P258, DOI 10.1093/wbro/lkq007
   Estache A, 2011, UTIL POLICY, V19, P104, DOI 10.1016/j.jup.2010.12.003
   Estache A, 2009, UTIL POLICY, V17, P153, DOI 10.1016/j.jup.2008.09.002
   Fisman R, 2007, J DEV ECON, V83, P63, DOI 10.1016/j.jdeveco.2005.09.009
   Flyvbjerg B, 2014, PROJ MANAG J, V45, P6, DOI 10.1002/pmj.21409
   Fredriksson PG, 2003, J PUBLIC ECON, V87, P1407, DOI 10.1016/S0047-2727(02)00016-6
   Guccio C, 2014, EUR J POLIT ECON, V34, P237, DOI 10.1016/j.ejpoleco.2014.01.010
   Gunduz M, 2013, SCI ENG ETHICS, V19, P505, DOI 10.1007/s11948-012-9356-9
   Gwatidzo T, 2014, INT REV FINANC ANAL, V31, P152, DOI 10.1016/j.irfa.2013.11.005
   Hakimi A, 2017, INT J EMERG MARK, V12, P550, DOI 10.1108/IJoEM-06-2015-0118
   Hosseini MR, 2020, SCI ENG ETHICS, V26, P205, DOI 10.1007/s11948-019-00089-0
   Hosseini MR, 2018, AUTOMAT CONSTR, V87, P235, DOI 10.1016/j.autcon.2017.12.002
   Hosseini MR, 2018, J MANAGE ENG, V34, DOI 10.1061/(ASCE)ME.1943-5479.0000587
   Jimenez A, 2017, MANAGE INT REV, V57, P775, DOI 10.1007/s11575-017-0312-4
   Kaufmann D, 2011, HAGUE J RULE LAW, V3, P220, DOI 10.1017/S1876404511200046
   Kaufmann D, 2011, ECON POLIT-OXFORD, V23, P195, DOI 10.1111/j.1468-0343.2010.00377.x
   Kaymak T, 2015, SOC INDIC RES, V124, P785, DOI 10.1007/s11205-014-0814-4
   Kenny C, 2009, TRANSPORT REV, V29, P21, DOI 10.1080/01441640802075760
   Kenny C, 2009, J DEV STUD, V45, P314, DOI 10.1080/00220380802265066
   Kinda T, 2011, DEV ECON, V49, P429, DOI 10.1111/j.1746-1049.2011.00146.x
   Le Y, 2014, J MANAGE ENG, V30, DOI 10.1061/(ASCE)ME.1943-5479.0000300
   Le Y, 2014, J CONSTR ENG M, V140, DOI 10.1061/(ASCE)CO.1943-7862.0000886
   Lin H, 2017, INT J PROJ MANAG, V35, P1415, DOI 10.1016/j.ijproman.2017.04.009
   Locatelli G, 2017, INT J PROJ MANAG, V35, P252, DOI 10.1016/j.ijproman.2016.09.010
   Ma HY, 2020, ENG CONSTR ARCHIT MA, V27, P975, DOI 10.1108/ECAM-07-2019-0363
   Mauro P, 1998, J PUBLIC ECON, V69, P263, DOI 10.1016/S0047-2727(98)00025-5
   MAURO P, 1995, Q J ECON, V110, P681, DOI 10.2307/2946696
   NYE JS, 1967, AM POLIT SCI REV, V61, P417, DOI 10.2307/1953254
   Olken BA, 2007, J POLIT ECON, V115, P200, DOI 10.1086/517935
   Olken BA, 2012, ANNU REV ECON, V4, P479, DOI 10.1146/annurev-economics-080511-110917
   Olken BA, 2009, J POLIT ECON, V117, P417, DOI 10.1086/599707
   Osei-Tutu E, 2010, INT J MANAG PROJ BUS, V3, P236, DOI 10.1108/17538371011036563
   Owusu E.K, 2019, J MANAG ENG, V35, P16
   Owusu EK, 2020, J INFRASTRUCT SYST, V26, DOI 10.1061/(ASCE)IS.1943-555X.0000531
   Owusu EK, 2020, J CIV ENG MANAG, V26, P66, DOI 10.3846/jcem.2020.11659
   Owusu EK, 2020, J CLEAN PROD, V246, DOI 10.1016/j.jclepro.2019.119078
   Owusu EK, 2020, CITIES, V96, DOI 10.1016/j.cities.2019.102435
   Owusu EK, 2019, J INFRASTRUCT SYST, V25, DOI 10.1061/(ASCE)IS.1943-555X.0000484
   Owusu EK, 2019, J CLEAN PROD, V216, P394, DOI 10.1016/j.jclepro.2019.01.124
   Owusu EK, 2019, SCI ENG ETHICS, V25, P1, DOI 10.1007/s11948-017-0002-4
   Owusu EK, 2019, PROJ MANAG J, V50, P40, DOI 10.1177/8756972818808983
   Panayides PM, 2015, TRANSPORT RES A-POL, V71, P110, DOI 10.1016/j.tra.2014.11.006
   Sempiterno M., 2021, J OPEN INNOV-TECHNOL, V7, P84
   Serenko A, 2010, J INFORMETR, V4, P447, DOI 10.1016/j.joi.2010.04.001
   Shan M, 2017, SCI ENG ETHICS, V23, P1643, DOI 10.1007/s11948-016-9865-z
   Shan M, 2015, J PROF ISS ENG ED PR, V141, DOI 10.1061/(ASCE)EI.1943-5541.0000241
   Shan M, 2015, SCI ENG ETHICS, V21, P683, DOI 10.1007/s11948-014-9560-x
   Signor R, 2020, J CONSTR ENG M, V146, DOI 10.1061/(ASCE)CO.1943-7862.0001737
   Sohail M, 2008, J CONSTR ENG M ASCE, V134, P729, DOI 10.1061/(ASCE)0733-9364(2008)134:9(729)
   Song JB, 2019, J MANAGE ENG, V35, DOI 10.1061/(ASCE)ME.1943-5479.0000664
   Tabish SZS, 2012, CONSTR MANAG ECON, V30, P21, DOI 10.1080/01446193.2011.654128
   Tabish SZS, 2011, CONSTR MANAG ECON, V29, P261, DOI 10.1080/01446193.2010.549138
   Wang HM, 2019, INT J PROJ MANAG, V37, P117, DOI 10.1016/j.ijproman.2018.10.008
   Xue J, 2020, ENG CONSTR ARCHIT MA, V27, P1505, DOI 10.1108/ECAM-07-2019-0353
   Yalcinkaya M, 2015, AUTOMAT CONSTR, V59, P68, DOI 10.1016/j.autcon.2015.07.012
   Younsi M, 2019, ECON TRANSIT, V27, P841, DOI 10.1111/ecot.12224
   Yu Y, 2019, SCI ENG ETHICS, V25, P1147, DOI 10.1007/s11948-018-0024-6
   Zhai Z, 2020, TECHNOL ECON DEV ECO, V26, P449, DOI 10.3846/tede.2020.11334
   Zhai Z, 2020, ADV CIV ENG, V2020, DOI 10.1155/2020/6813043
   Zhang B, 2017, J MANAGE ENG, V33, DOI 10.1061/(ASCE)ME.1943-5479.0000479
   Zhao XB, 2017, AUTOMAT CONSTR, V80, P37, DOI 10.1016/j.autcon.2017.04.002
NR 89
TC 3
Z9 3
U1 17
U2 31
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD APR
PY 2021
VL 13
IS 8
AR 4400
DI 10.3390/su13084400
PG 21
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA RU7SX
UT WOS:000645344500001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Li, AC
   Yang, B
AF Li, Anchen
   Yang, Bo
TI GSIRec: Learning with graph side information for recommendation
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Side information recommendation; Multi-task learning; Graph neural
   network; Social network; Knowledge graph
AB Collaborative filtering (CF) is one of the dominant techniques used in modern recommender systems. Traditional CF-based methods suffer from issues of data sparsity and cold start. Therefore, side information has been widely utilized by researchers to address these problems. Most side information is typically heterogeneous and in the form of the graph structure. In this work, we propose a deep end-to-end recommendation framework named GSIRec to make full use of the graph side information. Specifically, GSIRec derives a multi-task learning approach that introduces a side information task to assist the recommendation task. The key idea is that we design a delicate knowledge assistance module to be the bridge between tasks, which captures useful knowledge to complement each task. Also, we utilize a graph attention method to exploit the topological structure of side information to enhance recommendation. To show the wide application and flexibility of our framework, we integrate side information from two aspects: social networks (for users) and knowledge graphs (for items). We apply GSIRec in two recommendation scenarios: social-aware recommendation and knowledge-aware recommendation. To evaluate the effectiveness of our framework, we conduct extensive experiments with four real-world public datasets. The results reveal that GSIRec consistently outperforms the state-of-the-art methods on the rating prediction task and top-K recommendation task. Moreover, GSIRec can alleviate data sparsity and cold start issues to some extent.
C1 [Li, Anchen; Yang, Bo] Jilin Univ, Coll Comp Sci & Technol, Changchun, Peoples R China.
RP Yang, B (通讯作者)，Jilin Univ, Coll Comp Sci & Technol, Changchun, Peoples R China.
EM liac18@mails.jlu.edu.cn; ybo@jlu.edu.cn
FU National Natural Science Foundation of China [61876069]; Jilin Province
   Key Scientific and Technological Research and Development Project
   [20180201067GX, 20180201044GX]; Jilin Province Natural Science
   Foundation [20200201036JC]
FX This work was supported by the National Natural Science Foundation of
   China under Grant No. 61876069; Jilin Province Key Scientific and
   Technological Research and Development Project under Grant Nos.
   20180201067GX and 20180201044GX; and Jilin Province Natural Science
   Foundation under Grant No. 20200201036JC.
CR Anagnostopoulos A, 2008, P 14 ACM SIGKDD INT, P7
   Bond RM, 2012, NATURE, V489, P295, DOI 10.1038/nature11421
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Caruana R, 1997, MACH LEARN, V28, P41, DOI 10.1023/A:1007379606734
   Chaney A. J. B., 2015, P 9 ACM C REC SYST, P43, DOI DOI 10.1145/2792838.2800193
   Chen C., 2020, SIGIR
   Chen C, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P225, DOI 10.1145/3331184.3331192
   Chen C, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P177, DOI 10.1145/3289600.3290982
   Chen H., 2020, IEEE T KNOWL DATA EN
   Fan WQ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P417, DOI 10.1145/3308558.3313488
   Fan WQ, 2018, AAAI CONF ARTIF INTE, P8075
   Guo GB, 2015, AAAI CONF ARTIF INTE, P123
   Guo HF, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1725
   Guo Q, 2020, ARXIV PREPRINT ARXIV
   Haidong Chen, 2016, 2016 IEEE International Workshop on Electromagnetics (iWEM): Applications and Student Innovation Competition, P1, DOI 10.1109/iWEM.2016.7504980
   Hamilton Will, 2017, NEURIPS
   Han J., 2013, IJCAI
   He XN, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P355, DOI 10.1145/3077136.3080777
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   He XN, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P549, DOI 10.1145/2911451.2911489
   Hongxu Chen, 2021, WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, P1056, DOI 10.1145/3437963.3441762
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Hu GN, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P667, DOI 10.1145/3269206.3271684
   Jamali M., 2010, P 4 ACM C REC SYST, P135, DOI DOI 10.1145/1864708.1864736
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Jiang XQ, 2020, PROCEEDINGS OF THE 2020 SIAM INTERNATIONAL CONFERENCE ON DATA MINING (SDM), P91, DOI 10.1137/1.9781611976236.11
   Kingma D. P., 2017, ARXIV
   Kipf T.N., 2017, P 23 ACM SIGKDD INT
   Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944
   Krichene W, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1748, DOI 10.1145/3394486.340226
   Lewis K, 2012, P NATL ACAD SCI USA, V109, P68, DOI 10.1073/pnas.1109739109
   Lin TH, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1767, DOI 10.1145/3269206.3269234
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu BYYLD, 2013, P 23 INT JOINT C ART
   Liu Yang, 2020, IEEE T KNOWL DATA EN, V2020
   Long MS, 2017, ADV NEUR IN, V30
   Ma H, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P73
   Ma H, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P203, DOI 10.1145/1571941.1571978
   Ma Hao, 2011, P 4 ACM INT C WEB SE, P287
   Massa P, 2007, RECSYS 07: PROCEEDINGS OF THE 2007 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P17
   Misra I, 2016, PROC CVPR IEEE, P3994, DOI 10.1109/CVPR.2016.433
   Pan YT, 2020, WORLD WIDE WEB, V23, P2259, DOI 10.1007/s11280-020-00793-z
   Rendle Steffen, 2010, Proceedings 2010 10th IEEE International Conference on Data Mining (ICDM 2010), P995, DOI 10.1109/ICDM.2010.127
   Ruder Sebastian, 2017, ABS170605098 ARXIV
   Salakhutdinov R, 2007, ADV NEURAL INFORM PR, P1257
   Sarwar B, 2001, P 10 INT C WORLD WID, P285
   Shah X, 2017, P SIAM INT C DAT MIN, P381
   Shi C, 2019, WORLD WIDE WEB, V22, P153, DOI 10.1007/s11280-018-0553-6
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Tang JL, 2016, AAAI CONF ARTIF INTE, P251
   Tang XL, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1891, DOI 10.1145/3292500.3330705
   Tang YY, 2020, WORLD WIDE WEB, V23, P1319, DOI 10.1007/s11280-019-00693-x
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Wang HW, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P968, DOI 10.1145/3292500.3330836
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2000, DOI 10.1145/3308558.3313411
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang X, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P99, DOI 10.1145/3366423.3380098
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang Z, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P219, DOI 10.1145/3397271.3401141
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu L, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P235, DOI 10.1145/3331184.3331214
   Wu QT, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3877
   Yang B, 2017, IEEE T PATTERN ANAL, V39, P1633, DOI 10.1109/TPAMI.2016.2605085
   Ying HC, 2019, WORLD WIDE WEB, V22, P2209, DOI 10.1007/s11280-018-0596-8
   Yosinski J, 2014, ADV NEUR IN, V27
   Yu X, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P283, DOI 10.1145/2556195.2556259
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhang HW, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P325, DOI 10.1145/2911451.2911502
   Zhang J., 2019, ELECTRON COMMER R A
   Zhang ZY, 2019, WORLD WIDE WEB, V22, P1135, DOI 10.1007/s11280-018-0579-9
   Zhao H, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P635, DOI 10.1145/3097983.3098063
   Zheng XY, 2018, WORLD WIDE WEB, V21, P985, DOI 10.1007/s11280-017-0494-5
NR 78
TC 0
Z9 0
U1 8
U2 19
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
PD SEP
PY 2021
VL 24
IS 5
SI SI
BP 1411
EP 1437
DI 10.1007/s11280-021-00910-6
EA JUL 2021
PG 27
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WJ2YV
UT WOS:000669768800001
DA 2022-08-23
ER

PT J
AU Liu, ZG
   Li, XY
   Jomaas, G
AF Liu, Zhao-Ge
   Li, Xiang-Yang
   Jomaas, Grunde
TI Identifying Community Fire Hazards from Citizen Communication by
   Applying Transfer Learning and Machine Learning Techniques
SO FIRE TECHNOLOGY
LA English
DT Article
DE Community fire hazards; Hazard identification; Citizen communication;
   Text classification; Machine learning; Transfer learning
ID IDENTIFICATION
AB A cross-region transfer learning method is proposed to identify community (e.g. car parks, public spaces and shopping centers) fire hazards based on text input provided by community members. The key component of the method, which also accounts for data imbalance, is an improved transfer component analysis that is embedded with a local discriminant analysis to transfer non-local rich knowledge to the fire hazard identification of local communities with an insufficient number of samples. In addition, a fire hazard knowledge map is established and applied to supplement the missing key features for fire hazard identification, and ontology modeling is applied to standardize the text features and reduce the effect of semantic ambiguity brought by cross-region knowledge transfer. The proposed method is verified based on the text data of nine fire hazard classes from Lanzhou and Beidaihe in China. Machine learning experiments show that fire hazard identification performance of all nine classes were improved with the overall accuracy, precision, recall, F1 score and AUC increased by 12%, 15%, 16%, 15% and 15%, respectively. Under data imbalance scenarios, the proposed method outperforms the state of the art methods, such as sampling-based methods, FastText and ULMFiT. The results also show that the proposed method can achieve desired performance with only half of the training samples. These findings illustrate that the proposed method can assist regions by improving fire identification results significantly through knowledge transfer. The proposed approach can be followed to build smart systems for community fire risk management with reasonable performance and high efficiency.
C1 [Liu, Zhao-Ge; Li, Xiang-Yang] Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China.
   [Jomaas, Grunde] Univ Edinburgh, Sch Engn, Edinburgh EH9 3FG, Midlothian, Scotland.
RP Liu, ZG (通讯作者)，Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China.
EM zhaogeliu@hit.edu.cn
OI Jomaas, Grunde/0000-0003-4067-1610
FU Major Research Project of Nation Natural Science Foundation of China
   [91746207]; General Program of Nation Natural Science Foundation of
   China [71774043]; Emergency Management Major Research Project of Nation
   Natural Science Foundation of China [91024028]
FX This work is supported by the Major Research Project of Nation Natural
   Science Foundation of China named "Big data Driven Management and
   Decision-making Research'' (No. 91746207), the General Program of Nation
   Natural Science Foundation of China (No. 71774043) and the Emergency
   Management Major Research Project of Nation Natural Science Foundation
   of China (No. 91024028).
CR [Anonymous], 2015, ARXIV181004805V1
   [Anonymous], 2018, P 24 INT C MACH LEAR
   [Anonymous], 2014, XINHUA NEWS AGENCY
   [Anonymous], 2017, FRONT MICROBIOL
   Aziz A, 2019, PROCESS SAF ENVIRON, V123, P87, DOI 10.1016/j.psep.2018.12.008
   Bai XY, 2017, J SCI TECHNOL POLICY, V8, P2, DOI 10.1108/JSTPM-08-2015-0026
   Balubaid MA, 2015, INT J ADV COMPUT SC, V6, P133
   Chen DT, 2001, PROC CVPR IEEE, P621
   Crawley F, 2015, HAZOP: GUIDE TO BEST PRACTICE: GUIDELINES TO BEST PRACTICE FOR THE PROCESS AND CHEMICAL INDUSTRIES, 3RD EDITION, P1
   CTIF World Fire Statistics Center, 2020, WORLD FIRE STAT
   El Hindi K, 2018, ENTROPY-SWITZ, V20, DOI 10.3390/e20110857
   Gehandler J, 2015, FIRE TECHNOL, V51, P585, DOI 10.1007/s10694-014-0406-5
   Grubinger T, 2017, NEURAL PROCESS LETT, V46, P845, DOI 10.1007/s11063-017-9612-8
   Hodges JL, 2019, FIRE TECHNOL, V55, P2115, DOI 10.1007/s10694-019-00846-4
   Home Office, 2019, FIR RESC INC STAT EN
   Horvath I, 2013, FIRE SAFETY J, V57, P35, DOI 10.1016/j.firesaf.2012.10.009
   Howard J, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P328
   Joulin A., 2017, P C EUR CHAPT ASS CO, V2, P427, DOI [DOI 10.18653/V1/E17-2068, 10.18653/v1/e17-2068]
   Kamkarhaghighi M, 2017, EXPERT SYST APPL, V90, P241, DOI 10.1016/j.eswa.2017.08.021
   Koziarski M, 2020, PATTERN RECOGN, V102, DOI 10.1016/j.patcog.2020.107262
   Li M, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112923
   Liang XW, 2020, KNOWL-BASED SYST, V196, DOI 10.1016/j.knosys.2020.105845
   Ma J, 2012, IEEE T SYST MAN CY A, V42, P784, DOI 10.1109/TSMCA.2011.2172205
   Meng JN, 2011, EXPERT SYST APPL, V38, P10562, DOI 10.1016/j.eswa.2011.02.085
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Ministry of Emergency Management of the People's Republic of China, 2019, FIR STAT
   Moinuddin KAM, 2019, FIRE SAFETY J, V105, P204, DOI 10.1016/j.firesaf.2019.03.006
   Pan SJ, 2011, IEEE T NEURAL NETWOR, V22, P199, DOI 10.1109/TNN.2010.2091281
   Pan Sinno Jialin, 2008, AAAI, V8, P677
   Pareek NK, 2006, IMAGE VISION COMPUT, V24, P926, DOI 10.1016/j.imavis.2006.02.021
   Quanz B, 2009, P 18 ACM C INF KNOWL, DOI DOI 10.1145/1645953.1646121
   Soyata T, 2019, SUSTAIN CITIES SOC, V50, DOI 10.1016/j.scs.2019.101566
   Tong HZ, 2018, J COMPLEXITY, V49, P85, DOI 10.1016/j.jco.2018.08.001
   U.S. Fire Administration, 2016, US FIR STAT
   Wang DS, 2019, APPL SOFT COMPUT
   Wang YF, 2006, GCC 2006: FIFTH INTERNATIONAL CONFERENCE ON GRID AND COOPERATIVE COMPUTING WORKSHOPS, PROCEEDINGS, P13
   Xiang EW, 2010, IEEE T KNOWL DATA EN, V22, P770, DOI 10.1109/TKDE.2010.31
   Xiao M, 2012, P 26 AAAI C ART INT
   Xin J, 2013, FIRE SAFETY J, V62, P72, DOI 10.1016/j.firesaf.2013.09.022
   Xin PW, 2017, PROCESS SAF ENVIRON, V105, P143, DOI 10.1016/j.psep.2016.11.003
   Xu G, 2019, FIRE SAFETY J, V105, P277, DOI 10.1016/j.firesaf.2019.03.004
   Xu Z, 2019, IEEE T BIG DATA, V5, P529, DOI 10.1109/TBDATA.2017.2656918
   Yu CY, 2010, FIRE TECHNOL, V46, P651, DOI 10.1007/s10694-009-0110-z
   Yung DT, 2002, FIRE TECHNOL, V38, P231, DOI 10.1023/A:1019830015147
   Zhang DW, 2015, EXPERT SYST APPL, V42, P1857, DOI 10.1016/j.eswa.2014.09.011
NR 45
TC 5
Z9 5
U1 7
U2 22
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0015-2684
EI 1572-8099
J9 FIRE TECHNOL
JI Fire Technol.
PD NOV
PY 2021
VL 57
IS 6
SI SI
BP 2809
EP 2838
DI 10.1007/s10694-020-01035-4
EA SEP 2020
PG 30
WC Engineering, Multidisciplinary; Materials Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Materials Science
GA XA4ZY
UT WOS:000568712800002
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Yu, G
   Tabatabaei, M
   Mezei, J
   Zhong, QH
   Chen, SY
   Li, ZM
   Li, J
   Shu, LQ
   Shu, Q
AF Yu, Gang
   Tabatabaei, Mohammad
   Mezei, Jozsef
   Zhong, Qianhui
   Chen, Siyu
   Li, Zheming
   Li, Jing
   Shu, LiQi
   Shu, Qiang
TI Improving chronic disease management for children with knowledge graphs
   and artificial intelligence
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Chronic disease management; Artificial intelligence; Health care
   application; Big data; Machine learning
ID LOW-INCOME; ASTHMA; TECHNOLOGY; FRAMEWORK; MODEL; CARE
AB Chronic diseases for children pose serious challenges from a health management perspective. When not implemented in a well-designed manner, an inefficient management platform can have a significant negative impact on patients and the utilization of health care resources. Innovations of recent years in information technology, artificial intelligence and machine learning provide possibilities to design and implement knowledge-based systems and platforms that follow-up, monitor and advise child patients with a chronic disease in an automated manner. In this article we propose the Artificial Intelligence Chronic Management System that combines artificial intelligence, knowledge graph, big data and internet of things in a platform to offer an optimized solution from the perspective of treatment and utilization of resources. The system includes patient and hospital clients, data storage and analytic tools for decision support relying on AI-based services. We illustrate the functionality of the system through different situations frequently occurring in pediatric wards. To assess the feasibility of the AI component, we utilize real life health care data from a hospital in China to develop a classification model for patients with asthma. To provide a more qualitative assessment at the same time, we discuss how the Artificial Intelligence Chronic Management System conforms to the requirements set forth by the standard Chronic Care Model.
C1 [Yu, Gang; Li, Zheming; Li, Jing] Zhejiang Univ, Childrens Hosp, Dept IT Ctr, Sch Med, 3333 Binsheng Rd, Hangzhou 310052, Peoples R China.
   [Yu, Gang; Li, Zheming; Li, Jing; Shu, Qiang] Natl Clin Res Ctr Child Hlth, 3333 Binsheng Rd, Hangzhou 310052, Peoples R China.
   [Shu, LiQi] Brown Univ, Dept Neurol, Warren Alpert Med Sch, 593 Eddy St, Providence, RI 02903 USA.
   [Tabatabaei, Mohammad; Mezei, Jozsef; Zhong, Qianhui; Chen, Siyu] Avaintec Oy, Itamerenkatu 1, Helsinki 00180, Finland.
   [Mezei, Jozsef] Abo Akad Univ, Tuomiokirkontori 3, Turku 20500, Finland.
   [Yu, Gang; Mezei, Jozsef; Zhong, Qianhui; Li, Zheming; Li, Jing] Sino Finland Joint AI Lab, 3333 Binsheng Rd, Hangzhou 310052, Peoples R China.
RP Mezei, J (通讯作者)，Tuomiokirkontori 3, Turku 20500, Finland.; Shu, Q (通讯作者)，3333 Binsheng Rd, Hangzhou 310052, Zhejiang, Peoples R China.
EM yugbme@zju.edu.cn; mohammad.tabatabaei@avaintec.com; jmezei@abo.fi;
   qianhui.zhong@avaintec.com; siyu.chen@avaintec.com; 6513103@zju.edu.cn;
   lijing6@zju.edu.cn; liqi_shu@brown.edu; shuqiang@zju.edu.cn
OI Li, Jing/0000-0002-3626-5815
FU National Key R&D Program of China [2019YFE0126200]; National Natural
   Science Foundation of China [62076218]; Zhejiang Province Public Welfare
   Technology Application Research Project [LGF18H260004]
FX The authors are grateful to the editor and referees for their valuable
   comments and suggestions for improving the paper. This paper was
   partially supported by the National Key R&D Program of China
   (2019YFE0126200), the National Natural Science Foundation of China
   (62076218), and the Zhejiang Province Public Welfare Technology
   Application Research Project (LGF18H260004).
CR Abuel-Reesh J., 2017, INT J ENG INFORM SYS, V1, P102
   Barr N, 2017, J HEALTH SERV RES PO, V22, P250, DOI 10.1177/1355819617714292
   Bennett CC, 2013, ARTIF INTELL MED, V57, P9, DOI 10.1016/j.artmed.2012.12.003
   Emanet N., 2014, DECISION ANAL, V1, P6
   Faust O, 2018, COMPUT METH PROG BIO, V161, P1, DOI 10.1016/j.cmpb.2018.04.005
   Friedman Jerome, 2001, SPRINGER SERIES STAT, V1
   Goto T, 2018, AM J EMERG MED, V36, P1650, DOI 10.1016/j.ajem.2018.06.062
   Guo J, 2018, HEALTH EQUITY, V2, P174, DOI 10.1089/heq.2018.0037
   Hoang KH, 2019, KNOWL-BASED SYST, V181, DOI 10.1016/j.knosys.2019.05.031
   Holsapple C, 2014, DECIS SUPPORT SYST, V64, P130, DOI 10.1016/j.dss.2014.05.013
   Huang J, 2005, IEEE T KNOWL DATA EN, V17, P299, DOI 10.1109/TKDE.2005.50
   Huang MJ, 2007, EXPERT SYST APPL, V32, P856, DOI 10.1016/j.eswa.2006.01.038
   Jalaliniya S., 2012, P 7 NORD HUM COMP IN, P791
   Jiang JC, 2017, KNOWL-BASED SYST, V138, P58, DOI 10.1016/j.knosys.2017.09.030
   Kaplan A, 2021, J ALLER CL IMM-PRACT, V9, P2255, DOI 10.1016/j.jaip.2021.02.014
   Kocsis O, 2017, E-HEALTH BIOENG CONF, P571, DOI 10.1109/EHB.2017.7995488
   Kong Ling-Zhi, 2017, Chronic Dis Transl Med, V3, P135, DOI 10.1016/j.cdtm.2017.06.004
   Lei ZF, 2020, FUTURE GENER COMP SY, V102, P534, DOI 10.1016/j.future.2019.08.030
   Liang HY, 2019, NAT MED, V25, P433, DOI 10.1038/s41591-018-0335-9
   Liu HY, 2016, INT CONF SYST INFORM, P482, DOI 10.1109/ICSAI.2016.7811003
   [刘云娥 LIU Yun-e], 2009, [中华护理杂志, Chinese Journal of Nursing], V44, P645
   Malas TB, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-42806-6
   Mikolov T., 2013, ARXIV, DOI 10.48550/arXiv.1301.3781
   Milani RV, 2015, AM J MED, V128, P337, DOI 10.1016/j.amjmed.2014.10.047
   Miranda JJ, 2008, TROP MED INT HEALTH, V13, P1225, DOI 10.1111/j.1365-3156.2008.02116.x
   Moraitou M, 2017, ADV EXP MED BIOL, V989, P255, DOI 10.1007/978-3-319-57348-9_22
   Murdoch TB, 2013, JAMA-J AM MED ASSOC, V309, P1351, DOI 10.1001/jama.2013.393
   Ng CY, 2018, JAMA PEDIATR, V172, P1177, DOI 10.1001/jamapediatrics.2018.3039
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Patra R., 2019, INTRO KNOWLEDGE GRAP
   Perez-Ardanaz B, 2019, J NURS SCHOLARSHIP, V51, P518, DOI 10.1111/jnu.12499
   Prasadl B., 2011, INT J SOFT COMPUT, V2, P26, DOI [10.5121/ijsc.2011.2103, DOI 10.5121/IJSC.2011.2103]
   Pujara J, 2013, LECT NOTES COMPUT SC, V8218, P542, DOI 10.1007/978-3-642-41335-3_34
   Do Q, 2017, PROCEDIA COMPUT SCI, V113, P344, DOI 10.1016/j.procs.2017.08.343
   Reynolds R, 2018, BMC FAM PRACT, V19, DOI 10.1186/s12875-017-0692-3
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Samb B, 2010, LANCET, V376, P1785, DOI 10.1016/S0140-6736(10)61353-0
   Sendra S, 2018, INFORM FUSION, V40, P76, DOI 10.1016/j.inffus.2017.06.002
   Seol HY, 2020, BMJ OPEN RESPIR RES, V7, DOI 10.1136/bmjresp-2019-000524
   Sharma S., 2019, ARXIV
   Shu LQ, 2019, WORLD J PEDIATR, V15, P105, DOI 10.1007/s12519-019-00255-1
   Singhal A., 2012, INTRO KNOWLEDGE GRAP
   Spathis D, 2019, HEALTH INFORM J, V25, P811, DOI 10.1177/1460458217723169
   State Health and Family Planning Commission of the People's Republic of China, 2015, REP NUTR CHRON DIS C
   State Health and Family Planning Commission of the People's Republic of China, 2018, CHIN FAM HLTH BIG DA
   Tay Y, 2017, AAAI CONF ARTIF INTE, P1243
   Topalovic M., 2018, D28 RESP DIS DIAGNOS, pA6390
   Topalovic M, 2019, EUR RESPIR J, V53, DOI 10.1183/13993003.01660-2018
   TOREN K, 1993, CHEST, V104, P600, DOI 10.1378/chest.104.2.600
   Trivedi R, 2017, PR MACH LEARN RES, V70
   Van Cleave J, 2010, JAMA-J AM MED ASSOC, V303, P623, DOI 10.1001/jama.2010.104
   Wagner EH, 2001, HEALTH AFFAIR, V20, P64, DOI 10.1377/hlthaff.20.6.64
   Wahl B, 2018, BMJ GLOB HEALTH, V3, DOI 10.1136/bmjgh-2018-000798
   Weeks G, 2016, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD011227.pub2
   WHO, 2002, INN CAR CHRON COND B
   Wolff P, 2019, BIOMED RES INT, V2019, DOI 10.1155/2019/8532892
   World Health Organization, 2005, CHRON DIS FACT SHEET
   World Health Organization (2005b, PREVENTING CHRONIC D
   Wu HC, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361686
   Zhang JY, 2017, KNOWL-BASED SYST, V126, P56, DOI 10.1016/j.knosys.2017.03.029
   Zwar N., 2017, SYSTEMATIC REV CHRON
NR 61
TC 1
Z9 1
U1 0
U2 0
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD SEP 1
PY 2022
VL 201
AR 117026
DI 10.1016/j.eswa.2022.117026
PG 12
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA 3E6QO
UT WOS:000830107400006
OA hybrid
DA 2022-08-23
ER

PT J
AU Sheu, HS
   Chu, ZX
   Qi, DQ
   Li, S
AF Sheu, Heng-Shiou
   Chu, Zhixuan
   Qi, Daiqing
   Li, Sheng
TI Knowledge-Guided Article Embedding Refinement for Session-Based News
   Recommendation
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article; Early Access
DE Feature extraction; Recurrent neural networks; Recommender systems;
   Graph neural networks; Learning systems; Task analysis; Semantics; Graph
   embedding; knowledge graph; news recommendation; session-based
   recommendation
AB Personalized news recommendation aims to recommend news articles to customers, by exploiting the personal preferences and short-term reading interest of users. A practical challenge in personalized news recommendations is the lack of logged user interactions. Recently, the session-based news recommendation has attracted increasing attention, which tries to recommend the next news article given previous articles in an active session. Current session-based news recommendation methods mainly extract latent embeddings from news articles and user-item interactions. However, many existing methods could not exploit the semantic-level structural information among news articles. And the feature learning process simply relies on the news articles in training data, which may not be sufficient to learn semantically rich embeddings. This brief presents a context-aware graph embedding (CAGE) approach for session-based news recommendation. It employs external knowledge graphs to improve the semantic-level representations of news articles. Moreover, graph neural networks are incorporated to further enhance the article embeddings. In addition, we consider the similarity among sessions and design attention neural networks to model the short-term user preferences. Extensive results on multiple news recommendation benchmark datasets show that CAGE performs better than some competitive baselines in most cases.
C1 [Sheu, Heng-Shiou; Chu, Zhixuan; Qi, Daiqing] Univ Georgia, Dept Comp Sci, Athens, GA 30602 USA.
   [Li, Sheng] Univ Georgia, Inst Artificial Intelligence, Dept Comp Sci, Athens, GA 30602 USA.
RP Li, S (通讯作者)，Univ Georgia, Inst Artificial Intelligence, Dept Comp Sci, Athens, GA 30602 USA.
EM hs06573@uga.edu; zhixuan.chu@uga.edu; daiqing.qi@uga.edu;
   sheng.li@uga.edu
OI Li, Sheng/0000-0003-1205-8632
FU U.S. Army Research Office Award [W911NF-21-1-0109]
FX This work was supported in part by the U.S. Army Research Office Award
   under Grant Number W911NF-21-1-0109.
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   CHU Q, 2019, P CHIN NAT C CHIN CO, P221, DOI DOI 10.1007/978-3-030-32381-3_18
   Chu ZX, 2021, KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P176, DOI 10.1145/3447548.3467302
   Das A, 2007, P 16 INT C WORLD WID, P271
   Epure EV, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P121, DOI 10.1145/3109859.3109894
   Gulla JA, 2017, 2017 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2017), P1042, DOI 10.1145/3106426.3109436
   Han JY, 2020, IEEE T NEUR NET LEAR, V31, P737, DOI 10.1109/TNNLS.2019.2909432
   Hidasi B, 2015, ARXIV151106939
   Hidasi B, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P843, DOI 10.1145/3269206.3271761
   Huang J, 2018, ACM/SIGIR PROCEEDINGS 2018, P505, DOI 10.1145/3209978.3210017
   Jannach D, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P306, DOI 10.1145/3109859.3109872
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Jiang XD, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2656
   Jiang Xiaodong, 2020, IEEE Trans Pattern Anal Mach Intell, VPP, DOI 10.1109/TPAMI.2020.3029762
   Jugovac M, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P269, DOI 10.1145/3240323.3240384
   Kamehkhosh Iman, 2017, RECSYS, P50
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Li S., 2015, P 24 ACM INT C INFOR, P811, DOI DOI 10.1145/2806416.2806527
   Ludewig M, 2018, USER MODEL USER-ADAP, V28, P331, DOI 10.1007/s11257-018-9209-6
   MOREIRA GDP, 2019, ARXIV190410367
   Moreira GDP, 2018, PROCEEDINGS OF THE 3RD WORKSHOP ON DEEP LEARNING FOR RECOMMENDER SYSTEMS (DLRS), P15, DOI 10.1145/3270323.3270328
   Rezayi Saed, 2021, NAACL
   Sarwar B, 2001, P 10 INT C WORLD WID, P285
   Sheu HS, 2020, RECSYS 2020: 14TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, P657, DOI 10.1145/3383313.3418477
   Sottocornola G, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1395, DOI 10.1145/3184558.3191582
   Tan Y.K., 2016, DLRS, P17, DOI [10.1145/2988450.2988452, DOI 10.1145/2988450.2988452]
   Tang J, ARXIV190210197, V2019
   Vargas S., 2011, P 5 ACM C REC SYST, P109, DOI DOI 10.1145/2043932.2043955
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu FZ, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3597
   Wu S, 2019, AAAI CONF ARTIF INTE, P346
   Yin ZC, 2021, VIROL SIN, DOI 10.1007/s12250-021-00444-1
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhao H, 2020, P 29 INT JOINT C ART, P4997
NR 38
TC 2
Z9 2
U1 7
U2 15
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
DI 10.1109/TNNLS.2021.3084958
EA JUN 2021
PG 7
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA XS9QM
UT WOS:000733234100001
PM 34106870
DA 2022-08-23
ER

PT J
AU Han, X
   Zhang, CH
   Ji, Y
   Hu, Z
AF Han, Xiao
   Zhang, Chunhong
   Ji, Yang
   Hu, Zheng
TI A Dilated Recurrent Neural Network-Based Model for Graph Embedding
SO IEEE ACCESS
LA English
DT Article
DE Graph embedding; dilated recurrent neural network; knowledge graph
AB Graph embedding aims to preserve graphs into low-dimensional embedding space while preserving their properties. As thus, the embeddings can be easily exploited by downstream graph-based tasks. Most existing models of graph embedding strive to preserve some proximity properties of graphs such as the first/second-order proximity or higher-order proximity within a random walk sequence. However, the proximity relationship between different sequences has been rarely discussed, which is a significant property of graphs. For this reason, we propose a graph dilated recurrent neural network (G-DRNN) model to learn the inter-sequence proximity. Considering the inter-sequence proximity exists within subgraphs, we can also define it as subgraph-aware higher-order proximity. Especially, we note that a graph sequence consists of alternating nodes and edges and can be decomposed into a node subsequence and an edge sequence. To explore the special structure of a graph sequence, the G-DRNN exploits a skip connection network architecture to preserve the overall information of the graph sequence while maintaining the relative independence of each subsequence. To evaluate the effectiveness of the G-DRNN in multi-relational graphs, we empirically apply it to the popular research interest of knowledge graph embedding. The results demonstrate that the embeddings learned by the G-DRNN outperform previous approaches on both tasks of link prediction and node classification.
C1 [Han, Xiao; Zhang, Chunhong; Ji, Yang] Beijing Univ Posts & Telecommun, Key Lab Universal Wireless Commun, Minist Educ, Beijing, Peoples R China.
   [Hu, Zheng] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China.
RP Han, X (通讯作者)，Beijing Univ Posts & Telecommun, Key Lab Universal Wireless Commun, Minist Educ, Beijing, Peoples R China.
EM hanxiao1007@bupt.edu.cn
FU National Natural Science Foundation of China [61602048, 61601046,
   61520106007]; BUPT-SICE Excellent Graduate Students Innovation Fund
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61602048, Grant 61601046, and Grant
   61520106007, and in part by the BUPT-SICE Excellent Graduate Students
   Innovation Fund, in 2016.
CR Ahmed A., 2013, P 22 INT C WORLD WID, P37, DOI 10.1145/2488388.2488393
   Bahdanau D., 2014, 3 INT C LEARNING REP
   Belkin M, 2002, ADV NEUR IN, V14, P585
   Bordes A, 2011, AAAI, V6, P6
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Collobert R., 2008, P 25 ICML, P160
   Das R., 2016, CHAINS REASONING ENT
   Fan M, 2017, PATTERN RECOGN LETT, V93, P31, DOI 10.1016/j.patrec.2016.09.005
   Garcia-Duran A, 2015, THESIS
   Goyal P., 2017, GRAPH EMBEDDING TECH
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Guo Xiaoxiao, 2017, ADV NEURAL INF PROCE, P76
   HAN X, 2018, P PAC AS C KNOWL DIS, V938, P247, DOI DOI 10.1007/978-3-319-93037-4_20
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kingma D. P., 2017, ARXIV
   Lin Y, 2015, MODELING RELATION PA
   Lin YC, 2015, ADV SOC SCI EDUC HUM, V39, P2181
   Neelakantan A., 2015, P AAAI SPRING S SER
   Neelakantan A, 2015, INFERRING MISSING EN
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Perozzi B., 2014, P 20 ACM SIGKDD INT, P701, DOI DOI 10.1145/2623330.2623732
   RISTOSKI P, 2016, P INT SEM WEB C, V9981, P498, DOI DOI 10.1007/978-3-319-46523-4_30
   Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323
   Schlichtkrull M., 2017, MODELING RELATIONAL
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Wang DX, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1225, DOI 10.1145/2939672.2939753
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xie R, 2016, IMAGE EMBODIED KNOWL
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Zhang CH, 2017, TSINGHUA SCI TECHNOL, V22, P185
NR 32
TC 1
Z9 1
U1 1
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 32085
EP 32092
DI 10.1109/ACCESS.2019.2901804
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HR7UA
UT WOS:000463359300001
OA gold
DA 2022-08-23
ER

PT J
AU Zhu, R
   Janowicz, K
   Cai, L
   Mai, GC
AF Zhu, Rui
   Janowicz, Krzysztof
   Cai, Ling
   Mai, Gengchen
TI Reasoning over higher-order qualitative spatial relations via spatially
   explicit neural networks
SO INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
LA English
DT Article; Early Access
DE Geospatial knowledge graphs; higher-order spatial interactions;
   qualitative spatial representation and reasoning; spatially explicit
   methods; GeoAI
ID TERNARY PROJECTIVE RELATIONS; REPRESENTATION; ONTOLOGY
AB Qualitative spatial reasoning has been a core research topic in GIScience and AI for decades. It has been adopted in a wide range of applications such as wayfinding, question answering, and robotics. Most developed spatial inference engines use symbolic representation and reasoning, which focuses on small and densely connected data sets, and struggles to deal with noise and vagueness. However, with more sensors becoming available, reasoning over spatial relations on large-scale and noisy geospatial data sets requires more robust alternatives. This paper, therefore, proposes a subsymbolic approach using neural networks to facilitate qualitative spatial reasoning. More specifically, we focus on higher-order spatial relations as those have been largely ignored due to the binary nature of the underlying representations, e.g. knowledge graphs. We specifically explore the use of neural networks to reason over ternary projective relations such as between. We consider multiple types of spatial constraint, including higher-order relatedness and the conceptual neighborhood of ternary projective relations to make the proposed model spatially explicit. We introduce evaluating results demonstrating that the proposed spatially explicit method substantially outperforms the existing baseline by about 20%.
C1 [Zhu, Rui; Janowicz, Krzysztof; Cai, Ling] Univ Calif Santa Barbara, Dept Geog, STKO Lab, Santa Barbara, CA 93106 USA.
   [Zhu, Rui; Janowicz, Krzysztof; Cai, Ling] Univ Calif Santa Barbara, Ctr Spatial Studies, Santa Barbara, CA 93106 USA.
   [Janowicz, Krzysztof] Univ Vienna, Dept Geog & Reg Res, Vienna, Austria.
   [Mai, Gengchen] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA.
RP Zhu, R (通讯作者)，Univ Calif Santa Barbara, Dept Geog, STKO Lab, Santa Barbara, CA 93106 USA.
EM ruizhu@geog.ucsb.edu
FU National Science Foundation's Convergence Accelerator Program [1936677,
   2033521]
FX This work is funded by the National Science Foundation's Convergence
   Accelerator Program under [Grant No. 1936677 and No. 2033521].
CR Adams BJ, 2011, GIS 11 P 19 ACM SIGS, P309
   Bahdanau D., 2014, ARXIV
   Beall J, 2012, J PHILOS LOGIC, V41, P595, DOI 10.1007/s10992-011-9191-5
   Bennett B, 2008, FRONT ARTIF INTEL AP, V183, P280, DOI 10.3233/978-1-58603-923-3-280
   Bergstra J, 2012, J MACH LEARN RES, V13, P281
   Billen R, 2005, DEVELOPMENTS IN SPATIAL DATA HANDLING, P381, DOI 10.1007/3-540-26772-7_29
   Billen R, 2004, LECT NOTES COMPUT SC, V2992, P310
   Bloch I, 2006, IEEE T SYST MAN CY B, V36, P312, DOI 10.1109/TSMCB.2005.857095
   Bobadilla J, 2013, KNOWL-BASED SYST, V46, P109, DOI 10.1016/j.knosys.2013.03.012
   Calegari R, 2020, INTELL ARTIF, V14, P7, DOI 10.3233/IA-190036
   Chen H, 2018, ISPRS INT J GEO-INF, V7, DOI 10.3390/ijgi7060221
   Chen H, 2019, PROC CVPR IEEE, P12530, DOI 10.1109/CVPR.2019.01282
   Claramunt C, 2020, J SPAT INT SCI, P35, DOI 10.5311/JOSIS.2020.20.666
   Clementini E, 1997, ARTIF INTELL, V95, P317, DOI 10.1016/S0004-3702(97)00046-5
   Clementini E, 2006, IEEE T KNOWL DATA EN, V18, P799, DOI 10.1109/TKDE.2006.102
   CLEMENTINI E, 1995, INFORM SCI-APPL, V3, P149, DOI 10.1016/1069-0115(94)00033-X
   Clementini E, 2019, INF TECHNOL CONTROL, V48, P5, DOI 10.5755/j01.itc.48.1.22246
   Clementini E, 2010, IEEE T KNOWL DATA EN, V22, P161, DOI 10.1109/TKDE.2009.79
   Dube M.P., 2014, P 22 ACM SIGSPATIAL, P233
   EGENHOFER MJ, 1991, LECT NOTES COMPUT SC, V525, P144
   EGENHOFER MJ, 1991, INT J GEOGR INF SYST, V5, P161, DOI 10.1080/02693799108927841
   EGENHOFER MJ, 1989, LECT NOTES COMPUT SC, V367, P457
   EGENHOFER MJ, 1995, INT J GEOGR INF SYST, V9, P555, DOI 10.1080/02693799508902056
   Egenhofer MJ, 1995, LECT NOTES COMPUT SC, V988, P1
   Frank A. U., 1992, Journal of Visual Languages and Computing, V3, P343, DOI 10.1016/1045-926X(92)90007-9
   FREKSA C, 1991, DECISION SUPPORT SYSTEMS AND QUALITATIVE REASONING, P181
   FREKSA C, 1991, NATO ADV SCI I D-BEH, V63, P361
   Freksa C., 2016, INT ENCY GEOGRAPHY P, P1
   Freksa C, 2020, J SPAT INT SCI, P43, DOI 10.5311/JOSIS.2020.20.622
   Freksa C, 2018, INT J GEOGR INF SCI, V32, P2514, DOI 10.1080/13658816.2017.1420794
   Guan S., 2020, P 58 ANN M ASS COMPU, P6141, DOI [10. 18653/v1/2020.acl-main.546, DOI 10.18653/V1/2020.ACL-MAIN.546]
   Hernandez D, 1995, LECT NOTES COMPUT SC, V988, P45
   Hitzler P., 2009, FDN SEMANTIC WEB TEC
   Hitzler P, 2018, LECT NOTES ARTIF INT, V10872, P3, DOI 10.1007/978-3-319-91379-7_1
   Huang Z., 2019, P 2019 C EMPIRICAL M, P5866
   Hudson DA, 2019, PROC CVPR IEEE, P6693, DOI 10.1109/CVPR.2019.00686
   Janner M., 2018, T ASSOC COMPUT LING, V6, P49
   Janowicz K, 2020, INT J GEOGR INF SCI, V34, P625, DOI 10.1080/13658816.2019.1684500
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Kingma D. P., P 3 INT C LEARN REPR, P1
   Klippel A., 2013, COGN LINGUIST, P195
   Krishnaswamy N, 2019, AAAI CONF ARTIF INTE, P2911
   Kuhn W, 2014, LECT NOTES COMPUT SC, V8728, P173, DOI 10.1007/978-3-319-11593-1_12
   Landsiedel C, 2017, ADV ROBOTICS, V31, P222, DOI 10.1080/01691864.2016.1277554
   Li L., 2018, ARXIV181005934
   Lynch K., 1960, IMAGE CITY
   Mai G., 2020, P 8 INT C LEARNING R
   Mai GC, 2020, T GIS, V24, P623, DOI 10.1111/tgis.12629
   Mai GC, 2020, LECT NOTES GEOINF CA, P21, DOI 10.1007/978-3-030-14745-7_2
   Mark DM, 1999, INT J GEOGR INF SCI, V13, P747, DOI 10.1080/136588199241003
   Meyes R., 2019, ABLATION STUDIES ART
   MINSKY M, 1991, AI MAG, V12, P34
   Mirzaee R., 2021, P 2021 C N AM CHAPTE
   Mossakowski T, 2012, ARTIF INTELL, V180, P34, DOI 10.1016/j.artint.2011.10.003
   Nair V., 2010, INT C INT C MACH LEA, V32, P807
   Noy N., 2006, DEFINING N ARY RELAT
   Peng B., 2015, NEURAL NETWORK BASED
   Peyre J, 2019, IEEE I CONF COMP VIS, P1981, DOI 10.1109/ICCV.2019.00207
   Qiu PY, 2019, ISPRS INT J GEO-INF, V8, DOI 10.3390/ijgi8060254
   Regalia B, 2019, T GIS, V23, P601, DOI 10.1111/tgis.12548
   Renz J., 2002, QUALITATIVE SPATIAL
   Russell S. J, 2010, ARTIFICIAL INTELLIGE
   Santoro A, 2017, ADV NEUR IN, V30
   Scheider S, 2020, J SPAT INT SCI, P167, DOI 10.5311/JOSIS.2020.20.555
   Vasardani Maria, 2013, Spatial Information Theory. 11th International Conference, COSIT 2013. Proceedings: LNCS 8116, P299, DOI 10.1007/978-3-319-01790-7_17
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Worboys MF, 2001, INT J GEOGR INF SCI, V15, P633, DOI 10.1080/13658810110061162
   Yan B, 2019, T GIS, V23, P620, DOI 10.1111/tgis.12547
   Yang B., 2015, ICLR
   Zhu R, 2019, T GIS, V23, P398, DOI 10.1111/tgis.12550
   Zhu R, 2017, LECT NOTES GEOINF CA, P331, DOI 10.1007/978-3-319-56759-4_19
   Zhu R, 2016, T GIS, V20, P333, DOI 10.1111/tgis.12232
NR 73
TC 0
Z9 0
U1 0
U2 0
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1365-8816
EI 1362-3087
J9 INT J GEOGR INF SCI
JI Int. J. Geogr. Inf. Sci.
DI 10.1080/13658816.2022.2092115
EA JUL 2022
PG 32
WC Computer Science, Information Systems; Geography; Geography, Physical;
   Information Science & Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Geography; Physical Geography; Information Science &
   Library Science
GA 2V2PH
UT WOS:000823692600001
DA 2022-08-23
ER

PT J
AU Geng, ZQ
   Zhang, YH
   Han, YM
AF Geng, Zhiqiang
   Zhang, Yanhui
   Han, Yongming
TI Joint entity and relation extraction model based on rich semantics
SO NEUROCOMPUTING
LA English
DT Article
DE Rich semantics; Knowledge graphs; End-to-end method; Multi-head
   attention mechanism; Pretrained semantic embedding; Convolutional neural
   network
AB Extracting entities and relations from unstructured texts has become an important task in the natural language processing (NLP), especially knowledge graphs (KG). However, relation classification (RC) and named entity recognition (NER) tasks are usually considered separately, which lost a lot of associated contextual information. Therefore, a novel end-to-end method based on the attention mechanism integrating convolutional and recurrent neural networks is proposed for joint entity and relation extraction, which can obtain rich semantics and takes full advantage of the associated information between entities and relations without introducing external complicated features. The convolutional operation is employed to obtain character-level and word-level embeddings which are transferred to the multi-head attention mechanism. Then the multi-head attention mechanism can encode contextual semantics and embeddings to obtain efficient semantic representation. Moreover, the rich semantics are encoded to obtain final tag sequence based on recurrent neural networks. Finally, the experiments are performed on NYT10 and NYT11 benchmarks to demonstrate the proposed method. Compared with the current pipe-lined and joint approaches, the experimental results indicate that the proposed method can obtain state-of-the-art performance in terms of the standard F1-score. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Geng, Zhiqiang; Zhang, Yanhui; Han, Yongming] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China.
   [Geng, Zhiqiang; Zhang, Yanhui; Han, Yongming] Minist Educ China, Engn Res Ctr Intelligent PSE, Beijing 100029, Peoples R China.
RP Han, YM (通讯作者)，Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China.; Han, YM (通讯作者)，Minist Educ China, Engn Res Ctr Intelligent PSE, Beijing 100029, Peoples R China.
EM hanym@mail.buct.edu.cn
OI Han, Yongming/0000-0003-3209-725X
FU National Natural Science Foundation of China [61673046]; Fundamental
   Research Funds for the Central Universities [XK1802-4]; Science and
   Technology Major Project of Guizhou Province (Guizhou Branch) [[2018]
   3002]
FX This research was partly funded by National Natural Science Foundation
   of China (61673046), the Fundamental Research Funds for the Central
   Universities (XK1802-4) and Science and Technology Major Project of
   Guizhou Province (Guizhou Branch [2018] 3002).
CR Abdel-Hamid O, 2014, IEEE-ACM T AUDIO SPE, V22, P1533, DOI 10.1109/TASLP.2014.2339736
   Bansal M., 2016160100770 ARXIV
   Benajiba Y, 2009, IEEE T AUDIO SPEECH, V17, P926, DOI 10.1109/TASL.2009.2019927
   BROWN RW, 1957, J ABNORM SOC PSYCH, V55, P1, DOI 10.1037/h0041199
   Chang CH, 2006, IEEE T KNOWL DATA EN, V18, P1411, DOI 10.1109/TKDE.2006.152
   Chen H, 2018, NEUROCOMPUTING, V294, P12, DOI 10.1016/j.neucom.2017.11.064
   Cho K, 2015, IEEE T MULTIMEDIA, V17, P1875, DOI 10.1109/TMM.2015.2477044
   Choi YJ, P 2006 C EMP METH NA, P431
   FENG Y, 2017, COMPLEXITY, V2017, P1
   Geng ZQ, 2020, ENERGY, V194, DOI 10.1016/j.energy.2019.116851
   Geng ZQ, 2020, INFORM SCIENCES, V509, P183, DOI 10.1016/j.ins.2019.09.006
   Gormley MR, 2015150502419 ARXIV
   Greff K, 2017, IEEE T NEUR NET LEAR, V28, P2222, DOI 10.1109/TNNLS.2016.2582924
   Gupta P., 2016, P COLING 2016 26 INT, P2537
   Han YM, 2020, INFORM SCIENCES, V531, P1, DOI 10.1016/j.ins.2020.04.036
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Hu X, 2021, J CLEAN PROD, V278, DOI 10.1016/j.jclepro.2020.123611
   Jin LF, 2020, AAAI CONF ARTIF INTE, V34, P8034
   Kate R. J., 2010, P 14 C COMP NAT LANG, P203
   Lal A, 2019, ENG APPL ARTIF INTEL, V84, P11, DOI 10.1016/j.engappai.2019.05.007
   Lam W, 2010, P INT C COMP LING BE, P1399, DOI DOI 10.1061/41123(383)134
   Lei M, 2019, NEURAL COMPUT APPL, V31, P9113, DOI 10.1007/s00521-019-04430-3
   Levy O, 2014, NEURIPS, DOI DOI 10.5555/2969033.2969070
   Levy O., 201414023722 ARXIV
   Li Q, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P402, DOI 10.3115/v1/p14-1038
   Li WD, 2020, NEUROCOMPUTING, V382, P174, DOI 10.1016/j.neucom.2019.11.079
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Miwa M, P 2014 C EMP METH NA, P1858
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Raganato A., 2018, P 2018 EMNLP WORKSH, P287
   Ren X, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1015, DOI 10.1145/3038912.3052708
   Santos N, 2015150406580 ARXIV
   Schuster M, 1997, IEEE T SIGNAL PROCES, V45, P2673, DOI 10.1109/78.650093
   Singaravelan S., 2013, 2013 International Conference on Advanced Computing and Communication Systems (ICACCS), P1, DOI 10.1109/ICACCS.2013.6938716
   Song LF, 2019191104123 ARXIV
   Su S, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4389
   Takanobu R, 2019, AAAI CONF ARTIF INTE, P7072
   Tan ZX, 2018, AAAI CONF ARTIF INTE, P4929
   Tang J, 2015150303578 ARXIV
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Wei ZP, 2019190808672 ARXIV
   Yang  Bishan, 2013, P 51 ANN M ASS COMP, P1640
   Yang BS, 2019190403107 ARXIV
   Zeng XR, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P506
   Zhang S., 2015, P 29 PAC AS C LANG I, V1, P73
   Zheng SC, 2017170605075 ARXIV
   Zheng SC, 2017, NEUROCOMPUTING, V257, P59, DOI 10.1016/j.neucom.2016.12.075
   Zhou P, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2, P207, DOI 10.18653/v1/p16-2034
NR 49
TC 20
Z9 20
U1 8
U2 59
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD MAR 14
PY 2021
VL 429
BP 132
EP 140
DI 10.1016/j.neucom.2020.12.037
EA JAN 2021
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QD2QC
UT WOS:000615368600013
DA 2022-08-23
ER

PT J
AU Chen, SY
   Chen, DM
   Tan, ZX
   Chen, MZ
   Han, JW
AF Chen, Siying
   Chen, Demin
   Tan, Zhixiong
   Chen, Maozhi
   Han, Jingwei
TI Knowledge mapping of planetary boundaries based on bibliometrics
   analysis
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Review; Early Access
DE Planetary boundaries; Bibliometric; CiteSpace; Absolute sustainability
ID LIFE-CYCLE ASSESSMENT; EARTH SYSTEM GOVERNANCE; SAFE OPERATING SPACE;
   ENVIRONMENTAL SUSTAINABILITY; ABSOLUTE SUSTAINABILITY; ECO-EFFICIENCY;
   CARRYING-CAPACITY; IMPACT ASSESSMENT; INDICATORS; FOOTPRINTS
AB The planetary boundaries concept has triggered a vast amount of pure and applied scientific research, as well as policy and governance activities globally. Indeed, it has rapidly become a centerpiece of sustainability study. It is crucial to review the scientific state of the planetary boundaries (PB) concept systematically. However, there is a lack of research on drawing a scientific investigation map of planetary boundaries. Therefore, to clarify the spatial and temporal distribution characteristics, research hotspots, and frontiers of planetary boundaries, a scientometric analysis was performed based on 530 academic publications on planetary boundaries from 2009 to 2021. This paper conducted the analysis by visualizing the social network, dual-map overlay, co-cited references, structure variation article, and co-occurrence keywords with CiteSpace. The results show that as a new achievement and paradigm in sustainable development research, the planetary boundaries framework is gradually getting global attention and promotion, which has increasingly become an interdisciplinary hot research topic. The most productive authors and institutions are concentrated in England, the USA, Germany, and Sweden. Relevant articles were mainly published in journals focusing on ecology, earth, marine, veterinary, animal, economics, and politics. In addition, we summarized four predominant research themes by clustering keywords: the calculation of single boundary threshold and present value, the integration with assessment methods such as life cycle assessment and footprint families, the downscaling of planetary boundaries, and the expansion to economic and social domains. For scholars who are interested in this topic, this paper would be a useful reference and guideline.
C1 [Chen, Siying; Chen, Demin; Han, Jingwei] Chongqing Univ, Sch Econ & Business Adm, Chongqing, Peoples R China.
   [Tan, Zhixiong] Chongqing Univ, Sch Publ Policy & Adm, Chongqing, Peoples R China.
   [Chen, Maozhi] Chongqing Univ Educ, Sch Econ & Business Adm, Chongqing, Peoples R China.
RP Tan, ZX (通讯作者)，Chongqing Univ, Sch Publ Policy & Adm, Chongqing, Peoples R China.
EM 201701021024@cqu.edu.cn; chendm@cqu.edu.cn; tzxcqu@126.com;
   calcmz@hotmail.com; 773939382@qq.com
FU Fundamental Research Funds for the Central Universities of Chongqing
   University [2018CDYJSY0055, 2022CDJSKJC26, 2021CDSKXYGG013]; Science and
   Technology Research Project of Chongqing Education Commission
   [KJQN202001602]
FX The authors declare that the fund support was received from the
   Fundamental Research Funds for the Central Universities of Chongqing
   University (2018CDYJSY0055, 2022CDJSKJC26, 2021CDSKXYGG013) and from the
   Science and Technology Research Project of Chongqing Education
   Commission (KJQN202001602).
CR Ahmed Z, 2019, ENVIRON SCI POLLUT R, V26, P26782, DOI 10.1007/s11356-019-05911-7
   Algunaibet IM, 2019, ENERG ENVIRON SCI, V12, P1890, DOI 10.1039/c8ee03423k
   Antonini C, 2017, SUSTAIN DEV, V25, P123, DOI 10.1002/sd.1667
   Anuar A, 2022, ENVIRON SCI POLLUT R, V29, P13729, DOI 10.1007/s11356-021-16470-1
   Arrow KJ, 2012, ENVIRON DEV ECON, V17, P317, DOI 10.1017/S1355770X12000137
   Barbier EB, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9101858
   Barker F, 2017, POTENTIAL USE PLANET
   Baum SD, 2014, ECOL ECON, V107, P13, DOI 10.1016/j.ecolecon.2014.07.024
   Beard SJ, 2021, FUTURES, V127, DOI 10.1016/j.futures.2020.102673
   Biermann F, 2020, ANNU REV ENV RESOUR, V45, P497, DOI 10.1146/annurev-environ-012320-080337
   Biermann F, 2010, INT ENVIRON AGREEM-P, V10, P277, DOI 10.1007/s10784-010-9137-3
   Bjorn A, 2020, INT J LIFE CYCLE ASS, V25, P2241, DOI 10.1007/s11367-020-01823-8
   Bjorn A, 2020, ECOL INDIC, V110, DOI 10.1016/j.ecolind.2019.105865
   Bjorn A, 2019, SCI TOTAL ENVIRON, V696, DOI 10.1016/j.scitotenv.2019.133964
   Bjorn A, 2017, J CLEAN PROD, V163, P106, DOI 10.1016/j.jclepro.2015.12.095
   Bjorn A, 2015, INT J LIFE CYCLE ASS, V20, P1005, DOI 10.1007/s11367-015-0899-2
   Bjorn A, 2015, ENVIRON SCI TECHNOL, V49, P6370, DOI 10.1021/acs.est.5b02106
   Bradford S.C., 1934, ENGINEERING, V137, P85
   Brook BW, 2013, TRENDS ECOL EVOL, V28, P396, DOI 10.1016/j.tree.2013.01.016
   Brown K, 2017, PROG HUM GEOG, V41, P118, DOI 10.1177/0309132515604429
   Brown MT, 2001, POPUL ENVIRON, V22, P471, DOI 10.1023/A:1010756704612
   Bunsen J, 2021, ECOL INDIC, V121, DOI 10.1016/j.ecolind.2020.107022
   Burch S, 2021, SUSTAIN SCI, V16, P1963, DOI 10.1007/s11625-021-01037-3
   Butz C, 2018, SUSTAIN SCI, V13, P1031, DOI 10.1007/s11625-018-0574-1
   Carpenter SR, 2011, ENVIRON RES LETT, V6, DOI 10.1088/1748-9326/6/1/014009
   Carter-Templeton H, 2018, J NURS SCHOLARSHIP, V50, P582, DOI 10.1111/jnu.12399
   Chandrakumar C, 2019, J IND ECOL, V23, P906, DOI 10.1111/jiec.12830
   Chandrakumar C, 2018, ECOL INDIC, V90, P577, DOI 10.1016/j.ecolind.2018.03.063
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen CM, 2014, J ASSOC INF SCI TECH, V65, P334, DOI 10.1002/asi.22968
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Chen W., 2001, INFORM SCIENCES, V8, P884
   Clark N, 2014, SOCIOL REV, V62, P19, DOI 10.1111/1467-954X.12122
   Cohen F, 2019, ANNU REV ENV RESOUR, V44, P425, DOI 10.1146/annurev-environ-101718-033055
   Cole F.J., 1917, SCI PROGRESS-UK, V11, P578
   Cole MJ, 2017, SUSTAIN SCI, V12, P829, DOI 10.1007/s11625-016-0418-9
   Cole MJ, 2014, P NATL ACAD SCI USA, V111, pE4399, DOI 10.1073/pnas.1400985111
   Coote A, 2022, SOC POLICY SOC, V21, P473, DOI 10.1017/S1474746421000385
   Daly HE, 1994, COMMON GOOD, P492
   Dao H, 2018, GLOBAL ENVIRON CHANG, V52, P49, DOI 10.1016/j.gloenvcha.2018.06.005
   de Vries W, 2013, CURR OPIN ENV SUST, V5, P392, DOI 10.1016/j.cosust.2013.07.004
   Dearing JA, 2014, GLOBAL ENVIRON CHANG, V28, P227, DOI 10.1016/j.gloenvcha.2014.06.012
   DEVALL MS, 1990, FOREST ECOL MANAG, V33-4, P303, DOI 10.1016/0378-1127(90)90200-U
   Dillman KJ, 2021, GLOB SUSTAIN, V4, DOI 10.1017/sus.2021.28
   Ding AL, 2020, ACCOUNT FINANC, V60, P3, DOI 10.1111/acfi.12599
   Dong HW, 2022, SCI TOTAL ENVIRON, V807, DOI 10.1016/j.scitotenv.2021.151045
   Donges JF, 2021, EARTH SYST DYNAM, V12, P1115, DOI 10.5194/esd-12-1115-2021
   Donthu N, 2021, J BUS RES, V133, P285, DOI 10.1016/j.jbusres.2021.04.070
   Downing AS, 2019, ENVIRON RES LETT, V14, DOI 10.1088/1748-9326/ab22c9
   Ehrenstein M, 2020, J CLEAN PROD, V270, DOI 10.1016/j.jclepro.2020.121833
   Ellegaard O, 2015, SCIENTOMETRICS, V105, P1809, DOI 10.1007/s11192-015-1645-z
   Engstrom G, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-18342-7
   Ensor J, 2021, EARTH SYST GOV-NETH, V7, DOI 10.1016/j.esg.2020.100086
   Falkenmark M, 2019, J HYDROL X, V2, DOI 10.1016/j.hydroa.2018.100009
   Fan YP, 2017, RESOUR CONSERV RECY, V125, P293, DOI 10.1016/j.resconrec.2017.07.003
   Fang K, 2015, SUSTAINABILITY-BASEL, V7, P11285, DOI 10.3390/su70811285
   Fang K, 2015, ECOL ECON, V114, P218, DOI 10.1016/j.ecolecon.2015.04.008
   Fang K, 2014, ECOL INDIC, V36, P508, DOI 10.1016/j.ecolind.2013.08.017
   Fernandez EF, 2019, REV EUR COMP INT ENV, V28, P48, DOI 10.1111/reel.12256
   Galaz V, 2016, INT ENVIRON AGREEM-P, V16, P189, DOI 10.1007/s10784-014-9253-6
   Galaz V, 2012, ECOL ECON, V81, P1, DOI 10.1016/j.ecolecon.2012.02.023
   Galaz V, 2012, NATURE, V486, P191, DOI 10.1038/486191c
   Galaz V, 2012, CURR OPIN ENV SUST, V4, P80, DOI 10.1016/j.cosust.2012.01.006
   Geissdoerfer M, 2017, J CLEAN PROD, V143, P757, DOI 10.1016/j.jclepro.2016.12.048
   Gerten D, 2020, NAT SUSTAIN, V3, P200, DOI 10.1038/s41893-019-0465-1
   Gerten D, 2013, CURR OPIN ENV SUST, V5, P551, DOI 10.1016/j.cosust.2013.11.001
   Gleeson T, 2020, WATER RESOUR RES, V56, DOI 10.1029/2019WR024957
   Guinee JB., 2022, J IND ECOL, DOI [10.1111/jiec.13260, DOI 10.1111/JIEC.13260]
   Guo YM, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11133606
   Caiado RGG, 2017, J CLEAN PROD, V165, P890, DOI 10.1016/j.jclepro.2017.07.166
   Haffar M, 2018, BUS STRATEG ENVIRON, V27, P1079, DOI 10.1002/bse.2053
   Hajer M, 2015, SUSTAINABILITY-BASEL, V7, P1651, DOI 10.3390/su7021651
   Hayha T, 2016, GLOBAL ENVIRON CHANG, V40, P60, DOI 10.1016/j.gloenvcha.2016.06.008
   Heck V, 2018, GLOBAL ENVIRON CHANG, V49, P73, DOI 10.1016/j.gloenvcha.2018.02.004
   Hickel J, 2020, ECOL ECON, V167, DOI 10.1016/j.ecolecon.2019.05.011
   Hickel J, 2019, THIRD WORLD Q, V40, P18, DOI 10.1080/01436597.2018.1535895
   Hoffren J, 2009, SUSTAIN DEV, V17, P233, DOI 10.1002/sd.387
   Huang LH, 2020, ECOL INDIC, V114, DOI 10.1016/j.ecolind.2020.106339
   Hughes TP, 2013, TRENDS ECOL EVOL, V28, P389, DOI 10.1016/j.tree.2013.05.019
   Irwin EG, 2016, ANNU REV RESOUR ECON, V8, P77, DOI 10.1146/annurev-resource-100815-095351
   Jiang SY, 2020, CHIN J POPUL RESOUR, V18, P165, DOI 10.1016/j.cjpre.2021.04.023
   KIDD CV, 1992, J AGR ENVIRON ETHIC, V5, P1, DOI 10.1007/BF01965413
   Kim RE, 2021, REV EUR COMP INT ENV, V30, P3, DOI 10.1111/reel.12383
   Lade SJ, 2020, NAT SUSTAIN, V3, P119, DOI 10.1038/s41893-019-0454-4
   Leach M, 2018, GLOB SUSTAIN, V1, DOI 10.1017/sus.2018.12
   Leng GY, 2021, SCI TOTAL ENVIRON, V760, DOI 10.1016/j.scitotenv.2020.144035
   Lewis SL, 2012, NATURE, V485, P417, DOI 10.1038/485417a
   Li M, 2021, ENVIRON INT, V152, DOI 10.1016/j.envint.2021.106475
   Li M, 2020, WATER RES, V184, DOI 10.1016/j.watres.2020.116163
   Li Z, 2008, SCIENTOMETRICS, V75, P97, DOI 10.1007/s11192-007-1838-1
   Liu J, 2021, J IND INF INTEGR, V21, DOI 10.1016/j.jii.2020.100191
   Liu YJ, 2022, ENVIRON SCI POLLUT R, DOI 10.1007/s11356-022-20138-9
   Lucas E, 2021, SUSTAIN PROD CONSUMP, V28, P877, DOI 10.1016/j.spc.2021.07.003
   Lucas PL, 2020, GLOBAL ENVIRON CHANG, V60, DOI 10.1016/j.gloenvcha.2019.102017
   Mace GM, 2014, GLOBAL ENVIRON CHANG, V28, P289, DOI 10.1016/j.gloenvcha.2014.07.009
   MacLeo M, 2021, SCIENCE, V373, P61, DOI 10.1126/science.abg5433
   Malthus T, 2013, FUTURE NATURE DOCUME, P15, DOI [10.12987/9780300188479-004, DOI 10.12987/9780300188479-004]
   Manyangu G, 2021, EUR J PREV CARDIOL, V28, P1303, DOI 10.1177/2047487319854827
   Marsilio M, 2011, PUBLIC MANAG REV, V13, P763, DOI 10.1080/14719037.2010.539112
   Meadows D.H., 1972, LIMITS GROWTH
   Meijaard E, 2022, FRONT NUTR, V9, DOI 10.3389/fnut.2022.878644
   Miraux L, 2022, SCI TOTAL ENVIRON, V806, DOI 10.1016/j.scitotenv.2021.150862
   Moldan B, 2012, ECOL INDIC, V17, P4, DOI 10.1016/j.ecolind.2011.04.033
   Molden D., 2009, NAT CLIM CHANGE, V1, P116, DOI [10.1038/climate.2009.97., DOI 10.1038/CLIMATE.2009.97, 10.1038/climate.2009.97]
   Montoya JM, 2018, TRENDS ECOL EVOL, V33, P71, DOI 10.1016/j.tree.2017.10.004
   Mourao PR, 2020, J CLEAN PROD, V256, DOI 10.1016/j.jclepro.2020.120413
   Nash KL, 2017, NAT ECOL EVOL, V1, P1625, DOI 10.1038/s41559-017-0319-z
   Nath RD, 2021, FINANC INNOV, V7, DOI 10.1186/s40854-021-00286-6
   Neumayer E, 2003, WEAK VERSUS STRONG S
   O'Neill DW, 2018, NAT SUSTAIN, V1, P88, DOI 10.1038/s41893-018-0021-4
   Omoregbe O, 2020, ENERGY REP, V6, P1200, DOI 10.1016/j.egyr.2020.05.003
   Parsonsova A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13042164
   Pearce D.W., 1993, ECOL ECON, V8, P103, DOI 10.1016/0921-8009(93)90039-9
   Persson L, 2022, ENVIRON SCI TECHNOL, V56, P1510, DOI 10.1021/acs.est.1c04158
   Pincheira R, 2021, ENVIRON SCI POLLUT R, V28, P65573, DOI 10.1007/s11356-021-14795-5
   Rashidi K, 2015, ENERG ECON, V50, P18, DOI 10.1016/j.eneco.2015.04.018
   Raworth K., 2012, SAFE JUST SPACE HUMA, DOI DOI 10.5822/978-1-61091-458-1
   Raworth K, 2017, LANCET PLANET HEALTH, V1, pE48, DOI 10.1016/S2542-5196(17)30028-1
   Ricardo D., 1891, PRINCIPLES POLITICAL
   Rockstrom J, 2009, NATURE, V461, P472, DOI 10.1038/461472a
   Roos S, 2016, J CLEAN PROD, V133, P691, DOI 10.1016/j.jclepro.2016.05.146
   Rounsevell MDA, 2020, SCIENCE, V368, P1193, DOI 10.1126/science.aba6592
   Rowan R., 2014, PROG HUM GEOG, V38, P447
   Running SW, 2012, SCIENCE, V337, P1458, DOI 10.1126/science.1227620
   Ryberg MW, 2020, J CLEAN PROD, V276, DOI 10.1016/j.jclepro.2020.123287
   Ryberg MW, 2018, SCI TOTAL ENVIRON, V634, P1406, DOI 10.1016/j.scitotenv.2018.04.075
   Ryberg MW, 2016, J CLEAN PROD, V139, P450, DOI 10.1016/j.jclepro.2016.08.074
   Sala S, 2013, INTEGR ENVIRON ASSES, V9, P623, DOI 10.1002/ieam.1471
   Sandin G, 2015, INT J LIFE CYCLE ASS, V20, P1684, DOI 10.1007/s11367-015-0984-6
   Shaikh MA, 2021, ECOL INDIC, V121, DOI 10.1016/j.ecolind.2020.106981
   Shaler NS, 1905, MAN EARTH
   Singhal S, 2002, J ENVIRON MANAGE, V66, P19, DOI 10.1006/jema.2002.0571
   Sinha R, 2022, RESOUR CONSERV RECY, V178, DOI 10.1016/j.resconrec.2021.106031
   Sjafjell B, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12156245
   Springmann M, 2018, NATURE, V562, P519, DOI 10.1038/s41586-018-0594-0
   Steffen W, 2015, SCIENCE, V347, DOI 10.1126/science.1259855
   Steffen W, 2013, CURR OPIN ENV SUST, V5, P403, DOI 10.1016/j.cosust.2013.04.007
   Suarez-Eiroa B, 2022, ECOL ECON, V192, DOI 10.1016/j.ecolecon.2021.107258
   Tuomisto HL, 2012, J CLEAN PROD, V37, P147, DOI 10.1016/j.jclepro.2012.06.025
   Turner RK, 1992, SPECULATIONS WEAK ST
   Villarrubia-Gomez P, 2018, MAR POLICY, V96, P213, DOI 10.1016/j.marpol.2017.11.035
   Wackernagel M, 1998, OUR ECOLOGICAL FOOTP, V9
   Wang JY, 2022, SCI TOTAL ENVIRON, V806, DOI 10.1016/j.scitotenv.2021.151484
   Wheeler J, 2021, AICHE J, V67, DOI 10.1002/aic.17131
   Whiteman G, 2013, J MANAGE STUD, V50, P307, DOI 10.1111/j.1467-6486.2012.01073.x
   Willett W, 2019, LANCET, V393, P447, DOI 10.1016/S0140-6736(18)31788-4
   Wolff A, 2017, J CLEAN PROD, V166, P784, DOI 10.1016/j.jclepro.2017.08.057
   Zambrano-Monserrate MA, 2020, J ENVIRON MANAGE, V272, DOI 10.1016/j.jenvman.2020.111069
   Zhang M, 2018, ENVIRON IMPACT ASSES, V68, P90, DOI 10.1016/j.eiar.2017.11.002
   Zhang Q, 2021, ENVIRON SCI TECHNOL, V55, P15423, DOI 10.1021/acs.est.1c02513
   Zhang S, 2022, ECOL INDIC, V137, DOI 10.1016/j.ecolind.2022.108786
   Zheng MS, 2017, ENVIRON SCI POLLUT R, V24, P20409, DOI 10.1007/s11356-017-9711-0
   Zhu EY, 2021, ENVIRON SCI POLLUT R, V28, P31358, DOI 10.1007/s11356-021-12858-1
   Zupic I, 2015, ORGAN RES METHODS, V18, P429, DOI 10.1177/1094428114562629
NR 154
TC 0
Z9 0
U1 2
U2 2
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
DI 10.1007/s11356-022-22292-6
EA AUG 2022
PG 23
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 3Q1AK
UT WOS:000837965500001
PM 35945326
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Kastrin, A
   Hristovski, D
AF Kastrin, Andrej
   Hristovski, Dimitar
TI Scientometric analysis and knowledge mapping of literature-based
   discovery (1986-2020)
SO SCIENTOMETRICS
LA English
DT Article
DE Literature-based discovery; Scientometrics; Information visualization;
   CiteSpace
ID FISH-OIL; INFORMATION-RETRIEVAL; GENERATING HYPOTHESES; MAGNESIUM;
   PATTERNS; NETWORK; SCIENCE; ASSOCIATION; COCITATION; MIGRAINE
AB Literature-based discovery (LBD) aims to discover valuable latent relationships between disparate sets of literatures. This paper presents the first inclusive scientometric overview of LBD research. We utilize a comprehensive scientometric approach incorporating CiteSpace to systematically analyze the literature on LBD from the last four decades (1986-2020). After manual cleaning, we have retrieved a total of 409 documents from six bibliographic databases and two preprint servers. The 35 years' history of LBD could be partitioned into three phases according to the published papers per year: incubation (1986-2003), developing (2004-2008), and mature phase (2009-2020). The annual production of publications follows Price's law. The co-authorship network exhibits many subnetworks, indicating that LBD research is composed of many small and medium-sized groups with little collaboration among them. Science mapping reveals that mainstream research in LBD has shifted from baseline co-occurrence approaches to semantic-based methods at the beginning of the new millennium. In the last decade, we can observe the leaning of LBD towards modern network science ideas. In an applied sense, the LBD is increasingly used in predicting adverse drug reactions and drug repurposing. Besides theoretical considerations, the researchers have put a lot of effort into the development of Web-based LBD applications. Nowadays, LBD is becoming increasingly interdisciplinary and involves methods from information science, scientometrics, and machine learning. Unfortunately, LBD is mainly limited to the biomedical domain. The cascading citation expansion announces deep learning and explainable artificial intelligence as emerging topics in LBD. The results indicate that LBD is still growing and evolving.
C1 [Kastrin, Andrej; Hristovski, Dimitar] Univ Ljubljana, Fac Med, Inst Biostat & Med Informat, Ljubljana, Slovenia.
RP Kastrin, A (通讯作者)，Univ Ljubljana, Fac Med, Inst Biostat & Med Informat, Ljubljana, Slovenia.
EM andrej.kastrin@mf.uni-lj.si
RI Kastrin, Andrej/AAH-9234-2021
OI Kastrin, Andrej/0000-0002-3495-0165
FU Slovenian Research Agency [Z5-9352, J5-1780]
FX Authors were supported by the Slovenian Research Agency (Grant Nos.
   Z5-9352 (AK) and J5-1780 (DH)).
CR Abramo G, 2011, SCIENTOMETRICS, V86, P347, DOI 10.1007/s11192-010-0297-2
   Ahlers Caroline B, 2007, AMIA Annu Symp Proc, P6
   Ahmed A, 2016, INT J COMPUT SCI NET, V16, P11
   Andronis C, 2011, BRIEF BIOINFORM, V12, P357, DOI 10.1093/bib/bbr005
   Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007
   Arrieta AB, 2020, INFORM FUSION, V58, P82, DOI 10.1016/j.inffus.2019.12.012
   Bekhuis Tanja, 2006, Biomed Digit Libr, V3, P2, DOI 10.1186/1742-5581-3-2
   Berthold M. R., 2012, LECT NOTES ARTIF INT, DOI [10.1007/978-3-642-31830-6, DOI 10.1007/978-3-642-31830-6]
   Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bornmann L, 2015, J ASSOC INF SCI TECH, V66, P2215, DOI 10.1002/asi.23329
   Bradford S.C., 1934, ENGINEERING, V137, P85
   Bruza P, 2008, LIT BASED DISCOVERY, DOI [10.1007/978-3-540-68690-3, DOI 10.1007/978-3-540-68690-3]
   CALLON M, 1983, SOC SCI INFORM, V22, P191, DOI 10.1177/053901883022002003
   Cameron D, 2015, J BIOMED INFORM, V54, P141, DOI 10.1016/j.jbi.2015.01.014
   Cameron D, 2013, J BIOMED INFORM, V46, P238, DOI 10.1016/j.jbi.2012.09.004
   Chen C., 2017, REPRESENTING SCI KNO, DOI [10.1007/978-3-319-62543-0, DOI 10.1007/978-3-319-62543-0]
   Chen C., 2013, STRUCTURE DYNAMICS S, V2, DOI [10.1007/978-1-4471-5128-9, DOI 10.1007/978-1-4471-5128-9, DOI 10.1007/978-1-4471-5128-9_5]
   Chen CM, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0223994
   Chen Chaomei, 2018, Journal of Information Management, V6, P6, DOI 10.1633/JISTaP.2018.6.2.1
   Chen CM, 2014, J ASSOC INF SCI TECH, V65, P334, DOI 10.1002/asi.22968
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen CM, 2009, J INFORMETR, V3, P191, DOI 10.1016/j.joi.2009.03.004
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen H, 2004, BMC BIOINFORMATICS, V5, DOI 10.1186/1471-2105-5-147
   Cobo MJ, 2012, J AM SOC INF SCI TEC, V63, P1609, DOI 10.1002/asi.22688
   Cohen AM, 2005, BRIEF BIOINFORM, V6, P57, DOI 10.1093/bib/6.1.57
   Cohen T, 2017, J BIOMED INFORM, V68, P150, DOI 10.1016/j.jbi.2017.03.003
   Cohen T, 2012, J BIOMED INFORM, V45, P1049, DOI 10.1016/j.jbi.2012.07.003
   Cory KA, 1997, COMPUT HUMANITIES, V31, P1, DOI 10.1023/A:1000422220677
   Crichton G, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0232891
   Crichton G, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2163-9
   DAVIES R, 1989, J DOC, V45, P273, DOI 10.1108/eb026846
   DAVIES R, 1990, J DOC, V46, P368, DOI 10.1108/eb026868
   de Solla Price DJ, 1963, LITTLE SCI, DOI [10.7312/pric91844, DOI 10.7312/PRIC91844]
   DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
   Deftereos SN, 2011, WIRES SYST BIOL MED, V3, P323, DOI 10.1002/wsbm.147
   DeShazo JP, 2009, BMC MED INFORM DECIS, V9, DOI 10.1186/1472-6947-9-7
   DIGIACOMO RA, 1989, AM J MED, V86, P158, DOI 10.1016/0002-9343(89)90261-1
   Ding Y, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0071416
   Egghe L, 2006, SCIENTOMETRICS, V69, P131, DOI 10.1007/s11192-006-0144-7
   Frijters R, 2010, PLOS COMPUT BIOL, V6, DOI 10.1371/journal.pcbi.1000943
   Fuller Sherrilynne S, 2004, Biomed Digit Libr, V1, P2, DOI 10.1186/1742-5581-1-2
   GARFIELD E, 1955, SCIENCE, V122, P108, DOI 10.1126/science.122.3159.108
   Godin B, 2006, SCIENTOMETRICS, V68, P109, DOI 10.1007/s11192-006-0086-0
   Gopalakrishnan V, 2019, J BIOMED INFORM, V93, DOI 10.1016/j.jbi.2019.103141
   Gordon MD, 1996, J AM SOC INFORM SCI, V47, P116, DOI 10.1002/(SICI)1097-4571(199602)47:2<116::AID-ASI3>3.3.CO;2-P
   Gordon MD, 1998, J AM SOC INFORM SCI, V49, P674
   Gould R. V., 1989, SOCIOL METHODOL, V19, P89, DOI DOI 10.2307/270949
   Henry S, 2017, J BIOMED INFORM, V74, P20, DOI 10.1016/j.jbi.2017.08.011
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Hou JH, 2018, SCIENTOMETRICS, V115, P869, DOI 10.1007/s11192-018-2695-9
   Hristovski D, 2001, STUD HEALTH TECHNOL, V84, P1344
   Hristovski D, 2005, INT J MED INFORM, V74, P289, DOI 10.1016/j.ijmedinf.2004.04.024
   Hristovski D, 2015, PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015), P805, DOI 10.1145/2808797.2809300
   Hristovski Dimitar, 2006, AMIA Annu Symp Proc, P349
   Hristovski Dimitar, 2013, Cardiovascular & Hematological Agents in Medicinal Chemistry, V11, P14
   Hui W, 2019, 2019 2ND INTERNATIONAL CONFERENCE ON COMPUTING AND BIG DATA (ICCBD 2019), P7, DOI 10.1145/3366650.3366660
   Jensen LJ, 2006, NAT REV GENET, V7, P119, DOI 10.1038/nrg1768
   Jenssen TK, 2001, NAT GENET, V28, P21, DOI 10.1038/88213
   Jha K, 2016, LECT NOTES COMPUT SC, V9612, P310, DOI 10.1007/978-3-319-41754-7_29
   Kastrin A, 2016, METHOD INFORM MED, V55, P340, DOI 10.3414/ME15-01-0108
   Katukuri JR, 2012, BMC GENOMICS, V13, DOI 10.1186/1471-2164-13-S3-S5
   Kilicoglu H, 2012, BIOINFORMATICS, V28, P3158, DOI 10.1093/bioinformatics/bts591
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   Kostoff RN, 2008, TECHNOL FORECAST SOC, V75, P226, DOI 10.1016/j.techfore.2007.11.007
   Kostoff RN, 2014, SCIENTOMETRICS, V100, P623, DOI 10.1007/s11192-014-1298-3
   Kozomara A, 2011, NUCLEIC ACIDS RES, V39, pD152, DOI 10.1093/nar/gkq1027
   Lee S, 2005, SOC STUD SCI, V35, P673, DOI 10.1177/0306312705052359
   Lever J, 2018, BIOINFORMATICS, V34, P652, DOI 10.1093/bioinformatics/btx613
   Li JC, 2019, NAT REV PHYS, V1, P301, DOI 10.1038/s42254-019-0057-z
   Lindsay RK, 1999, J AM SOC INFORM SCI, V50, P574, DOI 10.1002/(SICI)1097-4571(1999)50:7<574::AID-ASI3>3.0.CO;2-Q
   Moher D., 2009, PLOS MED, V6, DOI [10.1371/journal.pmed.1000097, DOI 10.1371/JOURNAL.PMED.1000097]
   Mower J, 2017, AMIA ANN S P, V2016, P1940
   Nadri H, 2017, J MED SYST, V41, DOI 10.1007/s10916-017-0794-4
   Noyons ECM, 1999, J AM SOC INFORM SCI, V50, P115, DOI 10.1002/(SICI)1097-4571(1999)50:2<115::AID-ASI3>3.3.CO;2-A
   Petric I, 2012, COMPUT J, V55, P47, DOI 10.1093/comjnl/bxq074
   Petric I, 2009, J BIOMED INFORM, V42, P219, DOI 10.1016/j.jbi.2008.08.004
   Pratt Wanda, 2003, P 2 INT C KNOWL CAPT, P105, DOI DOI 10.1145/945645.945662
   PRICE DJD, 1965, SCIENCE, V149, P510
   PRITCHARD A, 1969, J DOC, V25, P348
   Pyysalo S, 2019, BIOINFORMATICS, V35, P1553, DOI 10.1093/bioinformatics/bty845
   Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003
   Sang ST, 2018, IEEE INT C BIOINFORM, P751, DOI 10.1109/BIBM.2018.8621423
   Schuemie MJ, 2009, METHOD INFORM MED, V48, P76, DOI 10.3414/ME0576
   SEBASTIAN Y, 2017, KNOWL ENG REV, V32, P1, DOI DOI 10.1017/S0269888917000042
   Sebastian Y, 2017, KNOWL-BASED SYST, V115, P66, DOI 10.1016/j.knosys.2016.10.015
   Shang N, 2014, J BIOMED INFORM, V52, P293, DOI 10.1016/j.jbi.2014.07.011
   Shneider AM, 2009, TRENDS BIOCHEM SCI, V34, P217, DOI 10.1016/j.tibs.2009.02.002
   Smalheiser NR, 2017, J DATA INFO SCI, V2, P43, DOI 10.1515/jdis-2017-0019
   Smalheiser NR, 2012, J AM SOC INF SCI TEC, V63, P218, DOI 10.1002/asi.21599
   SMALHEISER NR, 1994, NEUROSCI RES COMMUN, V15, P1
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Song DW, 2006, LECT NOTES COMPUT SC, V3841, P692
   Srinivasan P, 2004, J AM SOC INF SCI TEC, V55, P396, DOI 10.1002/asi.10389
   Stapley B J, 2000, Pac Symp Biocomput, P529
   Swanson DR, 2011, J BIOMED DISCOV COLL, V6, DOI 10.5210/disco.v6i0.3515
   SWANSON DR, 1986, PERSPECT BIOL MED, V30, P7
   SWANSON DR, 1986, LIBR QUART, V56, P103, DOI 10.1086/601720
   Swanson DR, 1999, LIBR TRENDS, V48, P48
   Swanson DR, 1997, ARTIF INTELL, V91, P183, DOI 10.1016/S0004-3702(97)00008-8
   SWANSON DR, 1988, PERSPECT BIOL MED, V31, P526
   Sybrandt J, 2018, IEEE INT CONF BIG DA, P1504, DOI 10.1109/BigData.2018.8621974
   Sybrandt J, 2018, IEEE INT CONF BIG DA, P1494, DOI [10.1109/bigdata.2018.8622637, 10.1109/BigData.2018.8622637]
   Thilakaratne M, 2020, ACM COMPUT SURV, V52, DOI 10.1145/3365756
   Thilakaratne M, 2019, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.235
   Thonon F, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0122239
   Uzzi B, 2013, SCIENCE, V342, P468, DOI 10.1126/science.1240474
   van der Eijk CC, 2004, J AM SOC INF SCI TEC, V55, P436, DOI 10.1002/asi.10392
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Weeber M, 2005, BRIEF BIOINFORM, V6, P277, DOI 10.1093/bib/6.3.277
   Weeber M, 2003, J AM MED INFORM ASSN, V10, P252, DOI 10.1197/jamia.M1158
   Weeber M, 2001, J AM SOC INF SCI TEC, V52, P548, DOI 10.1002/asi.1104.abs
   Wei CH, 2019, NUCLEIC ACIDS RES, V47, pW587, DOI 10.1093/nar/gkz389
   Widdows D, 2015, LOG J IGPL, V23, P141, DOI 10.1093/jigpal/jzu028
   Wilkowski Bartlomiej, 2011, AMIA Annu Symp Proc, V2011, P1514
   Wren JD, 2004, BIOINFORMATICS, V20, P389, DOI 10.1093/bioinformatics/btg421
   Wren JD, 2004, BMC BIOINFORMATICS, V5, DOI 10.1186/1471-2105-5-145
   Wu LF, 2019, NATURE, V566, P378, DOI 10.1038/s41586-019-0941-9
   Yang HT, 2017, BRIEF BIOINFORM, V18, P488, DOI 10.1093/bib/bbw030
   Ying Rex, 2019, Adv Neural Inf Process Syst, V32, P9240
   Zhao SD, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbaa057
   Zitnik M, 2019, INFORM FUSION, V50, P71, DOI 10.1016/j.inffus.2018.09.012
NR 123
TC 5
Z9 5
U1 26
U2 126
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD FEB
PY 2021
VL 126
IS 2
BP 1415
EP 1451
DI 10.1007/s11192-020-03811-z
EA JAN 2021
PG 37
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA QR4LJ
UT WOS:000604526600004
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Przesdzink, F
   Herzog, LM
   Fiebelkorn, F
AF Przesdzink, Felix
   Herzog, Laura Mae
   Fiebelkorn, Florian
TI Combining Stakeholder- and Social Network- Analysis to Improve Regional
   Nature Conservation: A Case Study from Osnabruck, Germany
SO ENVIRONMENTAL MANAGEMENT
LA English
DT Article
DE Conservation stakeholders; Regional conservation networks; SWOT
   analysis; Conservation collaboration; Conservation conflicts;
   Stakeholder network optimization
ID CITIZEN PARTICIPATION; COMMUNITY STRUCTURE; SWOT ANALYSIS; MANAGEMENT;
   COLLABORATION; GOVERNANCE; KNOWLEDGE; PERCEPTIONS; STRATEGIES; TOOL
AB Many nature conservation projects fail primarily not because of a lack of knowledge about upcoming threats or viable conservation concepts but rather because of the inability to transfer knowledge into the creation of effective measures. Therefore, an increase in information exchange and collaboration between theory- and practice-oriented conservation actors, as well as between conservation actors, land user groups, and authorities may enhance the effectiveness of conservation goals. By considering the interactions between conservation stakeholders as social networks, social network analysis (SNA) can help identify structural optimization potential in these networks. The present study combines SNA and stakeholder analysis (SA) to assess the interactions between 34 conservation stakeholders in the major city and district of Osnabruck in northwestern Germany and offers insights into cost/benefit optimizations of these stakeholder interactions. Data were acquired using a pile sort technique and guideline-based expert interviews. The SA, based on knowledge mapping and SWOT (strength, weaknesses, opportunities, and threats) analysis, identified individual stakeholder's complementary properties, indicating which among them would most benefit from mutual information exchange and collaboration. The SNA revealed discrepancies in information exchange and collaboration between theory- and practice-focused stakeholders. Conflicts were found predominantly between conservation associations, authorities and land user groups. Ecological research, funding, land-use conflicts, and distribution of conservation knowledge were identified as fields with high potential for increased information exchange and collaboration. Interviews also showed that the stakeholders themselves see many opportunities for increased networking in the region. The results are discussed in relation to the existing literature on nature conservation networks and used to recommend optimization measures for the studied network. Finally, the conclusion reflects upon the developed approach's implications and possibilities for conservation stakeholders and planners in general.
C1 [Przesdzink, Felix; Fiebelkorn, Florian] Osnabruck Univ, Sch Biol & Chem, Didact Biol, Osnabruck, Germany.
   [Herzog, Laura Mae] Osnabruck Univ, Inst Geog, Sch Cultural Studies & Social Sci, Osnabruck, Germany.
   [Herzog, Laura Mae] Osnabruck Univ, Res Ctr Inst Environm Syst Res, Osnabruck, Germany.
RP Przesdzink, F (通讯作者)，Osnabruck Univ, Sch Biol & Chem, Didact Biol, Osnabruck, Germany.
EM fprzesdzink@uos.de
RI Fiebelkorn, Florian/GNP-2191-2022
OI Fiebelkorn, Florian/0000-0002-7972-6925; Przesdzink,
   Felix/0000-0002-2633-9489
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
CR Amberscript BV, 2020, AMBERSCRIPT BV
   [Anonymous], 2015, REGIONAL CONSERVATIO
   Balmford A, 2006, CONSERV BIOL, V20, P692, DOI 10.1111/j.1523-1739.2006.00434.x
   Bazzoli GJ, 2003, MED CARE RES REV, V60, p63S, DOI 10.1177/1077558703259082
   Beierle TC, 2002, RISK ANAL, V22, P739, DOI 10.1111/0272-4332.00065
   Berardo R, 2016, PUBLIC ADMIN REV, V76, P738, DOI 10.1111/puar.12532
   Bertuol-Garcia D, 2018, BIOL REV, V93, P1032, DOI 10.1111/brv.12385
   Bode M, 2011, CONSERV BIOL, V25, P295, DOI 10.1111/j.1523-1739.2010.01610.x
   Bodin O, 2020, ANNU REV ENV RESOUR, V45, P471, DOI 10.1146/annurev-environ-011020-064352
   Bodin O, 2017, POLICY STUD J, V45, P289, DOI 10.1111/psj.12146
   Bogner A., 2005, EXPERTENINTERVIEW
   Boster, 1994, CULT ANTHR METHODS J, V6, P2
   BOSTER JS, 1987, SOC NETWORKS, V9, P375, DOI 10.1016/0378-8733(87)90005-0
   Cohen PJ, 2012, CONSERV LETT, V5, P376, DOI 10.1111/j.1755-263X.2012.00255.x
   Cooper CB, 2007, ECOL SOC, V12
   Cornwall A., 2008, COMMUNITY DEV J, V43, P269, DOI [DOI 10.1093/CDJ/BSN010, 10.1093/CDJ/BSN010]
   Crona BI, 2006, ECOL SOC, V11
   Csardi G., 2006, INTERJOURNAL COMPLEX, V1695, P1
   Davenport TH., 1998, WORKING KNOWLEDGE OR
   Dickson, 2006, GLOBAL ENV ASSESSMEN, P151
   Dresing T., 2018, PRAXISBUCH INTERVIEW, V8. Auflage
   Ellefson, 1996, STAFF PAPER SERIES
   Ernstson H, 2010, ECOL SOC, V15
   Ernstson H, 2008, ECOL SOC, V13
   Fabian Y, 2019, BIOL CONSERV, V235, P93, DOI 10.1016/j.biocon.2019.04.011
   Faust K., 1994, SOCIAL NETWORK ANAL
   FIORINO DJ, 1990, SCI TECHNOL HUM VAL, V15, P226, DOI 10.1177/016224399001500204
   Fischer M, 2015, POLICY SCI, V48, P363, DOI 10.1007/s11077-015-9224-y
   Freeman R. E., 1984, PITMAN SERIES BUSINE
   Friedman RS, 2020, LAND USE POLICY, V97, DOI 10.1016/j.landusepol.2020.104738
   Germain RH, 2001, FOREST POLICY ECON, V3, P113, DOI 10.1016/S1389-9341(01)00065-X
   Girvan M, 2002, P NATL ACAD SCI USA, V99, P7821, DOI 10.1073/pnas.122653799
   Gordon A, 2013, ECOL MODEL, V249, P19, DOI 10.1016/j.ecolmodel.2012.07.009
   Grimble R, 1997, AGR SYST, V55, P173, DOI 10.1016/S0308-521X(97)00006-1
   Guerrero AM, 2013, CONSERV BIOL, V27, P35, DOI 10.1111/j.1523-1739.2012.01964.x
   Hauck J, 2016, ECOL SOC, V21, DOI 10.5751/ES-08596-210249
   Herzog L.M., 2020, MICROPOLLUTANT REGUL
   Irvin RA, 2004, PUBLIC ADMIN REV, V64, P55, DOI 10.1111/j.1540-6210.2004.00346.x
   Johnson J.C, 2018, ANAL SOCIAL NETWORKS
   Kangas J, 2003, J ENVIRON MANAGE, V69, P349, DOI 10.1016/j.jenvman.2003.09.010
   Kaplan, 1999, RESOLVING LAND USE C
   Knight AT, 2006, CONSERV BIOL, V20, P408, DOI 10.1111/j.1523-1739.2006.00305.x
   Korfmacher KS, 2001, ENVIRON MANAGE, V27, P161, DOI 10.1007/s002670010141
   Lahteenmaki-Smith, 2001, 20014 NORDR, P4
   Laugero G., 1998, MANAGING KNOWLEDGE
   Lienert J, 2013, J ENVIRON MANAGE, V125, P134, DOI 10.1016/j.jenvman.2013.03.052
   Luyet V, 2012, J ENVIRON MANAGE, V111, P213, DOI 10.1016/j.jenvman.2012.06.026
   Martin-Collado D, 2013, ANIMAL, V7, P885, DOI 10.1017/S175173111200242X
   Mayring P., 2010, QUALITATIVE INHALTSA, DOI DOI 10.1007/978-3-531-92052-8_42
   Mbaru EK, 2017, BIOL CONSERV, V210, P222, DOI 10.1016/j.biocon.2017.03.031
   Mills M, 2014, BIOL CONSERV, V169, P6, DOI 10.1016/j.biocon.2013.10.015
   MINTZBERG H, 1994, HARVARD BUS REV, V72, P107
   Morgans CL, 2017, GLOB ECOL CONSERV, V11, P236, DOI 10.1016/j.gecco.2017.07.004
   Mostert E., 2003, INT J RIVER BASIN MA, V1, P267, DOI DOI 10.1080/15715124.2003.9635212
   Mushove P, 2005, GLOBAL ENVIRON CHANG, V15, P184, DOI 10.1016/j.gloenvcha.2004.12.008
   Newman MEJ, 2006, P NATL ACAD SCI USA, V103, P8577, DOI 10.1073/pnas.0601602103
   Newman M. E. J., 2004, PHYS REV, V69
   Niedersachsisches Landesamt fur Statistik, 2021, MEIN GEM MEIN STADT
   Nita A, 2018, SOC NETW ANAL MIN, V8, DOI 10.1007/s13278-018-0509-y
   Nouri J, 2008, INT J ENVIRON SCI TE, V5, P43, DOI 10.1007/BF03325996
   Olsson P, 2007, ECOL SOC, V12
   Pardalos PM, 2011, HDB OPTIMIZATION COM, V1
   Peckham, 2000, NEW WATERSHED SOURCE, P1
   Phillipson J, 2012, J ENVIRON MANAGE, V95, P56, DOI 10.1016/j.jenvman.2011.10.005
   Pons P, 2005, LECT NOTES COMPUT SC, V3733, P284
   Prell C, 2009, SOC NATUR RESOUR, V22, P501, DOI 10.1080/08941920802199202
   Pressey RL, 2009, ORYX, V43, P464, DOI 10.1017/S0030605309990500
   Primack R. B., 2008, PRIMER CONSERVATION
   R Development Core Team, 2016, R LANG ENV STAT COMP
   Reed MS, 2009, J ENVIRON MANAGE, V90, P1933, DOI 10.1016/j.jenvman.2009.01.001
   Reid W., 2006, BRIDGING SCALES KNOW
   Renn O., 2009, ENVIRON POLICY GOV, V19, P174, DOI [10.1002/eet.507, DOI 10.1002/EET.507]
   Schubert K., 2014, LEHRBUCH POLITIKFELD, P259
   Schuett MA, 2001, ENVIRON MANAGE, V27, P587, DOI 10.1007/s002670010172
   Scolozzi R, 2014, J ENVIRON MANAGE, V146, P543, DOI 10.1016/j.jenvman.2014.05.040
   Scott J., 2000, SOCIAL NETWORK ANAL
   Stern PC, 2005, BIOSCIENCE, V55, P976, DOI 10.1641/0006-3568(2005)055[0976:DMFUES]2.0.CO;2
   TERRA.vita Natur und Geopark, 2021, TERRA NATURA2000
   Turrini A, 2010, PUBLIC ADMIN, V88, P528, DOI 10.1111/j.1467-9299.2009.01791.x
   Vail EF, 1999, INFORM SYST MANAGE, V16, P16, DOI 10.1201/1078/43189.16.4.19990901/31199.3
   Vance-Borland K, 2011, CONSERV LETT, V4, P278, DOI 10.1111/j.1755-263X.2011.00176.x
   VERBI GmbH, 2020, MAXQDA
   WEIHRICH H, 1982, LONG RANGE PLANN, V15, P54, DOI 10.1016/0024-6301(82)90120-0
   Wexler M., 2001, J KNOWLEDGE MANAGEME, V5, P249, DOI [10.1108/EUM0000000005868, DOI 10.1108/EUM0000000005868]
NR 84
TC 0
Z9 0
U1 3
U2 8
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0364-152X
EI 1432-1009
J9 ENVIRON MANAGE
JI Environ. Manage.
PD FEB
PY 2022
VL 69
IS 2
BP 271
EP 287
DI 10.1007/s00267-021-01564-w
EA NOV 2021
PG 17
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA YN1JM
UT WOS:000724017100002
PM 34850249
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Joslin, AC
   Green, R
   German, JB
   Lange, MC
AF Joslin, A. C.
   Green, R.
   German, J. B.
   Lange, M. C.
TI Concept mapping One-Carbon Metabolism to model future ontologies for
   nutrient-gene-phenotype interactions
SO GENES AND NUTRITION
LA English
DT Article
DE One-Carbon Metabolism; Nutrigenomics; Ontology; CMAPs
ID METHIONINE SYNTHASE REDUCTASE; METHYLENETETRAHYDROFOLATE REDUCTASE;
   PLASMA HOMOCYSTEINE; MEGALOBLASTIC-ANEMIA; TRANSCOBALAMIN GENE;
   B-VITAMINS; FOLATE; RISK; POLYMORPHISMS; HYPERHOMOCYSTEINEMIA
AB Advances in the development of bioinformatic tools continue to improve investigators' ability to interrogate, organize, and derive knowledge from large amounts of heterogeneous information. These tools often require advanced technical skills not possessed by life scientists. User-friendly, low-barrier-to-entry methods of visualizing nutrigenomics information are yet to be developed. We utilized concept mapping software from the Institute for Human and Machine Cognition to create a conceptual model of diet and health-related data that provides a foundation for future nutrigenomics ontologies describing published nutrient-gene/polymorphism-phenotype data. In this model, maps containing phenotype, nutrient, gene product, and genetic polymorphism interactions are visualized as triples of two concepts linked together by a linking phrase. These triples, or "knowledge propositions," contextualize aggregated data and information into easy-to-read knowledge maps. Maps of these triples enable visualization of genes spanning the One-Carbon Metabolism (OCM) pathway, their sequence variants, and multiple literature-mined associations including concepts relevant to nutrition, phenotypes, and health. The concept map development process documents the incongruity of information derived from pathway databases versus literature resources. This conceptual model highlights the importance of incorporating information about genes in upstream pathways that provide substrates, as well as downstream pathways that utilize products of the pathway under investigation, in this case OCM. Other genes and their polymorphisms, such as TCN2 and FUT2, although not directly involved in OCM, potentially alter OCM pathway functionality. These upstream gene products regulate substrates such as B12. Constellations of polymorphisms affecting the functionality of genes along OCM, together with substrate and cofactor availability, may impact resultant phenotypes. These conceptual maps provide a foundational framework for development of nutrient-gene/polymorphism-phenotype ontologies and systems visualization.
C1 [Joslin, A. C.; German, J. B.; Lange, M. C.] Univ Calif Davis, Dept Food Sci & Technol, Davis, CA 95616 USA.
   [Green, R.] Univ Calif Davis, Sch Med, Dept Pathol & Lab Med, Sacramento, CA 95817 USA.
RP Lange, MC (通讯作者)，Univ Calif Davis, Dept Food Sci & Technol, 1 Shields Ave, Davis, CA 95616 USA.
EM mclange@ucdavis.edu
FU NIH [R01HD059127, R01HD065122, R01HD061923, R21AT006180, R01AT007079];
   EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN
   DEVELOPMENT [R01HD065122, R01HD061923] Funding Source: NIH RePORTER;
   NATIONAL CENTER FOR COMPLEMENTARY & ALTERNATIVE MEDICINE [R21AT006180]
   Funding Source: NIH RePORTER; National Center for Complementary &
   Integrative Health [R01AT007079] Funding Source: NIH RePORTER
FX The authors acknowledge support from the following NIH grants:
   R01HD059127, R01HD065122, R01HD061923, R21AT006180, R01AT007079.
CR Afman LA, 2002, EUR J HUM GENET, V10, P433, DOI 10.1038/sj.ejhg.5200830
   Amorim MR, 2013, GENET TEST MOL BIOMA, V17, P69, DOI 10.1089/gtmb.2012.0200
   Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Bailey LB, 1999, J NUTR, V129, P919, DOI 10.1093/jn/129.5.919
   Beedholm-Ebsen R, 2010, BLOOD, V115, P1632, DOI 10.1182/blood-2009-07-232587
   Blanton SH, 2011, BIRTH DEFECTS RES A, V91, P50, DOI 10.1002/bdra.20740
   Brunaud L, 2003, DIGESTION, V68, P133, DOI 10.1159/000075307
   CARMEL R, 1988, NEW ENGL J MED, V318, P1738, DOI 10.1056/NEJM198806303182607
   Cascalheira JF, 2009, CLIN BIOCHEM, V42, P783, DOI 10.1016/j.clinbiochem.2009.02.006
   Castro AG, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-267
   CLARKE R, 1991, NEW ENGL J MED, V324, P1149, DOI 10.1056/NEJM199104253241701
   Dharuri H, 2013, BMC GENOMICS, V14, DOI 10.1186/1471-2164-14-865
   Fillon-Emery N, 2004, AM J CLIN NUTR, V80, P1551, DOI 10.1093/ajcn/80.6.1551
   Garrod MG, 2010, EUR J CLIN NUTR, V64, P503, DOI 10.1038/ejcn.2010.20
   Green T, 2013, PROSTATE, V73, P1561, DOI 10.1002/pros.22706
   Gueant JL, 2007, J MED GENET, V44, P363, DOI 10.1136/jmg.2006.048041
   Gulati S, 1999, BIOCHEM BIOPH RES CO, V259, P436, DOI 10.1006/bbrc.1999.0696
   HALL CA, 1965, J LAB CLIN MED, V65, P459
   Haviv YS, 2002, NEPHRON, V92, P120, DOI 10.1159/000064485
   Hazra A, 2008, NAT GENET, V40, P1160, DOI 10.1038/ng.210
   HERBERT V, 1962, J CLIN INVEST, V41, P1263, DOI 10.1172/JCI104589
   Kaput J, 2008, CURR OPIN BIOTECH, V19, P110, DOI 10.1016/j.copbio.2008.02.005
   Kelemen LE, 2008, CANCER RES, V68, P2498, DOI 10.1158/0008-5472.CAN-07-5165
   Kozyraki R, 1998, BLOOD, V91, P3593
   Kronenberger T, 2014, BIOMED RES INT, V2014, DOI 10.1155/2014/108516
   Leclerc D, 1998, P NATL ACAD SCI USA, V95, P3059, DOI 10.1073/pnas.95.6.3059
   Levine AJ, 2010, CANCER EPIDEM BIOMAR, V19, P1812, DOI 10.1158/1055-9965.EPI-10-0151
   Li F, 2008, MOL GENET METAB, V94, P326, DOI 10.1016/j.ymgme.2008.03.013
   Li Q, 2013, CANCER CAUSE CONTROL, V24, P1875, DOI 10.1007/s10552-013-0264-3
   Li ST, 2007, NUCLEIC ACIDS RES, V35, pD707, DOI 10.1093/nar/gkl969
   Lim U, 2007, BLOOD, V109, P3050, DOI 10.1182/blood-2006-07-034330
   Lindblom A, 2011, HUM MUTAT, V32, P495, DOI 10.1002/humu.21468
   Matakidou A, 2007, BRIT J CANCER, V97, P247, DOI 10.1038/sj.bjc.6603830
   McNulty H, 2006, CIRCULATION, V113, P74, DOI 10.1161/CIRCULATIONAHA.105.580332
   Meigs JB, 2008, NEW ENGL J MED, V359, P2208, DOI 10.1056/NEJMoa0804742
   Morine Y, 2012, SURG TODAY, V42, P135, DOI 10.1007/s00595-011-0054-z
   Namour F, 2011, HAEMATOL-HEMATOL J, V96, P1715, DOI 10.3324/haematol.2011.043984
   NYGARD O, 1995, JAMA-J AM MED ASSOC, V274, P1526, DOI 10.1001/jama.274.19.1526
   O'Byrne MR, 2010, BIRTH DEFECTS RES A, V88, P689, DOI 10.1002/bdra.20706
   Oppeneer SJ, 2012, MOL GENET METAB, V105, P73, DOI 10.1016/j.ymgme.2011.09.035
   Pangilinan F, 2010, J MED GENET, V47, P677, DOI 10.1136/jmg.2009.073775
   Romero P, 2005, GENOME BIOL, V6
   ROSENBLATT DS, 1987, AM J MED GENET, V26, P377, DOI 10.1002/ajmg.1320260216
   Rutsch F, 2011, J INHERIT METAB DIS, V34, P121, DOI 10.1007/s10545-010-9083-9
   Sauberlich H E, 1980, Ann N Y Acad Sci, V355, P80, DOI 10.1111/j.1749-6632.1980.tb21329.x
   Seetharam B, 1999, J NUTR, V129, P1761, DOI 10.1093/jn/129.10.1761
   Shields DC, 1999, AM J HUM GENET, V64, P1045, DOI 10.1086/302310
   Silla Y, 2011, BIOCHEMISTRY-US, V50, P1396, DOI 10.1021/bi101095g
   Smith B, 2005, GENOME BIOL, V6, DOI 10.1186/gb-2005-6-5-r46
   Stover PJ, 2002, J NUTR, V132, p2476S, DOI 10.1093/jn/132.8.2476S
   Szegedi SS, 2008, J BIOL CHEM, V283, P8939, DOI 10.1074/jbc.M710449200
   Taboada M, 2012, BMC MED INFORM DECIS, V12, DOI 10.1186/1472-6947-12-78
   Unfried G, 2002, OBSTET GYNECOL, V99, P614, DOI 10.1016/S0029-7844(01)01789-6
   Vaughn JD, 2004, J NUTR, V134, P2985, DOI 10.1093/jn/134.11.2985
   Vlastos F, 2009, J THORAC ONCOL, V4, P1259, DOI 10.1097/JTO.0b013e3181aba6bd
   Ward M, 2011, INT J VITAM NUTR RES, V81, P240, DOI 10.1024/0300-9831/a000069
   Watkins D, 2002, AM J HUM GENET, V71, P143, DOI 10.1086/341354
   Wernimont SM, 2011, BMC MED GENET, V12, DOI 10.1186/1471-2350-12-150
   Whetzel PL, 2006, OMICS, V10, P199, DOI 10.1089/omi.2006.10.199
   Woeller CF, 2007, J BIOL CHEM, V282, P29927, DOI 10.1074/jbc.M706264200
   Wolthers KR, 2007, BIOCHEMISTRY-US, V46, P6696, DOI 10.1021/bi700339v
   Yakub M, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0033222
   Yamada K, 2001, P NATL ACAD SCI USA, V98, P14853, DOI 10.1073/pnas.261469998
   Yang QH, 2008, AM J CLIN NUTR, V88, P232, DOI 10.1093/ajcn/88.1.232
   Zittan E, 2007, AM J PHYSIOL-HEART C, V293, pH860, DOI 10.1152/ajpheart.01189.2006
NR 65
TC 2
Z9 2
U1 1
U2 15
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1555-8932
EI 1865-3499
J9 GENES NUTR
JI Genes Nutr.
PD SEP
PY 2014
VL 9
IS 5
AR 419
DI 10.1007/s12263-014-0419-1
PG 17
WC Genetics & Heredity; Nutrition & Dietetics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity; Nutrition & Dietetics
GA AP6XG
UT WOS:000342221100004
PM 25091042
OA Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Ma, ZR
   Zhao, LN
   Li, JQ
   Xu, X
   Li, J
AF Ma, Zerui
   Zhao, Linna
   Li, Jianqiang
   Xu, Xi
   Li, Jing
TI SiBERT: A Siamese-based BERT network for Chinese medical entities
   alignment
SO METHODS
LA English
DT Article
DE Entity alignment; Chinese medical entity alignment; Siamese network;
   BERT; Knowledge graph
ID NORMALIZATION; LINKAGE
AB Entity alignment aims at associating semantically similar entities in knowledge graphs from different sources. It is widely used in the integration and construction of professional medical knowledge. The existing deep learning methods lack term-level embedding representation, which limits the performance of entity alignment and causes a massive computational overhead. To address these problems, we propose a Siamese-based BERT (SiBERT) for Chinese medical entities alignment. SiBERT generates term-level embedding based on word embedding sequences to enhance the features of entities in similarity calculation. The process of entity alignment contains three steps. Specifically, the SiBERT is firstly pre-trained with synonym dictionary in the public domain, and transferred to the task of medical entity alignment. Secondly, four different categories of entities (disease, symptom, treatment, and examination) are labeled based on the standard terms selected from standard terms dataset. The entities and their standard terms form term pairs to train SiBERT. Finally, combined with the entity alignment algorithm, the most similar standard term is selected as the final result. To evaluate the effectiveness of our method, we conduct extensive experiments on real-world datasets. The experimental results illustrate that SiBERT network is superior to other compared algorithms both in alignment accuracy and computational efficiency.
C1 [Ma, Zerui; Li, Jing] Beijing Univ Technol, Fac Sci, Beijing, Peoples R China.
   [Zhao, Linna; Li, Jianqiang; Xu, Xi] Beijing Univ Technol, Fac Informat, Beijing, Peoples R China.
RP Li, J (通讯作者)，Beijing Univ Technol, Fac Sci, Beijing, Peoples R China.
EM leejing@bjut.edu.cn
FU National Key R & D Program of China [2020AAA0104900/2020AAA0104904];
   Beijing Municipal Commission of Education [KZ202010005010]; Beijing
   Chaoyang Collaborative Innovation [CYXC2010]
FX This study was supported by the National Key R & D Program of China with
   the project no.2020AAA0104900/2020AAA0104904, the Beijing Municipal
   Commission of Education with project No. KZ202010005010 and Beijing
   Chaoyang Collaborative Innovation project CYXC2010.
CR Arasu A., 2010, P 2010 ACM SIGMOD IN, DOI [10.1145/1807167.1807252, DOI 10.1145/1807167.1807252]
   Aroyo L., 2011, NATURE PRECEDINGS, DOI [10.1145/2166896.2166911, DOI 10.1145/2166896.2166911]
   Bertinetto L, 2016, LECT NOTES COMPUT SC, V9914, P850, DOI 10.1007/978-3-319-48881-3_56
   Bhattacharya I., 2005, P 2005 SIAM INT C DA, P47
   Bhattacharya I, 2006, SIAM PROC S, P47
   Bromley J., 1993, International Journal of Pattern Recognition and Artificial Intelligence, V7, P669, DOI 10.1142/S0218001493000339
   Cochinwala M, 2001, INFORM SCIENCES, V137, P1, DOI 10.1016/S0020-0255(00)00070-0
   Devlin J., 2018, ARXIV
   Disantostefano J., 2009, J NURSE PRACTITIONER, V5, P56, DOI [10.1016/j.nurpra.2008.09.020, DOI 10.1016/J.NURPRA.2008.09.020, 10.1097/hcm.0b013-31823b60fa, DOI 10.1097/HCM.0B013-31823B60FA]
   Elfeky MG, 2002, PROC INT CONF DATA, P17, DOI 10.1109/ICDE.2002.994694
   Ghiasvand O., 2014, SEMEVAL 2014, DOI [DOI 10.3115/V1/S14-2147, 10.109310.3115/v1/S14-2147]
   Guo JF, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P55, DOI 10.1145/2983323.2983769
   Hall  R., 2008, KDD, P310
   He K.X., 2019, ARXIV
   Herbrich R, 2000, ADV NEUR IN, P115
   Ji Z., 2019, ARXIV
   Lacoste-Julien S, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P572
   Leaman R, 2016, BIOINFORMATICS, V32, P2839, DOI 10.1093/bioinformatics/btw343
   Leaman R, 2013, BIOINFORMATICS, V29, P2909, DOI 10.1093/bioinformatics/btt474
   Li HD, 2017, BMC BIOINFORMATICS, V18, DOI 10.1186/s12859-017-1805-7
   Li JZ, 2013, KNOWL-BASED SYST, V50, P112, DOI 10.1016/j.knosys.2013.06.004
   Li JZ, 2009, IEEE T KNOWL DATA EN, V21, P1218, DOI 10.1109/TKDE.2008.202
   Luo Y, 2018, AAAI CONF ARTIF INTE, P5868
   Mccallum A., 2004, ADV NEURAL INFORM PR, V17, P905, DOI [10.5555/2976040.2976154, DOI 10.5555/2976040.2976154]
   National Center for Health Statistics (US) and Council on Clinical Classifications, 1980, INT CLASSIFICATION D, V2
   NEWCOMBE HB, 1959, SCIENCE, V130, P954, DOI 10.1126/science.130.3381.954
   Ning WX, 2016, BMC MED INFORM DECIS, V16, DOI 10.1186/s12911-016-0269-4
   Rastogi V, 2011, PROC VLDB ENDOW, V4, P208, DOI 10.14778/1938545.1938546
   Reimers N., 2019, ARXIV
   Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, P333, DOI 10.1561/1500000019
   Singla P, 2006, IEEE DATA MINING, P572
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Tang J, 2006, J WEB SEMANT, V4, P243, DOI 10.1016/j.websem.2006.06.001
   Wang Q, 2020, J BIOMED INFORM, V105, DOI 10.1016/j.jbi.2020.103418
   Wright D, 2019, NORMCO DEEP DIS NORM
NR 35
TC 0
Z9 0
U1 0
U2 0
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1046-2023
EI 1095-9130
J9 METHODS
JI Methods
PD SEP
PY 2022
VL 205
BP 133
EP 139
DI 10.1016/j.ymeth.2022.07.003
PG 7
WC Biochemical Research Methods; Biochemistry & Molecular Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology
GA 3Q2HC
UT WOS:000838053000001
PM 35798258
DA 2022-08-23
ER

PT J
AU Li, J
   Huang, QB
   Cai, Y
   Liu, YK
   Fu, MY
   Li, Q
AF Li, Jing
   Huang, Qingbao
   Cai, Yi
   Liu, Yongkang
   Fu, Mingyi
   Li, Qing
TI Topic-level knowledge sub-graphs for multi-turn dialogue generation
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge-based dialogue system; Multi-turn dialogue generation;
   Knowledge graph; Topic-level
ID DISCOVERY
AB Previous multi-turn dialogue approaches based on global Knowledge Graphs (KGs) still suffer from generic, uncontrollable, and incoherent responses generation. Most of them neither consider the local topic-level semantic information of KGs nor effectively merge the information of long dialogue contexts and KGs into the dialogue generation. To tackle these issues, we propose a Topic-level Knowledge aware Dialogue Generation model to capture context-aware topic-level knowledge information. Our method thus accounts for topic-coherence, fluency, and diversity of generated responses. Specifically, we first decompose the given KG into a set of topic-level sub-graphs, with each sub-graph capturing a semantic component of the input KG. Furthermore, we design a Topic-level Sub-graphs Attention Network to calculate the comprehensive representation of both sub-graphs and previous turns of dialogue utterances, which then decoded with the current turn into a response. By using sub-graphs, our model is able to attend to different topical components of the KG and enhance the topic-coherence. We perform extensive experiments on two datasets of DuRecDial and KdConv to demonstrate the effectiveness of our model. The experimental results demonstrate that our model outperforms existing strong baselines. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Li, Jing; Huang, Qingbao; Liu, Yongkang; Fu, Mingyi] Guangxi Univ, Sch Elect Engn, Nanning, Guangxi, Peoples R China.
   [Cai, Yi] South China Univ Technol, Sch Software Engn, Guangzhou, Peoples R China.
   [Huang, Qingbao; Cai, Yi] MOE China, Key Lab Big Data & Intelligent Robot SCUT, Guangzhou, Peoples R China.
   [Li, Qing] Hong Kong Polytech Univ, Dept Comp, Hung Hom, Kowloon, Hong Kong, Peoples R China.
   [Huang, Qingbao] Guangxi Key Lab Intelligent Control & Maintenance, Nanning, Guangxi, Peoples R China.
RP Huang, QB (通讯作者)，Guangxi Univ, Sch Elect Engn, Nanning, Guangxi, Peoples R China.; Huang, QB (通讯作者)，MOE China, Key Lab Big Data & Intelligent Robot SCUT, Guangzhou, Peoples R China.; Huang, QB (通讯作者)，Guangxi Key Lab Intelligent Control & Maintenance, Nanning, Guangxi, Peoples R China.
EM laizirise@gmail.com; qbhuang@gxu.edu.cn; zhkkun@gmail.com
OI Huang, Qingbao/0000-0001-7691-347X
FU National Natural Science Foundation of China [62076100]; Fundamental
   Research Funds for the Central Universities, China, SCUT [D2210010,
   D2200150, D2201300]; Science and Technology Planning Project of
   Guangdong Province, China [2020B0101100002]; Science and Technology
   Programs of Guangzhou, China [201704030076, 201707010223, 201802010027,
   201902010046]; Science and Technology Key Projects of Guangxi Province,
   China [2020AA21077007]; Hong Kong Research Grants Council, China
   [PolyU1121417, C1031-18G]; Hong Kong Polytechnic University, China
   [1.9B0V]
FX This work was supported by National Natural Science Foundation of China
   (62076100) , and the collaborative research grants from the Fundamental
   Research Funds for the Central Universities, China, SCUT (D2210010,
   D2200150, and D2201300) , the Science and Technology Planning Project of
   Guangdong Province, China (2020B0101100002) , the Science and Technology
   Programs of Guangzhou, China (201704030076, 201707010223, 201802010027,
   201902010046) , the Science and Technology Key Projects of Guangxi
   Province, China (2020AA21077007) , and the Hong Kong Research Grants
   Council, China (PolyU1121417, C1031-18G) , and an internal research
   grant from the Hong Kong Polytechnic University, China (1.9B0V) .
CR Bahdanau D., 2014, ARXIV
   Cai Y, 2016, NEUROCOMPUTING, V208, P25, DOI 10.1016/j.neucom.2016.02.070
   Cai Y, 2008, LECT NOTES COMPUT SC, V5231, P69, DOI 10.1007/978-3-540-87877-3_7
   Cho K., 2014, ARXIV14061078, P1724
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dolan B., 2016, P 2016 C N AM CHAPT, P110, DOI 10.18653/v1/N16-1014
   Feng JC, 2020, WORLD WIDE WEB, V23, P1553, DOI 10.1007/s11280-019-00760-3
   Ghazvininejad M, 2018, AAAI CONF ARTIF INTE, P5110
   Grave E, 2018, PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2018), P3483
   He W., KNOWL-BASED SYST, V213
   Jiang B, 2020, KNOWL-BASED SYST, V210, DOI 10.1016/j.knosys.2020.106479
   JieminWu Yanghui Rao, 2020, P 58 ANN M ASS COMP, P6159
   Jung J., 2020, P 2020 C EMP METH NA, P3484
   Li J., 2017, ADVERSARIAL LEARNING
   Li Z, 2021, KNOWL-BASED SYST, V212, DOI 10.1016/j.knosys.2020.106618
   Lian RZ, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5081
   Lin XX, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P41
   Liu ZM, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P1036
   Nakano M, 2020, KNOWL-BASED SYST, V204, DOI 10.1016/j.knosys.2020.106212
   Page L., 1999, 199966 STANF INFOLAB
   Pan LM, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P1463
   Papineni K, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P311, DOI 10.3115/1073083.1073135
   Sarkar R., 2020, COLING, P4179
   Serban IV, 2016, AAAI CONF ARTIF INTE, P3776
   Song Y., 2020, P 58 ANN M ASS COMP, P5832, DOI 10.18653/v1/2020.acl-main.517
   Sordoni A, 2015, P ACM INT C INF KNOW, P553, DOI 10.1145/2806416.2806493
   Sutskever Ilya, 2014, ADV NEURAL INFORM PR, V27, P3104
   Tang JH, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P5624
   Tian ZL, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P650
   Tu ZP, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P76
   Wang Jian, 2020, P 28 INT C COMP LING, P4100
   Wang X, 2019, DATA SCI ENG, V4, P24, DOI 10.1007/s41019-019-0090-z
   Wu WQ, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3794
   Xu J., 2020, IJCAI, P3941
   Xu J, 2020, AAAI CONF ARTIF INTE, V34, P9338
   Yin RP, 2019, KNOWL-BASED SYST, V185, DOI 10.1016/j.knosys.2019.105020
   Yu W., 2020, SURVEY KNOWLEDGE ENH
   Yu WR, 2013, IEEE DATA MINING, P1271, DOI 10.1109/ICDM.2013.32
   Zhang BW, 2020, KNOWL-BASED SYST, V207, DOI 10.1016/j.knosys.2020.106398
   Zhang FX, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2987
   Zhang HN, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3737
   Zhang HN, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3721
   Zhang HY, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P2031
   Zhang W., 2020, EMNLP FINDINGS, P1981
   Zhang Y, 2020, NEW ENGL J MED, V382, DOI 10.1056/NEJMc2007575
   Zhou H., 2020, P 58 ANN M ASS COMPU, P7098
   Zhou H, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4623
   Zhu X., 2020, P 2020 C EMP METH NA, P725, DOI 10.18653/v1/2020.emnlp-main.54
NR 48
TC 0
Z9 0
U1 11
U2 17
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD DEC 25
PY 2021
VL 234
AR 107499
DI 10.1016/j.knosys.2021.107499
EA OCT 2021
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WW7VC
UT WOS:000718118200002
DA 2022-08-23
ER

PT J
AU Perez-Rodriguez, G
   Perez-Perez, M
   Fdez-Riverola, F
   Lourenco, A
AF Perez-Rodriguez, Gael
   Perez-Perez, Martin
   Fdez-Riverola, Florentino
   Lourenco, Analia
TI Mining the sociome for Health Informatics: Analysis of therapeutic
   lifestyle adherence of diabetic patients in Twitter
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Sociome; Community detection; Topic modelling; Knowledge graphs;
   Diabetes; Twitter
ID TYPE-2; MANAGEMENT; DISTRESS; MELLITUS
AB In recent years, the number of active users in social media has grown exponentially. Despite the thematic diversity of the messages, social media have become an important vehicle to disseminate health information as well as to gather insights about patients' experiences and emotional intelligence. Therefore, the present work proposes a new methodology of analysis to identify and interpret the behaviour, perceptions and appreciations of patients and close relatives towards a health condition through their social interactions. At the core of this methodology are techniques of natural language processing and machine learning as well as the reconstruction of knowledge graphs, and further graph mining. The case study is the diabetes community, and more specifically, the patients communicating about type 1 diabetes (T1D) and type 2 diabetes (T2D). The results produced in this study show the effectiveness of the proposed method to discover useful and non-trivial knowledge about patient perceptions of disease. Such knowledge may be used in the context of Health Informatics to promote healthy lifestyles in more efficient ways as well as to improve communication with the patients. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Perez-Rodriguez, Gael; Perez-Perez, Martin; Fdez-Riverola, Florentino; Lourenco, Analia] Univ Vigo, Dept Comp Sci, ESEI, Campus As Lagoas, Orense 32004, Spain.
   [Perez-Rodriguez, Gael; Perez-Perez, Martin; Fdez-Riverola, Florentino; Lourenco, Analia] Biomed Res Ctr CINBIO, Campus Univ Lagoas Marcosende, Vigo 36310, Spain.
   [Perez-Rodriguez, Gael; Perez-Perez, Martin; Fdez-Riverola, Florentino; Lourenco, Analia] SERGAS UVIGO, Galicia Hlth Res Inst ISS Galicia Sur, SING Res Grp, Vigo, Spain.
   [Lourenco, Analia] Univ Minho, Ctr Biol Engn CEB, Campus Gualtar, P-4710057 Braga, Portugal.
RP Lourenco, A (通讯作者)，ESEI Escuela Super Ingn Informat, Edificio Politecn,Campus Univ As Lagoas S-N, Orense 32004, Spain.
EM gaeperez@uvigo.es; martiperez@uvigo.es; riverola@uvigo.es;
   analia@uvigo.es
RI Fdez-Riverola, Florentino/G-1411-2011; Lourenço, Anália/Y-9447-2019;
   Perez-Rodriguez, Gael/S-3079-2016
OI Fdez-Riverola, Florentino/0000-0002-3943-8013; Lourenço,
   Anália/0000-0001-8401-5362; Perez-Perez, Martin/0000-0003-1349-6562;
   Perez-Rodriguez, Gael/0000-0001-6740-3742
FU Portuguese Foundation for Science and Technology (FCT)
   [UID/BIO/04469/2013]; COMPETE 2020 [POCI-01-0145-FEDER-006684]; Xunta de
   Galicia (Centro singular de investigacion de Galicia accreditation
   2019-2022); European Union (European Regional Development Fund ERDF)
   [ED431G2019/06]; Conselleria de Educacion, Universidades e Formacion
   Profesional (Xunta de Galicia) [ED431C2018/55-GRC]; Xunta de Galicia
FX This work was partially supported by the Portuguese Foundation for
   Science and Technology (FCT) under the scope of the strategic funding of
   UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684), the
   Xunta de Galicia (Centro singular de investigacion de Galicia
   accreditation 2019-2022) and the European Union (European Regional
   Development Fund ERDF)-Ref. ED431G2019/06, and Conselleria de Educacion,
   Universidades e Formacion Profesional (Xunta de Galicia) under the scope
   of the strategic funding of ED431C2018/55-GRC Competitive Reference
   Group. The authors also acknowledge the Postdoc contract of Martin
   Perez-Perez, funded by the Xunta de Galicia.
CR Adimadhyam S, 2019, BRIT J CLIN PHARMACO, V85, P160, DOI 10.1111/bcp.13782
   Akimushkin C, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0170527
   Amancio DR, 2015, EPL-EUROPHYS LETT, V110, DOI 10.1209/0295-5075/110/68001
   American Diabetes Association, 2017, Diabetes Care, V40, pS44
   [Anonymous], 2018, NAT CELL BIOL, V20, P1329, DOI DOI 10.1038/S41556-018-0253-6
   Aristotelis, 2016, HUNSPELL DICT ENGLIS
   Beguerisse-Diaz M, 2017, DIGIT HEALTH, V3, DOI 10.1177/2055207616688841
   Bello-Orgaz G, 2017, FUTURE GENER COMP SY, V66, P125, DOI 10.1016/j.future.2016.06.032
   Berry E, 2018, DIABETES RES CLIN PR, V141, P244, DOI 10.1016/j.diabres.2018.05.020
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bommer C, 2018, DIABETES CARE, V41, P963, DOI 10.2337/dc17-1962
   Celardo L, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.09.005
   Chen L, 2012, NAT REV ENDOCRINOL, V8, P228, DOI 10.1038/nrendo.2011.183
   Cho NH, 2018, DIABETES RES CLIN PR, V138, P271, DOI 10.1016/j.diabres.2018.02.023
   Cho YR, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-S3-S3
   Chuang J., 2012, TERMITE VISUALIZATIO
   Clauset A, 2004, PHYS REV E, V70, DOI 10.1103/PhysRevE.70.066111
   Collins CR, 2003, COMP GEOM-THEOR APPL, V25, P233, DOI 10.1016/S0925-7721(02)00099-8
   Couto FM, 2018, J CHEMINFORMATICS, V10, DOI 10.1186/s13321-018-0312-9
   DAVENPORT EC, 1991, EDUC PSYCHOL MEAS, V51, P821, DOI 10.1177/001316449105100403
   De la Torre-Diez I, 2012, TELEMED E-HEALTH, V18, P404, DOI 10.1089/tmj.2011.0227
   Dooley DM, 2018, NPJ SCI FOOD, V2, DOI 10.1038/s41538-018-0032-6
   El-Sappagh S, 2018, J BIOMED SEMANT, V9, DOI 10.1186/s13326-018-0176-y
   Emnacen V.C., 2016, BE SMART DRINK WATER
   Ferreira RM, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0056579
   Franks MM, 2012, FAM SYST HEALTH, V30, P154, DOI 10.1037/a0028614
   Gabarron Elia, 2019, J Diabetes Sci Technol, V13, P439, DOI 10.1177/1932296818811679
   Gualdani R, 2016, MOLECULES, V21, DOI 10.3390/molecules21111530
   Househ M, 2014, HEALTH INFORM J, V20, P50, DOI 10.1177/1460458213476969
   Howarth C, 2007, DIABETIC MED, V24, P1229, DOI 10.1111/j.1464-5491.2007.02254.x
   Hua XY, 2016, JAMA-J AM MED ASSOC, V315, P1400, DOI 10.1001/jama.2016.0126
   Hutto C.J., 2014, 8 INT C WEBL SOC MED
   Kandimalla R, 2017, BBA-MOL BASIS DIS, V1863, P1078, DOI 10.1016/j.bbadis.2016.08.018
   Karami A, 2018, INT J INFORM MANAGE, V38, P1, DOI 10.1016/j.ijinfomgt.2017.08.002
   Kibbe WA, 2015, NUCLEIC ACIDS RES, V43, pD1071, DOI 10.1093/nar/gku1011
   KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694
   LaMacchia D., 2017, TWITTER TEXT LIB
   Lastra G, 2014, ENDOCRIN METAB CLIN, V43, P103, DOI 10.1016/j.ecl.2013.09.005
   Leung PS, 2016, NUTRIENTS, V8, DOI 10.3390/nu8030147
   Li YJ, 2007, IEEE T PATTERN ANAL, V29, P1091, DOI 10.1109/TPAMI.2007.1070
   Maahs DM, 2010, ENDOCRIN METAB CLIN, V39, P481, DOI 10.1016/j.ecl.2010.05.011
   Magbri A., 2019, GLOB J UROL NEPHROL, DOI [10.28933/gjun-2019-06-2605, DOI 10.28933/GJUN-2019-06-2605]
   Majidi S, 2015, CURR DIABETES REP, V15, DOI 10.1007/s11892-015-0619-0
   Malik FS, 2019, J MED INTERNET RES, V21, DOI 10.2196/12149
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   Martin F., 2015, P AUSTR LANG TECHN A, P111
   Masucci AP, 2006, PHYS REV E, V74, DOI 10.1103/PhysRevE.74.026102
   Mohammad R.Z., 2014, SOCIAL MEDIA MINING
   Moorhead SA, 2013, J MED INTERNET RES, V15, DOI 10.2196/jmir.1933
   Naumann J, 2017, NUTRIENTS, V9, DOI 10.3390/nu9080914
   Newman M. E. J., 2004, PHYS REV, V69
   Niknejad A., 2019, ANATOMY ONTOLOGY
   Perez-Perez M, 2019, J MED INTERNET RES, V21, DOI 10.2196/12610
   Pershad Y, 2018, J CLIN MED, V7, DOI 10.3390/jcm7060121
   Powers MA, 2017, DIABETES EDUCATOR, V43, P40, DOI 10.1177/0145721716689694
   Raffo J, 2016, Worldwide gender-name dictionary
   Riddle MC, 2018, DIABETES CARE, V41, P929, DOI 10.2337/dci18-0012
   Rosenstock J, 2018, ARCH ENDOCRIN METAB, V62, P424, DOI 10.20945/2359-3997000000056
   Rothe R, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOP (ICCVW), P252, DOI 10.1109/ICCVW.2015.41
   Shaw G., 2017, COMPUTATIONAL CONTEN, DOI [10.1002/pra2.2017.14505401039., DOI 10.1002/PRA2.2017.14505401039]
   Sievert C., 2014, LDAVIS METHOD VISUAL, DOI 10.13140/2.1.1394.3043
   Sigal RJ, 2004, DIABETES CARE, V27, P2518, DOI 10.2337/diacare.27.10.2518
   Silva FN, 2016, J INFORMETR, V10, P487, DOI 10.1016/j.joi.2016.03.008
   Sinnenberg L, 2017, AM J PUBLIC HEALTH, V107, pE1, DOI 10.2105/AJPH.2016.303512
   Statista, 2020, NUMB SOC US WORLDW 2
   Statista, 2019, TWITT NUMB ACT US 20
   Su G, 2010, BIOINFORMATICS, V26, P3135, DOI 10.1093/bioinformatics/btq596
   Tang L, 2018, AM J INFECT CONTROL, V46, P1375, DOI 10.1016/j.ajic.2018.05.019
   Tay J, 2015, AM J CLIN NUTR, V102, P780, DOI 10.3945/ajcn.115.112581
   Tharwat A., 2020, APPL COMPUT INFORM, V17, P168, DOI [DOI 10.1016/J.ACI.2018.08.003, 10.1016/j.aci.2018.08.003]
   Trump LJ, 2018, FAM SYST HEALTH, V36, P87, DOI 10.1037/fsh0000302
   Wallach H. M, 2009, P 26 ANN INT C MACHI, P1105, DOI DOI 10.1145/1553374.1553515
   Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918
   Whittemore R, 2018, CURR DIABETES REP, V18, DOI 10.1007/s11892-018-0986-4
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   World Health Organization, 2016, GLOBAL REPORT DIABET
   Wynn R, 2017, INT J INTEGR CARE, V17, DOI 10.5334/ijic.3261
   Xu GF, 2018, BMJ-BRIT MED J, V362, DOI 10.1136/bmj.k1497
   Yamamoto Y., 2018, TWITTER4J JAVA LIB T
   Youngji Kim, 2018, [Journal of Korean Academy of Community Health Nursing, 지역사회간호학회지], V29, P429, DOI 10.12799/jkachn.2018.29.4.429
NR 80
TC 1
Z9 1
U1 0
U2 23
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD SEP
PY 2020
VL 110
BP 214
EP 232
DI 10.1016/j.future.2020.04.025
PG 19
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA LZ3UK
UT WOS:000541153400018
OA Green Published
DA 2022-08-23
ER

PT J
AU Yu, L
   Qiu, PY
   Gao, JL
   Lu, F
AF Yu, Li
   Qiu, Peiyuan
   Gao, Jialiang
   Lu, Feng
TI A Knowledge-Based Filtering Method for Open Relations among Geo-Entities
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE geographical knowledge service; knowledge graphs; open relation
   extraction; confidence assessment
AB Knowledge graphs (KGs) are crucial resources for supporting geographical knowledge services. Given the vast geographical knowledge in web text, extraction of geo-entity relations from web text has become the core technology for construction of geographical KGs; furthermore, it directly affects the quality of geographical knowledge services. However, web text inevitably contains noise and geographical knowledge can be sparsely distributed, both of which greatly restrict the quality of geo-entity relationship extraction. We propose a method for filtering geo-entity relations based on existing knowledge bases (KBs). Accordingly, ontology knowledge, fact knowledge, and synonym knowledge are integrated to generate geo-related knowledge. Then, the extracted geo-entity relationships and the geo-related knowledge are transferred into vectors, and the maximum similarity between vectors is the confidence value of one extracted geo-entity relationship triple. Our method takes full advantage of existing KBs to assess the quality of geographical information in web text, which is helpful to improve the richness and freshness of geographical KGs. Compared with the Stanford OpenIE method, our method decreased the mean square error (MSE) from 0.62 to 0.06 in the confidence interval [0.7, 1], and improved the area under the receiver operating characteristic (ROC) curve (AUC) from 0.51 to 0.89.
C1 [Yu, Li] Chinese Acad Sci, Natl Sci Lib, Beijing 100190, Peoples R China.
   [Yu, Li; Qiu, Peiyuan; Gao, Jialiang; Lu, Feng] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.
   [Gao, Jialiang; Lu, Feng] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Lu, Feng] Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Fujian, Peoples R China.
   [Lu, Feng] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China.
RP Lu, F (通讯作者)，Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.; Lu, F (通讯作者)，Univ Chinese Acad Sci, Beijing 100049, Peoples R China.; Lu, F (通讯作者)，Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Fujian, Peoples R China.; Lu, F (通讯作者)，Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China.
EM yul@mail.las.ac.cn; qiupy@lreis.ac.cn; gaojl@lreis.ac.cn;
   luf@lreis.ac.cn
OI Yu, Li/0000-0002-4374-8743
FU National Natural Science Foundation of China [41631177, 41801320];
   National Key Research and Development Program [2016YFB0502104]; State
   Key Laboratory of Resources and Environmental Information System
FX This research was supported by the National Natural Science Foundation
   of China (Grant No. 41631177, No. 41801320), the National Key Research
   and Development Program (Grant No. 2016YFB0502104), and a grant from
   State Key Laboratory of Resources and Environmental Information System.
   Their supports are gratefully acknowledged. We also thank the anonymous
   referees for their helpful comments and suggestions.
CR Angeli G, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P344
   Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   COHEN J, 1960, EDUC PSYCHOL MEAS, V20, P37, DOI 10.1177/001316446002000104
   Egenhofer M., 1989, P 3 INT C FDN DAT OR, P457
   Ehrlinger L., 2016, P SEMANTICS 2016 LEI
   Ferrucci D, 2013, ARTIF INTELL, V199, P93, DOI 10.1016/j.artint.2012.06.009
   Galarraga L.A., 2013, P 22 INT C WORLD WID, P413, DOI DOI 10.1145/2488388.2488425
   Hu Y., 2014, P GEOGR INF RETR WOR
   Hu YJ, 2015, T GIS, V19, P398, DOI 10.1111/tgis.12151
   Huang B., 2013, P ACM SIGMOD INT C M, P1
   ISO, 191572013 ISO
   Ke<ss>ler C., 2012, P 7 INT C GEOGR INF
   Kruk S.R., 2009, SEMANTIC WEB ONTOLOG, DOI [10.1007/978-3-540-85434-0_3, DOI 10.1007/978-3-540-85434-0_3]
   Le Q., 2014, P 31 INT C MACH LEAR, P1188, DOI DOI 10.1007/978-1-4614-3223-4
   Lehmann J., 2015, SEMANTIC WEB, V5, P1
   Li FR, 2017, PROC VLDB ENDOW, V10, P1370, DOI 10.14778/3137628.3137646
   Li Xiang, 2011, Instrument Techniques and Sensor, P1
   Luciano DC., 2013, CLAUSIE CLAUSE BASED, P355, DOI DOI 10.1145/2488388.2488420
   Martinez-Minaya J, 2018, STOCH ENV RES RISK A, V32, P3227, DOI 10.1007/s00477-018-1548-7
   Pal H., 2016, P 5 WORKSH AUT KNOWL, P35, DOI DOI 10.18653/V1/W16-1307
   Saha S., 2018, P 27 INT C COMP LING, P2288
   Schmitz M., 2012, P 2012 JOINT C EMP M, P523
   Senaratne H, 2017, INT J GEOGR INF SCI, V31, P139, DOI 10.1080/13658816.2016.1189556
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Yu L, 2018, INT J DIGIT EARTH, V11, P845, DOI 10.1080/17538947.2017.1359688
   Zhang HT, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2729, DOI 10.1145/3219819.3219977
NR 26
TC 0
Z9 2
U1 4
U2 11
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 2220-9964
J9 ISPRS INT J GEO-INF
JI ISPRS Int. Geo-Inf.
PD FEB
PY 2019
VL 8
IS 2
AR 59
DI 10.3390/ijgi8020059
PG 14
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA HO2RD
UT WOS:000460762100008
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, M
   Wang, JL
AF Zhang, Min
   Wang, Juanle
TI Trend Analysis of Global Disaster Education Research Based on Scientific
   Knowledge Graphs
SO SUSTAINABILITY
LA English
DT Article
DE disaster education; scientific knowledge graph; bibliometrics; network
   analysis; geographic spatial distribution
ID PATTERNS; IMPACTS
AB Disaster reduction and prevention have become urgent issues worldwide. Disaster education is an effective way to deal with frequent global disaster risks, carry out disaster prevention and relief measures in a timely manner, and reduce disaster losses. Based on the Web of Science database, using bibliometrics and network analysis methods based on scientific knowledge graphs, we conducted a visual analysis of global disaster education research trends from the perspectives of national cooperation spatial distribution, research hotspot mining, hybrid network analysis, and institutional cooperation spatial distribution of disaster education. The following conclusions were drawn. (1) The spatial distribution of disaster education research is uneven: it is clustered in Europe, evenly distributed in Asia and Africa, and scattered in North America and Oceania. Moreover, the United States in North America, China and Japan in Asia, and Australia in Oceania have the largest number of articles. (2) The field of disaster education focuses mainly on the themes of education, disaster nursing, disaster risk and reduction, disaster awareness, and earthquakes. The general trend of research hotspots is disaster risk >> disaster preparedness >> disaster nurse >> disaster awareness >> disaster risk and reduction, realizing the great transformation from disaster rescue to disaster preparedness and then to disaster prevention awareness. (3) A hybrid network of keywords and countries revealed the research focus of various countries in the field of disaster education, and a hybrid network of keywords and categories showed that the research on disaster education primarily focuses on the disciplines of environment, nursing, geography, geology, atmosphere, ecology, and psychology. On this basis, the breadth and depth of the disaster education system should be further improved. (4) The spatial layout of disaster education research institutions showed a clustered distribution of research institutions in North America and Europe, even distribution in some regions in Asia, and sporadic distribution in Africa and Oceania. In-depth cooperation among institutions should be strengthened, the degree of attention paid to disaster education should be increased, and external cooperation should be actively carried out to improve the level of disaster education, particularly in Africa and Asia.
C1 [Zhang, Min; Wang, Juanle] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.
   [Zhang, Min] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China.
   [Wang, Juanle] China Pakistan Joint Res Ctr Earth Sci, Islamabad 45320, Pakistan.
   [Wang, Juanle] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.
RP Wang, JL (通讯作者)，Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.; Wang, JL (通讯作者)，China Pakistan Joint Res Ctr Earth Sci, Islamabad 45320, Pakistan.; Wang, JL (通讯作者)，Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.
EM zhangmin@lreis.ac.cn; wangjl@igsnrr.ac.cn
RI , Juanle/R-8881-2016
OI , Juanle/0000-0002-5641-0813
CR [Anonymous], 2009, CENTRAL PEOPLES GOVT
   Bastian M., 2009, P INT AAAI C WEB SOC, P361, DOI 10.13140/2.1.1341.1520
   Baytiyeh H, 2018, EDUC URBAN SOC, V50, P230, DOI 10.1177/0013124517713111
   Chen CM, 2014, J ASSOC INF SCI TECH, V65, P334, DOI 10.1002/asi.22968
   Chen CM, 2013, TECHNOL ANAL STRATEG, V25, P619, DOI 10.1080/09537325.2013.801949
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Chen Z.Q., 2020, GUIZHOU SOC SCI, V4, P101
   Cheng L.N., 2021, CHIN EVID BASED NURS, V7, P29, DOI [10.12102/j.issn.2095-8668.2021.01.005, DOI 10.12102/J.ISSN.2095-8668.2021.01.005]
   Chi P., 2016, INFORM SCI, V34, P108
   Du Y.H, 2010, ED RES EXP, V5, P70
   Hoffmann R, 2017, WORLD DEV, V96, P32, DOI 10.1016/j.worlddev.2017.02.016
   Jing Yang, 2017, 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), P169, DOI 10.1109/ICBDA.2017.8078800
   Kitagawa K, 2015, HIST EDUC, V44, P371, DOI 10.1080/0046760X.2014.979255
   Seo HJ, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13052505
   Shen S, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0191250
   Shi S.H, 2020, DISASTER REDUCT CHIN, V23, P54
   Tanaka K, 2005, APPL GEOGR, V25, P201, DOI 10.1016/j.apgeog.2005.07.001
   Wang W.Z, 2018, LIFE DISASTER, V04, P22
   Zhang C.Y, 2020, SHANGHAI ED, V17, P30
   Zhang D, 2016, CLASS MT RIVERS, V11, P196
   Zhang DS, 2014, ADV BUS FINANCE, V2, P156, DOI 10.5729/abf.vol2.156
   Zhang Y., 2012, J ED STUD, V8, P45, DOI [10.14082/j.cnki.1673-1298.2012.05.002, DOI 10.14082/J.CNKI.1673-1298.2012.05.002]
   [张英 Zhang Ying], 2011, [灾害学, Journal of Catastrophology], V26, P109
   Zhou Z.F., 2021, J INTELL, V40, P171, DOI [10.3969/j.issn.1002-1965.2021.10.024, DOI 10.3969/J.ISSN.1002-1965.2021.10.024]
NR 25
TC 1
Z9 1
U1 15
U2 15
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD FEB
PY 2022
VL 14
IS 3
AR 1492
DI 10.3390/su14031492
PG 13
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA ZA7AS
UT WOS:000756312600001
OA gold
DA 2022-08-23
ER

PT J
AU Li, BQ
   Chen, HM
AF Li, Baiqing
   Chen, Hongming
TI Prediction of Compound Synthesis Accessibility Based on Reaction
   Knowledge Graph
SO MOLECULES
LA English
DT Article
DE synthesis accessibility; reaction knowledge graph; graph model
ID DE-NOVO DESIGN; CHEMICAL SPACE; BALANCING NOVELTY; RANDOM FOREST; DRUG
   DESIGN; CLASSIFICATION; COMPLEXITY; DATABASE; UNIVERSE; TOOLS
AB With the increasing application of deep-learning-based generative models for de novo molecule design, the quantitative estimation of molecular synthetic accessibility (SA) has become a crucial factor for prioritizing the structures generated from generative models. It is also useful for helping in the prioritization of hit/lead compounds and guiding retrosynthesis analysis. In this study, based on the USPTO and Pistachio reaction datasets, a chemical reaction network was constructed for the identification of the shortest reaction paths (SRP) needed to synthesize compounds, and different SRP cut-offs were then used as the threshold to distinguish a organic compound as either an easy-to-synthesize (ES) or hard-to-synthesize (HS) class. Two synthesis accessibility models (DNN-ECFP model and graph-based CMPNN model) were built using deep learning/machine learning algorithms. Compared to other existing synthesis accessibility scoring schemes, such as SYBA, SCScore, and SAScore, our results show that CMPNN (ROC AUC: 0.791) performs better than SYBA (ROC AUC: 0.76), albeit marginally, and outperforms SAScore and SCScore. Our prediction models based on historical reaction knowledge could be a potential tool for estimating molecule SA.
C1 [Li, Baiqing; Chen, Hongming] Guangdong Lab Anim Monitoring Inst, Guangdong Prov Key Lab Lab Anim, Guangzhou 510663, Peoples R China.
   [Li, Baiqing] Chinese Acad Sci, Guangzhou Inst Biomed & Hlth, State Key Lab Resp Dis, Guangzhou 510530, Peoples R China.
   [Li, Baiqing; Chen, Hongming] Guangzhou Regenerat Med & Hlth Guangdong Lab, Bioland Lab, Guangzhou 510530, Peoples R China.
   [Chen, Hongming] Guangzhou Int Bio Isl, Guangzhou Lab, 9 XinDaoHuanBei Rd, Guangzhou 510005, Peoples R China.
RP Chen, HM (通讯作者)，Guangdong Lab Anim Monitoring Inst, Guangdong Prov Key Lab Lab Anim, Guangzhou 510663, Peoples R China.; Chen, HM (通讯作者)，Guangzhou Regenerat Med & Hlth Guangdong Lab, Bioland Lab, Guangzhou 510530, Peoples R China.; Chen, HM (通讯作者)，Guangzhou Int Bio Isl, Guangzhou Lab, 9 XinDaoHuanBei Rd, Guangzhou 510005, Peoples R China.
EM li_baiqing@grmh-gdl.cn; chen_hongming@grmh-gdl.cn
OI li, baiqing/0000-0001-8351-8267
CR [Anonymous], 2020, FILB V2 1
   [Anonymous], 2020, PIST REL V02
   [Anonymous], 2020, HAZELNUT V2 1
   Berthold MR, 2008, STUD CLASS DATA ANAL, P319, DOI 10.1145/1656274.1656280
   BERTZ SH, 1981, J AM CHEM SOC, V103, P3599, DOI 10.1021/ja00402a071
   Besnard J, 2012, NATURE, V492, P215, DOI 10.1038/nature11691
   Blomberg N, 2009, J COMPUT AID MOL DES, V23, P513, DOI 10.1007/s10822-009-9264-5
   Boda K, 2007, J COMPUT AID MOL DES, V21, P311, DOI 10.1007/s10822-006-9099-2
   Bohacek RS, 1996, MED RES REV, V16, P3, DOI 10.1002/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.3.CO;2-D
   Butina D, 1999, J CHEM INF COMP SCI, V39, P747, DOI 10.1021/ci9803381
   Chevillard F, 2015, J CHEM INF MODEL, V55, P1824, DOI 10.1021/acs.jcim.5b00203
   Coley CW, 2019, SCIENCE, V365, P557, DOI 10.1126/science.aax1566
   Coley CW, 2019, J CHEM INF MODEL, V59, P2529, DOI 10.1021/acs.jcim.9b00286
   Coley CW, 2018, J CHEM INF MODEL, V58, P252, DOI 10.1021/acs.jcim.7b00622
   Coley CW, 2017, ACS CENTRAL SCI, V3, P434, DOI 10.1021/acscentsci.7b00064
   De Coster W, 2018, BIOINFORMATICS, V34, P2666, DOI 10.1093/bioinformatics/bty149
   Deelder W, 2019, FRONT GENET, V10, DOI 10.3389/fgene.2019.00922
   Ertl P, 2009, J CHEMINFORMATICS, V1, DOI 10.1186/1758-2946-1-8
   Genheden S, 2020, J CHEMINFORMATICS, V12, DOI 10.1186/s13321-020-00472-1
   Gillet VJ, 1995, PERSPECT DRUG DISCOV, V3, P34, DOI 10.1007/BF02174466
   Green CP, 2018, FUTURE MED CHEM, V10, P2573, DOI 10.4155/fmc-2018-0161
   Gromski PS, 2019, NAT REV CHEM, V3, P119, DOI 10.1038/s41570-018-0066-y
   Grzybowski BA, 2009, NAT CHEM, V1, P31, DOI [10.1038/NCHEM.136, 10.1038/nchem.136]
   Heifets A., 2014, THESIS U TORONTO CAN
   Hoffmann T, 2019, DRUG DISCOV TODAY, V24, P1148, DOI 10.1016/j.drudis.2019.02.013
   Huang Q, 2011, J CHEM INF MODEL, V51, P2768, DOI 10.1021/ci100216g
   Kutchukian PS, 2010, EXPERT OPIN DRUG DIS, V5, P789, DOI 10.1517/17460441.2010.497534
   Li J, 2015, ORG BIOMOL CHEM, V13, P7164, DOI 10.1039/c5ob00709g
   Liu C.-H., 2011, ARXIV 2011 ARXIV2011
   Llanos EJ, 2019, P NATL ACAD SCI USA, V116, P12660, DOI 10.1073/pnas.1816039116
   Loving K, 2010, CURR TOP MED CHEM, V10, P14, DOI 10.2174/156802610790232305
   Lowe D.M., 2012, EXTRACTION CHEM STRU
   MATTHEWS BW, 1975, BIOCHIM BIOPHYS ACTA, V405, P442, DOI 10.1016/0005-2795(75)90109-9
   Medina-Franco JL, 2014, EXPERT OPIN DRUG DIS, V9, P151, DOI 10.1517/17460441.2014.872624
   NetworkX, US
   OpenEye Scientific Software Inc, US
   Plowright AT, 2012, DRUG DISCOV TODAY, V17, P56, DOI 10.1016/j.drudis.2011.09.012
   Polishchuk PG, 2013, J COMPUT AID MOL DES, V27, P675, DOI 10.1007/s10822-013-9672-4
   Popova M, 2018, SCI ADV, V4, DOI 10.1126/sciadv.aap7885
   PubChem Database, US
   reaxys, REAXYS DATABASE
   Reymond JL, 2010, MEDCHEMCOMM, V1, P30, DOI 10.1039/c0md00020e
   Rogers D, 2010, J CHEM INF MODEL, V50, P742, DOI 10.1021/ci100050t
   Ruddigkeit L, 2012, J CHEM INF MODEL, V52, P2864, DOI 10.1021/ci300415d
   Schneider G, 2005, NAT REV DRUG DISCOV, V4, P649, DOI 10.1038/nrd1799
   Simm GN, 2019, J PHYS CHEM A, V123, P385, DOI 10.1021/acs.jpca.8b10007
   Song Y, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2831
   Svetnik V, 2003, J CHEM INF COMP SCI, V43, P1947, DOI 10.1021/ci034160g
   Thakkar A, 2021, CHEM SCI, V12, P3339, DOI 10.1039/d0sc05401a
   Thakkar A, 2020, CHEM SCI, V11, P154, DOI 10.1039/c9sc04944d
   Vamathevan J, 2019, NAT REV DRUG DISCOV, V18, P463, DOI 10.1038/s41573-019-0024-5
   van Hilten N, 2019, J CHEM INF MODEL, V59, P644, DOI 10.1021/acs.jcim.8b00737
   Vorsilak M, 2020, J CHEMINFORMATICS, V12, DOI 10.1186/s13321-020-00439-2
   Vorsilak M, 2017, J CHEMINFORMATICS, V9, DOI 10.1186/s13321-017-0206-2
   Yang XF, 2017, SCI TECHNOL ADV MAT, V18, P972, DOI 10.1080/14686996.2017.1401424
   Zhang QY, 2007, J CHEM INF MODEL, V47, P1, DOI 10.1021/ci050520j
NR 56
TC 0
Z9 0
U1 10
U2 11
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1420-3049
J9 MOLECULES
JI Molecules
PD FEB
PY 2022
VL 27
IS 3
AR 1039
DI 10.3390/molecules27031039
PG 17
WC Biochemistry & Molecular Biology; Chemistry, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Chemistry
GA 2H6CH
UT WOS:000814379700001
PM 35164303
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Li, T
   Hu, YJ
   Ju, AK
   Hu, ZR
AF Li, Tao
   Hu, Yongjin
   Ju, Ankang
   Hu, Zhuoran
TI Adversarial Active Learning for Named Entity Recognition in
   Cybersecurity
SO CMC-COMPUTERS MATERIALS & CONTINUA
LA English
DT Article
DE Adversarial learning; active learning; named entity recognition; dynamic
   attention mechanism
AB Owing to the continuous barrage of cyber threats, there is a massive amount of cyber threat intelligence. However, a great deal of cyber threat intelligence come from textual sources. For analysis of cyber threat intelligence, many security analysts rely on cumbersome and time-consuming manual efforts. Cybersecurity knowledge graph plays a significant role in automatics analysis of cyber threat intelligence. As the foundation for constructing cybersecurity knowledge graph, named entity recognition (NER) is required for identifying critical threat-related elements from textual cyber threat intelligence. Recently, deep neural network-based models have attained very good results in NER. However, the performance of these models relies heavily on the amount of labeled data. Since labeled data in cybersecurity is scarce, in this paper, we propose an adversarial active learning framework to effectively select the informative samples for further annotation. In addition, leveraging the long short-term memory (LSTM) network and the bidirectional LSTM (BiLSTM) network, we propose a novel NER model by introducing a dynamic attention mechanism into the BiLSTM-LSTM encoder-decoder. With the selected informative samples annotated, the proposed NER model is retrained. As a result, the performance of the NER model is incrementally enhanced with low labeling cost. Experimental results show the effectiveness of the proposed method.
C1 [Li, Tao; Hu, Yongjin; Ju, Ankang] Zhengzhou Inst Informat Sci & Technol, Zhengzhou 450001, Peoples R China.
   [Hu, Zhuoran] Univ Wisconsin, Coll Letters & Sci, Madison, WI 53706 USA.
RP Hu, YJ (通讯作者)，Zhengzhou Inst Informat Sci & Technol, Zhengzhou 450001, Peoples R China.
EM thechanger@126.com
FU National Natural Science Foundation of China [61501515]
FX This work was supported by the National Natural Science Foundation of
   China under grant 61501515.
CR Chandola V., 2017, ENCY MACHINE LEARNIN, P9
   Cheng M., 2019, PATTERN RECOGN, V32, P90
   Chiu Jason PC, 2016, T ASS COMPUT LINGUIS, V4, P357, DOI 10.1162/tacl_a_00104
   Dionisio N, 2019, P INT JOINT C NEUR N, P1, DOI 10.1109/IJCNN.2019.8852475
   dos Santos CN, 2014, PR MACH LEARN RES, V32, P1818
   Erdogan TK, 2018, 2ND INTERNATIONAL CONGRESS OF NURSING (ICON-2018), P1
   Gal Y, 2017, PR MACH LEARN RES, V70
   Gasmi H, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9193945
   Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
   Gui T, 2017, P 2017 C EMP METH NA, P2411, DOI [10.18653/v1/D17-1256, DOI 10.18653/V1/D17-1256]
   Houlsby N., 2011, COMPUTING RES REPOSI
   Kuo WC, 2018, LECT NOTES COMPUT SC, V11072, P715, DOI 10.1007/978-3-030-00931-1_82
   Li J., 2020, IEEE T KNOWLEDGE DAT
   Lin Yang, 2017, Medical Image Computing and Computer Assisted Intervention - MICCAI 2017. 20th International Conference. Proceedings: LNCS 10435, P399, DOI 10.1007/978-3-319-66179-7_46
   Long M, 2020, ROUT STUD TW CEN LIT, P1
   Mesnil G., 2013, INTERSPEECH 2013 14, P3771
   Mizokami S., 2017, DEEP ACTIVE LEARNING, P79
   Phandi P., 2018, P 12 INT WORKSH SEM, P697, DOI 10.18653/v1/s18-1113
   Pingle A, 2019, PROCEEDINGS OF THE 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2019), P879, DOI 10.1145/3341161.3343519
   Pradhan B, 2017, LASER SCANNING APPL
   Satyapanich T., 2020, P 34 ASS ADV ART INT
   Shen Y, 2018, INT J SUSTAIN TRANSP, V12, P686, DOI 10.1080/15568318.2018.1429696
   Sinha S, 2019, IEEE I CONF COMP VIS, P5971, DOI 10.1109/ICCV.2019.00607
   Tang Z, 2015, CLUSTER COMPUT, V18, P493, DOI 10.1007/s10586-015-0426-z
   Wang KZ, 2017, IEEE T CIRC SYST VID, V27, P2591, DOI 10.1109/TCSVT.2016.2589879
   Zeng DJ, 2018, CMC-COMPUT MATER CON, V55, P121, DOI 10.3970/cmc.2018.055.121
   Zhai FF, 2017, AAAI CONF ARTIF INTE, P3365
   Zhang ZJ, 2018, PROCEEDINGS OF GEOSHANGHAI 2018 INTERNATIONAL CONFERENCE: FUNDAMENTALS OF SOIL BEHAVIOURS, P185, DOI 10.1007/978-981-13-0125-4_20
   Zhe Jin, 2019, Knowledge Science, Engineering and Management. 12th International Conference, KSEM 2019. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 11775), P537, DOI 10.1007/978-3-030-29551-6_48
   Zheng SC, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1227, DOI 10.18653/v1/P17-1113
   Zhu J., 2017, ARXIV170207956V5
NR 31
TC 1
Z9 1
U1 10
U2 31
PU TECH SCIENCE PRESS
PI HENDERSON
PA 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA
SN 1546-2218
EI 1546-2226
J9 CMC-COMPUT MATER CON
JI CMC-Comput. Mat. Contin.
PY 2021
VL 66
IS 1
BP 407
EP 420
DI 10.32604/cmc.2020.012023
PG 14
WC Computer Science, Information Systems; Materials Science,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Materials Science
GA OO7CT
UT WOS:000587534300026
OA gold
DA 2022-08-23
ER

PT J
AU Rossanez, A
   dos Reis, JC
   Torres, RD
   de Ribaupierre, H
AF Rossanez, Anderson
   dos Reis, Julio Cesar
   Torres, Ricardo da Silva
   de Ribaupierre, Helene
TI KGen: a knowledge graph generator from biomedical scientific literature
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article; Proceedings Paper
CT 4th International Workshop on Semantics-Powered Data Mining and
   Analytics (SEPDA)
CY OCT 26, 2019
CL Auckland, NEW ZEALAND
DE Knowledge Graphs; RDF Triples; Ontologies; Information Extraction
ID ALZHEIMERS-DISEASE; WEB; ONTOLOGIES; MASHUP; DESIGN
AB Background Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer's Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be produced. A proper representation of such knowledge brings great benefits to researchers, to the scientific community, and consequently, to society. Methods In this article, we study and evaluate a semi-automatic method that generates knowledge graphs (KGs) from biomedical texts in the scientific literature. Our solution explores natural language processing techniques with the aim of extracting and representing scientific literature knowledge encoded in KGs. Our method links entities and relations represented in KGs to concepts from existing biomedical ontologies available on the Web. We demonstrate the effectiveness of our method by generating KGs from unstructured texts obtained from a set of abstracts taken from scientific papers on the Alzheimer's Disease. We involve physicians to compare our extracted triples from their manual extraction via their analysis of the abstracts. The evaluation further concerned a qualitative analysis by the physicians of the generated KGs with our software tool. Results The experimental results indicate the quality of the generated KGs. The proposed method extracts a great amount of triples, showing the effectiveness of our rule-based method employed in the identification of relations in texts. In addition, ontology links are successfully obtained, which demonstrates the effectiveness of the ontology linking method proposed in this investigation. Conclusions We demonstrate that our proposal is effective on building ontology-linked KGs representing the knowledge obtained from biomedical scientific texts. Such representation can add value to the research in various domains, enabling researchers to compare the occurrence of concepts from different studies. The KGs generated may pave the way to potential proposal of new theories based on data analysis to advance the state of the art in their research domains.
C1 [Rossanez, Anderson; dos Reis, Julio Cesar] Univ Estadual Campinas, Inst Comp, Campinas, SP, Brazil.
   [Torres, Ricardo da Silva] NTNU Norwegian Univ Sci & Technol, Fac Informat Technol & Elect Engn, Dept ICT & Nat Sci, Alesund, Norway.
   [de Ribaupierre, Helene] Cardiff Univ, Sch Comp Sci & Informat, Cardiff, Wales.
RP Rossanez, A (通讯作者)，Univ Estadual Campinas, Inst Comp, Campinas, SP, Brazil.
EM anderson.rossanez@ic.unicamp.br
OI Rossanez, Anderson/0000-0001-7103-4281
FU Sao Paulo Research Foundation (FAPESP) [2017/02325-5]
FX This work is supported by the Sao Paulo Research Foundation (FAPESP)
   (Grant #2017/02325-5). The opinions expressed in this work do not
   necessarily reflect those of the funding agencies.
CR Angeli G, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P344
   [Anonymous], 2016, P 31 ANN ACM S APPL
   Auer S, 2007, P 2 AS C SEM WEB
   Baker C. F., 1998, P 36 ANN M ASS COMP, P86, DOI DOI 10.3115/980845.980860
   Barnickel T, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0006393
   Beckett D, 2002, COMPUT NETW, V39, P577, DOI 10.1016/S1389-1286(02)00221-9
   Belleau F, 2008, J BIOMED INFORM, V41, P706, DOI 10.1016/j.jbi.2008.03.004
   Bian D., 2019, BMC MED INFORM DECIS, V19
   Bird S, 2009, NATURAL LANGUAGE PRO
   Bizer C, 2009, IEEE INTELL SYST, V24, P87, DOI 10.1109/MIS.2009.102
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Braak H, 2011, J NEUROPATH EXP NEUR, V70, P960, DOI 10.1097/NEN.0b013e318232a379
   Candan K. S., 2001, SIGKDD EXPLOR NEWSL, V3, P6, DOI DOI 10.1145/507533.507536
   Caracciolo C, 2013, SEMANT WEB, V4, P341, DOI 10.3233/SW-130106
   Chang A. X., 2014, 201402 CSTR STANF U
   ChunYang Liu, 2013, Advanced Data Mining and Applications. 9th International Conference, ADMA 2013. Proceedings: LNCS 8347, P231, DOI 10.1007/978-3-642-53917-6_21
   Collobert R, 2011, J MACH LEARN RES, V12, P2493
   Corro Luciano Del, 2013, CLAUSE BASED OPEN IN, V13, P355, DOI DOI 10.1145/2488388.2488420
   Ehrlinger L., 2016, JOINT P POSTERS DEMO, V1695, P13
   Ellson J, 2004, MATH VIS, P127
   EVANS DA, 1989, JAMA-J AM MED ASSOC, V262, P2551, DOI 10.1001/jama.262.18.2551
   Exner P, 2012, WOLE ISWC
   Fader A, 2011, P C EMP METH NAT LAN
   Ferragina P., 2010, P 19 ACM INT C INF K, P1625, DOI DOI 10.1145/1871437.1871689
   Ferrucci DA, 2012, IBM J RES DEV, V56, DOI 10.1147/JRD.2012.2184356
   FILLMORE CJ, 1976, ANN NY ACAD SCI, V280, P20, DOI 10.1111/j.1749-6632.1976.tb25467.x
   Finkel J. R., 2005, P 43 ANN M ASS COMP, P363, DOI DOI 10.3115/1219840.1219885
   Gangemi A, 2017, SEMANT WEB, V8, P873, DOI 10.3233/SW-160240
   Gaugler J, 2019, ALZHEIMERS DEMENT, V15, P321, DOI 10.1016/j.jalz.2019.01.010
   Gitler AD, 2017, DIS MODEL MECH, V10, P499, DOI 10.1242/dmm.030205
   Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081
   He X, 2019, BMC MED INFORM DECIS, V19, DOI 10.1186/s12911-019-0857-1
   Kamdar MR, 2015, DATABASE-OXFORD, DOI 10.1093/database/bav049
   Kanza S, 2019, EXPERT OPIN DRUG DIS, V14, P433, DOI 10.1080/17460441.2019.1586880
   Kertkeidkachorn KI17] Natthawut, 2017, AAAI WORKSH
   Kochut A, 2011, IBM J RES DEV, V55, DOI 10.1147/JRD.2011.2170920
   Kumar A, 2005, LECT NOTES ARTIF INT, V3581, P213
   Lam HYK, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-S3-S4
   Lam HYK, 2006, P ASWC INT WORKSH SE, P158
   Li F, 2017, BMC BIOINFORMATICS, V18, DOI 10.1186/s12859-017-1609-9
   Liu YH, 2017, INFORM PROCESS MANAG, V53, P851, DOI 10.1016/j.ipm.2017.03.004
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   Martinez-Rodriguez JL, 2018, EXPERT SYST APPL, P113
   Martinez-Romero M, 2017, J BIOMED SEMANT, V8, DOI 10.1186/s13326-017-0128-y
   Moro A., 2014, TACL, V2, P231, DOI DOI 10.1162/TACL_A_00179
   Neumann M, 2019, SIGBIOMED WORKSHOP ON BIOMEDICAL NATURAL LANGUAGE PROCESSING (BIONLP 2019), P319
   Nolin MA, 2012, BRIEF BIOINFORM, V13, P98, DOI 10.1093/bib/bbr003
   Palmer M, 2005, COMPUT LINGUIST, V31, P71, DOI 10.1162/0891201053630264
   Peng Y, 2014, DATABASE, V2014, P2014
   Punyakanok V., 2008, COMPUT LINGUIST, V34, P2
   Regino A.G., 2019, P WORKSH SEM WEB SOL, V2477, P40
   Ropper AH, ADAMS VICTORS PRINCI, P1645
   Rossanez A, 2019, P 4 INT WORKSH SEM P, P12
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Ruan T, 2019, BMC MED INFORM DECIS, V19
   Salvadores M, 2012, INT SEM WEB C
   Schmitz M., 2012, P 2012 JOINT C EMP M, P523
   Schuler Karin Kipper, 2005, AAI3179808
   Schwartz Ariel S, 2003, Pac Symp Biocomput, P451
   Setia N, 2018, BUILD KNOWLEDGE GRAP
   Shi LX, 2017, BIOMED RES INT, V2017, DOI 10.1155/2017/2858423
   Sousa RT, 2020, BMC BIOINFORMATICS, V21, DOI 10.1186/s12859-019-3296-1
   Whetzel PL, 2011, NUCLEIC ACIDS RES, V39, pW541, DOI 10.1093/nar/gkr469
   Zhang D., 2015, ARXIV150801006
NR 64
TC 8
Z9 8
U1 4
U2 18
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6947
J9 BMC MED INFORM DECIS
JI BMC Med. Inform. Decis. Mak.
PD DEC 14
PY 2020
VL 20
SU 4
SI SI
AR 314
DI 10.1186/s12911-020-01341-5
PG 24
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Science (CPCI-S)
SC Medical Informatics
GA PG5QX
UT WOS:000599790500004
PM 33317512
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Ezaldeen, H
   Bisoy, SK
   Misra, R
   Alatrash, R
AF Ezaldeen, Hadi
   Bisoy, Sukant Kishoro
   Misra, Rachita
   Alatrash, Rawaa
TI Semantics-Aware Context-Based Learner Modelling Using Normalized PSO for
   Personalized E-learning
SO JOURNAL OF WEB ENGINEERING
LA English
DT Article
DE Personalized E-learning recommendation; contextual learner model;
   semantic analysis; knowledge graph; normalized PSO; prefix tree
ID RECOMMENDATION ALGORITHM
AB E-learning proves its importance in the diverse educational levels over traditional education. An adaptive e-learning system needs to deduce the learner model for adding personalization to instructional websites. The learner model is the perception repository about the e-content user, which can be inferred implicitly by employing meaningful semantic analysis of the text. In this research, a novel methodology is proposed to conceptually deduce the semantic learner model for personalized e-learning recommendations. Firstly, Conceptual Learner Model (CLM) is developed based on the learner's behavior and context-based text semantic representation by exploiting concepts from the ConceptNet knowledge base, with a significant association of patterns and rules. Then, Expanded Contextual Learner Model (ECLM) is developed by exploring the latent semantics in graphs to add concepts with the common-sense meanings that exceeded the named entities. The learner's knowledge graph is defined based on contextually associated concepts. Semantic relations in ConceptNet are exploited to extend learner models. The Normalized Particle Swarm Optimization (NPSO) algorithm is used to learn the importance of the relation types between the concepts. Thus, CLM and ECLM each are represented as a vector of weighted concepts in which updating is obtained automatically. The proposed recommendation system incorporates dynamic learner models to predict an appropriate e-content with the highest ranking, matching the true needs of a particular learner. Our simulation results show that the performance of ECLM is better Mean Reciprocal Rank (MRR) value 0.780 than other existing methods.
C1 [Ezaldeen, Hadi; Bisoy, Sukant Kishoro; Misra, Rachita; Alatrash, Rawaa] CV Raman Global Univ, Dept Comp Sci & Engn, Bhubaneswar 752054, Odisha, India.
RP Ezaldeen, H (通讯作者)，CV Raman Global Univ, Dept Comp Sci & Engn, Bhubaneswar 752054, Odisha, India.
EM hadi.talal@gmail.com; sukantabisoyi@yahoo.com;
   rachita.dhunu.misra@gmail.com; rawaa.alatrash@gmail.com
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Benzarti S, 2015, ADV INTELL SYST, V348, P227, DOI 10.1007/978-3-319-18503-3_23
   Boratto L, 2017, NEUROCOMPUTING, V254, P79, DOI 10.1016/j.neucom.2016.10.079
   Cui WJ, 2017, INT CONF BIG DATA, P455, DOI 10.1109/BIGCOMP.2017.7881756
   Ezaldeen Hadi, 2020, International Journal of Electronic Business, V15, P389, DOI 10.1504/IJEB.2020.111095
   Ezaldeen H., 2019, 2019 INT C INT COMP, P1, DOI [10.1109/ICICRS46726.2019.9 555866, DOI 10.1109/ICICRS46726.2019.9555866]
   Ezaldeen H, 2022, J WEB SEMANT, V72, DOI 10.1016/j.websem.2021.100700
   Guo JJ, 2016, PROCEEDINGS 2016 THIRD INTERNATIONAL CONFERENCE ON TRUSTWORTHY SYSTEMS AND THEIR APPLICATIONS (TSA), P48, DOI 10.1109/TSA.2016.18
   Havasi Catherine, 2007, RECENT ADV NATURAL L
   Ilievski F, 2021, KNOWL-BASED SYST, V229, DOI 10.1016/j.knosys.2021.107347
   Jiang HY, 2020, WORLD WIDE WEB, V23, P1693, DOI 10.1007/s11280-019-00752-3
   Karidi DP, 2018, J AMB INTEL HUM COMP, V9, P2035, DOI 10.1007/s12652-017-0491-7
   Keshavarz M., 2012, P AS PAC IND ENG MAN, P1917
   Kundu S. S., 2021, COMPUTATIONAL INTELL, V197, DOI [10.1007/978-981-15-8744-35, DOI 10.1007/978-981-15-8744-35]
   Liu H, 2004, BT TECHNOL J, V22, P211, DOI 10.1023/B:BTTJ.0000047600.45421.6d
   Liu XJ, 2019, CLUSTER COMPUT, V22, pS2823, DOI 10.1007/s10586-017-1560-6
   Ma HF, 2017, INFORM SCIENCES, V385, P325, DOI 10.1016/j.ins.2016.12.047
   Mangaroska K, 2019, IEEE T LEARN TECHNOL, V12, P516, DOI 10.1109/TLT.2018.2868673
   Martins AF, 2021, SOFT COMPUT, V25, P11019, DOI 10.1007/s00500-021-05836-9
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Normadhi NBA, 2019, COMPUT EDUC, V130, P168, DOI 10.1016/j.compedu.2018.11.005
   Ouf S, 2017, COMPUT HUM BEHAV, V72, P796, DOI 10.1016/j.chb.2016.08.030
   Pennacchiotti M., 2012, P 21 ACM INT C INF K, P165, DOI DOI 10.1145/2396761.2396786
   Piao G., 2016, P 2016 C US MOD AD P, P333, DOI [10.1145/2930238.2930367, DOI 10.1145/2930238.2930367]
   Piao GY, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2057, DOI 10.1145/2983323.2983908
   Raj NS, 2022, J COMPUT EDUC, V9, P113, DOI 10.1007/s40692-021-00199-4
   Shu JB, 2018, MULTIMEDIA SYST, V24, P163, DOI 10.1007/s00530-017-0539-8
   Speer R., 2013, PEOPLES WEB MEETS NL, P161, DOI DOI 10.1007/978-3-642-35085-6_6
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Wang J., 2021, COMPUTERS ED ARTIFIC, V2
   Zarrinkalam F, 2015, 2015 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT), VOL 1, P469, DOI 10.1109/WI-IAT.2015.182
   Zou D, 2021, INTERACT LEARN ENVIR, V29, P270, DOI 10.1080/10494820.2020.1789178
NR 32
TC 1
Z9 1
U1 1
U2 1
PU RIVER PUBLISHERS
PI GISTRUP
PA ALSBJERGVEJ 10, GISTRUP, 9260, DENMARK
SN 1540-9589
EI 1544-5976
J9 J WEB ENG
JI J. Web Eng.
PY 2022
VL 21
IS 4
BP 1187
EP 1223
DI 10.13052/jwe1540-9589.2148
PG 37
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0O8DM
UT WOS:000783753900009
DA 2022-08-23
ER

PT J
AU Wu, YZ
   Shen, XH
   Liu, Q
   Xiao, FL
   Li, CY
AF Wu, Yuezhong
   Shen, Xuehao
   Liu, Qiang
   Xiao, Falong
   Li, Changyun
TI A Garbage Detection and Classification Method Based on Visual Scene
   Understanding in the Home Environment
SO COMPLEXITY
LA English
DT Article
AB Garbage classification is a social issue related to people's livelihood and sustainable development, so letting service robots autonomously perform intelligent garbage classification has important research significance. Aiming at the problems of complex systems with data source and cloud service center data transmission delay and untimely response, at the same time, in order to realize the perception, storage, and analysis of massive multisource heterogeneous data, a garbage detection and classification method based on visual scene understanding is proposed. This method uses knowledge graphs to store and model items in the scene in the form of images, videos, texts, and other multimodal forms. The ESA attention mechanism is added to the backbone network part of the YOLOv5 network, aiming to improve the feature extraction ability of the network, combining with the built multimodal knowledge graph to form the YOLOv5-Attention-KG model, and deploying it to the service robot to perform real-time perception on the items in the scene. Finally, collaborative training is carried out on the cloud server side and deployed to the edge device side to reason and analyze the data in real time. The test results show that, compared with the original YOLOv5 model, the detection and classification accuracy of the proposed model is higher, and the real-time performance can also meet the actual use requirements. The model proposed in this paper can realize the intelligent decision-making of garbage classification for big data in the scene in a complex system and has certain conditions for promotion and landing.
C1 [Wu, Yuezhong; Shen, Xuehao; Xiao, Falong] Hunan Univ Technol, Coll Railway Transportat, Zhuzhou 412007, Peoples R China.
   [Wu, Yuezhong; Liu, Qiang; Li, Changyun] Hunan Univ Technol, Coll Comp Sci, Zhuzhou 412007, Peoples R China.
   [Liu, Qiang; Li, Changyun] Intelligent Informat Percept & Proc Technol Hunan, Zhuzhou, Peoples R China.
RP Liu, Q; Li, CY (通讯作者)，Hunan Univ Technol, Coll Comp Sci, Zhuzhou 412007, Peoples R China.; Liu, Q; Li, CY (通讯作者)，Intelligent Informat Percept & Proc Technol Hunan, Zhuzhou, Peoples R China.
EM liuqiang@hut.edu.cn; lichangyun@hut.edu.cn
RI Liu, Qiang/AGX-6422-2022
FU National Key R&D Program of China [2019YFE0122600, 2018YFB1700200,
   2019QY1604]; National Natural Science Foundation of China [U1836217];
   Hunan Provincial Key Research and Development Project of China
   [2019GK2133]; Natural Science Foundation of Hunan Province [2021JJ50050,
   2021JJ50058, 2020JJ6089]; Scientific Research Project of Hunan
   Provincial Department of Education [19B147]; Key Project of the
   Department of Education in Hunan Province [19A133]; Scientific Research
   Project of China Packaging Federation [17ZBLWT001KT010]; Open Platform
   Innovation Foundation of Hunan Provincial Education Department [20K046];
   Special Fund Support Project for the Construction of Innovative
   Provinces in Hunan [2019GK4009]
FX This research was supported by the National Key R&D Program of China
   (Grant nos. 2019YFE0122600, 2018YFB1700200, and 2019QY1604), National
   Natural Science Foundation of China (Grant no. U1836217), Hunan
   Provincial Key Research and Development Project of China (Grant no.
   2019GK2133), Natural Science Foundation of Hunan Province (Grant nos.
   2021JJ50050, 2021JJ50058, and 2020JJ6089), Scientific Research Project
   of Hunan Provincial Department of Education (Grant no. 19B147), Key
   Project of the Department of Education in Hunan Province (Grant no.
   19A133), Scientific Research Project of China Packaging Federation
   (Grant no. 17ZBLWT001KT010), Open Platform Innovation Foundation of
   Hunan Provincial Education Department (Grant no. 20K046), and Special
   Fund Support Project for the Construction of Innovative Provinces in
   Hunan (2019GK4009).
CR Abeywickrama T, 2016, PROC VLDB ENDOW, V9, P492, DOI 10.14778/2904121.2904125
   Algabri M, 2020, IEEE ACCESS, V8, P54663, DOI 10.1109/ACCESS.2020.2980452
   Bauer L., 2018, P 2018 C EMP METH NA, P4220
   Bochkovskiy A., 2020, ARXIV200410934
   Chen LX, 2017, SEC 2017: 2017 THE SECOND ACM/IEEE SYMPOSIUM ON EDGE COMPUTING (SEC'17), DOI 10.1145/3132211.3134451
   Chen LX, 2018, IEEE ACM T NETWORK, V26, P1619, DOI 10.1109/TNET.2018.2841758
   Chen TS, 2019, PROC CVPR IEEE, P6156, DOI 10.1109/CVPR.2019.00632
   Chen TS, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P627
   Chen ZH, 2017, CHIN CONTR CONF, P11223, DOI 10.23919/ChiCC.2017.8029147
   Dai J, 2016, PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), P1796, DOI 10.1109/ICIT.2016.7475036
   Fang Q, 2016, IEEE T MULTIMEDIA, V18, P702, DOI 10.1109/TMM.2016.2527602
   Girshick R, 2015, IEEE I CONF COMP VIS, P1440, DOI 10.1109/ICCV.2015.169
   Girshick R, 2014, PROC CVPR IEEE, P580, DOI 10.1109/CVPR.2014.81
   He JX, 2017, 2017 FIRST IEEE INTERNATIONAL CONFERENCE ON ENERGY INTERNET (ICEI 2017), P1, DOI 10.1109/ICEI.2017.8
   Jiang CH, 2018, ADV NEUR IN, V31
   Liu W, 2016, LECT NOTES COMPUT SC, V9905, P21, DOI 10.1007/978-3-319-46448-0_2
   Liu Y., 2019, MMKG MULTIMODAL KNOW
   Lu CW, 2016, LECT NOTES COMPUT SC, V9905, P852, DOI 10.1007/978-3-319-46448-0_51
   Lu JS, 2018, PROC CVPR IEEE, P7219, DOI 10.1109/CVPR.2018.00754
   Lu JS, 2017, PROC CVPR IEEE, P3242, DOI 10.1109/CVPR.2017.345
   Mao WL, 2021, RESOUR CONSERV RECY, V164, DOI 10.1016/j.resconrec.2020.105132
   Marino K, 2017, PROC CVPR IEEE, P20, DOI 10.1109/CVPR.2017.10
   Mikami K, 2018, 2018 IEEE 24TH INTERNATIONAL CONFERENCE ON EMBEDDED AND REAL-TIME COMPUTING SYSTEMS AND APPLICATIONS (RTCSA), P1, DOI 10.1109/RTCSA.2018.00010
   Pham Q. V., 2018, DECENTRALIZED COMPUT
   Prasad KNRSV, 2020, IEEE T WIREL COMMUN, V19, P4847, DOI 10.1109/TWC.2020.2987990
   Qilong Wang, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P11531, DOI 10.1109/CVPR42600.2020.01155
   Redmon J., 2018, IEEE T PATTERN ANAL, V15, P1125
   Redmon J, 2017, PROC CVPR IEEE, P6517, DOI 10.1109/CVPR.2017.690
   Redmon J, 2016, PROC CVPR IEEE, P779, DOI 10.1109/CVPR.2016.91
   Ren SQ, 2017, IEEE T PATTERN ANAL, V39, P1137, DOI 10.1109/TPAMI.2016.2577031
   Ren Z, 2017, PROC CVPR IEEE, P1151, DOI 10.1109/CVPR.2017.128
   Rezatofighi H, 2019, PROC CVPR IEEE, P658, DOI 10.1109/CVPR.2019.00075
   Seredkin AV, 2019, J PHYS CONF SER, V1359, DOI 10.1088/1742-6596/1359/1/012127
   Shen XH, 2021, MOB INF SYST, V2021, DOI 10.1155/2021/5231092
   Sun C, 2015, IEEE I CONF COMP VIS, P2596, DOI 10.1109/ICCV.2015.298
   Togacar M, 2020, MEASUREMENT, V153, DOI 10.1016/j.measurement.2019.107459
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   [王欢 Wang Huan], 2020, [工程科学学报, Chinese Journal of Engineering], V42, P476
   Wang XL, 2018, PROC CVPR IEEE, P6857, DOI 10.1109/CVPR.2018.00717
   Wang ZX, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1021
   Woo SH, 2018, LECT NOTES COMPUT SC, V11211, P3, DOI 10.1007/978-3-030-01234-2_1
   Wu Q, 2016, PROC CVPR IEEE, P4622, DOI 10.1109/CVPR.2016.500
   Wu YZ, 2020, SECUR COMMUN NETW, V2020, DOI 10.1155/2020/8843803
   [杨俊友 Yang Junyou], 2012, [中国图象图形学报, Journal of Image and Graphics], V17, P114
   Yang M., 2016, CLASSIFICATION TRASH
   Zhang PC, 2019, IEEE ACCESS, V7, P63550, DOI 10.1109/ACCESS.2019.2914270
   [张秋平 Zhang Qiuping], 2021, [计算机研究与发展, Journal of Computer Research and Development], V58, P1318
   Zhang Y, 2020, IEEE ACCESS, V8, P56607, DOI 10.1109/ACCESS.2020.2982456
   Zheng ZH, 2020, AAAI CONF ARTIF INTE, V34, P12993
   [周利坤 Zhou Likun], 2014, [机械科学与技术, Mechanical Science and Technology for Aerospace Engineering], V33, P635
   Zhou YM, 2019, IEEE WINT CONF APPL, P283, DOI 10.1109/WACV.2019.00036
   Zhu Da-qi, 2010, Control and Decision, V25, P961
NR 52
TC 0
Z9 0
U1 5
U2 5
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1076-2787
EI 1099-0526
J9 COMPLEXITY
JI Complexity
PD NOV 26
PY 2021
VL 2021
AR 1055604
DI 10.1155/2021/1055604
PG 14
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA 2R8AH
UT WOS:000821328100001
OA gold
DA 2022-08-23
ER

PT J
AU Gao, Z
   Fu, G
   Ouyang, CP
   Tsutsui, S
   Liu, XZ
   Yang, J
   Gessner, C
   Foote, B
   Wild, D
   Ding, Y
   Yu, Q
AF Gao, Zheng
   Fu, Gang
   Ouyang, Chunping
   Tsutsui, Satoshi
   Liu, Xiaozhong
   Yang, Jeremy
   Gessner, Christopher
   Foote, Brian
   Wild, David
   Ding, Ying
   Yu, Qi
TI edge2vec: Representation learning using edge semantics for biomedical
   knowledge discovery
SO BMC BIOINFORMATICS
LA English
DT Article
DE Knowledge graph; Heterogeneous network; Biomedical knowledge discovery;
   Representation learning; Graph embedding; Node embedding; Edge
   semantics; Applied machine learning; Data science; Linked data; Semantic
   web; Network science; Systems biology
ID GRAPH
AB BackgroundRepresentation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems.ResultsIn this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, edge2vec significantly outperforms state-of-the-art models on all three tasks.ConclusionsWe propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.
C1 [Gao, Zheng; Tsutsui, Satoshi; Liu, Xiaozhong; Yang, Jeremy; Gessner, Christopher; Wild, David; Ding, Ying] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN USA.
   [Fu, Gang; Yang, Jeremy] Microsoft Corp, Seattle, WA USA.
   [Ouyang, Chunping] Univ South China, Hengyang, Hunan, Peoples R China.
   [Yang, Jeremy] Univ New Mexico, Sch Med, Albuquerque, NM 87131 USA.
   [Foote, Brian; Wild, David; Ding, Ying] Data2Discovery Inc, Bloomington, IN USA.
   [Yu, Qi] Shanxi Med Univ, Sch Management, Taiyuan, Shanxi, Peoples R China.
RP Yu, Q (通讯作者)，Shanxi Med Univ, Sch Management, Taiyuan, Shanxi, Peoples R China.
EM yuqi@sxmu.edu.cn
OI Liu, Xiaozhong/0000-0003-3477-8323
FU National Natural Science Foundation of China [71573162]
FX The work was supported by National Natural Science Foundation of China
   (No. 71573162). The funding bodies had no role in the design of this
   study, the collection, analysis, and interpretation of data, or the
   writing of this manuscript.
CR [Anonymous], 2017, THESIS
   [Anonymous], INTRO LOCALLY UNPUB
   Belkin M, 2002, ADV NEUR IN, V14, P585
   Bhagat S, 2011, SOCIAL NETWORK DATA ANALYTICS, P115
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Chen B, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002574
   Chen B, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-255
   Chen M, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2707
   Collobert R., 2008, P 25 ICML, P160
   Courville A., 2016, DEEP LEARNING
   Cox T. F., 2000, MULTIDIMENSIONAL SCA, V2nd
   Dong YX, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P135, DOI 10.1145/3097983.3098036
   Fouss F, 2007, IEEE T KNOWL DATA EN, V19, P355, DOI 10.1109/TKDE.2007.46
   Fu G, 2016, BMC BIOINFORMATICS, V17, DOI 10.1186/s12859-016-1005-x
   Gao Zheng, 2017, ICONFERENCE 2017 P, V2
   Gao Zizhe, 2018, END TO END MODEL PRE
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Himmelstein DS, 2015, PLOS COMPUT BIOL, V11, DOI 10.1371/journal.pcbi.1004259
   Joanl F. B., 1987, STAT SCI, V2, P45
   Kedem D., 2012, ADV NEURAL INFORM PR, P2573
   Levy O, 2015, T ASSOC COMPUT LING, P3, DOI DOI 10.1162/TACL_A_00134
   Liang B, 2015, INT CONF SOFTW ENG, P894, DOI 10.1109/ICSESS.2015.7339198
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Liu XZ, 2016, PROCEEDINGS OF THE 27TH ACM CONFERENCE ON HYPERTEXT AND SOCIAL MEDIA (HT'16), P103, DOI 10.1145/2914586.2914665
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Mikolov T, 2010, NTFRSPEECH, V2, P3
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Perozzi B., 2014, P 20 ACM SIGKDD INT, P701, DOI DOI 10.1145/2623330.2623732
   Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323
   Seal A, 2015, J CHEMINFORMATICS, V7, DOI 10.1186/s13321-015-0089-z
   Sen P, 2008, AI MAG, V29, P93, DOI 10.1609/aimag.v29i3.2157
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Tang L, 2011, DATA MIN KNOWL DISC, V23, P447, DOI 10.1007/s10618-010-0210-x
   Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319
   Tu Cunchao, 2016, ARXIV161106645
   Wilcke Xander, 2017, DATA SCI, P1
   Yu X, 2014, INT C PATT RECOG, P2802, DOI 10.1109/ICPR.2014.483
   Zhang C., 2016, ICONFERENCE 2016 P
NR 40
TC 16
Z9 16
U1 10
U2 26
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD JUN 10
PY 2019
VL 20
AR 306
DI 10.1186/s12859-019-2914-2
PG 15
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
GA IF1NZ
UT WOS:000472846600001
PM 31238875
OA gold, Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Sitar-Taut, DA
   Mican, D
   Buchmann, RA
AF Sitar-Taut, Dan-Andrei
   Mican, Daniel
   Buchmann, Robert Andrei
TI A knowledge-driven digital nudging approach to recommender systems built
   on a modified Onicescu method
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Digital nudging; Agile Modeling Method Engineering; Knowledge Graph;
   Multi-criteria decisions; Recommender Systems; Cold start problem
ID SOCIAL NETWORKS; INFORMATION; CUSTOMER
AB Product recommendations are generally understood as data-driven - however, we argue that knowledge-driven management decisions may also play a role, especially in the cold start problem, which has been tackled with various degrees of success through a number of approaches. We hereby advocate an approach that captures managerial priorities in the act of recommendation building - i.e., the proposal is to complement the traditional customer-centric view (affected by uncertainty) with a machine-readable business-centric view. For this purpose, the paper reports on an engineered method for the "digital nudging" of recommendations - it starts by capturing a manager's priorities with diagrammatic means, which are further exposed as a Knowledge Graph to a recommender built on a modified version of the Onicescu method taking into consideration a business "utility" concept to influence decision-making.
   The research follows the Design Science methodology, resulting in a "method" artifact that tackles the cold start with the help of a (by-design) recommendation nudging mechanism. In terms of method engineering, the proposal orchestrates its ingredients into a coherent method with the help of (a) Agile Modeling Method Engineering, to setup up a diagrammatic tool for prioritization rules, (b) the Resource Description Framework, to capture the diagrammatic rules in knowledge graph form, and (c) the Onicescu multi-criteria decision method with modifications based on Zipf's Law. The evaluation was based on surveys with potential stakeholders, for the different steps of the method.
   The implications are that the notion of "digital nudging" can take a knowledge-driven form, engineered as an artifact that bridges the decision-makers' priorities (captured by diagrammatic means) with the customer-facing output (recommendations), instead of relying solely on the accumulated history of transactional data. This interpretation of digital nudging may be extended towards other "digital choice environments" where contextual decisions are called to influence the computational output.
C1 [Sitar-Taut, Dan-Andrei; Mican, Daniel; Buchmann, Robert Andrei] Babes Bolyai Univ, Fac Econ & Business Adm, Dept Business Informat Syst, Theodor Mihaly St 58-60, Cluj Napoca 400591, Romania.
RP Mican, D; Buchmann, RA (通讯作者)，Babes Bolyai Univ, Fac Econ & Business Adm, Dept Business Informat Syst, Theodor Mihaly St 58-60, Cluj Napoca 400591, Romania.
EM dan.sitar@econ.ubbcluj.ro; daniel.mican@econ.ubbcluj.ro;
   robert.buchmann@ubbcluj.ro
RI Sitar-Taut, Dan-Andrei/U-4746-2017; Buchmann, Robert/ABB-6735-2020;
   Mican, Daniel/K-6927-2018
OI Sitar-Taut, Dan-Andrei/0000-0001-5360-3874; Buchmann,
   Robert/0000-0002-7385-1610; Mican, Daniel/0000-0003-0277-9700
CR Abdollahpouri H., 2017, RECOMMENDER SYSTEMS, P347, DOI [10.1145/3079628.3079657, DOI 10.1145/3079628.3079657]
   Abdollahpouri H, 2020, USER MODEL USER-ADAP, V30, P127, DOI 10.1007/s11257-019-09256-1
   Alabool HM, 2021, NEURAL COMPUT APPL, V33, P8939, DOI 10.1007/s00521-021-05720-5
   Alexandridis G, 2017, DATA MIN KNOWL DISC, V31, P1031, DOI 10.1007/s10618-017-0504-3
   Anderson C, 2006, LONG TAIL WHY FUTURE
   Baur AW, 2014, IFIP ADV INF COMM TE, V445, P1
   Bouyahia T, 2017, LECT NOTES COMPUT SC, V10128, P51, DOI 10.1007/978-3-319-51966-1_4
   Brandtner P, 2015, LECT NOTES COMPUT SC, V9073, P295, DOI 10.1007/978-3-319-18714-3_19
   Brinkkemper S, 1996, INFORM SOFTWARE TECH, V38, P275, DOI 10.1016/0950-5849(95)01059-9
   Broderick M, 2015, COMMUN ACM, V58, P21, DOI 10.1145/2732421
   Buchmann RA, 2018, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING, P233, DOI 10.5220/0006694102330240
   Buchmann RA, 2017, REQUIR ENG, V22, P41, DOI 10.1007/s00766-015-0235-1
   Buchmann Robert Andrei, 2016, DOMAIN SPECIFIC CONC, P417, DOI DOI 10.1007/978-3-319-39417-6_19
   Burke R., 2000, ENCYCL LIBR INF SYST, V69, P175
   Carrer-Neto W, 2012, EXPERT SYST APPL, V39, P10990, DOI 10.1016/j.eswa.2012.03.025
   Chaffey D., 2016, DIGITAL MARKETING ST
   Chan Liu, 2019, 2019 IEEE International Conference on Computer Science and Educational Informatization (CSEI), P168
   Chelliah M, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P2979, DOI 10.1145/3357384.3360321
   CHEN PS, 1994, INFORM PROCESS MANAG, V30, P43, DOI 10.1016/0306-4573(94)90023-X
   Dashevsky E, 2018, DWS 2018 INSIDE AMEL
   Del Vasto-Terrientes L, 2016, J INTELL INF SYST, V46, P313, DOI 10.1007/s10844-015-0362-7
   Di Noia T, 2017, INFORM SCIENCES, V382, P234, DOI 10.1016/j.ins.2016.11.015
   Dobre I., 2002, ZONE ISSUE
   Eirinaki M, 2018, FUTURE GENER COMP SY, V78, P413, DOI 10.1016/j.future.2017.09.015
   Eshtehardian E, 2013, KSCE J CIV ENG, V17, P262, DOI 10.1007/s12205-013-1141-z
   Feng YH, 2021, EXPERT SYST APPL, V168, DOI 10.1016/j.eswa.2020.114418
   Fernandez-Tobias I, 2013, LECT NOTES COMPUT SC, V8109, P42, DOI 10.1007/978-3-642-40643-0_5
   Figueroa C, 2015, CONCURR COMP-PRACT E, V27, P4659, DOI 10.1002/cpe.3449
   Free University of Bozen-Bolzano, GITHUB ONT VIRT KNOW
   Gan MX, 2018, FUTURE GENER COMP SY, V78, P462, DOI 10.1016/j.future.2017.02.027
   GoodRelations, COOKB SCHEM GOODRELA
   Gossi D, DROP, V301, P310, DOI [10.1007/978-3-319-30569-1_23, DOI 10.1007/978-3-319-30569-1_23]
   Gunawardana Asela, 2015, RECOMMENDER SYSTEMS, P265, DOI DOI 10.1007/978-1-4899-7637-6_8
   Guo JP, 2018, FUTURE GENER COMP SY, V78, P451, DOI 10.1016/j.future.2017.02.031
   Gurini DF, 2018, FUTURE GENER COMP SY, V78, P430, DOI 10.1016/j.future.2017.03.020
   Han XX, 2020, J SUPERCOMPUT, V76, P9404, DOI 10.1007/s11227-020-03212-2
   Henderson-Sellers B., 2014, SITUATIONAL METHOD E, DOI [10.1007/978-3-642-41467-1_2, DOI 10.1007/978-3-642-41467-1_2]
   Huang SL, 2011, ELECTRON COMMER R A, V10, P398, DOI 10.1016/j.elerap.2010.11.003
   Hwangbo H, 2017, EXPERT SYST APPL, V89, P254, DOI 10.1016/j.eswa.2017.07.041
   Ilies L., 2006, MANAGEMENTUL FIRMEI
   Karagiannis D., 2016, FUNDAMENTAL CONCEPTU, P3
   Karagiannis D, 2018, LECT NOTES BUS INF P, V273, P3, DOI 10.1007/978-3-319-73459-0_1
   Karagiannis D, 2016, INFORM SYST, V56, P174, DOI 10.1016/j.is.2015.10.001
   Karpus A, 2016, KDIR: PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL. 1, P419, DOI 10.5220/0006083904190424
   Kilic HS, 2020, DECIS SUPPORT SYST, V137, DOI 10.1016/j.dss.2020.113360
   Kuchar J, 2017, KNOWL-BASED SYST, V135, P40, DOI 10.1016/j.knosys.2017.07.026
   Li J, 2020, MATHEMATICS-BASEL, V8, DOI 10.3390/math8091415
   Li W, 2021, ARCH COMPUT METHOD E, V28, P3781, DOI 10.1007/s11831-021-09562-1
   Lika B, 2014, EXPERT SYST APPL, V41, P2065, DOI 10.1016/j.eswa.2013.09.005
   Logesh R, 2020, NEURAL COMPUT APPL, V32, P2487, DOI 10.1007/s00521-019-04128-6
   Margaris D, 2018, FUTURE GENER COMP SY, V78, P440, DOI 10.1016/j.future.2017.03.015
   Marlinda Linda, 2017, 2017 5th International Conference on Cyber- and IT Service Management (CITSM). Proceedings, DOI 10.1109/CITSM.2017.8089271
   Martin A, 2017, MULT CRIT DECIS MAK, P83, DOI 10.1007/978-3-319-39292-9_5
   Megyesi E, 2014, INT MULTI SCI GEOCO, P65
   Mican D, 2020, DECIS SUPPORT SYST, V139, DOI 10.1016/j.dss.2020.113420
   Milano S, 2021, INFORM SOC, V37, P35, DOI 10.1080/01972243.2020.1832636
   Monti D, 2021, ARTIF INTELL REV, V54, P427, DOI 10.1007/s10462-020-09851-4
   Morawski J, 2017, INT J INTELL SYST, V32, P1062, DOI 10.1002/int.21884
   Morrison A, 2018, SEMANTICS 2018
   Musto C, 2017, KNOWL-BASED SYST, V136, P1, DOI 10.1016/j.knosys.2017.08.015
   Nadolski RJ, 2009, JASSS-J ARTIF SOC S, V12
   Natarajan S, 2020, EXPERT SYST APPL, V149, DOI 10.1016/j.eswa.2020.113248
   Nguyen PT, 2015, LECT NOTES COMPUT SC, V9366, P605, DOI 10.1007/978-3-319-25007-6_35
   Nilashi M, 2015, ELECTRON COMMER R A, V14, P542, DOI 10.1016/j.elerap.2015.08.004
   OMiLAB, ADOXX MET PLATF
   OMiLAB Repository, OMILAB RDF TRANSF
   Onicescu O, 1970, REV STAT, V4
   Ontotext, GRAPHDB DOWNL RES
   Palumbo E, 2020, EXPERT SYST APPL, V151, DOI 10.1016/j.eswa.2020.113235
   Popescu V., 2009, MANAGEMENT TECHNOLOG, V2
   Prat N., 2014, PACIS 2014 PROC, V23
   Rachieru N, 2018, IOP CONF SER-MAT SCI, V400, DOI 10.1088/1757-899X/400/6/062025
   Rad MH, 2020, SOFT COMPUT, V24, P15529, DOI 10.1007/s00500-020-04881-0
   Resteanu C, 2016, STUD INFORM CONTROL, V25, P433
   Rezaeimehr F, 2018, FUTURE GENER COMP SY, V78, P419, DOI 10.1016/j.future.2017.04.003
   ROY B, 1968, REV FR INFORM RECH O, V2, P57
   Saaty T. L, 1980, ANAL HIERARCHY PROCE
   Saenz-Perez F, 2018, EXPERT SYST APPL, V97, P106, DOI 10.1016/j.eswa.2017.12.007
   Sato M, 2020, PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20), P1461, DOI 10.1145/3341105.3375760
   Shambour Q, 2016, INT J ADV COMPUT SC, V7, P274
   Sitar-Taut A.-V., 2009, J APPL QUANT METHODS, V4, P175
   Sitar-Taut DA, 2020, ECON COMPUT ECON CYB, V54, P59, DOI 10.24818/18423264/54.3.20.04
   Sitar-Taut DA, 2020, INFORM TECHNOL MANAG, V21, P131, DOI 10.1007/s10799-019-00309-w
   Son J, 2017, EXPERT SYST APPL, V89, P404, DOI 10.1016/j.eswa.2017.08.008
   Stardog, US VIRT GRAPHS TUT
   Surer O, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P54, DOI 10.1145/3240323.3240350
   Suryavansh B. S., ZONE ISSUE
   Thaler Richard., 2012, BEHAV FDN PUBLIC POL, P428, DOI DOI 10.2139/SSRN.2536504
   Thomas O, 2009, BUS INFORM SYST ENG+, V1, P438, DOI 10.1007/s12599-009-0078-8
   W3C, 2013, SPARQL 1 1 QUERY LAN
   Wang GG, 2019, ARTIF INTELL REV, V51, P119, DOI 10.1007/s10462-017-9559-1
   Weinmann M, 2016, BUS INFORM SYST ENG+, V58, P433, DOI 10.1007/s12599-016-0453-1
   Weng SS, 2009, EXPERT SYST APPL, V36, P1268, DOI 10.1016/j.eswa.2007.11.056
   Wieringa RJ., 2014, DESIGN SCI METHODOLO, DOI [10.1007/978-3-662-43839-8, DOI 10.1007/978-3-662-43839-8]
   Zipf GK, 1950, J CLIN PSYCHOL, V6, P306
NR 95
TC 2
Z9 2
U1 6
U2 27
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD NOV 1
PY 2021
VL 181
AR 115170
DI 10.1016/j.eswa.2021.115170
EA MAY 2021
PG 16
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA SV1JY
UT WOS:000663582700008
DA 2022-08-23
ER

PT J
AU Zhang, XM
   Meng, MM
   Sun, XL
   Bai, Y
AF Zhang, Xiaoming
   Meng, Mingming
   Sun, Xiaoling
   Bai, Yu
TI FactQA: question answering over domain knowledge graph based on
   two-level query expansion
SO DATA TECHNOLOGIES AND APPLICATIONS
LA English
DT Article
DE DBpedia; Knowledge graph; Question answering; Question expansion;
   Question matching; Question understanding
AB Purpose With the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG. Design/methodology/approach The authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user's question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method. Findings The designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches. Originality/value Although the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user's information query based on the emerging KG.
C1 [Zhang, Xiaoming; Meng, Mingming; Sun, Xiaoling; Bai, Yu] Hebei Univ Sci & Technol, Sch Informat Sci & Engn, Shijiazhuang, Hebei, Peoples R China.
RP Meng, MM (通讯作者)，Hebei Univ Sci & Technol, Sch Informat Sci & Engn, Shijiazhuang, Hebei, Peoples R China.
EM zxmhebust@163.com; mengmming@163.com; 15028104457@163.com;
   baiyu0584@gmail.com
OI Zhang, Xiaoming/0000-0002-7091-1155
FU Natural Science Foundation of Hebei Province [F2018208116]; Hebei
   Science and Technology Support Program [16210312D]; Key Project of Hebei
   Education Department [ZD2015099]
FX This research was funded by the Natural Science Foundation of Hebei
   Province (Grant No. F2018208116), Hebei Science and Technology Support
   Program (No. 16210312D) and Key Project of Hebei Education Department
   (Grant No. ZD2015099).
CR Athira P. M., 2013, INT J WEB SEMANTIC T, V4, DOI [10.5121/ijwest.2013.4403, DOI 10.5121/IJWEST.2013.4403]
   Bordes A., 2015, LARGE SCALE SIMPLE Q
   Cheng X, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW), P295, DOI 10.1109/ICDMW.2015.181
   Cui W, 2016, P 25 INT JOINT C ART, P4240
   Diefenbach D., 2017, KNOWL INF SYST, V55, P526
   Gaikwad S, 2015, 2015 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), P1474, DOI 10.1109/ICACCI.2015.7275820
   Goodwin TR, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P297, DOI 10.1145/2983323.2983819
   Grappy A., 2011, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, P55, DOI 10.1109/WI-IAT.2011.210
   Han L., 2013, 2 JOINT C LEX COMP S
   Hristovski D, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-014-0365-3
   Ilievski I, 2017, PROCEEDINGS OF THE THEMATIC WORKSHOPS OF ACM MULTIMEDIA 2017 (THEMATIC WORKSHOPS'17), P415, DOI 10.1145/3126686.3126695
   Kahaduwa H, 2017, 2017 3RD INTERNATIONAL MORATUWA ENGINEERING RESEARCH CONFERENCE (MERCON), P449, DOI 10.1109/MERCon.2017.7980526
   Kots A, 2014, WIREL NETW, V20, P1, DOI 10.1007/s11276-013-0591-z
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Liu MH, 2010, PROCEEDINGS OF THE 17TH IAPRI WORLD CONFERENCE ON PACKAGING, P136
   Marnee M.C.D., 2010, STANFORD TYPED DEPEN
   Pathak S, 2016, PROCEEDINGS ON 2016 2ND INTERNATIONAL CONFERENCE ON NEXT GENERATION COMPUTING TECHNOLOGIES (NGCT), P534, DOI 10.1109/NGCT.2016.7877473
   Do QH, 2011, IEEE INT VEH SYM, P332, DOI 10.1109/IVS.2011.5940553
   Stoilos G, 2005, LECT NOTES COMPUT SC, V3729, P624
   Sun H, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1045, DOI 10.1145/2736277.2741651
   Thomas M, 2015, FRONT COGN PSYCHOL, P111
   Toti D, 2014, 2014 INTERNATIONAL CONFERENCE ON INTELLIGENT NETWORKING AND COLLABORATIVE SYSTEMS (INCOS), P716, DOI 10.1109/INCoS.2014.13
   Tran Q. H., 2015, P 9 INT WORKSH SEM E, V15, P215, DOI DOI 10.18653/V1/S15-2038
   Tsatsaronis George, 2012, FS1205 AAAI, P92
   Ture F, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P457, DOI 10.1145/2911451.2926729
   Unger C., 2012, P 21 INT C WORLD WID, P639, DOI DOI 10.1145/2187836.2187923
   Wang D.S., 2010, 11 ACIS INT C SOFTW, P639
   Yang H, 2005, PDCAT 2005: SIXTH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS AND TECHNOLOGIES, PROCEEDINGS, P40
   Yao TT, 2016, 2016 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), P39
   Yi T., 2017, 11 ACM INT C WEB SEA, P583
   Yin W, 2015, CHEM ENGINEER TRANS, V46, P1, DOI 10.3303/CET1546001
   Yu M, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P571, DOI 10.18653/v1/P17-1053
   Zaveri A., 2013, P 9 INT C SEM SYST, P97, DOI DOI 10.1145/2506182.2506195
   Zhao SS, 2016, 2016 12TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD), P1572, DOI 10.1109/FSKD.2016.7603410
   Zheng WG, 2016, PROC VLDB ENDOW, V9, P840, DOI 10.14778/2983200.2983201
   Zou L., 2007, P ACM 1 PH D WORKSH, P139, DOI DOI 10.1145/1316874.1316897
   Zou L, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P313, DOI 10.1145/2588555.2610525
   Zou L, 2011, PROC VLDB ENDOW, V4, P482, DOI 10.14778/2002974.2002976
NR 38
TC 4
Z9 4
U1 3
U2 45
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2514-9288
EI 2514-9318
J9 DATA TECHNOL APPL
JI Data Technol. Appl.
PD FEB 3
PY 2020
VL 54
IS 1
BP 34
EP 63
DI 10.1108/DTA-02-2019-0029
EA NOV 2019
PG 30
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA KW5CN
UT WOS:000501610500001
DA 2022-08-23
ER

PT J
AU Jung, W
   Kim, K
   Min, SK
   Nam, EM
   Lee, JK
AF Jung, Wonguen
   Kim, Kyubo
   Min, Seog Ki
   Nam, Eun Mi
   Lee, Jeong Kyong
TI Mapping of local recurrence after pancreaticoduodenectomy for distal
   extrahepatic cholangiocarcinoma: implications for adjuvant radiotherapy
SO BRITISH JOURNAL OF RADIOLOGY
LA English
DT Article
ID CLINICAL TARGET VOLUME; CONCURRENT CHEMORADIATION; CURATIVE RESECTION;
   RADIATION-THERAPY; ADENOCARCINOMA; CHEMORADIOTHERAPY; CARCINOMA;
   INVOLVEMENT; PANCREAS; PATTERNS
AB Objective: To generate a map of local recurrences after pancreaticoduodenectomy (PD) for patients with distal extrahepatic cholangiocarcinoma (DEHC) and to evaluate the adequate target volume coverage encompassing the majority of local recurrences when the clinical target volume (CTV) for pancreatic cancer was applied.
   Methods: We retrospectively reviewed the records of DEHC patients who underwent pancreaticoduodenectomy and had postoperative CT scans acquired between 1991 and 2015 available. The sites of local recurrence were delineated on individual CT scans, and then, mapping was manually performed onto template CT images. Coverage of each site of local recurrence was evaluated by applying the CTV defined according to Radiation Therapy Oncology Group (RTOG) consensus guidelines (CTVRTOG) for target delineation in the postoperative treatment of pancreatic head cancer.
   Results: Of the 99 patients, 36 patients had a total of 62 local relapses identifiable by postoperative CT scans; the relapses were the most frequent in the choledochojejunostomy (CJ) site, 11 sites (17.7%); para-aortic area, 10 sites (16.1%), superior mesenteric artery area, 10 sites (16.1%): and portal vein area, 9 sites (14.5%). 21 sites (33.9%) were not covered by the CTVRTOG, and the most common site of local recurrence outside the CTVRTOG was the CJ site.
   Conclusion: When mapping of local relapses was evaluated according to the CTVRTOG, the choledochojejunostomy site was identified as a high-risk area of local recurrence but was insufficiently covered within the CTVRTOG, These findings may help construct a target volume for postoperative radiotherapy in DEHC.
   Advances in knowledge: Mapping local recurrences can aid in defining appropriate target volume for postoperative radiotherapy in DEHC.
C1 [Jung, Wonguen; Kim, Kyubo] Ewha Womans Univ, Coll Med, Dept Radiat Oncol, Seoul, South Korea.
   [Min, Seog Ki] Ewha Womans Univ, Coll Med, Dept Surg, Seoul, South Korea.
   [Nam, Eun Mi] Ewha Womans Univ, Coll Med, Dept Internal Med, Seoul, South Korea.
   [Lee, Jeong Kyong] Ewha Womans Univ, Coll Med, Dept Radiol, Seoul, South Korea.
RP Kim, K (通讯作者)，Ewha Womans Univ, Coll Med, Dept Radiat Oncol, Seoul, South Korea.
EM kyubokim.ro@gmail.com
CR Amin M, 2017, AJCC CANC STAGING MA
   Bisello S, 2019, ONCOL LETT, V17, P1784, DOI 10.3892/ol.2018.9774
   Beltran MB, 2011, BMC CANCER, V11, DOI 10.1186/1471-2407-11-267
   Borghero Y, 2008, ANN SURG ONCOL, V15, P3147, DOI 10.1245/s10434-008-9998-7
   Choi HS, 2018, RADIAT ONCOL, V13, DOI 10.1186/s13014-018-1024-z
   Dholakia AS, 2013, INT J RADIAT ONCOL, V87, P1007, DOI 10.1016/j.ijrobp.2013.09.005
   Ethun CG, 2017, J AM COLL SURGEONS, V224, P406, DOI 10.1016/j.jamcollsurg.2016.12.006
   Ghiassi-Nejad Z, 2017, INT J RADIAT ONCOL, V99, P805, DOI 10.1016/j.ijrobp.2017.06.2467
   Goodman KA, 2012, INT J RADIAT ONCOL, V83, P901, DOI 10.1016/j.ijrobp.2012.01.022
   Hughes MA, 2007, INT J RADIAT ONCOL, V68, P178, DOI 10.1016/j.ijrobp.2006.11.048
   Kayahara M, 1999, ANN SURG, V229, P76, DOI 10.1097/00000658-199901000-00010
   Kim K, 2010, INT J RADIAT ONCOL, V77, P1186, DOI 10.1016/j.ijrobp.2009.06.033
   Kim S, 2002, INT J RADIAT ONCOL, V54, P414, DOI 10.1016/S0360-3016(02)02952-8
   Kim TH, 2011, INT J RADIAT ONCOL, V81, pE853, DOI 10.1016/j.ijrobp.2010.12.019
   Kim YS, 2016, CANCER CHEMOTH PHARM, V77, P979, DOI 10.1007/s00280-016-3014-x
   Klinkenbijl JH, 1999, ANN SURG, V230, P776, DOI 10.1097/00000658-199912000-00006
   Launois B, 2000, J Hepatobiliary Pancreat Surg, V7, P128, DOI 10.1007/s005340050166
   Lee J, 2018, CLIN TRANSL ONCOL, V20, P1011, DOI 10.1007/s12094-017-1815-y
   Marinelli I, 2017, ANTICANCER RES, V37, P955, DOI 10.21873/anticanres.11404
   Nakeeb A, 1996, ANN SURG, V224, P463, DOI 10.1097/00000658-199610000-00005
   Nelson JW, 2009, INT J RADIAT ONCOL, V73, P148, DOI 10.1016/j.ijrobp.2008.07.008
   Sasaki R, 2001, SURGERY, V129, P677, DOI 10.1067/msy.2001.114555
   Schmuck RB, 2016, CANCER MED-US, V5, P88, DOI 10.1002/cam4.566
   Socha J, 2017, RADIOTHER ONCOL, V125, P365, DOI 10.1016/j.radonc.2017.09.025
   Vern-Gross TZ, 2011, INT J RADIAT ONCOL, V81, P189, DOI 10.1016/j.ijrobp.2010.05.001
   Yoshida T, 1998, INT SURG, V83, P124
   Yoshida T, 1998, HEPATO-GASTROENTEROL, V45, P2388
NR 27
TC 6
Z9 6
U1 0
U2 0
PU BRITISH INST RADIOLOGY
PI LONDON
PA 36 PORTLAND PLACE, LONDON W1N 4AT, ENGLAND
SN 0007-1285
EI 1748-880X
J9 BRIT J RADIOL
JI Br. J. Radiol.
PY 2019
VL 92
IS 1100
AR 20190285
DI 10.1259/bjr.20190285
PG 6
WC Radiology, Nuclear Medicine & Medical Imaging
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Radiology, Nuclear Medicine & Medical Imaging
GA IK5JY
UT WOS:000476622700020
PM 31145644
OA Green Published
DA 2022-08-23
ER

PT J
AU Krishnan, C
   Higgins, JP
   West, RB
   Natkunam, Y
   Heerema-McKenney, A
   Arber, DA
AF Krishnan, Chandra
   Higgins, John P.
   West, Robert B.
   Natkunam, Yasodha
   Heerema-McKenney, Amy
   Arber, Daniel A.
TI Microtubule-associated Protein-2 is a Sensitive Marker of Primary and
   Metastatic Neuroblastoma
SO AMERICAN JOURNAL OF SURGICAL PATHOLOGY
LA English
DT Article
DE microtubule-associated protein-2; neuroblastoma; immunohistochemistry;
   small round blue cell tumors; tissue microarray
ID DIFFERENTIAL-DIAGNOSIS; EXPRESSION; TUMORS; MAP-2; CELLS;
   NEUROENDOCRINE; SPECIMENS
AB Background: Microtubule-associated protein-2 (MAP-2) is a protein expressed in high levels in cells derived from the neural crest. To the best of our knowledge, MAP-2 expression has not been thoroughly evaluated in tissues outside of the central nervous tissue. We examined the diagnostic utility of MAP-2 as a marker of neuroblastoma and attempted to characterize the expression of this protein in other tumors in the morphologic differential diagnosis of neuroblastoma.
   Results: MAP-2 showed significant cytoplasmic reactivity in 95% of primary and 100% of metastatic neuroblastomas. Included within thin set of tumors were 3 undifferentiated neuroblastomas, all of which showed strong staining. MAP-2 did not show significant staining in the majority of other small round blue cell tumors within the morphologic differential. Additionally, MAP-2 showed comparable sensitivity in staining primary neuroblastomas as compared with synaptophysin, chromogranin, CD56, and beta-catenin. In contrast to other markers of neuroblastoma, MAP-2 did not show significant cross reactivity to native bone marrow precursors, thus eliminating a potential source of confusion. In normal tissues, MAP-2 staining was essentially restricted to organs derived front the neural crest (adrenal medulla. endocrine organs). Variant patterns of staining were seen in exocrine organs, monocyte/macrophages and solitary fibrous tumor/hemangiopericytoma family of tumors. Rarely, high-grade adult sarcomas exhibiting strong cytoplasmic MAP-2 staining were seen.
   Conclusions: MAP-2 is a sensitive and specific marker of neuroblastoma, both in the primary tumor and bone marrow biopsy settings. We think that MAP-2, in conjunction with synaptophysin, is a very powerful immunohistochemical market in differentiating neuroblastoma from its morphologic mimics.
C1 [Krishnan, Chandra; Higgins, John P.; West, Robert B.; Natkunam, Yasodha; Heerema-McKenney, Amy; Arber, Daniel A.] Stanford Univ, Dept Pathol, Stanford, CA 94205 USA.
RP Krishnan, C (通讯作者)，Stanford Univ, Dept Pathol, 300 Pasteur Dr,Room L235, Stanford, CA 94205 USA.
EM Chandra.krishnan@yahoo.com
RI Natkunam, Yasodha/L-5454-2015
OI Natkunam, Yasodha/0000-0002-9816-1018; West, Robert/0000-0002-8330-8283
CR ARTLIEB U, 1985, LAB INVEST, V53, P684
   Blumcke I, 2001, J NEUROPATH EXP NEUR, V60, P984
   BURTELOW MA, 2009, AM J SURG PATHO 0409
   Fang D, 2001, AM J PATHOL, V158, P2107, DOI 10.1016/S0002-9440(10)64682-2
   FISCHER I, 1986, DEV BRAIN RES, V25, P99, DOI 10.1016/0165-3806(86)90156-2
   GOODMAN M, 1999, TUMORSCANCER INCIDEN
   HUBER G, 1984, J NEUROSCI, V4, P151
   Joshi VV, 2000, PEDIATR DEVEL PATHOL, V3, P184, DOI 10.1007/s100240050024
   Kononen J, 1998, NAT MED, V4, P844, DOI 10.1038/nm0798-844
   Krishnan C, 2009, AM J CLIN PATHOL, V131, P49, DOI 10.1309/AJCPAJODRJYD3OB2
   Liu YL, 2003, ADV ANAT PATHOL, V10, P101, DOI 10.1097/00125480-200303000-00005
   Liu YL, 2001, MODERN PATHOL, V14, P880, DOI 10.1038/modpathol.3880406
   Miettinen M, 1998, AM J SURG PATHOL, V22, P327, DOI 10.1097/00000478-199803000-00007
   MIETTINEN M, 1989, AM J SURG PATHOL, V13, P120, DOI 10.1097/00000478-198902000-00005
   MOLENAAR WM, 1990, AM J PATHOL, V136, P375
   Sanchez C, 2000, PROG NEUROBIOL, V61, P133, DOI 10.1016/S0301-0082(99)00046-5
   SHARMA N, 1994, CELL MOTIL CYTOSKEL, V27, P234, DOI 10.1002/cm.970270305
   Sukumar S, 2008, CELL TISSUE RES, V332, P89, DOI 10.1007/s00441-007-0566-4
   WIRNSBERGER GH, 1992, AM J SURG PATHOL, V16, P49
NR 19
TC 11
Z9 13
U1 0
U2 1
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 0147-5185
EI 1532-0979
J9 AM J SURG PATHOL
JI Am. J. Surg. Pathol.
PD NOV
PY 2009
VL 33
IS 11
BP 1695
EP 1704
DI 10.1097/PAS.0b013e3181b0ebdc
PG 10
WC Pathology; Surgery
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pathology; Surgery
GA 519UZ
UT WOS:000271795800016
PM 19701075
DA 2022-08-23
ER

PT J
AU Zheng, ZH
   Zhang, P
   Yuan, FZ
   Bo, YQ
AF Zheng, Zhonghui
   Zhang, Ping
   Yuan, Fangzheng
   Bo, Yunque
TI Scientometric Analysis of The Relationship between a Built Environment
   and Cardiovascular Disease
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE built environment; cardiovascular disease; scientometric analysis;
   walkability; physical activity; food environment
ID URBAN GREEN SPACE; BODY-MASS INDEX; PHYSICAL-ACTIVITY; NEIGHBORHOOD
   WALKABILITY; HEALTH-BENEFITS; AIR-POLLUTION; GLOBAL BURDEN;
   RISK-FACTORS; OBESITY; ASSOCIATION
AB The prevention and treatment of cardiovascular disease (CVD) are necessary to improve patient quality of life and to reduce the burden of medical and other social problems. Reducing the impact of CVD through environmental intervention was hailed as the most economical approach and research into such interventions is becoming key. The purpose of this article is to summarize the research topics and developments in the field of the built environment and CVD between 2000 and 2021 using scientometric analysis. In total, 1304 records retrieved from the Web of Science core database were analyzed using CiteSpace software, and the results were displayed using knowledge mapping. The number of publications and conferences relating to the built environment and CVD showed an upward trend over the study period, with the United States taking the lead. Physical activity and the food environment were used as mediators and entry points to map the relationship between the built environment and CVD. Walkability, residence characteristics, the food environment, and greenness were key research topics. Research shifted over the period to incorporate quantitative analyses of subjective feelings while focusing on decreasing sedentary behavior. Understanding the variability in the built environment is critical to improving the generalizability of the findings presented in the individual studies. Inter-disciplinary and multi-disciplinary research is conducive to innovation and ensuring the integration of real environmental elements. This study provides an overview and valuable guidance for researchers relating to how the built environment impacts CVD.
C1 [Zheng, Zhonghui; Zhang, Ping; Yuan, Fangzheng] Hebei Univ Technol, Sch Architecture & Art Design, Tianjin 300132, Peoples R China.
   [Bo, Yunque] Tianjin Med Informat Ctr, Policy Res Dept, Tianjin 300041, Peoples R China.
RP Zhang, P (通讯作者)，Hebei Univ Technol, Sch Architecture & Art Design, Tianjin 300132, Peoples R China.
EM 201922301004@stu.hebut.edu.cn; zhangping-a@hebut.edu.cn;
   a17302220763@163.com; puyixibo@163.com
OI zheng, zhong hui/0000-0001-6903-6499
FU National Natural Science Foundation of China [51508151]
FX This research was funded by the National Natural Science Foundation of
   China, grant number 51508151.
CR Abbate M, 2020, NUTR METAB CARDIOVAS, V30, P1080, DOI 10.1016/j.numecd.2020.02.020
   Ackerson K., 2005, GIS APPROACH EVALUAT
   Andersen LB, 2006, LANCET, V368, P299, DOI 10.1016/S0140-6736(06)69075-2
   Anjana RM, 2017, CURR SCI INDIA, V113, P1327, DOI 10.18520/cs/v113/i07/1327-1336
   Barton P, 2011, BMJ-BRIT MED J, V343, DOI 10.1136/bmj.d4044
   Berke EM, 2007, AM J PUBLIC HEALTH, V97, P486, DOI 10.2105/AJPH.2006.085837
   Black JL, 2010, AM J EPIDEMIOL, V171, P765, DOI 10.1093/aje/kwp458
   Brook RD, 2010, CIRCULATION, V121, P2331, DOI 10.1161/CIR.0b013e3181dbece1
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Cohen DA, 2006, PEDIATRICS, V118, pE1381, DOI 10.1542/peds.2006-1226
   Colom A, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15061285
   Crane R, 1998, TRANSPORT RES D-TR E, V3, P225, DOI 10.1016/S1361-9209(98)00001-7
   Cubbin Catherine, 2001, Ethnicity and Disease, V11, P687
   DeGuzman PB, 2013, PUBLIC HEALTH NURS, V30, P478, DOI 10.1111/phn.12051
   den Braver NR, 2018, BMC MED, V16, DOI 10.1186/s12916-017-0997-z
   Dolle H., 2021, UNDERGRAD SCH SHOWC, V3, P1
   Dong Y, 2019, ANGIOLOGY, V70, P523, DOI 10.1177/0003319718813448
   Eichinger M, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0126010
   Evenson KR, 2020, PREV CHRONIC DIS, V17, DOI 10.5888/pcd17.200118
   Falagas ME, 2008, FASEB J, V22, P338, DOI 10.1096/fj.07-9492LSF
   Feidong L., 2015, URBAN PLAN INT, V30, P62
   Frank LD, 2006, J AM PLANN ASSOC, V72, P75, DOI 10.1080/01944360608976725
   Frank LD, 2004, AM J PREV MED, V27, P87, DOI 10.1016/j.amepre.2004.04.011
   Frank LD, 2005, AM J PREV MED, V28, P117, DOI 10.1016/j.amepre.2004.11.001
   GARFIELD E, 1972, SCIENCE, V178, P471, DOI 10.1126/science.178.4060.471
   Gary-Webb TL, 2010, BMC PUBLIC HEALTH, V10, DOI 10.1186/1471-2458-10-312
   Gee GC, 2004, ENVIRON HEALTH PERSP, V112, P1645, DOI 10.1289/ehp.7074
   Gheorghe A, 2018, BMC PUBLIC HEALTH, V18, DOI 10.1186/s12889-018-5806-x
   Glanz K, 2016, SSM-POPUL HLTH, V2, P24, DOI 10.1016/j.ssmph.2016.02.002
   Grazuleviciene R, 2015, BIOMED RES INT, V2015, DOI 10.1155/2015/403012
   Grieco Elisabeth Poubel, 2016, Ambient. constr., V16, P215, DOI 10.1590/s1678-86212016000400115
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   James P, 2017, EPIDEMIOLOGY, V28, P780, DOI [10.1097/ede.0000000000000724, 10.1097/EDE.0000000000000724]
   James Peter, 2015, Curr Epidemiol Rep, V2, P131
   Jeefoo P, 2011, INT J ENV RES PUB HE, V8, P51, DOI 10.3390/ijerph8010051
   Joseph P, 2017, CIRC RES, V121, P677, DOI 10.1161/CIRCRESAHA.117.308903
   Kaiser P, 2016, AM J EPIDEMIOL, V183, P988, DOI 10.1093/aje/kwv296
   Ke X., 2020, URBAN HLTH WELLBEING, P65
   Keegan THM, 2012, AM J PUBLIC HEALTH, V102, P689, DOI 10.2105/AJPH.2011.300150
   Koya DL, 2007, J GEN INTERN MED, V22, P841, DOI 10.1007/s11606-007-0163-y
   Krizek KJ, 2009, ENVIRON PLANN B, V36, P725, DOI 10.1068/b34023
   Lamichhane AP, 2012, INT J BEHAV NUTR PHY, V9, DOI 10.1186/1479-5868-9-81
   Leal C, 2011, OBES REV, V12, P217, DOI 10.1111/j.1467-789X.2010.00726.x
   Lee JJ, 2017, OBES SCI PRACT, V3, P333, DOI 10.1002/osp4.115
   Lee RE, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0051081
   [雷杰 Lei Jie], 2019, [中华疾病控制杂志, Chinese Journal of Disease Control & Prevention], V23, P1102
   Lin X., 2015, PLANNER, V31, P12
   Lopez AD, 2006, GLOBAL BURDEN OF DISEASE AND RISK FACTORS, P1, DOI 10.1596/978-0-8213-6262-4
   Lovasi GS, 2011, J URBAN HEALTH, V88, P1143, DOI 10.1007/s11524-011-9604-3
   Lu Y., 2012, INT URBAN PLAN, V1, P10
   Lwin KK, 2011, COMPUT ENVIRON URBAN, V35, P408, DOI 10.1016/j.compenvurbsys.2011.05.002
   Malambo P, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0166846
   McGuire S, 2012, ADV NUTR, V3, P56, DOI 10.3945/an.111.001347
   McNeill LH, 2006, SOC SCI MED, V63, P1011, DOI 10.1016/j.socscimed.2006.03.012
   Meline J, 2017, BMC PUBLIC HEALTH, V17, DOI 10.1186/s12889-017-4962-8
   Millington C, 2009, HEALTH PLACE, V15, P474, DOI 10.1016/j.healthplace.2008.09.007
   Murray CJL, 2003, LANCET, V361, P717, DOI 10.1016/S0140-6736(03)12655-4
   Nieuwenhuijsen MJ, 2018, NAT REV CARDIOL, V15, P432, DOI 10.1038/s41569-018-0003-2
   Papas MA, 2007, EPIDEMIOL REV, V29, P129, DOI 10.1093/epirev/mxm009
   Paquet C, 2010, PSYCHOSOM MED, V72, P324, DOI 10.1097/PSY.0b013e3181cdf439
   Pearson AL, 2014, BMC PUBLIC HEALTH, V14, DOI 10.1186/1471-2458-14-553
   Picavet HSJ, 2016, PREV MED, V89, P7, DOI 10.1016/j.ypmed.2016.04.021
   Pouliou T, 2010, HEALTH PLACE, V16, P389, DOI 10.1016/j.healthplace.2009.11.011
   Richardson EA, 2013, PUBLIC HEALTH, V127, P318, DOI 10.1016/j.puhe.2013.01.004
   Rodrigues André Ricardo Prazeres, 2014, J. Transp. Lit., V8, P62, DOI 10.1590/S2238-10312014000300004
   Roth GA, 2020, J AM COLL CARDIOL, V76, P2980, DOI 10.1016/j.jacc.2020.11.021
   Saelens BE, 2008, MED SCI SPORT EXER, V40, pS550, DOI 10.1249/MSS.0b013e31817c67a4
   Sallis JF, 2012, CIRCULATION, V125, P729, DOI 10.1161/CIRCULATIONAHA.110.969022
   Seglen PO, 1997, BRIT MED J, V314, P497, DOI DOI 10.1136/BMJ.314.7079.497
   Singh GK, 2010, HEALTH AFFAIR, V29, P503, DOI 10.1377/hlthaff.2009.0730
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Talen E., 2013, INT J SUSTAINABLE LA, V1, P42, DOI DOI 10.24102/IJSLUP.V1I1.211
   Tamosiunas A, 2014, ENVIRON HEALTH-GLOB, V13, DOI 10.1186/1476-069X-13-20
   Twohig-Bennett C, 2018, ENVIRON RES, V166, P628, DOI 10.1016/j.envres.2018.06.030
   Vale DS, 2017, ENVIRON PLAN B-URBAN, V44, P740, DOI 10.1177/0265813516641685
   Van Dyck D, 2010, AM J PREV MED, V39, P25, DOI 10.1016/j.amepre.2010.03.004
   Vert C, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16030462
   Villeneuve PJ, 2012, ENVIRON RES, V115, P51, DOI 10.1016/j.envres.2012.03.003
   WHO, REV LEAD CAUS DEATH
   World Health Organization, 2020, GLOB ACT PLAN PREV C, DOI 10.1590/0034-7167.2016690422i
   Zapata-Diomedi B, 2016, HEALTH PLACE, V42, P19, DOI 10.1016/j.healthplace.2016.08.010
   陈伟伟, 2017, [中国循环杂志, Chinese Circulation Journal], V32, P521
   向剑锋, 2009, [中国运动医学杂志, Chinese Journal of Sports Medicine], V28, P575
NR 83
TC 0
Z9 0
U1 8
U2 8
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD MAY
PY 2022
VL 19
IS 9
AR 5625
DI 10.3390/ijerph19095625
PG 18
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA 1G9AE
UT WOS:000796139700001
PM 35565017
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wang, LC
   Zhou, SY
   Liu, YX
   Li, YF
   Sun, XG
AF Wang, Luchen
   Zhou, Sangyu
   Liu, Yanxiang
   Li, Yunfeng
   Sun, Xiaogang
TI Bibliometric analysis of the inflammatory mechanism in aortic disease
SO REVIEWS IN CARDIOVASCULAR MEDICINE
LA English
DT Article
DE aortic disease; inflammation; inflammatory response markers; knowledge
   mapping analysis; research hotspots
ID C-REACTIVE PROTEIN; CORONARY-HEART-DISEASE; NECROSIS-FACTOR-ALPHA;
   KAPPA-B ACTIVATION; OXIDATIVE STRESS; SIGNALING PATHWAY; KNOWLEDGE
   DOMAIN; D-DIMER; ANEURYSM; ATHEROSCLEROSIS
AB Background: In view of the key role of inflammation in the pathogenesis of aortic disease, we visually analyzed the research hotspots of inflammatory mechanism in aortic disease in this work through the method of bibliometrics from the Web of Science (WOS) Core database over the past three decades. Methods: A visual bibliometric network of research articles on inflammatory mechanisms in aortic disease was obtained from VOSviewer and Citespace based on the WOS Core Collection. Results: A total of 1278 documents from January 1990 to February 2021 were selected for analysis. The United States and China had the highest percentage of articles, comprising 34.01% and 24.92% of articles worldwide, respectively. Harvard University has published the most articles in this field, followed by the University of Michigan and Huazhong University of Science and Technology. The top 3 research hotspots were atherosclerosis, oxidative stress, and macrophages. The journal with the most articles in this area was Arteriosclerosis Thrombosis and Vascular Biology, followed by Atherosclerosis and PLOS One. The research trend on inflammatory mechanisms in the aortic system has 5 distinct directions: (1) atherosclerosis, NF-kappa B, expression, smooth muscle cell, and oxidative stress; (2) coronary artery disease, C-reactive protein, risk factors, endothelial dysfunction, and aortic stenosis; (3) abdominal aortic aneurysm, matrix metalloproteinases, macrophage, and pathogenesis; (4) cholesterol, metabolism, low-density lipoprotein, gene expression, and a therosclerotic lesions; and (5) calcific aortic valve disease, interstitial cells, calcification, and stenosis. Conclusions: Inflammatory mechanism research has shown a tendency to rise gradually in the aortic field. Numerous studies have explored the role of inflammatory responses in aortic disease, which may increase the risk of endothelial dysfunction (aortic fibrosis and stiffness) and induce plaque formation. Among them, NF kappa B activation, nitric-oxide synthase expression, and oxidative stress are particularly essential.
C1 [Wang, Luchen; Zhou, Sangyu; Liu, Yanxiang; Li, Yunfeng; Sun, Xiaogang] Chinese Acad Med Sci & Peking Union Med Coll, Natl Ctr Cardiovasc Dis, Fuwai Hosp, Aort & Vasc Surg Ctr, Beijing 100037, Peoples R China.
   [Li, Yunfeng] Shandong Univ, Qilu Hosp, Jinan 250012, Shandong, Peoples R China.
RP Sun, XG (通讯作者)，Chinese Acad Med Sci & Peking Union Med Coll, Natl Ctr Cardiovasc Dis, Fuwai Hosp, Aort & Vasc Surg Ctr, Beijing 100037, Peoples R China.
EM xiaogangsun2006@vip.sina.com
FU Beijing Municipal Science and Technology Commission, China
   [Z181100001718197]
FX This study was supported by Beijing Municipal Sci-ence and Technology
   Commission, China, Major Special Project #Z181100001718197.
CR Abplanalp WT, 2020, JAMA CARDIOL, V5, P1170, DOI 10.1001/jamacardio.2020.2468
   Agharazii M, 2015, AM J HYPERTENS, V28, P746, DOI 10.1093/ajh/hpu225
   Aikawa M, 2001, CIRCULATION, V103, P276
   Akhavanpoor M, 2014, FRONT PHYSIOL, V5, DOI 10.3389/fphys.2014.00296
   Albanese I, 2017, ARTERIOSCL THROM VAS, V37, P543, DOI 10.1161/ATVBAHA.116.308394
   AlMahameed ST, 2010, HEART ASIA, V2, P136, DOI 10.1136/ha.2010.002881
   Alushi B, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.00685
   Bai T, 2020, FREE RADICAL BIO MED, V160, P92, DOI 10.1016/j.freeradbiomed.2020.07.026
   Balistreri CR, 2017, J MOL CELL CARDIOL, V110, P38, DOI 10.1016/j.yjmcc.2017.06.011
   Balogh E, 2021, BIOMEDICINES, V9, DOI 10.3390/biomedicines9040427
   Banach M, 2018, J AM COLL CARDIOL, V72, P96, DOI 10.1016/j.jacc.2018.04.040
   Batra R, 2018, ARTERIOSCL THROM VAS, V38, P457, DOI 10.1161/ATVBAHA.117.310333
   Baxter BT, 2020, JAMA-J AM MED ASSOC, V323, P2029, DOI 10.1001/jama.2020.5230
   Berliner JA, 1996, FREE RADICAL BIO MED, V20, P707, DOI 10.1016/0891-5849(95)02173-6
   Bonnet AL, 2018, FRONT MED-LAUSANNE, V5, DOI 10.3389/fmed.2018.00335
   Bossone E, 2021, NAT REV CARDIOL, V18, P331, DOI 10.1038/s41569-020-00472-6
   Bowler MA, 2018, AM J PHYSIOL-HEART C, V315, pH1614, DOI 10.1152/ajpheart.00277.2018
   Bucerius J, 2019, EUR J NUCL MED MOL I, V46, P251, DOI 10.1007/s00259-018-4176-z
   Butt HZ, 2016, EUR J VASC ENDOVASC, V52, P47, DOI 10.1016/j.ejvs.2016.03.016
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Chen DP, 2019, J BIOMED MATER RES A, V107, P2756, DOI 10.1002/jbm.a.36778
   Chen YQ, 2021, TRIALS, V22, DOI 10.1186/s13063-021-05237-1
   Chen YL, 2019, CIRC RES, V125, P1087, DOI 10.1161/CIRCRESAHA.119.315833
   Cheng F, 2013, INT J CARDIOL, V167, P936, DOI 10.1016/j.ijcard.2012.03.090
   Chistiakov DA, 2016, J CELL MOL MED, V20, P17, DOI 10.1111/jcmm.12689
   Choke E, 2009, EUR J VASC ENDOVASC, V37, P305, DOI 10.1016/j.ejvs.2008.11.017
   Cochain C, 2018, CIRC RES, V122, P1661, DOI 10.1161/CIRCRESAHA.117.312509
   Del Porto F, 2010, ANN MED, V42, P622, DOI 10.3109/07853890.2010.518156
   Despres JP, 2000, ATHEROSCLEROSIS, V153, P263, DOI 10.1016/S0021-9150(00)00603-1
   Duan B, 2019, ACS BIOMATER SCI ENG, V5, P1416, DOI 10.1021/acsbiomaterials.8b01280
   Einarson TR, 2018, CARDIOVASC DIABETOL, V17, DOI 10.1186/s12933-018-0728-6
   Emeto TI, 2016, CLIN SCI, V130, P301, DOI 10.1042/CS20150547
   Feng J, 2016, EUR J PHARMACOL, V791, P632, DOI 10.1016/j.ejphar.2016.09.041
   Fernandez-Friera L, 2017, J AM COLL CARDIOL, V70, P2979, DOI 10.1016/j.jacc.2017.10.024
   Garcia-Rodriguez C, 2018, FRONT PHYSIOL, V9, DOI 10.3389/fphys.2018.00201
   Gendron N, 2021, ARTERIOSCL THROM VAS, V41, P415, DOI 10.1161/ATVBAHA.120.314287
   Golledge J, 2019, NAT REV CARDIOL, V16, P225, DOI 10.1038/s41569-018-0114-9
   Grebe A, 2018, CIRC RES, V122, P1722, DOI 10.1161/CIRCRESAHA.118.311362
   Guo LL, 2020, EUR J VASC ENDOVASC, V60, P916, DOI 10.1016/j.ejvs.2020.08.032
   Guo YN, 2021, J CELL PHYSIOL, V236, P2333, DOI 10.1002/jcp.30025
   Huang YM, 2020, BIOMED PHARMACOTHER, V125, DOI 10.1016/j.biopha.2020.109985
   Joanna G, 2017, SWISS MED WKLY, V147, DOI 10.4414/smw.2017.14489
   Joseph J, 2017, AM J MED, V130, P253, DOI 10.1016/j.amjmed.2016.10.005
   Ju XX, 2013, ARTERIOSCL THROM VAS, V33, P1612, DOI 10.1161/ATVBAHA.112.301049
   Jung JJ, 2016, SCI REP-UK, V6, DOI 10.1038/srep32659
   Kasashima S, 2018, HEART VESSELS, V33, P1471, DOI 10.1007/s00380-018-1203-8
   Kurihara T, 2012, CIRCULATION, V126, P3070, DOI 10.1161/CIRCULATIONAHA.112.097097
   Laguna-Fernandez A, 2016, EUR HEART J, V37, P3532, DOI 10.1093/eurheartj/ehw122
   Li GX, 2017, AM J PHYSIOL-HEART C, V312, pH1110, DOI 10.1152/ajpheart.00677.2016
   Li SN, 2009, HEART VESSELS, V24, P446, DOI 10.1007/s00380-008-1137-7
   Lindman BR, 2016, NAT REV DIS PRIMERS, V2, DOI 10.1038/nrdp.2016.6
   Liu C, 2018, CLIN SCI, V132, P655, DOI 10.1042/CS20171529
   Liu H, 2020, SCAND CARDIOVASC J, V54, P37, DOI 10.1080/14017431.2019.1689289
   Liu S, 2020, J AM HEART ASSOC, V9, DOI 10.1161/JAHA.119.014757
   Liu YY, 2020, BIOMED RES INT, V2020, DOI 10.1155/2020/5073762
   Loperena R, 2018, CARDIOVASC RES, V114, P1547, DOI 10.1093/cvr/cvy112
   Lutjohann D, 2019, PHARMACOL THERAPEUT, V199, P111, DOI 10.1016/j.pharmthera.2019.03.004
   Magni P, 2019, J MOL CELL CARDIOL, V129, P231, DOI 10.1016/j.yjmcc.2019.03.004
   Malecki C, 2020, INT J MOL SCI, V21, DOI 10.3390/ijms21207678
   Marquis-Gravel G, 2016, CIRCULATION, V134, P1766, DOI 10.1161/CIRCULATIONAHA.116.023997
   Marvisi C, 2020, PRESSE MED, V49, DOI 10.1016/j.lpm.2020.104018
   Mateuszuk L, 2020, BIOCHEM PHARMACOL, V178, DOI 10.1016/j.bcp.2020.114019
   Matilla L, 2019, CLIN SCI, V133, P1537, DOI 10.1042/CS20190475
   Meher AK, 2018, ARTERIOSCL THROM VAS, V38, P843, DOI 10.1161/ATVBAHA.117.309897
   Mori D, 2015, MICROVASC RES, V98, P68, DOI 10.1016/j.mvr.2015.01.003
   Mosch J, 2017, HISTOL HISTOPATHOL, V32, P293, DOI 10.14670/HH-11-797
   Moubarak M, 2012, PHYSIOL RES, V61, P13, DOI 10.33549/physiolres.932146
   Narang N, 2017, FRONT CARDIOVASC MED, V4, DOI 10.3389/fcvm.2017.00014
   Ndrepepa G, 2019, CLIN CHIM ACTA, V493, P36, DOI 10.1016/j.cca.2019.02.022
   Nogi M, 2018, CIRCULATION, V138, P2413, DOI 10.1161/CIRCULATIONAHA.118.035648
   Nordskog Brian K, 2004, Cardiovasc Toxicol, V4, P75, DOI 10.1385/CT:4:1:75
   Ohno T, 2018, ANN VASC DIS, V11, P84, DOI 10.3400/avd.oa.17-00086
   Ouchi N, 2000, CIRCULATION, V102, P1296, DOI 10.1161/01.CIR.102.11.1296
   Pacini D, 2013, INT J CARDIOL, V167, P2806, DOI 10.1016/j.ijcard.2012.07.008
   Purroy A, 2018, ATHEROSCLEROSIS, V278, P124, DOI 10.1016/j.atherosclerosis.2018.09.022
   Raffort J, 2017, NAT REV CARDIOL, V14, P457, DOI 10.1038/nrcardio.2017.52
   Rahman K, 2017, J CLIN INVEST, V127, P2904, DOI 10.1172/JCI75005
   Ramirez CM, 2019, CIRCULATION, V140, P225, DOI 10.1161/CIRCULATIONAHA.118.038571
   Robbins CS, 2018, CLIN SCI, V132, P1253, DOI 10.1042/CS20180185
   Ruehm SG, 2001, CIRCULATION, V103, P415
   Sakakura K, 2010, HYPERTENSION, V55, P422, DOI 10.1161/HYPERTENSIONAHA.109.143131
   Sato H, 2008, J BIOL CHEM, V283, P33483, DOI 10.1074/jbc.M804628200
   Sbarouni E, 2007, INT J CARDIOL, V122, P170, DOI 10.1016/j.ijcard.2006.11.056
   Seldin MM, 2016, J AM HEART ASSOC, V5, DOI 10.1161/JAHA.115.002767
   Shimizu K, 2004, J CLIN INVEST, V114, P300, DOI 10.1172/JCI200419855
   Shimizu K, 2001, NAT MED, V7, P738, DOI 10.1038/89121
   Sidloff D, 2014, CIRCULATION, V130, P2287, DOI 10.1161/CIRCULATIONAHA.114.010890
   Soto ME, 2018, FRONT PHYSIOL, V9, DOI 10.3389/fphys.2018.00077
   Sprague AH, 2009, BIOCHEM PHARMACOL, V78, P539, DOI 10.1016/j.bcp.2009.04.029
   Sun HJ, 2017, CELL DEATH DIS, V8, DOI 10.1038/cddis.2017.470
   Sun L, 2020, J CELL PHYSIOL, V235, P8747, DOI 10.1002/jcp.29718
   Sun XH, 2014, CIRC RES, V114, P32, DOI 10.1161/CIRCRESAHA.113.302089
   Sun YL, 2019, J MOL CELL CARDIOL, V131, P66, DOI 10.1016/j.yjmcc.2019.04.004
   Sverdlov AL, 2011, AM J CARDIOVASC DIS, V1, P185
   Tieu BC, 2009, J CLIN INVEST, V119, P3637, DOI 10.1172/JCI38308
   Tomiyama H, 2005, HYPERTENSION, V45, P997, DOI 10.1161/01.HYP.0000165018.63523.8a
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vidavsky N, 2021, ADV HEALTHC MATER, V10, DOI 10.1002/adhm.202001271
   Warden BA, 2020, TRENDS CARDIOVAS MED, V30, P179, DOI 10.1016/j.tcm.2019.05.007
   Watanabe R, 2020, FRONT IMMUNOL, V11, DOI 10.3389/fimmu.2020.587089
   Wei LH, 2008, AM J PHYSIOL-CELL PH, V294, pC1198, DOI 10.1152/ajpcell.00325.2007
   Wen D, 2013, HEART, V99, P1192, DOI 10.1136/heartjnl-2013-304158
   Wen D, 2012, CLIN CHIM ACTA, V413, P198, DOI 10.1016/j.cca.2011.09.029
   Wen D, 2011, CLIN CHIM ACTA, V412, P688, DOI 10.1016/j.cca.2010.12.039
   Wu BR, 2017, ANNU REV PHYSIOL, V79, P21, DOI 10.1146/annurev-physiol-022516-034001
   Xie XJ, 2020, EVID-BASED COMPL ALT, V2020, DOI 10.1155/2020/3568756
   Xu K, 2018, FRONT PHARMACOL, V9, DOI 10.3389/fphar.2018.01328
   Xu YJ, 2020, CONSERV BIOL, V34, P416, DOI 10.1111/cobi.13383
   Zeisbrich M, 2018, ANN RHEUM DIS, V77, P1053, DOI 10.1136/annrheumdis-2017-212647
   Zhan Q, 2017, AM J PHYSIOL-CELL PH, V312, pC407, DOI 10.1152/ajpcell.00230.2016
   Zhang DF, 2020, FRONT PEDIATR, V8, DOI 10.3389/fped.2020.00493
   Zhang ZD, 2019, MOL CELLS, V42, P218, DOI 10.14348/molcells.2018.0162
   Zheng DD, 2019, CLIN RES CARDIOL, V108, P691, DOI 10.1007/s00392-018-1398-9
   Zhong SS, 2019, FREE RADICAL BIO MED, V144, P266, DOI 10.1016/j.freeradbiomed.2019.03.036
   Zhou B, 2020, OXID MED CELL LONGEV, V2020, DOI 10.1155/2020/6384803
   Zhu KF, 2016, EUR J PREV CARDIOL, V23, P530, DOI 10.1177/2047487315587402
   Zou X, 2018, ACCIDENT ANAL PREV, V118, P131, DOI 10.1016/j.aap.2018.06.010
NR 118
TC 0
Z9 0
U1 8
U2 8
PU IMR PRESS
PI ROBINSON
PA 112 ROBINSON RD, ROBINSON, SINGAPORE
SN 1530-6550
EI 2153-8174
J9 REV CARDIOVASC MED
JI Rev. Cardiovasc. Med.
PD FEB 22
PY 2022
VL 23
IS 2
AR 067
DI 10.31083/j.rcm2302067
PG 16
WC Cardiac & Cardiovascular Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Cardiovascular System & Cardiology
GA ZX9AE
UT WOS:000772184700002
PM 35229558
OA gold
DA 2022-08-23
ER

PT J
AU Haldeman, G
   Babes-Vroman, M
   Tjang, A
   Nguyen, TD
AF Haldeman, Georgiana
   Babes-Vroman, Monica
   Tjang, Andrew
   Nguyen, Thu D.
TI CSF2: Formative Feedback in Autograding
SO ACM TRANSACTIONS ON COMPUTING EDUCATION
LA English
DT Article
DE Autograding; computer science concepts and skills map; error extraction
   and categorization; code clustering; formative and corrective feedback
ID GENERATION; PROGRAM; REPAIR
AB Autograding systems are being increasingly deployed to meet the challenges of teaching programming at scale. Studies show that formative feedback can greatly help novices learn programming. This work extends an autograder, enabling it to provide formative feedback on programming assignment submissions. Our methodology starts with the design of a knowledge map, which is the set of concepts and skills that are necessary to complete an assignment, followed by the design of the assignment and that of a comprehensive test suite for identifying logical errors in the submitted code. Test cases are used to test the student submissions and learn classes of common errors. For each assignment, we train a classifier that automatically categorizes errors in a submission based on the outcome of the test suite. The instructor maps the errors to corresponding concepts and skills and writes hints to help students find their misconceptions and mistakes. We apply this methodology to two assignments in our Introduction to Computer Science course and find that the automatic error categorization has a 90% average accuracy. We report and compare data from two semesters, one semester when hints are given for the two assignments and one when hints are not given. Results show that the percentage of students who successfully complete the assignments after an initial erroneous submission is three times greater when hints are given compared to when hints are not given. However, on average, even when hints are provided, almost half of the students fail to correct their code so that it passes all the test cases. The initial implementation of the framework focuses on the functional correctness of the programs as reflected by the outcome of the test cases. In our future work, we will explore other kinds of feedback and approaches to automatically generate feedback to better serve the educational needs of the students.
C1 [Haldeman, Georgiana; Babes-Vroman, Monica; Tjang, Andrew; Nguyen, Thu D.] Rutgers State Univ, Comp Sci Dept, 110 Frelinghuysen Rd, Piscataway, NJ 08854 USA.
RP Haldeman, G (通讯作者)，Rutgers State Univ, Comp Sci Dept, 110 Frelinghuysen Rd, Piscataway, NJ 08854 USA.
EM mgh80@cs.rutgers.edu; babes@cs.rutgers.com;
   atjang@scarletmail.rutgers.edu; tdnguyen@cs.rutgers.edu
OI Nguyen, Thu/0000-0003-2843-1796; Vroman, Monica/0000-0002-8817-0279
CR Allamanis M., 2017, ARXIV PREPRINT ARXIV
   Ambrose S. A., 2010, LEARNING WORKS 7 RES
   Beverly Park Woolf, 2007, BUILDING INTELLIGENT
   Brennan K., 2012, P 2012 ANN M AM ED R, P1
   Computing Research Association, GEN CS COMP SCI UND
   D'Antoni L, 2016, LECT NOTES COMPUT SC, V9780, P383, DOI 10.1007/978-3-319-41540-6_21
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Edwards Stephen H, 2008, ACM SIGCSE B, V40
   Gerdes A, 2017, INT J ARTIF INTELL E, V27, P65, DOI 10.1007/s40593-015-0080-x
   Glassman EL, 2016, ACM CONFERENCE ON COMPUTER-SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING (CSCW 2016), P1626, DOI 10.1145/2818048.2820011
   Glassman EL, 2015, UIST'15: PROCEEDINGS OF THE 28TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, P609, DOI 10.1145/2807442.2807495
   Glassman EL, 2015, ACM T COMPUT-HUM INT, V22, DOI 10.1145/2699751
   Guo PJ, 2015, UIST'15: PROCEEDINGS OF THE 28TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, P599, DOI 10.1145/2807442.2807469
   Hartmann B, 2010, CHI2010: PROCEEDINGS OF THE 28TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P1019
   Head A, 2017, PROCEEDINGS OF THE FOURTH (2017) ACM CONFERENCE ON LEARNING @ SCALE (L@S'17), P89, DOI 10.1145/3051457.3051467
   Holland Jay, 2009, 2009 INT C COMP ED
   Huang Jonathan, 2013, P 2013 WORKSH MASS O
   Ihantola P, 2010, P 10 KOL CALL INT C, P86, DOI 10.1145/1930464.1930480
   Kaleeswaran S, 2016, FSE'16: PROCEEDINGS OF THE 2016 24TH ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON FOUNDATIONS OF SOFTWARE ENGINEERING, P739, DOI 10.1145/2950290.2950363
   Keuning Hieke, 2016, P 2016 C INN TECHN C
   Le N.-T., 2014, P 2 WORKSH AI SUPP E
   Marwan S, 2019, ICER '19 - PROCEEDINGS OF THE 2019 ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH, P61, DOI 10.1145/3291279.3339420
   MAYER RE, 1986, COMMUN ACM, V29, P605, DOI 10.1145/6138.6142
   McBroom Jessica, 2019, ABS190811566 ARXIV
   Milojicic D, 2011, IEEE INTERNET COMPUT, V15, P9, DOI 10.1109/MIC.2011.2
   Moghadam J.B., 2015, SER L S 15, P261, DOI [10.1145/2724660.2728672, DOI 10.1145/2724660.2728672]
   National Center for Women & Information Technology, PROJ COMP JOBS CIS D
   Nguyen A, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P491, DOI 10.1145/2566486.2568023
   Le NT, 2013, IEEE T LEARN TECHNOL, V6, P258, DOI 10.1109/TLT.2013.16
   Paassen Benjamin, ABS170806564 CORR
   Piech Chris, 2015, ARXIV PREPRINT ARXIV
   Rivers K, 2017, INT J ARTIF INTELL E, V27, P37, DOI 10.1007/s40593-015-0070-z
   Shute VJ, 2008, REV EDUC RES, V78, P153, DOI 10.3102/0034654307313795
   Singh G, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P263, DOI 10.1145/2939672.2939696
   Singh R, 2013, ACM SIGPLAN NOTICES, V48, P15, DOI 10.1145/2499370.2462195
   Stamper J., 2008, P 9 INT C INT TUT SY, P71
   Suzuki R., 2017, P 2017 CHI C EXT ABS, P2951
   Wang K, 2018, ACM SIGPLAN NOTICES, V53, P481, DOI [10.1145/3192366.3192384, 10.1145/3296979.3192384]
   Zhi Rui, 2019, 12 INT C ED DAT MIN, P218
NR 39
TC 0
Z9 0
U1 2
U2 5
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 1946-6226
J9 ACM T COMPUT EDUC
JI ACM Trans. Comput. Educ.
PD JUN
PY 2021
VL 21
IS 3
AR 21
DI 10.1145/3445983
PG 30
WC Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research
GA TE0EJ
UT WOS:000669690000005
DA 2022-08-23
ER

PT J
AU Peng, J
   Xu, D
   Lee, R
   Xu, SW
   Zhou, YY
   Wang, K
AF Peng, Jacqueline
   Xu, David
   Lee, Ryan
   Xu, Siwei
   Zhou, Yunyun
   Wang, Kai
TI Expediting knowledge acquisition by a web framework for Knowledge Graph
   Exploration and Visualization (KGEV): case studies on COVID-19 and Human
   Phenotype Ontology
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article
DE Knowledge graph; COVID-19; Information extraction; Data visualization;
   Knowledge discovery
ID BIOMEDICAL TEXT; GENES; UMLS
AB Background: Knowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for down-stream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses.
   Method: We developed a web framework for Knowledge Graph Exploration and Visualization (KGEV), to construct and visualize KGs in five stages: triple extraction, triple filtration, metadata preparation, knowledge integration, and graph database preparation. The application has convenient user interface tools, such as node and edge search and filtering, data source filtering, neighborhood retrieval, and shortest path calculation, that work by querying a backend graph database. Unlike other KGs, our framework allows fast retrieval of relevant texts supporting the relationships in the KG, thus allowing human reviewers to judge the reliability of the knowledge extracted.
   Results: We demonstrated a case study of using the KGEV framework to perform research on COVID-19. The COVID-19 pandemic resulted in an explosion of relevant literature, making it challenging to make full use of the vast and heterogenous sources of information. We generated a COVID-19 KG with heterogenous information, including literature information from the CORD-19 dataset, as well as other existing knowledge from eight data sources. We showed the utility of KGEV in three intuitive case studies to explore and query knowledge on COVID-19. A demo of this web application can be accessed at http://covid19nlp.wglab.org. Finally, we also demonstrated a turn-key adaption of the KGEV framework to study clinical phenotypic presentation of human diseases by Human Phenotype Ontology (HPO), illustrating the versatility of the framework.
   Conclusion: In an era of literature explosion, the KGEV framework can be applied to many emerging diseases to support structured navigation of the vast amount of newly published biomedical literature and other existing biological knowledge in various databases. It can be also used as a general-purpose tool to explore and query gene-phenotype-disease-drug relationships interactively.
C1 [Peng, Jacqueline; Zhou, Yunyun; Wang, Kai] Childrens Hosp Philadelphia, Raymond G Perelman Ctr Cellular & Mol Therapeut, Philadelphia, PA 19104 USA.
   [Xu, David; Lee, Ryan] Univ Penn, Sch Arts & Sci, Philadelphia, PA 19104 USA.
   [Xu, Siwei] Emory Univ, Coll Arts & Sci, Atlanta, GA 30322 USA.
   [Wang, Kai] Univ Penn, Perelman Sch Med, Dept Pathol & Lab Med, Philadelphia, PA 19104 USA.
RP Zhou, YY; Wang, K (通讯作者)，Childrens Hosp Philadelphia, Raymond G Perelman Ctr Cellular & Mol Therapeut, Philadelphia, PA 19104 USA.; Wang, K (通讯作者)，Univ Penn, Perelman Sch Med, Dept Pathol & Lab Med, Philadelphia, PA 19104 USA.
EM zhouy6@chop.edu; wangk@chop.edu
FU NIH/NLM/NHGRI [LM012895]; Penn Undergraduate Research Mentoring Program;
   CHOP Research Institute
FX The study is supported by NIH/NLM/NHGRI grant LM012895, Penn
   Undergraduate Research Mentoring Program, and the CHOP Research
   Institute. The funding bodies had no role in the design of the study and
   collection, analysis, and interpretation of data and in writing the
   manuscript. Publication costs are funded by NIH/NLM/NHGRI grant
   LM012895.
CR Aronson AR, 2001, J AM MED INFORM ASSN, P17
   Bateman A, 2019, NUCLEIC ACIDS RES, V47, pD506, DOI 10.1093/nar/gky1049
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bouaziz J, 2018, BIOMED RES INT, V2018, DOI 10.1155/2018/6217812
   Carbon S, 2019, NUCLEIC ACIDS RES, V47, pD330, DOI 10.1093/nar/gky1055
   Cernile G, 2021, BMJ HEALTH CARE INFO, V28, DOI 10.1136/bmjhci-2020-100254
   Chahrour M, 2020, CUREUS J MED SCIENCE, V12, DOI 10.7759/cureus.7357
   Chen QY, 2020, NATURE, V579, P193, DOI 10.1038/d41586-020-00694-1
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Cotto Kelsy C, 2018, Nucleic Acids Res, V46, pD1068, DOI 10.1093/nar/gkx1143
   COVIDSurg Collaborative, 2020, BRIT J SURG, V107, P1097, DOI 10.1002/bjs.11646
   Dai YF, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9050750
   Daszak P, 2021, HEALTH AFFAIR, V40, P204, DOI 10.1377/hlthaff.2020.01544
   Davis AP, 2021, NUCLEIC ACIDS RES, V49, pD1138, DOI 10.1093/nar/gkaa891
   Domingo-Fernandez D., 2020, BIOINFORMATICS, V18, P551
   Else H, 2020, NATURE, V588, P553, DOI 10.1038/d41586-020-03564-y
   Gleeson LE, 2021, BRIT J NUTR, V125, P628, DOI 10.1017/S0007114520003529
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Kazemi Rashed S, 2020, ARXIV200309865
   Kejriwal M., 2020, HARV DATA SCI REV, DOI [10.1162/99608f92.e45650b8, DOI 10.1162/99608F92.E45650B8]
   Lu Wang L., 2020, ARXIV200410706
   McArthur DB, 2019, NURS CLIN N AM, V54, P297, DOI 10.1016/j.cnur.2019.02.006
   Nii-Trebi NI, 2017, BIOMED RES INT, V2017, P1, DOI 10.1155/2017/5245021
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Peng J, 2020, BMC MED INFORM DECIS, V20, DOI 10.1186/s12911-020-01352-2
   Pinero J, 2017, NUCLEIC ACIDS RES, V45, pD833, DOI 10.1093/nar/gkw943
   Porter Alan L, 2020, Front Res Metr Anal, V5, P594060, DOI 10.3389/frma.2020.594060
   Qilong Wang, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P11531, DOI 10.1109/CVPR42600.2020.01155
   Reese JT, 2021, PATTERNS, V2, DOI 10.1016/j.patter.2020.100155
   Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003
   Robinson PN, 2008, AM J HUM GENET, V83, P610, DOI 10.1016/j.ajhg.2008.09.017
   Scheen AJ, 2021, DIABETES METAB, V47, DOI 10.1016/j.diabet.2020.11.005
   Schriml LM, 2019, NUCLEIC ACIDS RES, V47, pD955, DOI 10.1093/nar/gky1032
   Shang J, 2020, P NATL ACAD SCI USA, V117, P11727, DOI 10.1073/pnas.2003138117
   Sheikhalishahi S, 2019, JMIR MED INF, V7, P15, DOI 10.2196/12239
   Shrivastava SR, 2013, INT J PREVENTIVE MED, V4, P736
   Simmons M, 2016, ADV EXP MED BIOL, V939, P139, DOI 10.1007/978-981-10-1503-8_7
   Singhal A, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw161
   Son JH, 2018, AM J HUM GENET, V103, P58, DOI 10.1016/j.ajhg.2018.05.010
   Szklarczyk D, 2019, NUCLEIC ACIDS RES, V47, pD607, DOI 10.1093/nar/gky1131
   Tweedie S, 2021, NUCLEIC ACIDS RES, V49, pD939, DOI 10.1093/nar/gkaa980
   Ursu O, 2017, NUCLEIC ACIDS RES, V45, pD932, DOI 10.1093/nar/gkw993
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wise C, ARXIVABS200712731, V2020
   Zhang R, 2021, J BIOMED INFORM, V115, DOI 10.1016/j.jbi.2021.103696
   Zhu F, 2013, J BIOMED INFORM, V46, P200, DOI 10.1016/j.jbi.2012.10.007
NR 46
TC 0
Z9 0
U1 3
U2 3
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6947
J9 BMC MED INFORM DECIS
JI BMC Med. Inform. Decis. Mak.
PD JUN 2
PY 2022
VL 22
IS SUPPL 2
SU 2
SI SI
AR 147
DI 10.1186/s12911-022-01848-z
PG 13
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA 1U1KN
UT WOS:000805177800001
PM 35655307
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Al Sayed, K
   Cheng, PCH
   Penn, A
AF Al Sayed, Kinda
   Cheng, Peter C. H.
   Penn, Alan
TI An investigation into the cognitive, metacognitive, and spatial markers
   of creativity and efficiency in architectural design
SO AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND
   MANUFACTURING
LA English
DT Article
DE Architectural design; design creativity; knowledge graph; linkography;
   metacognition
ID LINKOGRAPHY; THINKING
AB This paper presents a preliminary study into the spatial features that can be used to distinguish creativity andefficiency in design layouts, and the distinct pattern of cognitive and metacognitive activity that is associated with creative design. In a design experiment, a group of 12 architects were handed a design brief. Their drawing activity was recorded and they were required to externalize their thoughts during the design process. Both design solutions and verbal comments were analysed and modelled. A separate group of experienced architects used their expert knowledge to assign creativity and efficiency scores to the 12 design solutions. The design solutions were evaluated spatially. Protocol analysis studies including linkography and macroscopic analysis were used to discern distinctive patterns in the cognitive and metacognition activity of designs marked with the highest and least creativity scores. Entropy models of the linkographs and knowledge graphs were further introduced Finally, we assessed how creativity and efficiency correlates to experiment variables, cognitive activity, metacognitive activity, spatial and functional distribution of spaces in the design solutions, and the number and type of design constraints applied through the course of design. Through this investigation, we suggest that expert knowledge can be used to assess creativity and efficiency in designs. Our findings indicate that efficient layouts have distinct spatial features, and that cognitive and metacognitive activity in design that yields a highly creative outcome corresponds to higher frequencies of design moves and higher linkages between design moves.
C1 [Al Sayed, Kinda] Univ Sussex, Sch Engn & Informat, Chichester 1, Brighton BN1 9QJ, E Sussex, England.
   [Cheng, Peter C. H.] Univ Sussex, Brighton BN1 9QJ, E Sussex, England.
   [Penn, Alan] UCL, London, England.
RP Al Sayed, K (通讯作者)，Univ Sussex, Sch Engn & Informat, Chichester 1, Brighton BN1 9QJ, E Sussex, England.
EM k.al-sayed@sussex.ac.uk
OI Al Sayed, Kinda/0000-0003-2047-8565
FU University of Sussex
FX The authors of this paper would like to express their gratitude to Ruth
   Conroy Dalton and Professor Christoph Holscher for their contributions
   to the initial part of the research conducted at UCL. The authors would
   also like to express their gratitude to all the participants in the
   research experiment and the panel of expert architects that have
   contributed to evaluating the creativity and efficiency of the design
   solutions. This research is partly funded by the University of Sussex.
CR Al Sayed, 2014, DESIGN COMPUTING COG, V12, P393
   Al-Sayed K, 2010, AI EDAM, V24, P211, DOI 10.1017/S0890060410000065
   Al-Sayed K, 2008, DESIGN COMPUTING AND COGNITION '08, P635, DOI 10.1007/978-1-4020-8728-8_33
   Alexander Christopher., 1964, NOTES SYNTHESIS FORM, V5
   Boden M. A., 1990, CREATIVE MIND MYTHS
   Flavell J.H., 1976, NATURE INTELLIGENCE, P231, DOI DOI 10.1038/HDY.1976.85
   Fleming SM, 2012, PHILOS T R SOC B, V367, P1280, DOI 10.1098/rstb.2012.0021
   GOEL V, 1992, COGNITIVE SCI, V16, P395, DOI 10.1016/0364-0213(92)90038-V
   Goldschmidt G., 1992, EVALUATING PREDICTIN, P67
   Gong Y, 2009, INT C COMP AID IND D, P531, DOI 10.1109/CAIDCD.2009.5374933
   Hanna S, 2007, INT J ARCHIT COMPUT, V5, P1
   Heyes C, 2020, TRENDS COGN SCI, V24, P349, DOI 10.1016/j.tics.2020.02.007
   Hillier B., 1974, J ARCHITECTURAL RES, V3, P4
   JACOBS JE, 1987, EDUC PSYCHOL, V22, P255, DOI 10.1207/s15326985ep2203&4_4
   Kan JWT, 2008, DESIGN STUD, V29, P315, DOI 10.1016/j.destud.2008.03.001
   Kan JWT, 2007, AI EDAM, V21, P367, DOI 10.1017/S0890060407000339
   KLAHR D, 1988, COGNITIVE SCI, V12, P1, DOI 10.1016/0364-0213(88)90007-9
   Kuhn T. S., 1962, STRUCTURE SCI REVOLU
   Legrand N, 2021, COGNITION EMOTION, V35, P705, DOI 10.1080/02699931.2020.1859993
   Penn A, 2012, P 8 SPAC SYNT S SANT, P8242
   Rodriguez RN, 1998, PROCEEDINGS OF THE TWENTY-THIRD ANNUAL SAS USERS GROUP INTERNATIONAL CONFERENCE, P1262
   Schunn C.D., 1995, P 17 ANN C COGNITIVE, P106
   Shannon C.E., 1948, Bell System Technical Journal, V27, P379
   Shea N, 2019, TRENDS COGN SCI, V23, P560, DOI 10.1016/j.tics.2019.04.007
   Shea N, 2014, TRENDS COGN SCI, V18, P186, DOI 10.1016/j.tics.2014.01.006
   Sherratt S, 2011, PRINCIPLES PRACTICE, V17
   Shpuza, 2006, THESIS GEORGIA I TEC
   Simon H., 1957, MODELS MAN SOCIAL RA
   Simon H. A., 1977, MODELS OF DISCOVERY, P304
   Suwa M, 2003, PROCEEDINGS OF THE TWENTY-FIFTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, PTS 1 AND 2, P1140
   Suwa M., 1998, DESIGN STUDIES, V19, P455, DOI DOI 10.1016/S0142-694X(98)00016-7
   Yeung N, 2012, PHILOS T R SOC B, V367, P1310, DOI 10.1098/rstb.2011.0416
NR 32
TC 0
Z9 0
U1 3
U2 4
PU CAMBRIDGE UNIV PRESS
PI NEW YORK
PA 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA
SN 0890-0604
EI 1469-1760
J9 AI EDAM
JI AI EDAM-Artif. Intell. Eng. Des. Anal. Manuf.
PD NOV
PY 2021
VL 35
IS 4
BP 423
EP 437
AR PII S0890060421000251
DI 10.1017/S0890060421000251
EA JAN 2022
PG 15
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Engineering, Multidisciplinary;
   Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA YR0OG
UT WOS:000737585800001
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Du, W
   Yan, Q
   Zhang, WP
   Ma, J
AF Du, Wei
   Yan, Qiang
   Zhang, Wenping
   Ma, Jian
TI Leveraging online behaviors for interpretable knowledge-aware patent
   recommendation
SO INTERNET RESEARCH
LA English
DT Article
DE Interpretable knowledge-aware recommendation; Patent recommendation;
   Online behaviors
ID INNOVATION; SYSTEMS; MODELS; FIRMS
AB Purpose Patent trade recommendations necessitate recommendation interpretability in addition to recommendation accuracy because of patent transaction risks and the technological complexity of patents. This study designs an interpretable knowledge-aware patent recommendation model (IKPRM) for patent trading. IKPRM first creates a patent knowledge graph (PKG) for patent trade recommendations and then leverages paths in the PKG to achieve recommendation interpretability. Design/methodology/approach First, we construct a PKG to integrate online company behaviors and patent information using natural language processing techniques. Second, a bidirectional long short-term memory network (BiLSTM) is utilized with an attention mechanism to establish the connecting paths of a company - patent pair in PKG. Finally, the prediction score of a company - patent pair is calculated by assigning different weights to their connecting paths. The semantic relationships in connecting paths help explain why a candidate patent is recommended. Findings Experiments on a real dataset from a patent trading platform verify that IKPRM significantly outperforms baseline methods in terms of hit ratio and normalized discounted cumulative gain (nDCG). The analysis of an online user study verified the interpretability of our recommendations. Originality/value A meta-path-based recommendation can achieve certain explainability but suffers from low flexibility when reasoning on heterogeneous information. To bridge this gap, we propose the IKPRM to explain the full paths in the knowledge graph. IKPRM demonstrates good performance and transparency and is a solid foundation for integrating interpretable artificial intelligence into complex tasks such as intelligent recommendations.
C1 [Du, Wei; Zhang, Wenping] Renmin Univ China, Sch Informat, Beijing, Peoples R China.
   [Yan, Qiang] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China.
   [Ma, Jian] City Univ Hong Kong, Coll Business, Informat Syst, Hong Kong, Peoples R China.
RP Zhang, WP (通讯作者)，Renmin Univ China, Sch Informat, Beijing, Peoples R China.
EM ahduwei@ruc.edu.cn; yanqiang@ict.ac.cn; wpzhang@ruc.edu.cn;
   isjian@cityu.edu.hk
OI MA, Jian/0000-0003-2644-5756; DU, Wei/0000-0003-3272-3222
FU National Natural Science Foundation of China [71901208, 71771212,
   U1711262, 71801217]; Humanities and Social Sciences Foundation of the
   Ministry of Education [18YJC630025]; Key Projects of Philosophy and
   Social Sciences Research of Chinese Ministry of Education [19JZD021];
   Ministry of Education, Science and Technology Development Center
   [2019J01010]
FX This work was supported in part by the National Natural Science
   Foundation of China (Grant Nos. 71901208, 71771212, U1711262, 71801217),
   Humanities and Social Sciences Foundation of the Ministry of Education
   (No. 18YJC630025), Key Projects of Philosophy and Social Sciences
   Research of Chinese Ministry of Education (No. 19JZD021), and Ministry
   of Education, Science and Technology Development Center (No.
   2019J01010).
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
   Anandkumar A, 2014, J MACH LEARN RES, V15, P2773
   Anning-Dorson T, 2018, INT MARKET REV, V35, P580, DOI 10.1108/IMR-11-2015-0262
   Bellini V, 2018, PROCEEDINGS OF THE 3RD WORKSHOP ON DEEP LEARNING FOR RECOMMENDER SYSTEMS (DLRS), P24, DOI 10.1145/3270323.3270327
   Benassi M, 2009, R&D MANAGE, V39, P68, DOI 10.1111/j.1467-9310.2008.00537.x
   Bobadilla J, 2013, KNOWL-BASED SYST, V46, P109, DOI 10.1016/j.knosys.2013.03.012
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Carvalho DV, 2019, ELECTRONICS-SWITZ, V8, DOI 10.3390/electronics8080832
   Cavallucci D., 2020, COMPUT IND, V115, P1
   Chiu J.P.C., 2015, ABS151108308 CORR
   Povoa LMC, 2010, SCI PUBL POLICY, V37, P147, DOI 10.3152/030234210X496619
   Crestani, 2014, P 23 ACM INT C C INF, P1659
   Ellis D. P., 2015, ARXIV151208756
   Erdi P, 2013, SCIENTOMETRICS, V95, P225, DOI 10.1007/s11192-012-0796-4
   Fabry B, 2006, WORLD PAT INF, V28, P215, DOI 10.1016/j.wpi.2005.10.004
   Fall C.J., 2003, ACM SIGIR FORUM, V37, P10, DOI [10.1145/945546.945547, DOI 10.1145/945546.945547]
   Fu TY, 2015, IEEE DATA MINING, P751, DOI 10.1109/ICDM.2015.151
   Guellec, 2009, EMERGING PATENT MARK, DOI [10.1787/218413152254, DOI 10.1787/218413152254]
   He XJ, 2019, KNOWL-BASED SYST, V184, DOI 10.1016/j.knosys.2019.104899
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774
   Jeh G., 2002, P 8 ACM SIGKDD INT C, P538, DOI DOI 10.1145/775047.775126
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Karki M. M. S., 1997, WORLD PATENT INFORMA, V19, P269, DOI DOI 10.1016/S0172-2190(97)00033-1
   Kim D, 2019, INFORM SCIENCES, V477, P15, DOI 10.1016/j.ins.2018.10.006
   Kim SY, 2019, WORLD PAT INF, V59, DOI 10.1016/j.wpi.2019.101933
   Klasnja-Milicevic A, 2011, COMPUT EDUC, V56, P885, DOI 10.1016/j.compedu.2010.11.001
   Lehmann, 2019, AAAI 2019 WORKSH REC
   Leydesdorff L, 2014, SCIENTOMETRICS, V98, P1583, DOI 10.1007/s11192-012-0923-2
   Li GC, 2014, RES POLICY, V43, P941, DOI 10.1016/j.respol.2014.01.012
   Lichtenthaler U, 2009, J MANAGE STUD, V46, P1315, DOI 10.1111/j.1467-6486.2009.00854.x
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu X., 2014, P 23 ACM INT C C INF, P121, DOI DOI 10.1145/2661829.2661965
   MacKenzie I., 2013, RETAILERS CAN KEEP C
   Mahdabi P, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P113
   McCormick C., 2016, WORD2VEC TUTORIAL TH
   Min, 2016, ADV COMPUTER SCI UBI, P843
   Oh S, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P2291, DOI 10.1145/2505515.2505659
   Park, 2016, SUSTAINABILITY-BASEL, V8, P1
   Rose S., 2010, TEXT MINING APPL THE, P1, DOI [DOI 10.1002/9780470689646.CH1, 10.1002/9780470689646.ch1, 10.1002/9780470689646.CH1]
   Rudin C, 2019, NAT MACH INTELL, V1, P206, DOI 10.1038/s42256-019-0048-x
   Schafer J. B., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P291
   Shalaby W, 2018, ACM/SIGIR PROCEEDINGS 2018, P957, DOI 10.1145/3209978.3210106
   Su Xiaoyuan, 2009, ADV ARTIFICIAL INTEL, DOI DOI 10.1155/2009/421425
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Trappey AJC, 2013, J NETW COMPUT APPL, V36, P1441, DOI 10.1016/j.jnca.2013.02.035
   Wang F, 2017, 2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD), P2195, DOI 10.1109/FSKD.2017.8393111
   Wang F, 2013, 2013 10TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), P572, DOI 10.1109/FSKD.2013.6816262
   Wang Heng, 2019, P 2019 C EMP METH NA, P2623
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2000, DOI 10.1145/3308558.3313411
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang Q, 2019, INT J ELECTRON COMM, V23, P147, DOI 10.1080/10864415.2018.1564549
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu S, 2013, P 6 ACM INT C WEB SE, P43, DOI DOI 10.1145/2433396.2433404
   Xian YK, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P285, DOI 10.1145/3331184.3331203
   Xiang Ji, 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011), P1699, DOI 10.1109/FSKD.2011.6019821
   Yu X, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P283, DOI 10.1145/2556195.2556259
   Yun JJ, 2018, KNOWL MAN RES PRACT, V16, P305, DOI 10.1080/14778238.2018.1471328
   Zhang YD, 2020, FRONT COMPUT SCI-CHI, V14, DOI 10.1007/s11704-018-8106-9
NR 61
TC 0
Z9 0
U1 11
U2 31
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1066-2243
J9 INTERNET RES
JI Internet Res.
PD MAR 15
PY 2022
VL 32
IS 2
SI SI
BP 568
EP 587
DI 10.1108/INTR-08-2020-0473
EA JUN 2021
PG 20
WC Business; Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science; Telecommunications
GA ZY6TT
UT WOS:000660627500001
DA 2022-08-23
ER

PT J
AU Yang, ZX
   Dong, SB
   Hu, JL
AF Yang, Zuoxi
   Dong, Shoubin
   Hu, Jinlong
TI GFE: General Knowledge Enhanced Framework for Explainable Sequential
   Recommendation
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Explainable recommendation; Hierarchical self-attention; Knowledge
   graph; Sequential recommendation
AB It is vital for sequential recommendation to provide accurate and explainable results for user, which can help them make better decisions. In this paper, we develop a General Knowledge Enhanced Framework for Explainable Sequential Recommendation (GFE) to capture user's fine-grained preferences and dynamic preferences evolution. Specifically, the fine-grained preferences are modeled as intrinsic interests and external potential interests, which can be captured by sequential-aware interest and knowledge-aware interest modules respectively. Moreover, the high-order paths between each user item pair are generated with the help of the knowledge graph, which contain abundant high-order semantic relevance among entities. To make better use of this character, we propose a hierarchical self attention mechanism to aggregate the high-order semantic information from these knowledge paths, thus discovering user's dynamic preferences evolution. According to these abilities, GFE can provide more reasonable explanations from the views of microcosm and macrocosm. Unlike other traditional explainable sequential recommendation models, GFE has the strong generalization to integrate with other pure sequential recommendation models and endow explainability to them. Based on this nature, we combine the GFE with two state-of-the-art sequential recommendation models and further propose two GFE-based models, called GFE-SASRec and GFE-TiSASRec, to show the availability of GFE. Finally, experiments conducted on three real-world public datasets demonstrate the state-of-the-art performance and the strong explainability of GFE. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Yang, Zuoxi; Dong, Shoubin; Hu, Jinlong] South China Univ Technol, Sch Comp Sci & Engn, Guangdong Key Lab Comp Network, Guangzhou, Peoples R China.
RP Dong, SB (通讯作者)，South China Univ Technol, Sch Comp Sci & Engn, Guangdong Key Lab Comp Network, Guangzhou, Peoples R China.
EM csyangzuoxi@mail.scut.edu.cn; sbdong@scut.edu.cn
FU National Natural Science Foundation of China [61976239]; Science and
   Technology Project in Guangzhou of China [201903010046]; Innovation
   Foundation of High-end Scientific Research Institutions in Zhongshan of
   China [2019AG031]
FX This work is supported by the National Natural Science Foundation of
   China (Grant No. 61976239), Science and Technology Project in Guangzhou
   of China (Grant No. 201903010046) and Innovation Foundation of High-end
   Scientific Research Institutions in Zhongshan of China (Grant No.
   2019AG031).
CR Ali Z, 2020, KNOWL-BASED SYST, V210, DOI 10.1016/j.knosys.2020.106438
   Bellini V., 2017, P 2 WORKSH DEEP LEAR, P60, DOI [10.1145/3125486.3125496, DOI 10.1145/3125486.3125496]
   Beutel A, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P46, DOI 10.1145/3159652.3159727
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Cheng X, 2013, IEEE IJCNN, DOI 10.1109/IJCNN.2013.6706853
   Gazdar A, 2020, KNOWL-BASED SYST, V188, DOI 10.1016/j.knosys.2019.105058
   He RN, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P507, DOI 10.1145/2872427.2883037
   He RN, 2016, IEEE DATA MINING, P191, DOI [10.1109/ICDM.2016.88, 10.1109/ICDM.2016.0030]
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   Hidasi B., 2015, P 4 INT C LEARN REPR
   Hidasi B, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P843, DOI 10.1145/3269206.3271761
   Huang J, 2018, ACM/SIGIR PROCEEDINGS 2018, P505, DOI 10.1145/3209978.3210017
   Huang XW, 2019, PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19), P548, DOI 10.1145/3343031.3350893
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Ji K, 2015, KNOWL-BASED SYST, V88, P134, DOI 10.1016/j.knosys.2015.07.039
   Kang WC, 2018, IEEE DATA MINING, P197, DOI 10.1109/ICDM.2018.00035
   Karatzoglou A., 2010, P 4 ACM C REC SYST, P79, DOI DOI 10.1145/1864708.1864727
   Kingman D., 2015, 3 INT C LEARN REPR 2
   Li JC, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P322, DOI 10.1145/3336191.3371786
   Li J, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1419, DOI 10.1145/3132847.3132926
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu L., KNOWL-BASED SYST, V212
   Ma C, 2020, AAAI CONF ARTIF INTE, V34, P5045
   Pei WJ, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1459, DOI 10.1145/3132847.3133005
   Quadrana M, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P130, DOI 10.1145/3109859.3109896
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Rendle S., 2010, P 19 INT C WORLD WID, P811, DOI [/10.1145 /1772690.1772773, DOI 10.1145/1772690.1772773]
   Salamat A., KNOWL-BASED SYST, V217
   Shani G, 2005, J MACH LEARN RES, V6, P1265
   Sun F, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P1441, DOI 10.1145/3357384.3357895
   Tang JX, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P565, DOI 10.1145/3159652.3159656
   Tang XL, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1891, DOI 10.1145/3292500.3330705
   Uszkoreit Jakob, 2018, P C N AM CHAPT ASS C
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2000, DOI 10.1145/3308558.3313411
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xin X, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P125, DOI 10.1145/3331184.3331188
   Yan A, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P2173, DOI 10.1145/3357384.3358113
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhao WX, 2019, DATA INTELLIGENCE, V1, P121, DOI 10.1162/dint_a_00008
   Zhao ZY, 2020, KNOWL-BASED SYST, V204, DOI 10.1016/j.knosys.2020.106218
NR 44
TC 3
Z9 3
U1 10
U2 18
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD OCT 27
PY 2021
VL 230
AR 107375
DI 10.1016/j.knosys.2021.107375
EA AUG 2021
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UR8PO
UT WOS:000697004300003
DA 2022-08-23
ER

PT J
AU Zhang, F
   Li, JW
   Cheng, JW
AF Zhang, Fu
   Li, Jianwei
   Cheng, Jingwei
TI Improving entity alignment via attribute and external knowledge
   filtering
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Entity alignment; Selective filtering; Knowledge graph; Neural networks
AB The goal of entity alignment is to find the equivalent entity pairs in different Knowledge Graphs (KGs), which is a key step of KG fusion. Recent developments often take embedding-based methods, which mainly focus on embedding structure information (relationship triples) of KGs to align entities. However, attribute information (attribute triples) and external knowledge (some public knowledge bases) can also provide extremely valuable information but have not been well explored yet. In this paper, we propose a Selective Filtering Entity Alignment (SFEA) framework for improving entity alignment via attribute and external knowledge filtering. Our framework proposes two kinds of selective filtering mechanisms to filter the candidate set by using attribute information and external knowledge, so as to delete most of wrong entities in the candidate set. The framework mainly uses selective filtering mechanism and external knowledge for entity alignment. Experiments show that our framework achieves state-of-the-art on the dataset DBP15K compared to the existing similar methods of using structure and attribute information. Specifically, our framework outperforms the best state-of-the-art methods by 1.2%-4.9% in terms of Hits@1, and also achieves better or comparable performance when compared with other methods of incorporating multiple sources of information.
C1 [Zhang, Fu; Li, Jianwei; Cheng, Jingwei] Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Peoples R China.
RP Cheng, JW (通讯作者)，Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Peoples R China.
EM zhangfu216@126.com; chengjingwei@mail.neu.edu.cn
FU National Natural Science Foundation of China [61672139]; Natural Science
   Foundation of Ningxia Province [2020AAC03212]; Fundamental Research
   Funds for the Central Universities [N2216008, N2116018, N2016009]
FX The authors sincerely thank the anonymous referees for their valuable
   comments and suggestions, which improved the paper. The work is
   supported by the National Natural Science Foundation of China
   (61672139), the Natural Science Foundation of Ningxia Province (No.
   2020AAC03212), and the Fundamental Research Funds for the Central
   Universities (No. N2216008, N2116018, N2016009).
CR Ahmed U, 2022, FUTURE GENER COMP SY, V127, P70, DOI 10.1016/j.future.2021.08.028
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cao YX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1452
   Chen MH, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1511
   Creswell A, 2019, IEEE T NEUR NET LEAR, V30, P1967, DOI 10.1109/TNNLS.2018.2875194
   Jia Chen, 2020, Database Systems for Advanced Applications. 25th International Conference, DASFAA 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12112), P452, DOI 10.1007/978-3-030-59410-7_31
   Li Chengjiang, 2019, P 2019 C EMPIRICAL M, P2723
   Lin JCW, 2021, KNOWL-BASED SYST, V212, DOI 10.1016/j.knosys.2020.106548
   Lin XX, 2019, IEEE DATA MINING, P429, DOI 10.1109/ICDM.2019.00053
   Mikolov T., 2013, ARXIV PREPRINT ARXIV, P1
   Pei SC, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3231
   Pei SC, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3130, DOI 10.1145/3308558.3313646
   Peng YH, 2020, 11TH IEEE INTERNATIONAL CONFERENCE ON KNOWLEDGE GRAPH (ICKG 2020), P123, DOI 10.1109/ICBK50248.2020.00027
   Shao YN, 2021, PATTERN RECOGN LETT, V145, P157, DOI 10.1016/j.patrec.2021.02.008
   Shi Xiaofei, 2019, EMNLP IJCNLP, P813
   Sun ZQ, 2020, PROC VLDB ENDOW, V13, P2326, DOI 10.14778/3407790.3407828
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Tang XB, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3174
   Trisedya BD, 2019, AAAI CONF ARTIF INTE, P297
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang Zhichun, 2020, P 2020 C EMP METH NA, P1672, DOI DOI 10.18653/V1/2020.EMNLP-MAIN.130
   Wu YT, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5278
   Wu Yuting, 2019, EMNLP IJCNLP, P240
   Wu Yuting, 2020, ACL, P6477
   Xie Z., 2020, COLING
   Xu Y, 2021, IEEE T IND ELECTRON
   Xu YD, 2020, OPTIK, V223, DOI 10.1016/j.ijleo.2020.165494
   Xu YD, 2021, INFORM SCIENCES, V548, P378, DOI 10.1016/j.ins.2020.09.066
   Xu YD, 2020, DIGIT SIGNAL PROCESS, V106, DOI 10.1016/j.dsp.2020.102821
   Yang Hsiu-Wei, 2019, P 2019 C EMP METH NA, P4430
   Ye R, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4135
   Zeng WX, 2020, PROC INT CONF DATA, P1870, DOI 10.1109/ICDE48307.2020.00191
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhang QH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5429
   Zhang YY, 2018, AAAI CONF ARTIF INTE, P6069
   Zhu H, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4258
   Zhu R, 2021, ARXIV 210300791
NR 38
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
DI 10.1007/s10489-022-03744-5
EA JUL 2022
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2U7SF
UT WOS:000823355800001
DA 2022-08-23
ER

PT J
AU Hiebel, G
   Aspock, E
   Kopetzky, K
AF Hiebel, Gerald
   Aspock, Edeltraud
   Kopetzky, Karin
TI Ontological Modeling for Excavation Documentation and Virtual
   Reconstruction of an Ancient Egyptian Site
SO ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE
LA English
DT Article
DE Cultural heritage; CIDOC CRM; archaeology; ARIADNE; interpretation;
   virtual reconstruction
ID SEMANTIC INTEROPERABILITY
AB In this article we introduce our semantic modeling approach for data from over 50 years of excavations at Tell el-Daba in Egypt. The CIDOC CRM with some of its extensions is used as an ontological framework to provide the semantics for creating a knowledge graph containing material remains, excavated areas, and documentation resources. An objective of the project A Puzzle in 4D is to digitize the documentation and create metadata for analog and digital resources in order to provide the data to the research community and facilitate future work for this important archaeological site. Using an example of 3D reconstruction of a tomb, we show how the knowledge graph linked to digital resources can be exploited for a specific task to encounter available information that is essential for a virtual reconstruction. Moreover, we show an approach of modeling to represent the interpretations supporting reconstructions as well as relate them to the sources used, thus providing transparency for the model and provenance data. Modeling for excavation documentation as well as virtual reconstruction has been tailored to the large amount of data processed from the project. The goal is to propose a semantic modeling feasible even on a large scale while still preserving the basic underlying ontological structures.
C1 [Hiebel, Gerald] Univ Innsbruck, Digital Sci Ctr, Inst Archaeol, Langer Weg 11, A-6020 Innsbruck, Austria.
   [Aspock, Edeltraud] Austrian Acad Sci, Austrian Ctr Digital Humanities & Cultural Herita, Sonnenfelsgasse 19, A-1010 Vienna, Austria.
   [Kopetzky, Karin] Austrian Acad Sci, Inst Oriental & European Archaeol, Hollandstr 11 13, A-1020 Vienna, Austria.
RP Hiebel, G (通讯作者)，Univ Innsbruck, Digital Sci Ctr, Inst Archaeol, Langer Weg 11, A-6020 Innsbruck, Austria.
EM gerald.hiebel@uibk.a.c.at; Edeltraud.Aspoeck@oeaw.ac.at;
   Karin.Kopetzky@oeaw.ac.at
FU Austrian National Foundation for Research, Technology and Development
   [DH 2014/12]; EU FP7 project ARIADNE [FP7-INFRASTRUCTURES-2012-1-313193]
FX The A Puzzle in 4D project has been made possible through funding from
   the Austrian National Foundation for Research, Technology and
   Development (DH 2014/12), with support also from the EU FP7 project
   ARIADNE (FP7-INFRASTRUCTURES-2012-1-313193). We thank Professor Manfred
   Bietak for his support and engagement during the project. We thank in
   particular Karl Burkhart, Brigit Danthine, Stefanie Fragner, and Irene
   Petschko for their contributions to the research.
CR Aspock E., 2020, OREA SERIES, P79
   Aspock E., 2019, MITTEILUNGEN VOB, V72, P538, DOI [10.31263/voebm.v72i2.3249, DOI 10.31263/VOEBM.V72I2.3249]
   Aspock E., 2019, ARIADNE IMPACT, P51, DOI [10.5281/zenodo.3476712, DOI 10.5281/ZENODO.3476712]
   Bietak Manfred, 1996, AVARIS CAPITAL HYKSO
   Binding C, 2008, LECT NOTES COMPUT SC, V5173, P280
   Bruseker G., 2015, ISPRS ANN PHOTOGRAMM
   Catalano CE, 2020, INT J DIGIT LIBRARIE, V21, P359, DOI 10.1007/s00799-020-00287-3
   Cripps P., 2004, ONTOLOGICAL MODELLIN
   D'Andrea Andrea, 2013, 2013 Digital Heritage International Congress (DigitalHeritage). Federating the 19th Int'I VSMM, 10th Eurographics GCH, & 2nd UNESCO Memory of the World Conferences, plus special sessions from CAA, Arqueologico 2.0, Space2Place, ICOMOS ICIP & CIPA, EU projects, et al. Proceedings, P137
   Demetrescu E, 2018, COMM COM INF SC, V817, P102, DOI 10.1007/978-3-319-76992-9_7
   Doerr M, 2003, AI MAG, V24, P75
   Doerr M., DEFINITION CRM SCI E
   Doerr M., DEFINITION CRMARCHAE
   Doerr M, 2011, TAPP, V11, P20
   Doerr M., DEFINITION CRMDIG EX
   Felicetti A., 2018, CIODOC MUS C
   Gabellone F., 2015, REMOTE SENSING ICT C, P77
   Hartig O., 2017, C INT SEM WEB
   Hiebel G, 2017, INT J DIGIT LIBRARIE, V18, P271, DOI 10.1007/s00799-016-0192-4
   Moraitou E, 2019, HERITAGE-BASEL, V2, P611, DOI 10.3390/heritage2010040
   Niccolucci F, 2013, INT J HUMANIT ARTS C, V7, P70, DOI 10.3366/ijhac.2013.0082
   Ponchio F, 2020, COMPUT GRAPH FORUM, V39, P89, DOI 10.1111/cgf.13664
   Scalas A., 2018, SHAPE ANAL TECHNIQUE, DOI [10.2312/gch.20181373, DOI 10.2312/GCH.20181373]
   Schiffer M. B., 1987, FORMATION PROCESSES
   Stead St., CRMINF ARGUMENTATION
   Uleberg E., 2020, EAA2020 VIRT
   Van Ruymbeke Muriel., 2017, ISPRS ANN PHOTOGRAMM, VIV-2/W2, P287
NR 27
TC 1
Z9 1
U1 6
U2 13
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 1556-4673
EI 1556-4711
J9 ACM J COMPUT CULT HE
JI ACM J. Comput. Cult. Herit.
PD JUL
PY 2021
VL 14
IS 3
AR 32
DI 10.1145/3439735
PG 14
WC Humanities, Multidisciplinary; Computer Science, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Arts & Humanities - Other Topics; Computer Science
GA TS7DJ
UT WOS:000679805600007
DA 2022-08-23
ER

PT J
AU Zeng, ZX
   Shen, YT
   Hengsadeekul, T
   Yang, JJ
AF Zeng, Zixuan
   Shen, Yongtao
   Hengsadeekul, Thammanoon
   Yang, Jijun
TI Web of science based visual knowledge mapping analysis of synergy
   development of environment and society
SO ANNALS OF OPERATIONS RESEARCH
LA English
DT Article; Early Access
DE Environmental innovation; Social responsibility; Citespace;
   Bibliometrics; CSR
ID CORPORATE SOCIAL-RESPONSIBILITY; MANAGEMENT SYSTEM; CLIMATE-CHANGE;
   INNOVATION; GREEN; SUSTAINABILITY; PERFORMANCE; PRESSURES; CSR
AB Certain types of innovation are likely the requirement for innovation in the environment, which scientists all over the world have been particularly concerned about. This report employs the bibliometrics method to review social responsibility and environmental innovation research. Following refining the English language documents, the sorts and types of article are reviewed, environmental studies, environmental science, management, business, sustainable green science technology, economics, ethics and ecology are categorised and 559 literature data have finally been picked. In the field of journals, nations, authors, keywords, co-citation, research hot spots and research field evolution, the CiteSpace software was employed for visual analysis. The results show: (1) research on social accountabilities and environmental innovation has undergone three stages: germination, mild growth, and a breakthrough stage in growth, and (2) the USA, Germany and France have started in this subject for the first time. Whereas China began to grow late, with the support of numerous other countries, China quickly flourished within five years, playing a central part in this area. (3) The stages of introduction, development and maturity have been experienced through hotspots research and fault phenomena between distinct phases constitute breakthroughs in this area. The current research areas include the "perspective, performance and policy"; (4) There is tremendous worry about improving the geographical position of the overall ecosystem. In order to support the sustainability of economic development, the improvement of social and natural ecosystems will promote institutional variations and procedures in diverse social, cultural and political areas.
C1 [Zeng, Zixuan] Guangxi Univ Finance & Econ, Guangxi ASEAN Res Ctr Finance & Econ, Nanning, Peoples R China.
   [Shen, Yongtao] Guangxi Univ Finance & Econ, Fac Finance & Insurance, Nanning, Peoples R China.
   [Zeng, Zixuan; Shen, Yongtao; Hengsadeekul, Thammanoon] Naresuan Univ, Fac Logist & Digital Supply Chain, Phitsanulok, Thailand.
   [Yang, Jijun] Guangdong Univ Finance & Econ, Fac Publ Adm, Guangzhou, Peoples R China.
RP Shen, YT (通讯作者)，Guangxi Univ Finance & Econ, Fac Finance & Insurance, Nanning, Peoples R China.; Shen, YT (通讯作者)，Naresuan Univ, Fac Logist & Digital Supply Chain, Phitsanulok, Thailand.
EM 13zengzixuan@163.com; shenyongtao1989@163.com; thammanoonh@gmail.com;
   zixuanz61@nu.ac.th
OI Zeng, Zixuan/0000-0002-1439-5520
FU National Natural Science Foundation of China [11761012]; National Social
   Science Fund of China [16AGL017]
FX The research was supported by the National Natural Science Foundation of
   China (No. 11761012) and the National Social Science Fund of China (No.
   16AGL017).
CR Abbas J, 2020, J CLEAN PROD, V242, DOI 10.1016/j.jclepro.2019.118458
   Adams R, 2016, INT J MANAG REV, V18, P180, DOI 10.1111/ijmr.12068
   Aguinis H, 2012, J MANAGE, V38, P932, DOI 10.1177/0149206311436079
   Alonso-Virgos L, 2020, IBER CONF INF SYST
   Ambec S, 2008, ACAD MANAGE PERSPECT, V23, P45, DOI 10.5465/AMP.2008.35590353
   Babiak K, 2011, CORP SOC RESP ENV MA, V18, P11, DOI 10.1002/csr.229
   Bartolacci F, 2020, BUS STRATEG ENVIRON, V29, P1297, DOI 10.1002/bse.2434
   Baumgartner RJ, 2014, CORP SOC RESP ENV MA, V21, P258, DOI 10.1002/csr.1336
   Benito-Hernandez S, 2016, BUS ETHICS, V25, P416, DOI 10.1111/beer.12126
   Berrone P, 2013, STRATEGIC MANAGE J, V34, P891, DOI 10.1002/smj.2041
   Bocken NMP, 2014, J CLEAN PROD, V65, P42, DOI 10.1016/j.jclepro.2013.11.039
   Boons F, 2013, J CLEAN PROD, V45, P9, DOI 10.1016/j.jclepro.2012.07.007
   Boons F, 2013, J CLEAN PROD, V45, P1, DOI 10.1016/j.jclepro.2012.08.013
   Chang CH, 2011, J BUS ETHICS, V104, P361, DOI 10.1007/s10551-011-0914-x
   Chen C, 2016, CITESPACE PRACTICAL
   Chen CM, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0223994
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Cordeiro JJ, 2015, J BUS ETHICS, V130, P833, DOI 10.1007/s10551-014-2115-x
   Dangelico R, 2010, J BUS ETHICS, V95, P471, DOI 10.1007/s10551-010-0434-0
   De Marchi V, 2012, RES POLICY, V41, P614, DOI 10.1016/j.respol.2011.10.002
   Edge D, 1979, Hist Sci, V17, P102
   Espada JP, 2015, IEEE LAT AM T, V13, P503, DOI 10.1109/TLA.2015.7055571
   Fang Y, 2018, J SUSTAIN TOUR, V26, P108, DOI 10.1080/09669582.2017.1329310
   Fiorentino R, 2020, MANAGE DECIS, V58, P1715, DOI 10.1108/MD-09-2019-1327
   Flammer C, 2013, ACAD MANAGE J, V56, P758, DOI 10.5465/amj.2011.0744
   Gao J., 2020, IEEE IC COMP COM NET, P1, DOI 10.1109/icccn49398.2020.9209730
   Graafland J, 2020, CORP SOC RESP ENV MA, V27, P137, DOI 10.1002/csr.1779
   Groening C, 2013, J BUS RES, V66, P1852, DOI 10.1016/j.jbusres.2013.02.006
   Hu D, 2019, ORGAN ENVIRON, V32, P391, DOI 10.1177/1086026618773879
   Jain A, 2020, SMART INNOVATION WEB, DOI [10.1201/9780429298462, DOI 10.1201/9780429298462]
   Kardooni R, 2018, RENEW ENERG, V116, P659, DOI 10.1016/j.renene.2017.09.073
   Klewitz J, 2014, J CLEAN PROD, V65, P57, DOI 10.1016/j.jclepro.2013.07.017
   Kostarakis, 2018, ANN GEN PSYCHIATR, V17, pA12, DOI [10.1186/s12991-018-0182-6, DOI 10.1186/S12991-018-0182-6]
   Kumar P. M, 2021, IEEE J BIOMED HEALTH
   Liang CC, 2018, SAGE OPEN MED, V6, DOI 10.1177/2050312118800199
   Lyon TP, 2008, REV ENV ECON POLICY, V2, P240, DOI 10.1093/reep/ren004
   Martinez-Conesa I, 2017, J CLEAN PROD, V142, P2374, DOI 10.1016/j.jclepro.2016.11.038
   Naseem T, 2020, CORP SOC RESP ENV MA, V27, P501, DOI 10.1002/csr.1815
   Nishitani K, 2020, SUSTAIN PROD CONSUMP, V21, P156, DOI 10.1016/j.spc.2019.12.002
   Pan X, 2021, CORP SOC RESP ENV MA, V28, P214, DOI 10.1002/csr.2043
   Pan X, 2020, BUS STRATEG ENVIRON, V29, P197, DOI 10.1002/bse.2358
   Saeidi SP, 2015, J BUS RES, V68, P341, DOI 10.1016/j.jbusres.2014.06.024
   Salim HK, 2018, J CLEAN PROD, V170, P645, DOI 10.1016/j.jclepro.2017.09.017
   Schaltegger S, 2011, BUS STRATEG ENVIRON, V20, P222, DOI 10.1002/bse.682
   Schneider S, 2020, ORGAN ENVIRON, V33, P384, DOI 10.1177/1086026619854217
   Shahzad M, 2020, J CLEAN PROD, V253, DOI 10.1016/j.jclepro.2019.119938
   Shakeel PM, 2018, HEALTH INF SCI SYST, V6, DOI 10.1007/s13755-018-0054-0
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Sulkowski AJ, 2018, ORGAN ENVIRON, V31, P223, DOI 10.1177/1086026617722129
   Wei FW, 2015, PROF GEOGR, V67, P374, DOI 10.1080/00330124.2014.983588
   Wei Z., 2018, CITESPACE, V38, P4504
   Wu WW, 2020, CORP SOC RESP ENV MA, V27, P1487, DOI 10.1002/csr.1902
   Ye N, 2020, J CLEAN PROD, V272, DOI 10.1016/j.jclepro.2020.122679
   Yue C, 2015, CITESPACE, V33, P242
   Zeng ZX, 2020, ENTREP SUSTAIN ISS, V8, P1419, DOI 10.9770/jesi.2020.8.2(83)
   Zhang JX, 2020, CORP SOC RESP ENV MA, V27, P2920, DOI 10.1002/csr.2012
   Zhao XB, 2019, ARCHIT SCI REV, V62, P74, DOI 10.1080/00038628.2018.1485548
   Zutshi A, 2004, TECHNOVATION, V24, P371, DOI 10.1016/S0166-4972(02)00115-3
NR 58
TC 0
Z9 0
U1 15
U2 31
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0254-5330
EI 1572-9338
J9 ANN OPER RES
JI Ann. Oper. Res.
DI 10.1007/s10479-021-04324-5
EA OCT 2021
PG 20
WC Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Operations Research & Management Science
GA WK4AX
UT WOS:000709670800001
DA 2022-08-23
ER

PT J
AU Lin, CL
   Chen, ZZ
   Jiang, XY
   Chen, GL
   Jin, PQ
AF Lin, Chien-Liang
   Chen, Zezhou
   Jiang, Xinyue
   Chen, Guan Lin
   Jin, Peiqi
TI Roles and Research Trends of Neuroscience on Major Information Systems
   Journal: A Bibliometric and Content Analysis
SO FRONTIERS IN NEUROSCIENCE
LA English
DT Article
DE NeuroIS; neuroscience; bibliometric study; management information
   systems; VOSviewer
ID EYE-TRACKING; SECURITY WARNINGS; RESEARCH AGENDA; NEUROIS; FMRI; SEARCH;
   BEHAVIOR; SCIENCE; IMPACT; ANTECEDENTS
AB Over the past decade, neuroscience has been integrated into information systems as a new methodology and perspective to study and solve related problems. Therefore, NeuroIS has emerged as a new cutting-edge research field. This review aimed to identify, summarize, and classify existing NeuroIS publications through knowledge mapping and bibliometric analysis. To effectively understand the development trend of NeuroIS, this study referred to the journal selection index of the Association of Business Schools in 2021 and journals above three stars in the field of information management as the main selection basis. A total of 99 neuroscience papers and their citation data were included from 19 major information systems journals of SCI/SSCI. This study analyzed bibliometric data from 2010 to 2021 to identify the most productive countries, universities, authors, journals, and prolific publications in NeuroIS. To this end, VOSviewer was used to visualize mapping based on co-citation, bibliographic coupling, and co-occurrence. Keywords with strong citation bursts were also identified in this study. This signifies the evolution of this research field and may reveal potential research directions in the near future. In selecting research methods and analysis tools for NeuroIS, content analysis was used to further conclude and summarize the relevant trends. Moreover, a co-citation network analysis was conducted to help understand how the papers, journals, and authors in the field were connected and related, and to identify the seminal or pioneering major literature. For researchers, network maps visualized mainstream research and provided a structural understanding of NeuroIS. The review concludes by discussing potential research topics in this field.
C1 [Lin, Chien-Liang] Ningbo Univ, Coll Sci & Technol, Ningbo, Peoples R China.
   [Lin, Chien-Liang] Ningbo Univ, Res Ctr Ningbo Bay Area Dev, Cixi, Peoples R China.
   [Chen, Zezhou] Zhejiang Univ Technol, Sch Econ, Hangzhou, Peoples R China.
   [Jiang, Xinyue] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou, Peoples R China.
   [Chen, Guan Lin] Shu Te Univ, Dept Distribut Management, Kaohsiung, Taiwan, Provinc.
   [Jin, Peiqi] Zhejiang Univ Technol, Sch Foreign Language, Hangzhou, Peoples R China.
RP Chen, ZZ (通讯作者)，Zhejiang Univ Technol, Sch Econ, Hangzhou, Peoples R China.; Chen, GL (通讯作者)，Shu Te Univ, Dept Distribut Management, Kaohsiung, Taiwan, Provinc.
EM zezhou_chen@163.com; glchen@stu.edu.tw
FU Ministry of Education [202002143051]; K.C. Wong Magna Fund in Ningbo
   University [RC190015]
FX This research was supported by 2020, the first batch of
   Industry-University collaborative education project of the Ministry of
   Education-"Social Practice Training Camp Plan Based on Science and
   Technology Innovation and Entrepreneurship Projects" (No. 202002143051),
   K.C. Wong Magna Fund in Ningbo University (RC190015).
CR Ahn JH, 2018, J MANAGE INFORM SYST, V35, P1218, DOI 10.1080/07421222.2018.1523595
   Anderson BB, 2016, DECIS SUPPORT SYST, V92, P3, DOI 10.1016/j.dss.2016.09.010
   Anderson BB, 2016, J MANAGE INFORM SYST, V33, P713, DOI 10.1080/07421222.2016.1243947
   Anderson BB, 2016, EUR J INFORM SYST, V25, P364, DOI 10.1057/ejis.2015.21
   Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007
   Astor PJ, 2013, J MANAGE INFORM SYST, V30, P247, DOI 10.2753/MIS0742-1222300309
   Bera P, 2019, EUR J INFORM SYST, V28, P663, DOI 10.1080/0960085X.2019.1673972
   Berget G, 2016, J ASSOC INF SCI TECH, V67, P2320, DOI 10.1002/asi.23572
   Browne GJ, 2021, DECIS SUPPORT SYST, V143, DOI 10.1016/j.dss.2021.113498
   Browne GJ, 2020, MIS QUART, V44, P747, DOI 10.25300/MISQ/2020/14718
   Cheung MYM, 2017, J ASSOC INF SYST, V18, P605, DOI 10.17705/1jais.00464
   Cucino V, 2021, EUR J INNOV MANAG, V25, P295, DOI 10.1108/EJIM-01-2021-0015
   Cyr D, 2009, MIS QUART, V33, P539
   Day RF, 2010, DECIS SUPPORT SYST, V49, P396, DOI 10.1016/j.dss.2010.05.001
   De Bakker F.G., 2005, BUS SOC, V44, P283, DOI DOI 10.1177/0007650305278086
   de Guinea AO, 2014, J MANAGE INFORM SYST, V30, P179, DOI 10.2753/MIS0742-1222300407
   de Rezende LB, 2018, PROJ MANAG J, V49, P42, DOI 10.1177/875697281804900104
   Dimoka A., 2009, P 30 INT C INFORM SY, P133
   Dimoka A, 2012, MIS QUART, V36, P811
   Dimoka A, 2012, MIS QUART, V36, P679
   Dimoka A, 2011, INFORM SYST RES, V22, P687, DOI 10.1287/isre.1100.0284
   Dimoka A, 2010, MIS QUART, V34, P373
   Djamasbi S, 2011, INT J ELECTRON COMM, V15, P121, DOI 10.2753/JEC1086-4415150404
   Donthu N, 2020, J BUS RES, V109, P1, DOI 10.1016/j.jbusres.2019.10.039
   Ellison NB, 2020, J COMPUT-MEDIAT COMM, V25, P402, DOI 10.1093/jcmc/zmaa013
   Galvez-Sanchez FJ, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13063156
   Gao ZR, 2021, J MICROBIOL IMMUNOL, V54, P12, DOI 10.1016/j.jmii.2020.05.001
   Hariharan A, 2017, J ASSOC INF SYST, V18, P264, DOI 10.17705/1jais.00457
   Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625
   Hong WY, 2021, J ASSOC INF SYST, V22, P204, DOI 10.17705/1jais.00659
   Huang YF, 2011, INTERNET RES, V21, P541, DOI 10.1108/10662241111176362
   Jaeger L, 2021, INFORM SYST J, V31, P429, DOI 10.1111/isj.12317
   Jenkins JL, 2016, INFORM SYST RES, V27, P880, DOI 10.1287/isre.2016.0644
   Kalgotra P, 2019, INFORM SYST FRONT, V21, P1019, DOI 10.1007/s10796-017-9812-9
   Karampournioti E, 2021, INTERNET RES, V32, P228, DOI 10.1108/INTR-09-2019-0377
   Kim J, 2016, J ASSOC INF SCI TECH, V67, P2607, DOI 10.1002/asi.23628
   Kim J, 2015, J ASSOC INF SCI TECH, V66, P526, DOI 10.1002/asi.23187
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   Kocak M, 2019, SCIENTOMETRICS, V121, P1339, DOI 10.1007/s11192-019-03259-w
   Kuan KKY, 2014, J MANAGE INFORM SYST, V30, P151, DOI 10.2753/MIS0742-1222300406
   Leger PM, 2014, J ASSOC INF SYST, V15, P651
   Li MX, 2014, J MANAGE INFORM SYST, V30, P115, DOI 10.2753/MIS0742-1222300405
   Liang TP, 2014, J MANAGE INFORM SYST, V30, P7, DOI 10.2753/MIS0742-1222300401
   Lotka AJ., 1926, J WASH ACAD SCI, V16, P317
   Cunill OM, 2019, INT J HOSP MANAG, V78, P89, DOI 10.1016/j.ijhm.2018.10.013
   Miau S, 2018, TECHNOL ANAL STRATEG, V30, P1029, DOI 10.1080/09537325.2018.1434138
   Mikalef P, 2021, INFORM SYST FRONT, V23, P1273, DOI 10.1007/s10796-020-10034-3
   Minas RK, 2014, J MANAGE INFORM SYST, V30, P49, DOI 10.2753/MIS0742-1222300403
   Moody GD, 2015, J MANAGE INFORM SYST, V32, P192, DOI 10.1080/07421222.2015.1029391
   Moravec PL, 2019, MIS QUART, V43, P1343, DOI 10.25300/MISQ/2019/15505
   Moshfeghi Y, 2019, J ASSOC INF SCI TECH, V70, P954, DOI 10.1002/asi.24242
   Mumu JR, 2022, J INTERNET COMMER, V21, P217, DOI 10.1080/15332861.2021.1927437
   Nagariya R, 2021, BENCHMARKING, V28, P333, DOI 10.1108/BIJ-04-2020-0137
   Nunamaker JE, 2011, J MANAGE INFORM SYST, V28, P17, DOI 10.2753/MIS0742-1222280102
   Pfeiffer J, 2020, INFORM SYST RES, V31, P675, DOI 10.1287/isre.2019.0907
   Proudfoot JG, 2016, J MANAGE INFORM SYST, V33, P332, DOI 10.1080/07421222.2016.1205929
   Rayner K, 1998, PSYCHOL BULL, V124, P372, DOI 10.1037/0033-2909.124.3.372
   Riedl R., 2016, FUNDAMENTALS NEUROIS
   Riedl R, 2010, MIS QUART, V34, P397
   Schultheiss S, 2021, J ASSOC INF SCI TECH, V72, P285, DOI 10.1002/asi.24410
   Song YH, 2020, LIBR HI TECH, DOI 10.1108/LHT-06-2020-0126
   Straub D., 2004, COMMUN ASS INF SYST, V13, P380, DOI [10.17705/1cais.01324, DOI 10.17705/1CAIS.01324]
   Su YS, 2020, LIBR HI TECH, V38, P420, DOI 10.1108/LHT-01-2019-0028
   Tams S, 2014, J ASSOC INF SYST, V15, P723
   Tang KY, 2021, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2021.1875001
   Taskin Z, 2019, ONLINE INFORM REV, V43, P676, DOI 10.1108/OIR-07-2018-0217
   Turel O, 2016, J MANAGE INFORM SYST, V33, P1087, DOI 10.1080/07421222.2016.1267529
   Twyman NW, 2014, J MANAGE INFORM SYST, V31, P106, DOI 10.1080/07421222.2014.995535
   Twyman NW, 2014, J MANAGE INFORM SYST, V31, P173, DOI 10.2753/MIS0742-1222310108
   Vance A, 2018, MIS QUART, V42, P355, DOI 10.25300/MISQ/2018/14124
   Vance A, 2014, J ASSOC INF SYST, V15, P679
   vom Brocke J, 2020, EUR J INFORM SYST, V29, P9, DOI 10.1080/0960085X.2019.1708218
   vom Brocke J, 2014, J MANAGE INFORM SYST, V30, P211, DOI 10.2753/MIS0742-1222300408
   Voon-Hsien Lee, 2017, International Journal of Services, Economics and Management, V8, P227
   Walden E, 2018, MIS QUART, V42, P1097, DOI 10.25300/MISQ/2018/14419
   Wang Q, 2020, IND MANAGE DATA SYST, V120, P1863, DOI 10.1108/IMDS-12-2019-0671
   Wang QZ, 2014, DECIS SUPPORT SYST, V62, P1, DOI 10.1016/j.dss.2014.02.007
   Warkentin M, 2016, J ASSOC INF SYST, V17, P194, DOI 10.17705/1jais.00424
   Willems J, 2019, DECIS SUPPORT SYST, V124, DOI 10.1016/j.dss.2019.113080
   Wu J, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2021.102562
   Xu C, 2019, J ASSOC INF SCI TECH, V70, P942, DOI 10.1002/asi.24118
   Xu Q, 2020, DECIS SUPPORT SYST, V131, DOI 10.1016/j.dss.2020.113247
   Xu ZS, 2021, J BUS RES, V135, P304, DOI 10.1016/j.jbusres.2021.06.051
   Ye Q, 2013, DECIS SUPPORT SYST, V56, P502, DOI 10.1016/j.dss.2012.11.007
   Yeung AWK, 2017, FRONT NEUROSCI-SWITZ, V11, DOI 10.3389/fnins.2017.00120
   Zhang CY, 2020, TOUR MANAG PERSPECT, V35, DOI 10.1016/j.tmp.2020.100715
   Zhang YY, 2021, J ASSOC INF SCI TECH, V72, P611, DOI 10.1002/asi.24430
NR 87
TC 0
Z9 0
U1 0
U2 0
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1662-453X
J9 FRONT NEUROSCI-SWITZ
JI Front. Neurosci.
PD AUG 3
PY 2022
VL 16
AR 872532
DI 10.3389/fnins.2022.872532
PG 17
WC Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA 3V1IJ
UT WOS:000841414200001
OA Green Accepted, gold
DA 2022-08-23
ER

PT J
AU Mao, LH
   Zhu, YL
   Ju, CH
   Bao, FG
   Xu, CH
AF Mao, Linghao
   Zhu, Yiling
   Ju, Chunhua
   Bao, Fuguang
   Xu, Chonghuan
TI Visualization and Bibliometric Analysis of Carbon Neutrality Research
   for Global Health
SO FRONTIERS IN PUBLIC HEALTH
LA English
DT Article
DE carbon neutrality; knowledge map; research hotspots; CiteSpace; human
   health
ID DEPENDENT ELECTROCATALYTIC REDUCTION; HYDROCARBON FUELS; CO2; DIOXIDE;
   EMISSIONS; ELECTROREDUCTION; BIOENERGY; CATALYSTS; STATE; WATER
AB The visual analysis of carbon neutrality research can help better understand the development of the research field and explore the difficulties and hot spots in the research, thus making contributions to "carbon emission reduction," environmental protection and human health. This paper makes a visual quantitative analysis of 2,819 research papers published in top international journals from 2008 to 2021 in the WOS core database. It is found that China, the United States, Britain, and Germany are leading the way in carbon neutrality research. The research hotspots are mainly divided into three dimensions: (1) biomass energy and the negative effects it might bring; (2) ways and methods of electrochemical reduction of carbon dioxide; (3) catalysts and catalytic environment. The research mainly went through the conceptual period of 1997-2007, the exploration period of bioenergy from 2008 to 2021, the criticized period of bioenergy sources from 2011 to 2013, and the carbon dioxide electroreduction period from 2013 to the present. In the future, the research direction of biomass energy is to find one kind of biomass energy source which can be stored in a low-carbon way, produced in large quantities at a low cost, and will not occupy forestland. The electrolysis of water to produce hydrogen and the synthesis of fuel with CO2 are two major research directions at present, whose aims are to find the suitable catalyst and environment for the reaction. Besides, more research can be done on "carbon neutrality" policies so as to reduce carbon dioxide emissions from the source, develop a low-carbon economy and protect human health.
C1 [Mao, Linghao; Ju, Chunhua; Bao, Fuguang] Zhejiang Gongshang Univ, Sch Management Engn & E Business, Hangzhou, Peoples R China.
   [Zhu, Yiling] Zhejiang Gongshang Univ, Sch Foreign Languages, Hangzhou, Peoples R China.
   [Ju, Chunhua; Bao, Fuguang] Zhejiang Gongshang Univ, Contemporary Business & Trade Res Ctr, Hangzhou, Peoples R China.
   [Bao, Fuguang; Xu, Chonghuan] Zhejiang Gongshang Univ, Acad Zhejiang Culture Ind Innovat & Dev, Hangzhou, Peoples R China.
   [Xu, Chonghuan] Zhejiang Gongshang Univ, Sch Business Adm, Hangzhou, Peoples R China.
RP Bao, FG (通讯作者)，Zhejiang Gongshang Univ, Sch Management Engn & E Business, Hangzhou, Peoples R China.; Bao, FG (通讯作者)，Zhejiang Gongshang Univ, Contemporary Business & Trade Res Ctr, Hangzhou, Peoples R China.; Bao, FG; Xu, CH (通讯作者)，Zhejiang Gongshang Univ, Acad Zhejiang Culture Ind Innovat & Dev, Hangzhou, Peoples R China.; Xu, CH (通讯作者)，Zhejiang Gongshang Univ, Sch Business Adm, Hangzhou, Peoples R China.
EM baofuguang@126.com; talentxch@zjgsu.edu.cn
FU Natural Science Foundation of Zhejiang Province [LQ20G010002,
   LY20G010001]; Soft Science Research Program of Zhejiang Province
   [2021C25010]; project of China (Hangzhou) cross-border electricity
   business school [2021KXYJ06]; Philosophy and Social Science Foundation
   of Zhejiang Province [21NDJC083YB]; Contemporary Business and Trade
   Research Center of Zhejiang Gongshang University [XT202103, XT202105]
FX Funding This research is funded by Natural Science Foundation of
   Zhejiang Province (Nos. LQ20G010002 and LY20G010001), and the Soft
   Science Research Program of Zhejiang Province (No. 2021C25010). This
   research is supported by the project of China (Hangzhou) cross-border
   electricity business school (No. 2021KXYJ06), the Philosophy and Social
   Science Foundation of Zhejiang Province (No. 21NDJC083YB). Contemporary
   Business and Trade Research Center of Zhejiang Gongshang University
   (Nos. XT202103 and XT202105).
CR Asadi M, 2016, SCIENCE, V353, P467, DOI 10.1126/science.aaf4767
   Bao FG, 2022, AXIOMS, V11, DOI 10.3390/axioms11010017
   Brennan L, 2010, RENEW SUST ENERG REV, V14, P557, DOI 10.1016/j.rser.2009.10.009
   Cai C, 2022, FRONT PUBLIC HEALTH, V10, DOI 10.3389/fpubh.2022.814656
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Cherubini F, 2011, GCB BIOENERGY, V3, P413, DOI 10.1111/j.1757-1707.2011.01102.x
   Chisti Y, 2007, BIOTECHNOL ADV, V25, P294, DOI 10.1016/j.biotechadv.2007.02.001
   Cifre PG, 2007, ENERG CONVERS MANAGE, V48, P519, DOI 10.1016/j.enconman.2006.06.011
   De Luna P, 2018, NAT CATAL, V1, P103, DOI 10.1038/s41929-017-0018-9
   Dinh CT, 2018, SCIENCE, V360, P783, DOI 10.1126/science.aas9100
   Doherty WOS, 2011, IND CROP PROD, V33, P259, DOI 10.1016/j.indcrop.2010.10.022
   Durrett TP, 2008, PLANT J, V54, P593, DOI 10.1111/j.1365-313X.2008.03442.x
   Fargione J, 2008, SCIENCE, V319, P1235, DOI 10.1126/science.1152747
   Gao DF, 2015, J AM CHEM SOC, V137, P4288, DOI 10.1021/jacs.5b00046
   Gao S, 2016, NATURE, V529, P68, DOI 10.1038/nature16455
   Graves C, 2011, RENEW SUST ENERG REV, V15, P1, DOI 10.1016/j.rser.2010.07.014
   Halim R, 2011, BIORESOURCE TECHNOL, V102, P178, DOI 10.1016/j.biortech.2010.06.136
   Han XL, 2022, PSYCHOL RES BEHAV MA, V15, P1147, DOI 10.2147/PRBM.S362920
   Hoang TTH, 2018, J AM CHEM SOC, V140, P5791, DOI 10.1021/jacs.8b01868
   Holtsmark B, 2012, CLIMATIC CHANGE, V112, P415, DOI 10.1007/s10584-011-0222-6
   Jiang CR, 2017, CHEM SOC REV, V46, P4645, DOI 10.1039/c6cs00306k
   Ju CH, 2022, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.788475
   Kanan MW, 2008, SCIENCE, V321, P1072, DOI 10.1126/science.1162018
   Kortlever R, 2015, J PHYS CHEM LETT, V6, P4073, DOI 10.1021/acs.jpclett.5b01559
   Lee SK, 2008, CURR OPIN BIOTECH, V19, P556, DOI 10.1016/j.copbio.2008.10.014
   Liu SB, 2017, J AM CHEM SOC, V139, P2160, DOI 10.1021/jacs.6b12103
   Lu Q, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms4242
   Mckechnie J, 2011, ENVIRON SCI TECHNOL, V45, P789, DOI 10.1021/es1024004
   Qiao JL, 2014, CHEM SOC REV, V43, P631, DOI 10.1039/c3cs60323g
   Ren D, 2016, ACS CATAL, V6, P8239, DOI 10.1021/acscatal.6b02162
   Repo A, 2011, GCB BIOENERGY, V3, P107, DOI 10.1111/j.1757-1707.2010.01065.x
   Scott SA, 2010, CURR OPIN BIOTECH, V21, P277, DOI 10.1016/j.copbio.2010.03.005
   Searchinger T, 2008, SCIENCE, V319, P1238, DOI 10.1126/science.1151861
   Searchinger TD, 2009, SCIENCE, V326, P527, DOI 10.1126/science.1178797
   Song QW, 2017, GREEN CHEM, V19, P3707, DOI 10.1039/c7gc00199a
   Tu WG, 2014, ADV MATER, V26, P4607, DOI 10.1002/adma.201400087
   Wang J, 2022, INT J MOB COMMUN, V20, P483, DOI 10.1504/IJMC.2022.123794
   Xiang KH, 2021, PSYCHOL RES BEHAV MA, V14, P483, DOI 10.2147/PRBM.S303239
   Xu CH, 2021, MATHEMATICS-BASEL, V9, DOI 10.3390/math9212673
   Xu CH, 2021, ELECTRON COMMER R A, V48, DOI 10.1016/j.elerap.2021.101060
   Zeman F, 2007, ENVIRON SCI TECHNOL, V41, P7558, DOI 10.1021/es070874m
   Zhang S, 2014, J AM CHEM SOC, V136, P1734, DOI 10.1021/ja4113885
NR 42
TC 0
Z9 0
U1 5
U2 5
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2296-2565
J9 FRONT PUBLIC HEALTH
JI Front. Public Health
PD JUL 6
PY 2022
VL 10
AR 896161
DI 10.3389/fpubh.2022.896161
PG 13
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA 3D6FI
UT WOS:000829395000001
PM 35874983
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wu, D
   Dong, J
   Shi, L
   Liu, CX
   Ding, JY
AF Wu, Dan
   Dong, Jing
   Shi, Li
   Liu, Chunxiang
   Ding, Jiangyun
TI Credibility assessment of good abandonment results in mobile search
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Credibility assessment; Good abandonment; Mobile search
ID EYE-MOVEMENTS; WEB; INFORMATION; QUALITY; PERCEPTIONS; HEURISTICS;
   FRAMEWORK; SELECTION; USER
AB Search engine optimization allows for users' needs to be directly met by result snippets or a "knowledge map" without clicking any results. This behavior is called "good abandonment" and is found to frequently occur during mobile searching. Users exhibit such a behavior when they trust the result that addresses their information need without bothering to click it. Therefore, this study examines how users judge a result's credibility without clicking. This study proposes a model for assessing the credibility of good abandonment results, making a hypothesis about the measures that may affect credibility assessments in mobile searches. A credibility assessment experiment was conducted to collect users' eye movements, perceived credibility and feedback on different credibility measures. Users' search behaviors were recorded by a screen recorder, in order to see whether a search was good abandonment. Then the initially proposed model was validated in terms of users' perceived credibility, search behaviors and feedback, and further improved. The revised model found that the credibility assessment of good abandonment results in mobile searching is determined by six credibility measures distributed across three aspects of content, operator and design. Content-related measures show that users tend to believe the results if there is detailed and updated context information and the content is neutral. Operator-related measures indicate the impact of trust in the search engine on the credibility assessment. Design-related measures indicate that users tend to trust results with interactive functions and optimal layouts. How each of the six measures influence users' assessment of credibility is discussed in this paper.
C1 [Wu, Dan; Dong, Jing; Liu, Chunxiang] Wuhan Univ, Sch Informat Management, Luojia Hill, Wuhan 430072, Peoples R China.
   [Shi, Li] Univ Texas Austin, Sch Informat, Austin, TX 78701 USA.
   [Ding, Jiangyun] Wuhan Univ, Sch Comp Sci, Luojia Hill, Wuhan 430072, Peoples R China.
RP Dong, J (通讯作者)，Wuhan Univ, Sch Informat Management, Luojia Hill, Wuhan 430072, Peoples R China.
EM woodan@whu.edu.cn; d_jing@whu.edu.cn
FU National Natural Science Foundation of China [71673204]
FX This work was supported in part by the National Natural Science
   Foundation of China (No. 71673204).
CR Acres DJ, 2013, ARTICLES FROM THE 13TH WORLD CONGRESS ON PUBLIC HEALTH, P13
   Bhattacharya N, 2018, 2018 ACM SYMPOSIUM ON EYE TRACKING RESEARCH & APPLICATIONS (ETRA 2018), DOI 10.1145/3204493.3204579
   Buscher G, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P42
   Cheung CMK, 2006, J AM SOC INF SCI TEC, V57, P479, DOI 10.1002/asi.20312
   Choi W, 2015, J ASSOC INF SCI TECH, V66, P2399, DOI 10.1002/asi.23543
   Chuklin A., 2012, P 21 INT C WORLD WID
   Chuklin A., 2012, P 21 INT C WORLD WID
   Cutrell E, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P407
   Fogg B., 2001, P SIGCHI C HUM FACT
   Fogg B. J., 2002, COMPUTERS PERSUASIVE, P89
   Fogg B. J., 2003, CHI 03 HUM FACT COMP, P722, DOI DOI 10.1145/765891.765951
   Fogg B. J., 2002, FUNCTIONAL TRIAD COM, P23
   Fogg BJ, 2003, P 2003 C DES US EXP, DOI DOI 10.1145/997078.997097
   Ginsca AL, 2015, FOUND TRENDS INF RET, V9, P355, DOI 10.1561/1500000046
   Granka L. A., 2004, P 27 ANN INT ACM SIG
   Hilligoss B, 2008, INFORM PROCESS MANAG, V44, P1467, DOI 10.1016/j.ipm.2007.10.001
   Hong T, 2006, J HEALTH COMMUN, V11, P149, DOI 10.1080/10810730500526679
   Jacob RJK, 2003, MIND'S EYE: COGNITIVE AND APPLIED ASPECTS OF EYE MOVEMENT RESEARCH, P573, DOI 10.1016/B978-044451020-4/50031-1
   Jansen BJ, 2006, J AM SOC INF SCI TEC, V57, P1949, DOI 10.1002/asi.20425
   Jeon G. Y., 2014, P ICONFERENCE 2014
   Kakol M, 2017, INFORM PROCESS MANAG, V53, P1043, DOI 10.1016/j.ipm.2017.04.003
   Khabsa M, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1893, DOI 10.1145/2983323.2983867
   Kim J, 2016, J ASSOC INF SCI TECH, V67, P2607, DOI 10.1002/asi.23628
   Kim J, 2015, J ASSOC INF SCI TECH, V66, P526, DOI 10.1002/asi.23187
   Lee H, 2017, INFORM RES, V22
   Li J, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P43, DOI 10.1145/1571941.1571951
   Lim S, 2013, INFORM PROCESS MANAG, V49, P405, DOI 10.1016/j.ipm.2012.10.004
   Liu ZM, 2005, INT INF LIBR REV, V37, P99, DOI 10.1016/j.iilr.2005.05.004
   Luo SJ, 2017, FRONT COMPUT SCI-CHI, V11, P290, DOI 10.1007/s11704-016-5022-8
   Metzger MJ, 2003, COMPUT EDUC, V41, P271, DOI 10.1016/S0360-1315(03)00049-6
   Oyibo K., 2016, P 2016 C US MOD AD P
   Poole A, 2005, BCS CONF SERIES, P363, DOI 10.1007/1-84628-062-1_23
   Radlinski F., 2005, P 11 ACM SIGKDD INT
   Radlinski F, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P570
   Rayner K, 2009, Q J EXP PSYCHOL, V62, P1457, DOI 10.1080/17470210902816461
   Rieh S. Y., 2014, P 8 INT AAAI C WEBL
   Rieh S. Y., 2010, CREDIBILITY COGNITIV
   Robins D, 2008, INFORM PROCESS MANAG, V44, P386, DOI 10.1016/j.ipm.2007.02.003
   Stvilia B, 2008, J AM SOC INF SCI TEC, V59, P983, DOI 10.1002/asi.20813
   Stvilia B, 2007, J AM SOC INF SCI TEC, V58, P1720, DOI 10.1002/asi.20652
   Sundar S. S., 2008, MAIN MODEL HEURISTIC, P73
   Unkel J, 2017, J ASSOC INF SCI TECH, V68, P1850, DOI 10.1002/asi.23820
   Wathen CN, 2002, J AM SOC INF SCI TEC, V53, P134, DOI 10.1002/asi.10016
   Williams K., 2016, P 25 INT C WORLD WID
   Yaari E, 2011, J INF SCI, V37, P487, DOI 10.1177/0165551511416065
   Yaneva V, 2018, 15TH INTERNATIONAL WEB FOR ALL CONFERENCE (W4A) 2018, DOI 10.1145/3192714.3192819
   Zhang Y, 2014, J ASSOC INF SCI TECH, V65, P911, DOI 10.1002/asi.23023
NR 47
TC 2
Z9 2
U1 10
U2 31
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD NOV
PY 2020
VL 57
IS 6
AR 102350
DI 10.1016/j.ipm.2020.102350
PG 15
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA OG9PN
UT WOS:000582206800064
DA 2022-08-23
ER

PT J
AU Ting, SL
   Wang, WM
   Tse, YK
   Ip, WH
AF Ting, S. L.
   Wang, W. M.
   Tse, Y. K.
   Ip, W. H.
TI Knowledge elicitation approach in enhancing tacit knowledge sharing
SO INDUSTRIAL MANAGEMENT & DATA SYSTEMS
LA English
DT Article
DE Honk Kong; Medical diagnosis; Medical treatment; Doctors; Knowledge
   elicitation; Electronic medical records; Knowledge sharing; Concept map;
   Medical decision support
ID INFORMATION-SEEKING BEHAVIOR; ACQUISITION; SYSTEMS; NEEDS
AB Purpose - The purpose of this paper is to present an automatic Medical Knowledge Elicitation System (MediKFS), which is designed to improve elicitation and sharing of tacit knowledge acquired by physicians. The system leverages the clinical information stored in electronic medical record systems, by representing the acquired information in a series of knowledge maps.
   Design/methodology/approach - The system architecture of the proposed MediKES is first discussed, and then a case study on an application of the proposed system in a Hong Kong medical organization is presented to illustrate the adoption process and highlight the benefits that can be realized from deployment of the MediKES.
   Findings - The results of the case study show that the proposed solution is more reliable and powerful than traditional knowledge elicitation approaches in capturing physicians' tacit knowledge, transforming it into a machine-readable form, as well as enhancing the quality of the medical judgment made by physicians.
   Practical implications - A prototype system has been constructed and implemented on a trial basis in a medical organization. It has proven to be of benefit to healthcare professionals through its automatic functions in representing and visualizing physicians' diagnostic decisions.
   Originality/value - Knowledge is key to improving the quality of the medical judgment of physicians. However, researchers and practitioners are still striving for more effective ways of capturing tacit knowledge and transforming it into a machine-readable form so as to enhance knowledge sharing. In this paper, the authors reveal that the knowledge retrieval and the visual knowledge representation functions of the proposed system are able to facilitate knowledge sharing among physicians. Thus, junior physicians can use it as a decision support tool in making better diagnostic decisions.
C1 [Ting, S. L.; Wang, W. M.; Ip, W. H.] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Kowloon, Hong Kong, Peoples R China.
   [Tse, Y. K.] Univ Nottingham, Sch Business, Nottingham NG7 2RD, England.
RP Ting, SL (通讯作者)，Hong Kong Polytech Univ, Dept Ind & Syst Engn, Kowloon, Hong Kong, Peoples R China.
EM jacky.ting@polyu.edu.hk
RI Wang, W.M./G-4543-2018; IP, W.H./J-2941-2013
OI Wang, W.M./0000-0003-1188-4537; IP, W.H./0000-0001-6609-0713; Tse, Ying
   Kei (Mike)/0000-0001-6174-0326
FU Research Committee of the Hong Kong Polytechnic University
FX Acknowledgement is given to Dr Peter Lo, Dr Francis Liu, Dr C.W. Lo and
   Miss Maggie Poon for their guidance on issues in clinical coding and
   medical knowledge in general. The authors would also like to express
   their sincere thanks to the Research Committee of the Hong Kong
   Polytechnic University for providing the financial support for this
   research work.
CR Agorastos Theodoros, 2009, Cancer Inform, V8, P31
   Barbosa José Geraldo Pereira, 2009, BAR, Braz. Adm. Rev., V6, P247
   Berrar DP, 2003, P 4 INT C CRIT ASS M, P43
   Cooke N.J., 1999, HDB APPL COGNITION, P479
   COOKE NJ, 1994, INT J HUM-COMPUT ST, V41, P801, DOI 10.1006/ijhc.1994.1083
   Cooper DR, 2011, BUSINESS RES METHODS
   D'Alessandro DM, 2004, PEDIATRICS, V113, P64, DOI 10.1542/peds.113.1.64
   Dawes M, 2003, INT J MED INFORM, V71, P9, DOI 10.1016/S1386-5056(03)00023-6
   Downing GJ, 2009, BMC MED INFORM DECIS, V9, DOI 10.1186/1472-6947-9-44
   Ely JW, 2005, J AM MED INFORM ASSN, V12, P217, DOI 10.1197/jamia.M1608
   Ford DN, 1998, SYST DYNAM REV, V14, P309, DOI 10.1002/(SICI)1099-1727(199824)14:4<309::AID-SDR154>3.0.CO;2-5
   GAINES BR, 1993, IEEE T KNOWL DATA EN, V5, P4, DOI 10.1109/69.204087
   Gebus S, 2009, EXPERT SYST APPL, V36, P93, DOI 10.1016/j.eswa.2007.09.058
   Gonzalez-Gonzalez AI, 2007, ANN FAM MED, V5, P345, DOI 10.1370/afm.681
   Grefsheim SF, 2007, J MED LIBR ASSOC, V95, P426, DOI 10.3163/1536-5050.95.4.426
   Han I, 1996, EXPERT SYST APPL, V10, P209, DOI 10.1016/0957-4174(95)00047-X
   Haux R, 2006, INT J MED INFORM, V75, P268, DOI 10.1016/j.ijmedinf.2005.08.002
   Herschel RT., 2001, J KNOWL MANAG, V5, P107, DOI [DOI 10.1108/13673270110384455, 10.1108/13673270110384455]
   Hersh W, 2009, BMC MED INFORM DECIS, V9, DOI 10.1186/1472-6947-9-24
   Hudlicka E, 1996, PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON REQUIREMENTS ENGINEERING, P4, DOI 10.1109/ICRE.1996.491424
   Hung SY, 2001, IND MANAGE DATA SYST, V101, P227, DOI 10.1108/02635570110394626
   Kaur H, 2010, ANN INFORM SYST, V7, P215, DOI 10.1007/978-1-4419-1274-9_15
   Kawamoto K, 2009, BMC MED INFORM DECIS, V9, DOI 10.1186/1472-6947-9-17
   KIRKWOOD CA, 1989, J BIOMED ENG, V11, P511, DOI 10.1016/0141-5425(89)90046-0
   KLEIN GA, 1989, IEEE T SYST MAN CYB, V19, P462, DOI 10.1109/21.31053
   LAI JK, 2009, BMC MED J, V51, P114
   MCKEE TE, 1995, J INFORM TECHNOL, V10, P26, DOI 10.1057/jit.1995.4
   Meltzer D, 2002, ANN INTERN MED, V137, P866, DOI 10.7326/0003-4819-137-11-200212030-00007
   NAEEMA HJ, 2007, COMPETITIVENESS REV, V17, P248
   National Alliance for Health Information Technology (U.S.), 2008, REP OFF NAT COORD HL
   Payne PRO, 2007, J BIOMED INFORM, V40, P582, DOI 10.1016/j.jbi.2007.03.005
   Pizzi NJ, 2009, STUD COMPUT INTELL, V182, P289
   SHADBOLT NR, 1995, EVALUATION HUMAN WOR
   SHAW M, 1998, FINANC MANAGE, V17, P45
   Sternberg RJ, 1999, TACIT KNOWLEDGE IN PROFESSIONAL PRACTICE, P231
   Thompson ML, 1997, B MED LIBR ASSOC, V85, P187
   Ting SL, 2010, EXPERT SYST APPL, V37, P5336, DOI 10.1016/j.eswa.2010.01.023
   Toussi M, 2009, BMC MED INFORM DECIS, V9, DOI 10.1186/1472-6947-9-28
   Wilson J.R., 2005, EVALUATION HUMAN WOR
   Wozniak M, 2006, LECT NOTES COMPUT SC, V4345, P433
   Zrimec T, 2007, IFMBE PROC, V16, P830
NR 41
TC 11
Z9 12
U1 1
U2 49
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0263-5577
EI 1758-5783
J9 IND MANAGE DATA SYST
JI Ind. Manage. Data Syst.
PY 2011
VL 111
IS 7
BP 1039
EP 1064
DI 10.1108/02635571111161280
PG 26
WC Computer Science, Interdisciplinary Applications; Engineering,
   Industrial
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA 829CN
UT WOS:000295553200004
DA 2022-08-23
ER

PT J
AU Liang, JC
   Xie, JC
   Wang, X
   Wang, R
   Jin, T
   Wang, SJ
AF Liang, Jichao
   Xie, Jiancang
   Wang, Xue
   Wang, Rui
   Jin, Tao
   Wang, Shaojiu
TI Research on the Construction and Application Mode of Digital Plans for
   Sudden Water Pollution Events
SO PROCESSES
LA English
DT Article
DE sudden water pollution events; digital emergency plan; emergency
   response; emergency plan management; knowledge visualization; decision
   support system
ID EMERGENCY; SYSTEM; MANAGEMENT; ACCIDENTS
AB Water pollution is an important aspect of a national water treatment. Sudden water safety incidents are random and destructive, often bringing about huge losses of life and property. Due to the uncertainty of sudden water pollution, it is difficult to respond in a timely and rapid manner. Emergency personnel must deal with emergencies quickly and effectively to reduce the harm caused by these emergencies. The randomness and uncertainty of sudden water pollution events make emergency work more complicated; it is difficult for current emergency plans to play guiding roles in complex responses. The decision-making and use of traditional water safety procedures largely depend on the experiences of command personnel, as well as on the emergency plan, which often has poor applicability. This can result in ineffective implementation of emergency actions and use of resources stemming from the high subjectivity and low efficiency of emergency plans. In this paper, we summarize previous research on digital planning and platform component technology exploration in order to evaluate the use of sudden water safety emergency procedures. We first analyze the main problems in the construction and use of emergency plans (e.g., the lack of experience and adaptability). Secondly, based on the decision-making support platform, a digital emergency plan database for water pollution emergencies was established by using component technology and knowledge map technology. In doing so, the decision support platform could enable the rapid construction of digital plans that improve application efficiency in an actual response scenario. Finally, through the system example, this system model can be quickly matched from the plan database to the emergency plan that meets the current scenario. It is a recommended model used to provide rapid and effective assistance for emergency management and improve emergency efficiency.
C1 [Liang, Jichao; Xie, Jiancang; Wang, Xue; Wang, Rui; Jin, Tao; Wang, Shaojiu] Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg China, Xian 710048, Peoples R China.
   [Wang, Xue] Coordinat Ctr Water Resources Dev Survey & Hanjia, Xian 710004, Peoples R China.
RP Xie, JC (通讯作者)，Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg China, Xian 710048, Peoples R China.
EM xutljchao@163.com; jcxie@mail.xaut.edu.cn; doraemon2129@126.com;
   wr92333@163.com; jintao20200515@163.com; shaojiuwang01@163.com
OI Liang, Jichao/0000-0002-5743-0584
FU National Key Research and Development Programof China [2016YFC0401409];
   Natural Science Basic Research Program of Shaanxi province [2019JLZ-16];
   Science and Technology Program of Shaanxi Province [2020slkj-16]
FX This research was funded by The National Key Research and Development
   Programof China, grant no. 2016YFC0401409, the Natural Science Basic
   Research Program of Shaanxi province, grant no. 2019JLZ-16, and the
   Science and Technology Program of Shaanxi Province, grant no.
   2020slkj-16.
CR Aureli F, 2020, WATER-SUI, V12, DOI 10.3390/w12030637
   CAHN M, 1993, APPL GEOGR, V13, P285, DOI 10.1016/0143-6228(93)90005-L
   Claborn DM, 2011, J HOMEL SECUR EMERG, V8, DOI 10.2202/1547-7355.1895
   Deng S., 2008, J CHIN ACAD GOV, P102, DOI [10.3969/j.issn.1008-9314.2008.03.027, DOI 10.3969/J.ISSN.1008-9314.2008.03.027]
   Deroux JM, 1997, SCI TOTAL ENVIRON, V203, P261, DOI 10.1016/S0048-9697(97)00154-X
   Ding HC, 2018, ANN NUCL ENERGY, V111, P347, DOI 10.1016/j.anucene.2017.08.039
   Duan WL, 2015, INT J ENV RES PUB HE, V12, P7868, DOI 10.3390/ijerph120707868
   Fan W., 2006, INF CONSTR, V9, P14
   Fu L, 2020, J COASTAL RES, P529, DOI 10.2112/JCR-SI104-092.1
   He X., 2016, J WATER RESOUR WATER, V27, P55, DOI [10.11705/j.issn.1672-643X.2016.03.11, DOI 10.11705/J.ISSN.1672-643X.2016.03.11]
   Hoogendoorn M., 2005, P 2 INT ISCRAM C, P98
   [姜仁贵 Jiang Rengui], 2017, [灾害学, Journal of Catastrophology], V32, P12
   Li WP, 2018, ACTA GEOL SIN-ENGL, V92, P1231, DOI 10.1111/1755-6724.13601
   Li X, 2010, ADM MANAG REFORM, V3, P54
   Liu D., 2009, CHINA PUBLIC SAF ACA, V1, P167, DOI [10.3969/j.issn.1672-2396.2009.01.041, DOI 10.3969/J.ISSN.1672-2396.2009.01.041]
   Long Y, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11020510
   Long Y, 2016, ENVIRON SCI POLLUT R, V23, P12332, DOI 10.1007/s11356-016-6448-0
   [罗军刚 LUO Jungang], 2008, [系统工程理论与实践, Systems Engineering-Theory & Practice], V28, P122
   Ni JJ, 2012, IEEE T INSTRUM MEAS, V61, P980, DOI 10.1109/TIM.2011.2173000
   Robert H., 2004, CRISIS MANAGEMENT, P35
   Scaioni M, 2009, LECT NOTES COMPUT SC, V5592, P79, DOI 10.1007/978-3-642-02454-2_6
   Shafiee M., 2011, AM SOC CIV ENG, V414, P276, DOI [10.1061/41173(414)30, DOI 10.1061/41173(414)30]
   Shao Y., 2005, INF CONSTR, V8, P46
   Sun BZ, 2016, KYBERNETES, V45, P461, DOI 10.1108/K-03-2014-0055
   Tecuci G., 2007, PROC HAWAII INT CONF
   Timperio G, 2020, INT J DISAST RISK RE, V51, DOI 10.1016/j.ijdrr.2020.101773
   Wu XD, 2020, J COASTAL RES, P89, DOI 10.2112/JCR-SI111-015.1
   Xie J.C., 2010, WATER RESOUR INFORM, V5, P18
   Yu Z., 2014, YELLOW RIVER, V36, P37
   Zhang Xiu-Ju, 2017, International Journal of Environmental Studies, V74, P412, DOI 10.1080/00207233.2017.1298335
   Zhang X, 2020, WATER ENVIRON RES, V92, P278, DOI 10.1002/wer.1242
NR 31
TC 0
Z9 0
U1 7
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-9717
J9 PROCESSES
JI Processes
PD MAY
PY 2022
VL 10
IS 5
AR 833
DI 10.3390/pr10050833
PG 19
WC Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 1P3SC
UT WOS:000801931800001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, ZY
   Huang, C
   Li, XM
AF Wang, Zhenyu
   Huang, Chen
   Li, Xuemin
TI Research Trends and Hotspot Analysis of Conjunctival Bacteria Based on
   CiteSpace Software
SO BIOMED RESEARCH INTERNATIONAL
LA English
DT Article
ID COAGULASE-NEGATIVE STAPHYLOCOCCI; PARS-PLANA VITRECTOMY; RISK-FACTORS;
   CAUSATIVE ORGANISMS; 3-DAY APPLICATION; CATARACT-SURGERY;
   POVIDONE-IODINE; MEIBOMIAN GLAND; POSTOPERATIVE ENDOPHTHALMITIS;
   SCIENTOMETRIC ANALYSIS
AB Objective. To sort out the literature related to conjunctival bacteria and summarize research hotspots and trends of this field.Materials and Methods. The relevant literature data from 1900 to 2019 was retrieved from the Web of Science Core Collection database. After manual selection, each document record includes title, author, keywords, abstract, year, organization, and citation. We imported the downloaded data into CiteSpace V (version 5.5R2) to draw the knowledge map and conduct cooperative network analysis, discipline and journal analysis, cluster analysis, and burst keyword analysis.Results. After manual screening, there were 285 relevant papers published in the last 28 years (from 1991 to 2019), and the number is increasing year by year. The publications of conjunctival bacteria were dedicated by 1381 authors of 451 institutions in 56 countries/regions. The United States dominates this field (82 literatures), followed by Germany (23 literatures) and Japan (23 literatures). Overall, most cited papers were published with a focus on molecular biology, genetics, nursing, and toxicology. Most papers fall into the category of ophthalmology, veterinary sciences, and pharmacology and pharmacy. The only organized cluster is the "postantibiotic effect," and the top 5 keywords with the strongest citation bursts include "postoperative endophthalmiti(s)," "infectious keratoconjunctiviti(s)," "conjunctiviti(s)," "resistance," and "diversity".Conclusion. The global field of conjunctival bacteria has expanded in the last 28 years. The United States contributes most. However, there are little cooperation among authors and institutions. Overall, this bibliometric study organized one cluster, "postantibiotic effect", and identified the top 5 hotspots in conjunctival bacteria research: "postoperative endophthalmiti(s)," "infectious keratoconjunctiviti(s)," "conjunctiviti(s)," "resistance," and "diversity". Thus, further research focuses on these topics that may be more helpful to prevent ocular infection and improve prophylaxis strategies to bring a benefit to patients in the near future.
C1 [Wang, Zhenyu; Huang, Chen; Li, Xuemin] Peking Univ, Dept Ophthalmol, Hosp 3, Beijing, Peoples R China.
   [Wang, Zhenyu; Huang, Chen; Li, Xuemin] Peking Univ, Beijing Key Lab Restorat Damaged Ocular Nerve, Hosp 3, Beijing, Peoples R China.
   [Huang, Chen] Peking Univ, Med Res Ctr, Hosp 3, Beijing, Peoples R China.
RP Li, XM (通讯作者)，Peking Univ, Dept Ophthalmol, Hosp 3, Beijing, Peoples R China.; Li, XM (通讯作者)，Peking Univ, Beijing Key Lab Restorat Damaged Ocular Nerve, Hosp 3, Beijing, Peoples R China.
EM 13911254862@163.com
OI Wang, Zhenyu/0000-0001-6458-3944
FU National Science and Technology Major Project [2018ZX10101004]
FX This research was funded by National Science and Technology Major
   Project, grant number 2018ZX10101004.
CR Acar N, 2008, RETINA-J RET VIT DIS, V28, P545, DOI 10.1097/IAE.0b013e318162b008
   Asbell PA, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0205814
   Boudry C, 2018, OCUL SURF, V16, P173, DOI 10.1016/j.jtos.2017.10.002
   Caglar C, 2016, INT J OPHTHALMOL-CHI, V9, P1663, DOI 10.18240/ijo.2016.11.21
   Cai H Y, 2018, Zhonghua Yan Ke Za Zhi, V54, P445, DOI 10.3760/cma.j.issn.0412-4081.2018.06.011
   Cao HL, 2013, PLOS ONE, V8, DOI [10.1371/journal.pone.0052853, 10.1371/journal.pone.0076946]
   Chen CM, 2014, EXPERT OPIN BIOL TH, V14, P1295, DOI 10.1517/14712598.2014.920813
   Chen JK, 2009, EYE, V23, P780, DOI 10.1038/eye.2008.160
   Dave VP, 2014, CLIN OPHTHALMOL, V8, P2183, DOI 10.2147/OPTH.S71293
   De Kaspar HM, 2008, AM J OPHTHALMOL, V145, P136, DOI 10.1016/j.ajo.2007.08.031
   De Kaspar HM, 2009, EUR J OPHTHALMOL, V19, P717, DOI 10.1177/112067210901900505
   de Kaspar HM, 2005, AM J OPHTHALMOL, V139, P730
   de Kaspar HM, 2005, ARCH OPHTHALMOL-CHIC, V123, P161, DOI 10.1001/archopht.123.2.161
   de Kaspar HM, 2004, J GLAUCOMA, V13, P507, DOI 10.1097/01.ijg.0000137872.19942.cf
   de Kaspar HM, 2004, OPHTHALMOLOGY, V111, P1352, DOI 10.1016/j.ophtha.2003.10.032
   de Kaspar HM, 2003, GRAEF ARCH CLIN EXP, V241, P730, DOI 10.1007/s00417-003-0742-5
   de Paiva CS, 2016, SCI REP-UK, V6, DOI 10.1038/srep23561
   Deepthi KG, 2018, EXP EYE RES, V174, P1, DOI 10.1016/j.exer.2018.05.011
   Dong XJ, 2019, INVEST OPHTH VIS SCI, V60, P4774, DOI 10.1167/iovs.19-27719
   Dufault B, 2011, AM J EPIDEMIOL, V174, P1101, DOI 10.1093/aje/kwr241
   Durand ML, 2017, CLIN MICROBIOL REV, V30, P597, DOI 10.1128/CMR.00113-16
   Eifrig CWG, 2004, AM J OPHTHALMOL, V138, P799, DOI 10.1016/j.ajo.2004.06.035
   Fujii GY, 2002, OPHTHALMOLOGY, V109, P1814, DOI 10.1016/S0161-6420(02)01119-3
   Garg P, 2017, CURR OPIN OPHTHALMOL, V28, P67, DOI 10.1097/ICU.0000000000000326
   Gong Lan, 2010, Zhonghua Yan Ke Za Zhi, V46, P525
   Ham B, 2018, CURR EYE RES, V43, P314, DOI 10.1080/02713683.2017.1406528
   Han DP, 1996, AM J OPHTHALMOL, V122, P1
   He LM, 2009, J CATARACT REFR SURG, V35, P1715, DOI 10.1016/j.jcrs.2009.04.042
   He LM, 2009, J OCUL PHARMACOL TH, V25, P373, DOI 10.1089/jop.2008.0102
   Inoue Y, 2008, JPN J OPHTHALMOL, V52, P151, DOI 10.1007/s10384-008-0517-y
   Jiang XD, 2018, INFECT DRUG RESIST, V11, P1729, DOI 10.2147/IDR.S162135
   Kandi V, 2016, CUREUS, V8, DOI 10.7759/cureus.731
   Kleinschmidt S, 2015, FUTURE MICROBIOL, V10, P1859, DOI 10.2217/fmb.15.98
   Lalitha P, 2019, OPHTHALMOLOGY, V126, P1090, DOI 10.1016/j.ophtha.2019.03.039
   Lazicka-Galecka Monika, 2015, Klin Oczna, V117, P123
   Li XX, 2017, ASIA-PAC J OPHTHALMO, V6, P40, DOI 10.22608/APO.2015197
   Lu KN, 2018, MED SCI MONITOR, V24, P8357, DOI 10.12659/MSM.913644
   Lu KN, 2018, MED SCI MONITOR, V24, P3405, DOI 10.12659/MSM.910724
   Lundstrom M, 2015, J CATARACT REFR SURG, V41, P2410, DOI 10.1016/j.jcrs.2015.05.027
   Moss JM, 2008, OPHTHALMOLOGY, V115, P2013, DOI 10.1016/j.ophtha.2008.06.024
   Moss JM, 2010, OPHTHALMOLOGY, V117, P2141, DOI 10.1016/j.ophtha.2010.02.030
   Moss JM, 2009, OPHTHALMOLOGY, V116, P1498, DOI 10.1016/j.ophtha.2009.02.024
   Mund M, 2014, INT J ENV RES PUB HE, V11, P5792, DOI 10.3390/ijerph110605792
   Nentwich MM, 2015, J CATARACT REFR SURG, V41, P58, DOI 10.1016/j.jcrs.2014.04.040
   Nentwich MM, 2012, EUR J OPHTHALMOL, V22, P541, DOI 10.5301/ejo.5000093
   Niu GQ, 2019, TRENDS BIOCHEM SCI, V44, P961, DOI 10.1016/j.tibs.2019.05.005
   ORMEROD LD, 1993, OPHTHALMOLOGY, V100, P724
   ORMEROD LD, 1993, OPHTHALMOLOGY, V100, P715
   Ortega-Pena S, 2020, MOL BIOL REP, V47, P771, DOI 10.1007/s11033-019-05139-1
   Qin Y, 2020, BRAIN RES BULL, V156, P15, DOI 10.1016/j.brainresbull.2019.12.004
   Rahmani S, 2018, SEMIN OPHTHALMOL, V33, P95, DOI 10.1080/08820538.2017.1353826
   Ramin S, 2016, INT J OPHTHALMOL-CHI, V9, P1329, DOI 10.18240/ijo.2016.09.17
   Razeghinejad MR, 2017, SURV OPHTHALMOL, V62, P591, DOI 10.1016/j.survophthal.2017.01.009
   Rieger-Fackeldey E, 2002, EUR J PEDIATR, V161, P649, DOI 10.1007/s00431-002-1080-z
   Schargus M, 2018, OCUL SURF, V16, P180, DOI 10.1016/j.jtos.2017.09.006
   Scott IU, 2008, RETINA-J RET VIT DIS, V28, P138, DOI 10.1097/IAE.0b013e31815e9313
   Shi SQ, 2020, MEDICINE, V99, DOI 10.1097/MD.0000000000018971
   Srimani JK, 2017, MOL SYST BIOL, V13, DOI 10.15252/msb.20177723
   Suzuki T, 2016, J INFECT CHEMOTHER, V22, P360, DOI 10.1016/j.jiac.2016.01.011
   Suzuki T, 2013, J OCUL PHARMACOL TH, V29, P887, DOI 10.1089/jop.2013.0146
   Synnestvedt Marie B, 2005, AMIA Annu Symp Proc, P724
   Ta CN, 2008, J OCUL PHARMACOL TH, V24, P427, DOI 10.1089/jop.2008.0018
   Ta CN, 2008, J CATARACT REFR SURG, V34, P171, DOI 10.1016/j.jcrs.2007.08.035
   Ta CN, 2007, ANN OPHTHALMOL, V39, P313, DOI 10.1007/s12009-007-9007-y
   Ta CN, 2003, OPHTHALMOLOGY, V110, P1946, DOI 10.1016/S0161-6420(03)00735-8
   Ta CN, 2003, CORNEA, V22, P545, DOI 10.1097/00003226-200308000-00011
   Ta CN, 2002, OPHTHALMOLOGY, V109, P2036, DOI 10.1016/S0161-6420(02)01236-8
   TICHO U, 1993, AM J OPHTHALMOL, V115, P506, DOI 10.1016/S0002-9394(14)74454-X
   VanBuskirk EM, 1996, AM J OPHTHALMOL, V122, P751, DOI 10.1016/S0002-9394(14)70507-0
   Vuong C, 2002, MICROBES INFECT, V4, P481, DOI 10.1016/S1286-4579(02)01563-0
   Yactayo-Miranda Y, 2009, GRAEF ARCH CLIN EXP, V247, P993, DOI 10.1007/s00417-009-1039-0
   Zhang SD, 2018, OCUL SURF, V16, P70, DOI 10.1016/j.jtos.2017.09.001
   Zhao YQ, 2018, MEDICINE, V97, DOI 10.1097/MD.0000000000012643
   Zheng MS, 2017, ENVIRON SCI POLLUT R, V24, P20409, DOI 10.1007/s11356-017-9711-0
   Zou YD, 2019, MEDICINE, V98, DOI 10.1097/MD.0000000000016817
NR 75
TC 2
Z9 2
U1 6
U2 36
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 2314-6133
EI 2314-6141
J9 BIOMED RES INT
JI Biomed Res. Int.
PD OCT 5
PY 2020
VL 2020
AR 2580795
DI 10.1155/2020/2580795
PG 14
WC Biotechnology & Applied Microbiology; Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biotechnology & Applied Microbiology; Research & Experimental Medicine
GA OK4QO
UT WOS:000584637300012
PM 33083458
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Murillo, J
   Villegas, LM
   Ulloa-Murillo, LM
   Rodriguez, AR
AF Murillo, Julieth
   Maria Villegas, Lina
   Marcela Ulloa-Murillo, Leidy
   Rocio Rodriguez, Alejandra
TI Recent trends on omics and bioinformatics approaches to study
   SARS-CoV-2: A bibliometric analysis and mini-review
SO COMPUTERS IN BIOLOGY AND MEDICINE
LA English
DT Article
DE Omics sciences; COVID-19; SARS-CoV-2; VOSviewer; Bibliometrics
ID CORONAVIRUS; PNEUMONIA
AB Background: The successful sequencing of SARS-CoV-2 cleared the way for the use of omics technologies and integrative biology research for combating the COVID-19 pandemic. Currently, many research groups have slowed down their respective projects to concentrate efforts in the study of the biology of SARS-CoV-2. In this bibliometric analysis and mini-review, we aimed to describe how computational methods or omics approaches were used during the first months of the COVID-19 pandemic.
   Methods: We analyzed bibliometric data from Scopus, BioRxiv, and MedRxiv (dated June 19th, 2020) using quantitative and knowledge mapping approaches. We complemented our analysis with a manual process of carefully reading the selected articles to identify either the omics or bioinformatic tools used and their purpose.
   Results: From a total of 184 articles, we found that metagenomics and transcriptomics were the main sources of data to perform phylogenetic analysis aimed at corroborating zoonotic transmission, identifying the animal origin and taxonomic allocation of SARS-CoV-2. Protein sequence analysis, immunoinformatics and molecular docking were used to give insights about SARS-CoV-2 targets for drug and vaccine development. Most of the publications were from China and USA. However, China, Italy and India covered the top 10 most cited papers on this topic.
   Conclusion: We found an abundance of publications using omics and bioinformatics approaches to establish the taxonomy and animal origin of SARS-CoV-2. We encourage the growing community of researchers to explore other lesser-known aspects of COVID-19 such as virus-host interactions and host response.
C1 [Murillo, Julieth] Pontificia Univ Javeriana Cali, Fac Engn, Cali, Colombia.
   [Maria Villegas, Lina; Rocio Rodriguez, Alejandra] Univ Valle, Fac Hlth, Cali, Colombia.
   [Marcela Ulloa-Murillo, Leidy] Czech Univ Life Sci Prague, Fac Agrobiol Food & Nat Resources, Prague, Czech Republic.
RP Murillo, J (通讯作者)，Pontificia Univ Javeriana Cali, Fac Engn, Cali, Colombia.
EM juliethmurillo@javerianacali.edu.co;
   lina.villegas@correounivalle.edu.co; ulloa_murillo@af.czu.cz;
   alejandra.rodriguez@correounivalle.edu.co
RI Ulloa-Murillo, Leidy Marcela/AAO-3002-2020
OI Ulloa-Murillo, Leidy Marcela/0000-0002-7175-4251
FU Colombian National "Departamento Administrativo de Ciencia, Tecnologia e
   Innovacion" (COLCIENCIAS) [756-2016]; Centre for Infectious Animal
   Diseases Initiative of CZU Prague, Ph.D program in Agricultural
   Specialization
FX JM was supported by the Colombian National "Departamento Administrativo
   de Ciencia, Tecnologia e Innovacion" (COLCIENCIAS), Ph.D program, number
   756-2016. MU was supported by the Centre for Infectious Animal Diseases
   Initiative of CZU Prague, Ph.D program in Agricultural Specialization.
   The content is solely the responsibility of the authors and does not
   necessarily represent the official views of funders.
CR Ahmed SF, 2020, VIRUSES-BASEL, V12, DOI 10.3390/v12030254
   Al-Qahtani, 2015, BMC GENOMICS, V17, P487
   Arisan ED, 2020, VIRUSES-BASEL, V12, DOI 10.3390/v12060614
   Belouzard S, 2012, VIRUSES-BASEL, V4, P1011, DOI 10.3390/v4061011
   Bhattacharya M, 2020, J MED VIROL, V92, P618, DOI 10.1002/jmv.25736
   Bojkova D, 2020, NATURE, V583, P469, DOI 10.1038/s41586-020-2332-7
   Chan JFW, 2020, EMERG MICROBES INFEC, V9, P221, DOI 10.1080/22221751.2020.1719902
   Chasapi A, 2020, BIOINFORMATICS, V36, P2963, DOI 10.1093/bioinformatics/btaa132
   Chen LJ, 2020, EMERG MICROBES INFEC, V9, P313, DOI 10.1080/22221751.2020.1725399
   Fanelli V, 2020, CRIT CARE, V24, DOI 10.1186/s13054-020-02872-z
   Ghosh, 2019, J PROTEIN PROTEONOMI, V10, P257, DOI DOI 10.1007/S42485-019-00017-7
   Jin X, 2020, GUT, V69, P1002, DOI 10.1136/gutjnl-2020-320926
   Khan AA, 2020, BIOINFORMATICS, V36, P4065, DOI 10.1093/bioinformatics/btaa285
   KUMAR V, 2020, J BIOMOL STRUCT 0530, DOI DOI 10.1080/07391102.2020.1775704
   Lam TTY, 2020, NATURE, V583, P282, DOI 10.1038/s41586-020-2169-0
   Letko M, 2020, NAT MICROBIOL, V5, P562, DOI [10.1038/s41564-020-0688-y, 10.1101/2020.01.22.915660]
   Malik YS, 2020, VET QUART, V40, P68, DOI 10.1080/01652176.2020.1727993
   Neogi U, 2020, PATHOGENS, V9, DOI 10.3390/pathogens9050320
   Qi FR, 2020, BIOCHEM BIOPH RES CO, V526, P135, DOI 10.1016/j.bbrc.2020.03.044
   Ray S, 2020, OMICS, V24, P457, DOI 10.1089/omi.2020.0073
   Rehman SU, 2020, PATHOGENS, V9, DOI 10.3390/pathogens9030240
   Robson B, 2020, COMPUT BIOL MED, V122, DOI 10.1016/j.compbiomed.2020.103849
   Robson B, 2020, COMPUT BIOL MED, V121, DOI 10.1016/j.compbiomed.2020.103749
   Robson B, 2020, COMPUT BIOL MED, V119, DOI 10.1016/j.compbiomed.2020.103670
   Sepay N, 2020, STRUCT CHEM, V31, P1831, DOI 10.1007/s11224-020-01537-5
   Srinivasan S, 2020, VIRUSES-BASEL, V12, DOI 10.3390/v12040360
   Sungnak W, 2020, NAT MED, V26, P681, DOI 10.1038/s41591-020-0868-6
   UMESH U, 2020, J BIOMOL STRUCT DYN, P1
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Venkatakrishnan AJ, 2020, ELIFE, V9, DOI 10.7554/eLife.58040
   Wahba L, 2020, MSPHERE, V5, DOI 10.1128/mSphere.00160-20
   Wan YS, 2020, J VIROL, V94, DOI 10.1128/JVI.02015-19
   Wang MX, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17082963
   World Health Organization, 2020, WEEKL EP UPD 27 OCT
   Wu F, 2020, NATURE, V579, P265, DOI 10.1038/s41586-020-2008-3
   Xiong Y, 2020, EMERG MICROBES INFEC, V9, P761, DOI 10.1080/22221751.2020.1747363
   [杨小林 Yang Xiaolin], 2020, [中草药, Chinese Traditional and Herbal Drugs], V51, P1795
   Zhang CX, 2020, J PROTEOME RES, V19, P1351, DOI 10.1021/acs.jproteome.0c00129
   Zhang YH, 2020, BIOMED PHARMACOTHER, V127, DOI 10.1016/j.biopha.2020.110195
NR 39
TC 5
Z9 5
U1 8
U2 23
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0010-4825
EI 1879-0534
J9 COMPUT BIOL MED
JI Comput. Biol. Med.
PD JAN
PY 2021
VL 128
AR 104162
DI 10.1016/j.compbiomed.2020.104162
PG 8
WC Biology; Computer Science, Interdisciplinary Applications; Engineering,
   Biomedical; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Life Sciences & Biomedicine - Other Topics; Computer Science;
   Engineering; Mathematical & Computational Biology
GA PN6DU
UT WOS:000604568300001
PM 33310371
OA Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Kotze, SH
   Mole, CG
AF Kotze, Sanet Henriet
   Mole, Calvin Gerald
TI Making large class basic histology lectures more interactive: The use of
   draw-along mapping techniques and associated educational activities
SO ANATOMICAL SCIENCES EDUCATION
LA English
DT Article
DE histology education; undergraduate education; large class teaching;
   mapping techniques; cognitive processes; mind maps; concept maps
ID HEALTH-PROFESSIONAL EDUCATION; CONCEPT MAPS; MEDICAL-EDUCATION;
   TEACHING/LEARNING ACTIVITY; LEARNING-STRATEGY; CRITICAL THINKING;
   MULTIPLE-CHOICE; KNOWLEDGE MAPS; MIND MAP; STUDENT
AB At Stellenbosch University, South Africa, basic histology is taught to a combination class of almost 400 first-year medical, physiotherapy, and dietetic students. Many students often find the amount of work in basic histology lectures overwhelming and consequently loose interest. The aim was to determine if a draw-along mapping activity would focus students during large class lectures. After each lecture on three basic histology tissues, a guided draw-along mapping session covering the work from the lecture was introduced in the form of a click-advance PowerPoint presentation which was used to demonstrate the unfolding of an ideal map. The lecturer simultaneously drew a similar map using an overhead projector allowing the students to draw their own maps on blank sheets of paper along with the lecturer. Students remained attentive during the activity and many participated in answering informal questions posed by the lecturer as the map-making session progressed. After the last session, students completed an anonymous, voluntary questionnaire (response rate of 78%). The majority of students found the draw-along maps useful (94%) and believed that its use should be continued in the future (93%). A significant increase (P < 0.001) was found in the test results of student cohorts who were given the current intervention compared to cohorts from previous years who were given mind maps as handouts only or had no intervention. The use of the draw-along mapping sessions were successful in focusing students during large class lectures while also providing them with a useful tool for their studies. Anat Sci Educ 8: 463-470. (c) 2015 American Association of Anatomists.
C1 [Kotze, Sanet Henriet; Mole, Calvin Gerald] Univ Stellenbosch, Dept Biomed Sci, Fac Med & Hlth Sci, Div Anat & Histol, ZA-7505 Tygerberg, South Africa.
RP Kotze, SH (通讯作者)，Univ Stellenbosch, Fac Med & Hlth Sci, Dept Biomed Sci, POB 241, ZA-8000 Cape Town, South Africa.
EM shk@sun.ac.za
OI Kotze, Sanet/0000-0003-0853-2178; Mole, Calvin/0000-0001-8483-8190
CR AUSUBEL DP, 1960, J EDUC PSYCHOL, V51, P267, DOI 10.1037/h0046669
   AUSUBEL DP, 1962, J EDUC PSYCHOL, V53, P243, DOI 10.1037/h0040210
   Biggs J., 1987, RES MONOGRAPH
   Boley DA, 2008, NURS EDUC, V33, P220, DOI 10.1097/01.NNE.0000312223.97955.4c
   Budd JW, 2010, J ECON EDUC, V35, P35
   Buzan B, 1995, MIND MAP BOOK USE RA
   Canas A. J, 2003, SUMMARY LIT PERTAINI
   Canfield PJ, 2002, J VET MED EDUC, V29, P105, DOI 10.3138/jvme.29.2.105
   Cantillon P, 2003, BMJ-BRIT MED J, V326, P437, DOI 10.1136/bmj.326.7386.437
   Chase CC, 2001, MED EDUC, V35, P1071
   Chmielewski TL, 1998, J EDUC PSYCHOL, V90, P407, DOI 10.1037/0022-0663.90.3.407
   Cooper J.L., 2000, NEW DIR TEACH LEARN, V2000, P5, DOI 10.1002/tl.8101
   Cyr SKS, 2009, J NURSES PROF DEV, V25, P70, DOI 10.1097/NND.0b013e31819e1082
   D'Antoni AV, 2010, BMC MED EDUC, V10, DOI 10.1186/1472-6920-10-61
   Daley BJ, 2010, MED EDUC, V44, P440, DOI 10.1111/j.1365-2923.2010.03628.x
   Daley BJ, 1999, J NURS EDUC, V38, P42
   Davies M, 2011, HIGH EDUC, V62, P279, DOI 10.1007/s10734-010-9387-6
   Dearmon V, 2011, J NURS EDUC, V50, P656, DOI 10.3928/01484834-20110817-01
   DiLullo C, 2011, ANAT SCI EDUC, V4, P214, DOI 10.1002/ase.240
   EbertMay D, 1997, BIOSCIENCE, V47, P601, DOI 10.2307/1313166
   Entwistle N., 1983, UNDERSTANDING STUDEN
   Eppler M.J., 2006, INFORM VISUAL, V5, P202, DOI DOI 10.1057/PALGRAVE.IVS.9500131
   Fang N, 2011, INT J ENG EDUC, V27, P1352
   Farrand P, 2002, MED EDUC, V36, P426, DOI 10.1046/j.1365-2923.2002.01205.x
   Fleming N. D., 1995, P RES DEV HIGH ED AN, V18, P308, DOI DOI 10.1016/J.NEDT.2008.06.007
   Geski J., 1992, COLL TEACHING, V40, P151, DOI DOI 10.1080/87567555.1992.10532239
   Gonzalez HL, 2008, ADV PHYSIOL EDUC, V32, P312, DOI 10.1152/advan.00021.2007
   Greenberg J, 1998, IMPROVE ACAD, V17, P331
   HETTIARATCHI ESG, 1978, MED EDUC, V12, P290, DOI 10.1111/j.1365-2923.1978.tb00353.x
   Hsu LL, 2005, J PROF NURS, V21, P141, DOI 10.1016/j.profnurs.2005.04.006
   Jonassen D. H., 1997, Journal of Interactive Learning Research, V8, P289
   Karakuyu Y, 2010, INT J PHYS SCI, V5, P724
   Karpicke JD, 2011, SCIENCE, V331, P772, DOI 10.1126/science.1199327
   Kent A, 2004, MED EDUC, V38, P121, DOI 10.1111/j.1365-2923.2004.01748.x
   Laight DW, 2006, MED TEACH, V28, pE64, DOI 10.1080/01421590600617574
   Laight DW, 2004, MED TEACH, V26, P229, DOI 10.1080/0142159042000192064
   Lujan HL, 2006, ADV PHYSIOL EDUC, V30, P13, DOI 10.1152/advan.00045.2005
   Mattick K, 2004, MED EDUC, V38, P535, DOI 10.1111/j.1365-2929.2004.01836.x
   MCCONNELL CR, 1984, J ECON EDUC, V15, P181, DOI 10.2307/1181927
   McGaghie WC, 2000, ADV PHYSIOL EDUC, V23, P72
   Messineo M., 2007, COLL TEACHING, V55, P125, DOI DOI 10.3200/CTCH.55.3.125-133
   Molitor T, 2007, TEACH PROF, V21, P1
   Mujeeb A Momin, 2010, Indian J Med Sci, V64, P118
   Mulryan-Kyne C, 2010, TEACH HIGH EDUC, V15, P175, DOI 10.1080/13562511003620001
   Ncayiyana D, 1999, MED EDUC, V33, P713
   Nesbit JC, 2006, REV EDUC RES, V76, P413, DOI 10.3102/00346543076003413
   NEWBLE DI, 1979, MED EDUC, V13, P263, DOI 10.1111/j.1365-2923.1979.tb01511.x
   Norman GR, 1996, TEACH LEARN MED, V8, P208, DOI 10.1080/10401339609539799
   Novak J. D., 1998, LEARNING CREATING US
   Novak J. D., 2006, INFORM VISUALIZATION, V5, P175, DOI DOI 10.1057/PALGRAVE.IVS.9500126
   NOVAK JD, 1990, J RES SCI TEACH, V27, P937, DOI 10.1002/tea.3660271003
   Novak JD, 2006, 200601 IHMC FLOR I H
   O'Donnell AM, 2002, EDUC PSYCHOL REV, V14, P71, DOI 10.1023/A:1013132527007
   Pilcher J, 2011, NEONATAL NETW, V30, P336, DOI 10.1891/0730-0832.30.5.336
   Pinto AJ, 1997, MED TEACH, V19, P114, DOI 10.3109/01421599709019363
   Pottier P, 2010, MED EDUC, V44, P926, DOI 10.1111/j.1365-2923.2010.03748.x
   Pudelko B, 2012, MED EDUC, V46, P1215, DOI 10.1111/medu.12032
   Qadir F, 2011, JCPSP-J COLL PHYSICI, V21, P476, DOI 08.2011/JCPSP.476481
   Reese AC, 1998, MED ED ONLINE, V3, P1
   Revell SMH, 2012, NURS EDUC, V37, P131, DOI 10.1097/NNE.0b013e31825041ba
   Schmidt HJ, 2004, ADV HEALTH SCI EDUC, V9, P251, DOI 10.1023/B:AHSE.0000038309.92212.44
   Taylor LA, 2011, NURS EDUC, V36, P84, DOI 10.1097/NNE.0b013e31820b5308
   Torre DM, 2007, MED TEACH, V29, P949, DOI 10.1080/01421590701689506
   Wheeler LA, 2003, J PROF NURS, V19, P339, DOI 10.1016/S8755-7223(03)00134-0
   Wickramasinghe A., 2007, J MED EDUC, V1, P30
   Wulf D. H., 1987, NEW DIRECTIONS TEACH, V38, P17, DOI DOI 10.1002/T1.37219873204
NR 66
TC 3
Z9 3
U1 0
U2 12
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1935-9772
EI 1935-9780
J9 ANAT SCI EDUC
JI Anat. Sci. Educ.
PD SEP-OCT
PY 2015
VL 8
IS 5
BP 463
EP 470
DI 10.1002/ase.1514
PG 8
WC Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA CQ7ZE
UT WOS:000360824700009
PM 25650015
DA 2022-08-23
ER

PT J
AU Kumar, S
   Nanelia, A
   Mariappan, R
   Rajagopal, A
   Rajan, V
AF Kumar, Sajit
   Nanelia, Alicia
   Mariappan, Ragunathan
   Rajagopal, Adithya
   Rajan, Vaibhav
TI Patient Representation Learning From Heterogeneous Data Sources and
   Knowledge Graphs Using Deep Collective Matrix Factorization: Evaluation
   Study
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE representation learning; deep collective matrix factorization;
   electronic medical records; knowledge graphs; multiview learning; graph
   embeddings; clinical decision support
AB Background: Patient representation learning aims to learn features, also called representations, from input sources automatically, often in an unsupervised manner, for use in predictive models. This obviates the need for cumbersome, time-and resource-intensive manual feature engineering, especially from unstructured data such as text, images, or graphs. Most previous techniques have used neural network-based autoencoders to learn patient representations, primarily from clinical notes in electronic medical records (EMRs). Knowledge graphs (KGs), with clinical entities as nodes and their relations as edges, can be extracted automatically from biomedical literature and provide complementary information to EMR data that have been found to provide valuable predictive signals. Objective: This study aims to evaluate the efficacy of collective matrix factorization (CMF), both the classical variant and a recent neural architecture called deep CMF (DCMF), in integrating heterogeneous data sources from EMR and KG to obtain patient representations for clinical decision support tasks. Methods: Using a recent formulation for obtaining graph representations through matrix factorization within the context of CMF, we infused auxiliary information during patient representation learning. We also extended the DCMF architecture to create a task-specific end-to-end model that learns to simultaneously find effective patient representations and predictions. We compared the efficacy of such a model to that of first learning unsupervised representations and then independently learning a predictive model. We evaluated patient representation learning using CMF-based methods and autoencoders for 2 clinical decision support tasks on a large EMR data set. Results: Our experiments show that DCMF provides a seamless way for integrating multiple sources of data to obtain patient representations, both in unsupervised and supervised settings. Its performance in single-source settings is comparable with that of previous autoencoder-based representation learning methods. When DCMF is used to obtain representations from a combination of EMR and KG, where most previous autoencoder-based methods cannot be used directly, its performance is superior to that of previous nonneural methods for CMF. Infusing information from KGs into patient representations using DCMF was found to improve downstream predictive performance. Conclusions: Our experiments indicate that DCMF is a versatile model that can be used to obtain representations from single and multiple data sources and combine information from EMR data and KGs. Furthermore, DCMF can be used to learn representations in both supervised and unsupervised settings. Thus, DCMF offers an effective way of integrating heterogeneous data sources and infusing auxiliary knowledge into patient representations.
C1 [Kumar, Sajit] Great Learning, Bengaluru, India.
   [Nanelia, Alicia; Mariappan, Ragunathan; Rajan, Vaibhav] Natl Univ Singapore, Dept Informat Syst & Analyt, 13 Comp Dr, Singapore 117417, Singapore.
   [Rajagopal, Adithya] Natl Inst Technol, Thiruchirappalli, India.
RP Rajan, V (通讯作者)，Natl Univ Singapore, Dept Informat Syst & Analyt, 13 Comp Dr, Singapore 117417, Singapore.
EM vaibhav.rajan@nus.edu.sg
OI Kumar, Sajit/0000-0002-1331-5277; Nanelia, Alicia/0000-0002-3140-5575
FU Singapore Ministry of Education Academic Research Fund
   [R-253-000-159-114]
FX This work was supported by the Singapore Ministry of Education Academic
   Research Fund (R-253-000-159-114) . The principal investigator was VR.
CR Adams DL, 2002, J NATL MED ASSOC, V94, P430
   Almeida JR, 2021, J BIOMED INFORM, V120, DOI 10.1016/j.jbi.2021.103849
   Arnaud E, 2020, IEEE INT CONF BIG DA, P4836, DOI 10.1109/BigData50022.2020.9378073
   Bhattacharya S, 2017, P AAAI C ART INT FEB
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Che ZP, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P507, DOI 10.1145/2783258.2783365
   Choi E, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P787, DOI 10.1145/3097983.3098126
   Cohen T, 2017, J BIOMED INFORM, V68, P150, DOI 10.1016/j.jbi.2017.03.003
   Cui P, 2019, IEEE T KNOWL DATA EN, V31, P833, DOI 10.1109/TKDE.2018.2849727
   Dasgupta S, 2021, JMIR MED INF, V9, DOI 10.2196/32730
   Dong YX, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P135, DOI 10.1145/3097983.3098036
   Dubois S, 2018, EFFECTIVE REPRESENTA
   Ernst P, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0549-5
   Gartner D, 2015, INFORMS J COMPUT, V27, P718, DOI 10.1287/ijoc.2015.0655
   Ghanvatkar S, 2019, P ICIS C 2019 DEC 15
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Huddar V, 2016, IEEE ACCESS, V4, P7988, DOI 10.1109/ACCESS.2016.2618775
   Johnson AEW, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.35
   Kilicoglu H, 2012, BIOINFORMATICS, V28, P3158, DOI 10.1093/bioinformatics/bts591
   King DB, 2015, ACS SYM SER, V1214, P1
   Klami A, 2020, CMF COLLECTIVE MATRI
   Li F, 2020, AAAI CONF ARTIF INTE, V34, P8180, DOI 10.1609/aaai.v34i05.6331
   Liany H, 2020, BIOINFORMATICS, V36, P2209, DOI 10.1093/bioinformatics/btz893
   Liu X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P375, DOI 10.1145/3289600.3291029
   Ma FL, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P743, DOI 10.1145/3269206.3271701
   Mariappan R, 2019, MACH LEARN, V108, P1395, DOI 10.1007/s10994-019-05801-6
   Miotto R, 2016, SCI REP-UK, V6, DOI 10.1038/srep26094
   Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
   Quan HD, 2005, MED CARE, V43, P1130, DOI 10.1097/01.mlr.0000182534.19832.83
   Le Q, 2014, PR MACH LEARN RES, V32, P1188
   Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003
   Roth A. V., 1995, Production and Operations Management, V4, P2
   Ruan T, 2019, BMC MED INFORM DECIS, V19, DOI 10.1186/s12911-019-0985-7
   Savova GK, 2010, J AM MED INFORM ASSN, V17, P507, DOI 10.1136/jamia.2009.001560
   Singh A, 2008, INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, P650
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   Suresh H, 2017, USE AUTOENCODERS DIS
   Sushil M, 2018, J BIOMED INFORM, V84, P103, DOI 10.1016/j.jbi.2018.06.016
   The National Center for Biomedical Ontology, RXNAV BROWSER APPL P
   Tian HM, 2019, WORLD WIDE WEB, V22, P1325, DOI 10.1007/s11280-018-0548-3
   U.S. National Library of Medicine, FACT SHEET MEDLINE
   Vincent P, 2010, J MACH LEARN RES, V11, P3371
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang WQ, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P545, DOI 10.1145/3331184.3331258
   Xu K, 2019, P 4 MACH LEARN HEALT
   Yang C., 2020, IEEE T KNOWL DATA EN, V1
   Yin CC, 2019, IEEE DATA MINING, P738, DOI 10.1109/ICDM.2019.00084
   Zhang CX, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P793, DOI 10.1145/3292500.3330961
   Zhao J, 2018, P 27 INT C COMP LING
NR 53
TC 0
Z9 0
U1 2
U2 3
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Suite 1102, TORONTO, ON M5A 0P6, CANADA
EI 2291-9694
J9 JMIR MED INF
JI JMIR Med. Inf.
PD JAN
PY 2022
VL 10
IS 1
AR e28842
DI 10.2196/28842
PG 19
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA ZS3NM
UT WOS:000768375200018
PM 35049514
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Alnedawi, A
   Ullah, S
   Azam, A
   Mousa, E
   Obaid, I
   Yosri, A
AF Alnedawi, Ali
   Ullah, Saad
   Azam, Abdelhalim
   Mousa, Eman
   Obaid, Ihsan
   Yosri, Ahmed
TI Integrated and holistic knowledge map of resilient modulus studies for
   pavement materials: A scientometric analysis and bibliometric review of
   research frontiers and prospects
SO TRANSPORTATION GEOTECHNICS
LA English
DT Review
DE Bibliometric review; Scientometric analysis; Citespace; Vosviewer;
   Pavement materials; Unbound granular materials; Resilient modulus
ID RECYCLED ASPHALT PAVEMENT; FREEZE-THAW CYCLES; OF-THE-ART; MATRIC
   SUCTION; COHESIVE SOILS; UNSATURATED SUBGRADE; SEASONAL-VARIATION;
   DEMOLITION DEBRIS; MOISTURE-CONTENT; COMPACTED CLAY
AB Since the establishment of the American Association of State Highway and Transportation Officials (AASHTO) in 1960, the design of pavement structures has improved tremendously. The characterization of pavement materials has gained significant popularity all around the world. Throughout several decades, researchers have conducted extensive research in material characterization and unanimously agree that resilient modulus (MR) is one of the prime factors in predicting the pavement layers' performance. This study aims to systematically analyze and visualize the status of research conducted on pavement material. The analysis was performed based on the web of science by investigating the existing research undertaken to date on the MR of pavement materials using VOSvieweer and CiteSpace tools. This study examined two keywords, "resilient modulus" and their frequency of occurrence using the function of keyword co-occurrence analysis. The search result successfully recognized three clusters utilizing the process of cluster analysis based on a total of 1810 documents that contained "resilient modulus" in the title, abstract, or keywords. The study explored the growth pattern in the field of MR by using citation bursts analysis to reveal how research related to pavement materials has evolved during time. This study is unique as it provides the reader with detailed information on the existing literature related to MR studies to better classify the status quo and uptrend in pavement materials. In addition, future research topics related to the MR characterization were highlighted.
C1 [Alnedawi, Ali] Deakin Univ, Sch Engn, Geelong, Vic 3220, Australia.
   [Ullah, Saad] Tetra Tech Inc, Orlando, FL USA.
   [Azam, Abdelhalim; Yosri, Ahmed] Jouf Univ, Coll Engn, Civil Engn Dept, Sakakah, Saudi Arabia.
   [Azam, Abdelhalim; Mousa, Eman] Mansoura Univ, Fac Engn, Publ Works Engn Dept, Mansoura 35516, Egypt.
   [Mousa, Eman] Horus Univ, Fac Engn, Civil Engn Dept, Dumyat Al Jadidah 34511, Egypt.
   [Obaid, Ihsan] Oregon State Univ, Sch Civil & Construct Engn, Corvallis, OR 97331 USA.
   [Obaid, Ihsan] Univ Al Qadisiyah, Coll Engn, Rd & Transport Dept, Al Diwaniyah, Iraq.
   [Yosri, Ahmed] Delta Univ Sci & Technol, Coll Engn, Civil Engn Dept, Talkha, Egypt.
RP Alnedawi, A (通讯作者)，Deakin Univ, Sch Engn, Geelong, Vic 3220, Australia.
EM a.alnedawi@deakin.edu.au; Amazam@ju.edu.sa; Emousa@horus.edu.eg;
   amyosri@ju.edu.sa
OI Mousa, Eman/0000-0003-0486-5661
CR Abu-Farsakh MY, 2015, TRANSPORT RES REC, P44, DOI 10.3141/2510-06
   Ahmadinia E, 2011, MATER DESIGN, V32, P4844, DOI 10.1016/j.matdes.2011.06.016
   Ahmedzade P, 2009, J HAZARD MATER, V165, P300, DOI 10.1016/j.jhazmat.2008.09.105
   Akmaz E, 2020, TRANSPORT RES REC, V2674, P989, DOI 10.1177/0361198120934473
   Al-Hadidy AI, 2009, MATER DESIGN, V30, P1133, DOI 10.1016/j.matdes.2008.06.021
   Alam TB, 2010, INT J PAVEMENT ENG, V11, P123, DOI 10.1080/10298430902731362
   Allen JJ., 1974, TRANSP RES REC
   Allen JJ., 1973, EFFECTS NONCONSTANT
   Alnedawi Ali, 2019, Transportation Infrastructure Geotechnology, V6, P70, DOI 10.1007/s40515-019-00070-1
   Alnedawi A, 2018, INT J CIV ENG TECHNO, V9, P1856
   Alnedawi A, 2020, THESIS DEAKIN U
   Alnedawi A, 2019, J ROCK MECH GEOTECH, V11, P172, DOI 10.1016/j.jrmge.2018.05.004
   Alnedawi A, 2018, INT J GEOMATE, V14, P124, DOI 10.21660/2018.45.7336
   Andrei D., 2009, CONT TOP GR MODIF PR, P401
   Andrei D, 2003, DEV PREDICTIVE MODEL
   [Anonymous], 1973, DEV PROCEDURES CHARA
   Arab MG, 2019, J MATER CIVIL ENG, V31, DOI 10.1061/(ASCE)MT.1943-5533.0002565
   Arab MG, 2020, CONSTR BUILD MATER, V261, DOI 10.1016/j.conbuildmat.2020.119792
   Arabani M, 2010, CONSTR BUILD MATER, V24, P1060, DOI 10.1016/j.conbuildmat.2009.11.011
   Arulrajah A, 2013, J MATER CIVIL ENG, V25, P1077, DOI 10.1061/(ASCE)MT.1943-5533.0000652
   Arulrajah A, 2011, J MATER CIVIL ENG, V23, P1444, DOI 10.1061/(ASCE)MT.1943-5533.0000319
   Arulrajah A, 2016, CONSTR BUILD MATER, V114, P864, DOI 10.1016/j.conbuildmat.2016.03.150
   Arulrajah A, 2015, CONSTR BUILD MATER, V84, P146, DOI 10.1016/j.conbuildmat.2015.03.048
   Arulrajah A, 2014, CONSTR BUILD MATER, V58, P245, DOI 10.1016/j.conbuildmat.2014.02.025
   Azam AM, 2013, CAN GEOTECH J, V50, P1143, DOI 10.1139/cgj-2012-0406
   Ba M, 2013, GEOTECH GEOL ENG, V31, P1497, DOI 10.1007/s10706-013-9674-y
   Nguyen BT, 2015, INT J PAVEMENT ENG, V16, P180, DOI 10.1080/10298436.2014.937807
   Barskale RD, 1989, TRANSPORT RES RECORD, V1227, P173
   Bejarano M, 2001, EVALUATION RECYCLED
   Bennert T, 2000, TRANSPORT RES REC, P33, DOI 10.3141/1714-05
   Bilodeau JP, 2016, MATER STRUCT, V49, P4363, DOI 10.1617/s11527-016-0793-9
   Bilodeau JP, 2012, INT J PAVEMENT ENG, V13, P459, DOI 10.1080/10298436.2011.573556
   Boyce JR, 1976, THESIS U NOTTINGHAM
   Bozyurt Ozlem, 2012, GeoCongress 2012 State of the Art and Practice in Geotechnical Engineering, P3901, DOI 10.1061/9780784412121.400
   Brown SF., 1991, CYCLIC LOADING SOILS, V1, P249
   Brown SF, 1975, TRANSP RES REC
   Burczyk JM., 1994, TRANSP RES REC
   Butalia TS, 2003, AM SOC TEST MATER, V1437, P70, DOI 10.1520/STP12523S
   Camargo FF, 2013, INT J PAVEMENT ENG, V14, P223, DOI 10.1080/10298436.2012.655737
   Carmichael RF, 1985, RES REC, P1043
   Cary CE, 2011, ROAD MATER PAVEMENT, V12, P615, DOI 10.3166/RMPD.12.615-638
   Cetin A, 2014, ROAD MATER PAVEMENT, V15, P220, DOI 10.1080/14680629.2013.869505
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen CM, 1999, INFORM PROCESS MANAG, V35, P401, DOI 10.1016/S0306-4573(98)00068-5
   Chen JG, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15112350
   Chen WB, 2020, CONSTR BUILD MATER, V236, DOI 10.1016/j.conbuildmat.2019.117542
   Coleri E., 2007, RELATIONSHIP RESILIE
   Cosentino PJ, 2001, DEV SPECIFICATIONS U, P32309
   Cunningham CN, 2013, INT J PAVEMENT ENG, V14, P231, DOI 10.1080/10298436.2012.690518
   Dai ST, 2002, TRANSPORT RES REC, P20, DOI 10.3141/1786-03
   Darko A, 2020, AUTOMAT CONSTR, V112, DOI 10.1016/j.autcon.2020.103081
   Dawson A R, 1996, FLEXIBLE PAVEMENTS, P35
   Dehlen GL., 1970, HIGHW RES REC
   Disfani MM, 2012, J CLEAN PROD, V20, P170, DOI 10.1016/j.jclepro.2011.07.020
   Disfani MM, 2014, CONSTR BUILD MATER, V68, P667, DOI 10.1016/j.conbuildmat.2014.07.007
   Domitrovic J, 2019, TRANSP GEOTECH, V18, P83, DOI 10.1016/j.trgeo.2018.11.008
   Doucet F., 2004, P 57 CAN GEOT 5 JOIN
   Drumm EC, 1997, J GEOTECH GEOENVIRON, V123, P663, DOI 10.1061/(ASCE)1090-0241(1997)123:7(663)
   EDIL TB, 1994, GEOTECH TEST J, V17, P453
   Edil TB, 2002, P INT WORKSH LIGHT W, P17
   Ekblad J, 2008, MATER STRUCT, V41, P509, DOI 10.1617/s11527-007-9262-9
   El-Ashwah AS, 2022, INT J PAVEMENT ENG, V23, P476, DOI 10.1080/10298436.2020.1754416
   El-Ashwah AS, 2019, CONSTR BUILD MATER, V194, P372, DOI 10.1016/j.conbuildmat.2018.10.212
   Elliott R.P., 1988, TRANSPORT RES REC, V1192, P1
   Erlingsson S, 2010, ROAD MATER PAVEMENT, V11, P863, DOI 10.1080/14680629.2010.9690310
   Esfahani MA., 2018, INT J TRANSP ENG, V5, P367
   Fang Y, 2018, J SUSTAIN TOUR, V26, P108, DOI 10.1080/09669582.2017.1329310
   Figueroa JL, 1994, FINAL REPORT CHARACT
   Fredlund DG, 1977, TRANSP RES REC
   Garg N., 1996, TRANSP RES REC, V1547, P89
   George KP., 2004, PREDICTION RESILIENT
   Ghabchi R, 2013, INT J PAVEMENT ENG, V14, P274, DOI 10.1080/10298436.2012.711475
   Ghorbani B, 2021, CONSTR BUILD MATER, V280, DOI 10.1016/j.conbuildmat.2021.122499
   Ghorbani B, 2021, SOIL DYN EARTHQ ENG, V142, DOI 10.1016/j.soildyn.2020.106544
   Ghorbani B, 2021, TRANSP GEOTECH, V27, DOI 10.1016/j.trgeo.2020.100481
   Gorkem C, 2009, CONSTR BUILD MATER, V23, P2227, DOI 10.1016/j.conbuildmat.2008.12.001
   Gray JE., 1962, CHARACTERISTICS GRAD
   GROOS OV, 1969, J DOC, V25, P344, DOI 10.1108/eb026482
   Gu F, 2015, J MATER CIVIL ENG, V27, DOI 10.1061/(ASCE)MT.1943-5533.0001147
   Gupta S.C., 2009, HYDRAULIC MECH PROPE
   Han Z, 2016, ENG GEOL, V211, P85, DOI 10.1016/j.enggeo.2016.06.020
   Han Z, 2016, INT J GEOMECH, V16, DOI 10.1061/(ASCE)GM.1943-5622.0000631
   Han Z, 2015, CAN GEOTECH J, V52, P1605, DOI 10.1139/cgj-2014-0339
   Hardcastle JH., 1992, SUBGRADE RESILIENT M
   Haynes J.H., 1961, EFFECTS REPEATED LOA
   He QH, 2017, INT J PROJ MANAG, V35, P670, DOI 10.1016/j.ijproman.2016.08.001
   Heydinger AG, 2003, TRANSPORT RES REC, P47
   Heydinger AG, 1996, TRANSP RES REC, V1547, P1
   Hicks R., 1971, HIGHWAY RES RECORD
   Hicks RG., 1971, HIGHWAY RES
   Hoff I, 2004, P 6 INT S PAV UNB UN, P6
   Hopkins T. C, 2001, KTC0107SPR163951F
   Hosseini MR, 2018, AUTOMAT CONSTR, V87, P235, DOI 10.1016/j.autcon.2017.12.002
   Houston WN., 1993, TRANSP RES REC
   Hoyos LR, 2011, J MATER CIVIL ENG, V23, P977, DOI 10.1061/(ASCE)MT.1943-5533.0000267
   Huang YH., 2004, PAVEMENT ANAL DESIGN, Vsecond
   Ishikawa T, 2019, TRANSP GEOTECH, V21, DOI 10.1016/j.trgeo.2019.100284
   Ji R, 2014, SCI WORLD J, DOI 10.1155/2014/372838
   JIN MS, 1994, J TRANSP ENG-ASCE, V120, P603, DOI 10.1061/(ASCE)0733-947X(1994)120:4(603)
   Johnson TC, 1986, TRANSP RES REC, V1089, P147
   Jones MP., 1977, TRANSP RES REC, P641
   Kamal M., 1993, TRANSP RES REC, V1406, P88
   Kancherla A., 2005, RESILIENT MODULUS PE
   Kang DH, 2011, SOIL SCI SOC AM J, V75, P1276, DOI 10.2136/sssaj2010.0296
   Kaya Zulkuf, 2012, GeoCongress 2012 State of the Art and Practice in Geotechnical Engineering, P1486, DOI 10.1061/9780784412121.153
   Ke YJ, 2009, J CONSTR ENG M, V135, P1076, DOI 10.1061/(ASCE)0733-9364(2009)135:10(1076)
   Khasawneh MA, 2019, INT J PAVEMENT ENG, V20, P1193, DOI 10.1080/10298436.2017.1394101
   Khogali WEI, 2000, UNBOUND AGGREGATES IN ROAD CONSTRUCTION, P29
   Khoury CN, 2011, TRANSPORT RES REC, P68, DOI 10.3141/2232-07
   Khoury N, 2012, INT J GEOMECH, V12, P519, DOI 10.1061/(ASCE)GM.1943-5622.0000140
   Khoury N, 2009, TRANSPORT RES REC, P72, DOI 10.3141/2101-09
   Khoury NN, 2004, TRANSPORT RES REC, P99
   Kim W, 2007, TRANSP RES RECORD, P27, DOI 10.3141/2005-04
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   Kolay Prabir K., 2016, Geo-Chicago 2016. Geotechnics for Sustainable Energy. Selected Papers from Sessions of Geo-Chicago 2016, P677
   Kolisoja P., 1997, THESIS
   Lee W, 1997, J GEOTECH GEOENVIRON, V123, P131, DOI 10.1061/(ASCE)1090-0241(1997)123:2(131)
   LEE W, 1995, CAN GEOTECH J, V32, P559, DOI 10.1139/t95-059
   Leite FD, 2011, CONSTR BUILD MATER, V25, P2972, DOI 10.1016/j.conbuildmat.2010.11.105
   Lekarp F, 2000, J TRANSP ENG-ASCE, V126, P76, DOI 10.1061/(ASCE)0733-947X(2000)126:1(76)
   Lekarp F, 2000, J TRANSP ENG-ASCE, V126, P66, DOI 10.1061/(ASCE)0733-947X(2000)126:1(66)
   LI DQ, 1994, J GEOTECH ENG-ASCE, V120, P939, DOI 10.1061/(ASCE)0733-9410(1994)120:6(939)
   Liang RY, 2008, J TRANSP ENG, V134, P34, DOI 10.1061/(ASCE)0733-947X(2008)134:1(34)
   Liu HX, 2012, ADV MATER RES-SWITZ, V374-377, P1796, DOI 10.4028/www.scientific.net/AMR.374-377.1796
   Liu JW, 2008, I C WIREL COMM NETW, P11435
   Luo C, 2014, HIGH PERFORMANCE GRA
   Lytton RL., 1995, P 1 INT C UNS SOILS, P3
   MacGregor J., 1999, TRANSP RES REC, V1687, P22
   Maher A., 2000, RESILIENT MODULUS PR
   May R.W., 1981, TRANSPORT RES REC, VVol. 810, P1
   Mellizo C., 2010, 11 INT C ASPH PAV NA
   Mitry FG, 1965, DETERMINATION MODULU
   Moghadas Nejad F, 2010, CONSTR BUILD MATER, V24, P1158, DOI 10.1016/j.conbuildmat.2009.12.025
   Mohammad LN., 1995, TRANSP RES REC, P1504
   Mohammad LN., 1995, TRANSP RES REC
   Mohammadinia A, 2015, J MATER CIVIL ENG, V27, DOI 10.1061/(ASCE)MT.1943-5533.0001148
   Moore WM., 1970, LAB STUDY RELATION S
   Moossazadeh J., 1981, TRANSP RES REC
   Morgan JR., 1966, AUST ROAD RES BOARD
   Mousa E, 2017, CAN J CIVIL ENG, V44, P417, DOI 10.1139/cjce-2016-0435
   Mousa E, 2021, TRANSP GEOTECH, V26, DOI 10.1016/j.trgeo.2020.100414
   Mustafee N, 2014, SCIENTOMETRICS, V98, P2145, DOI 10.1007/s11192-013-1136-z
   Naji K, 2018, INT J PAVEMENT ENG, V19, P651, DOI 10.1080/10298436.2016.1199877
   NCHRP, 2004, RES RES DIG
   NCHRP, 2002, REC STAND METH ROUT
   Ng CWW, 2014, GEOTECHNIQUE, V64, P709, DOI 10.1680/geot.14.P.015
   Ng CWW, 2013, CAN GEOTECH J, V50, P223, DOI 10.1139/cgj-2012-0052
   Ni B., 2002, MODELLING RESILIENT, V2, P1131
   Nokkaew K, 2014, TRANSPORT RES REC, P68, DOI 10.3141/2433-08
   Oh JH, 2012, INT J PAVEMENT ENG, V13, P472, DOI 10.1080/10298436.2011.633170
   Ooi PSK, 2004, TRANSPORT RES REC, P115, DOI 10.3141/1874-13
   Pan TY, 2006, TRANSPORT RES REC, V1952, P12, DOI 10.3141/1952-02
   Papp WJ, 1998, GEOTECH SP, P122
   Perez-Acebo H, 2018, APPL SCI-BASEL, V8, DOI 10.3390/app8071041
   PEZO R, 1994, GEOTECH TEST J, V17, P349
   Plaistow LC., 1994, NONLINEAR BEHAV SOME
   Punith VS, 2007, J MATER CIVIL ENG, V19, P500, DOI 10.1061/(ASCE)0899-1561(2007)19:6(500)
   Puppala A.J., 1999, TRANSPORT RES REC, V1442, P47
   Puppala AJ, 2011, J MATER CIVIL ENG, V23, P990, DOI 10.1061/(ASCE)MT.1943-5533.0000268
   Qu YL, 2019, J MT SCI-ENGL, V16, P2159, DOI 10.1007/s11629-019-5426-7
   RAAD L, 1980, TRANSPORT ENG-J ASCE, V106, P111
   Raad L., 1992, CHARACTERIZATION SAT
   Rada G, 1981, COMPREHENSIVE EVALUA
   Rahim A.M., 2005, INT J PAVEMENT ENG, V6, P163, DOI [10.1080/10298430500140891, DOI 10.1080/10298430500140891]
   Rahim AM., MODELS ESTIMATE SUBG
   Rezapour M, 2018, J MOD TRANSP, V26, P268, DOI 10.1007/s40534-018-0166-x
   Richter CA., 2006, FHWAHRT04079 US DEP
   Robinson RG., 1974, MEASUREMENT ELASTIC
   Romero E, 2003, GEOTECHNIQUE, V53, P65, DOI 10.1680/geot.2003.53.1.65
   Saberian M, 2021, TRANSP GEOTECH, V27, DOI 10.1016/j.trgeo.2020.100477
   Salour F, 2015, ROAD MATER PAVEMENT, V16, P553, DOI 10.1080/14680629.2015.1021107
   Salour F, 2014, CAN GEOTECH J, V51, P1413, DOI 10.1139/cgj-2013-0484
   Sargand SM., 2000, EVALUATION INITIAL S
   Sawangsuriya A, 2009, TRANSPORT RES REC, P82, DOI 10.3141/2101-10
   Seed H.B., 1962, INT C STRUCT DES ASP, P611
   Seed HB., 1967, NCHRP REP
   Shi YL, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11133716
   Si HY, 2019, J CLEAN PROD, V213, P415, DOI 10.1016/j.jclepro.2018.12.157
   Sivakumar V, 2013, GEOTECHNIQUE, V63, P628, DOI [10.1680/geot.10.P.103, 10.1680/geot.10.P103]
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Song JB, 2016, SCIENTOMETRICS, V107, P1111, DOI 10.1007/s11192-016-1918-1
   Stolle D, 2009, CAN J CIVIL ENG, V36, P639, DOI 10.1139/L08-141
   Suarez CAM, 2010, P 11 INT C ASPH PAV, P1
   Sweere GTH., 1990, FAC CIV ENG GEOSCI, P431
   Tang AM, 2008, GEOTECHNIQUE, V58, P45, DOI 10.1680/geot.2008.58.1.45
   Tarefder RA, 2005, J MATER CIVIL ENG, V17, P19, DOI 10.1061/(ASCE)0899-1561(2005)17:1(19)
   Tayfur S, 2007, CONSTR BUILD MATER, V21, P328, DOI 10.1016/j.conbuildmat.2005.08.014
   Thadkamalla GB., 1995, TRANSP RES REC
   Thom N, 1988, DESIGN ROAD FDN
   Thom NH., 1988, 14 ARRB C 28 AUG 2 1, P14
   Thom NH., 1987, EFFECT MOISTURE STRU
   Thom R, 2008, GEOTECH TEST J, V31, P107
   THOMPSON MR, 1979, TRANSPORT ENG-J ASCE, V105, P71
   Thompson MR., 1989, P WORK RES MOD
   Thompson MR, 1976, FHWAILUI160
   Tian P, 1998, TRANSPORT RES REC, P75
   Titi H., 2006, 00920311 ID U WISC M
   Transportation Officials, 2008, MECH EMP PAV DES GUI
   Trollope EH, 1962, P ARRB, V1, P693
   Duong TV, 2016, ACTA GEOTECH, V11, P51, DOI 10.1007/s11440-014-0341-0
   Uchaipichat A, 2009, GEOTECHNIQUE, V59, P339, DOI 10.1680/geot.2009.59.4.339
   Uzan J., 1985, TRANSPORT RES REC, VVol. 1022, P52
   Van Eck NJ, 2007, STUD CLASS DATA ANAL, P299
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Van Niekerk AA., 1998, P 5 INT C BEAR CAP R
   Vuong B., 1992, ROAD TRANSP RES, V1
   Walubita LF, 2021, ROAD MATER PAVEMENT, V22, P2369, DOI 10.1080/14680629.2020.1756901
   Wang WN, 2018, ROAD MATER PAVEMENT, V19, P898, DOI 10.1080/14680629.2017.1283352
   Werkmeister S., 2004, PERMANENT DEFORMATIO
   Witczak M., UNIVERSAL AIRPORT DE
   Wolfe WE., 2004, SEASONAL INSTRUMENTA
   Wu M, 2011, THESIS WASHINGTON ST
   Xiao FP, 2007, J MATER CIVIL ENG, V19, P475, DOI 10.1061/(ASCE)0899-1561(2007)19:6(475)
   Xiao Y, 2012, BEST VALUE GRANULAR
   Xiao YJ, 2017, INT J GEOMECH, V17, DOI 10.1061/(ASCE)GM.1943-5622.0000735
   Xu YD, 2018, CONSTR BUILD MATER, V171, P291, DOI 10.1016/j.conbuildmat.2018.03.147
   Yaghoubi E, 2016, PROCEDIA ENGINEER, V143, P323, DOI 10.1016/j.proeng.2016.06.041
   Yang SR, 2008, J GEOTECH GEOENVIRON, V134, P1375, DOI 10.1061/(ASCE)1090-0241(2008)134:9(1375)
   Yang SR, 2005, TRANSPORT RES REC, P99, DOI 10.3141/1913-10
   Yau A, 2004, TRANSPORT RES REC, P47
   Yau A., 2002, STUDY LTPP LAB RESIL
   Zaman M., 2007, EFFECT SOIL SUCTION
   Zou X, 2020, ACCIDENT ANAL PREV, V144, DOI 10.1016/j.aap.2020.105568
   Zou X, 2019, ACCIDENT ANAL PREV, V132, DOI 10.1016/j.aap.2019.07.019
   Zou X, 2018, ACCIDENT ANAL PREV, V118, P131, DOI 10.1016/j.aap.2018.06.010
NR 225
TC 0
Z9 0
U1 4
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-3912
J9 TRANSP GEOTECH
JI Transp. Geotech.
PD MAR
PY 2022
VL 33
AR 100711
DI 10.1016/j.trgeo.2021.100711
PG 22
WC Engineering, Civil; Engineering, Geological
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 0U6DN
UT WOS:000787739800001
DA 2022-08-23
ER

PT J
AU Zhang, Y
   Liu, XY
   Wang, JP
   Du, L
   Ma, YY
   Liu, W
   Ye, R
   Yang, YL
   Xu, H
AF Zhang, Yan
   Liu, Xiaoyu
   Wang, Jingpu
   Du, Le
   Ma, Yanyun
   Liu, Wei
   Ye, Rui
   Yang, Yali
   Xu, Hui
TI Analysis of Multi-Part Phenotypic Changes in Skin to Characterize the
   Trajectory of Skin Aging in Chinese Women
SO CLINICAL COSMETIC AND INVESTIGATIONAL DERMATOLOGY
LA English
DT Article
DE aging; Chinese women; phenotypes; skin; wrinkles
ID ANTI-WRINKLE EFFICACY; AGE-RELATED-CHANGES; FACIAL WRINKLES; NECK;
   JAPANESE
AB Purpose: As the human body's largest organ exposed to the external environment, the skin suffers from internal and external aging factors, leading to wrinkles, loss of elasticity, sagging, and rough appearance. However, little is known of the characteristics of skin aging of different body parts in Chinese women. Here, we study the signs of extrinsic skin aging in different body parts to identify the knowledge map of manifestations of aging in Chinese women.
   Patients and Methods: Wrinkle and texture phenotypes and collagen samples from the face, neck, hands, and arms of 326 Chinese women were collected. The correlations between phenotypes and ages and the differences in phenotypes by age were evaluated.
   Results: The wrinkle and texture phenotypes around the eyes and mouth and of the hands were strongly correlated with age. Ages 32 and 58 showed the largest number of differentially changed aging phenotypes. The number of aging phenotypes increased sharply between the ages of 24 and 30, suggesting that the skin was undergoing rapid aging. Eye aging was the most rapidly changing phenotype between 19 and 30 years old. Wrinkles at the corner of the eyes showed a significant difference in the older group, suggesting an early onset and long-term effects.
   Conclusion: This is the first study to be performed on the characteristics of skin aging among Chinese women that takes account of multiple areas of the body. It was found that 24 years old was the time point at which the skin begins to age in Chinese women. This provides important clues for aging-related research and personalized skin care.
C1 [Zhang, Yan; Yang, Yali; Xu, Hui] Shanghai Jiao Tong Univ, Dept Dermatol, Sch Med, Shanghai Peoples Hosp 9, Shanghai, Peoples R China.
   [Liu, Xiaoyu] Shanghai Jiao Tong Univ, Dept Mol Diagnost & Endocrinol, Core Lab Med Ctr Clin Res,Sch Med, State Key Lab Med Genom,Shanghai Peoples Hosp 9, Shanghai, Peoples R China.
   [Liu, Xiaoyu; Wang, Jingpu; Du, Le; Ye, Rui] Inertiabiotech Co LTD, Shanghai, Peoples R China.
   [Ma, Yanyun] Fudan Univ, Human Phenome Inst, Shanghai, Peoples R China.
   [Ma, Yanyun] Fudan Univ, Collaborat Innovat Ctr Genet & Dev, Sch Life Sci, State Key Lab Genet Engn, Shanghai, Peoples R China.
   [Ma, Yanyun] Fudan Univ, Inst Six Sect Econ, Shanghai, Peoples R China.
   [Liu, Wei] Med Ctr Air Force PLA, Dept Dermatol, Beijing, Peoples R China.
RP Ye, R (通讯作者)，Inertiabiotech Co LTD, Shanghai, Peoples R China.; Xu, H (通讯作者)，Shanghai Ninth Peoples Hosp, Dept Dermatol, 639 Mfg Bur Rd, Shanghai, Peoples R China.
EM rye@inertiabiotech.com; XUHI786@sh9hospital.org.cn
FU industry Support Foundation of Huangpu District, Shanghai, Big Data
   Construction and Application of Artificial Intelligence Skin Diagnosis
   and Treatment [XK2020007]
FX This work was supported by the industry Support Foundation of Huangpu
   District, Shanghai, Big Data Construction and Application of Artificial
   Intelligence Skin Diagnosis and Treatment (XK2020007).
CR Dibernardo BE, 2013, J COSMET LASER THER, V15, P56, DOI 10.3109/14764172.2012.758383
   Dobos G, 2015, BRIT J DERMATOL, V172, P1249, DOI 10.1111/bjd.13509
   Fang RY, 2021, J COSMET DERMATOL-US, V20, P338, DOI 10.1111/jocd.13486
   Flament F, 2020, SKIN RES TECHNOL, V26, P905, DOI 10.1111/srt.12893
   Gao Q, 2011, SKIN RES TECHNOL, V17, P420, DOI 10.1111/j.1600-0846.2011.00513.x
   HARMAN D, 1991, P NATL ACAD SCI USA, V88, P5360, DOI 10.1073/pnas.88.12.5360
   Huertas ACM, 2016, BIOCHIMIE, V128, P163, DOI 10.1016/j.biochi.2016.08.010
   Kazanci A, 2017, SKIN RES TECHNOL, V23, P48, DOI 10.1111/srt.12300
   Kim E, 2013, SKIN RES TECHNOL, V19, P236, DOI 10.1111/srt.12020
   Krutmann J, 2017, J DERMATOL SCI, V85, P152, DOI 10.1016/j.jdermsci.2016.09.015
   Lee Sang-Un, 2017, [The Journal of The Institute of Internet, Broadcasting and Communication, 한국인터넷방송통신학회 논문지], V17, P1, DOI 10.7236/JIIBC.2017.17.3.1
   Lehallier B, 2019, NAT MED, V25, P1843, DOI 10.1038/s41591-019-0673-2
   Messaraa C, 2019, INT J COSMETIC SCI, V41, P79, DOI 10.1111/ics.12514
   Nam JH, 2017, LASER MED SCI, V32, P109, DOI 10.1007/s10103-016-2092-7
   Nouveau-Richard S, 2005, J DERMATOL SCI, V40, P187, DOI 10.1016/j.jdermsci.2005.06.006
   Poetschke Julian, 2016, MMW Fortschr Med, V158 Suppl 4, P1, DOI 10.1007/s15006-016-8302-1
   Porcheron A, 2014, INT J COSMETIC SCI, V36, P312, DOI 10.1111/ics.12128
   Puizina-Ivic N, 2008, ACTA DERMATOVEN ALP, V17, P47
   Sattler G, 2012, DERMATOL SURG, V38, P343, DOI 10.1111/j.1524-4725.2011.02253.x
   Tsukahara K, 2007, J DERMATOL SCI, V47, P19, DOI 10.1016/j.jdermsci.2007.03.007
   Vierkotter A, 2016, J DERMATOL SCI, V83, P219, DOI [10.1016/j.jdermsci.2016.05:011, 10.1016/j.jdermsci.2016.05.011]
   Wang Y, 2013, J COSMET LASER THER, V15, P237, DOI 10.3109/14764172.2013.769273
   Wang Y, 2013, AM J CLIN DERMATOL, V14, P147, DOI 10.1007/s40257-013-0009-9
   Xie XY, 2018, J COSMET DERMATOL-US, V17, P935, DOI 10.1111/jocd.12762
   Ye R, 2021, J COSMET DERMATOL-US, V20, P256, DOI 10.1111/jocd.13461
   Yoo MA, 2016, SKIN RES TECHNOL, V22, P69, DOI 10.1111/srt.12230
   Zhang JC, 2017, J BIOMED RES, V31, P108, DOI 10.7555/JBR.31.20150175
   Zhang SB, 2018, CELL TRANSPLANT, V27, P729, DOI 10.1177/0963689717725755
NR 28
TC 0
Z9 0
U1 4
U2 4
PU DOVE MEDICAL PRESS LTD
PI ALBANY
PA PO BOX 300-008, ALBANY, AUCKLAND 0752, NEW ZEALAND
SN 1178-7015
J9 CLIN COSMET INV DERM
JI CLIN. COSMET. INVESTIG. DERMATOL.
PY 2022
VL 15
BP 631
EP 642
DI 10.2147/CCID.S349401
PG 12
WC Dermatology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Dermatology
GA 0W8SC
UT WOS:000789289500002
PM 35444440
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Li, X
   Li, Y
   Li, GJ
AF Li, Xia
   Li, Yang
   Li, Guojin
TI A scientometric review of the research on the impacts of climate change
   on water quality during 1998-2018
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Review
DE Climate change; Water quality; Scientometrics; Bibliometrics; Hotspots;
   Prediction
ID DISSOLVED ORGANIC-CARBON; MACKENZIE DELTA REGION; RIVER-ICE HYDROLOGY;
   GREAT-BARRIER-REEF; LAND-USE CHANGES; POTENTIAL IMPACTS;
   ASSESSMENT-TOOL; CHESAPEAKE BAY; CORAL-REEFS; ECOSYSTEM SERVICES
AB Research on the impacts of climate change on water quality helps to better formulate water quality strategies under the challenge of an uncertain future, which is critical for human survival and development. As a result, in recent years, there has been growing attention given to research in the field, and the attention has led to an increasing number of publications, which is why a systematic literature review on this topic has been proposed in the current paper. This study reviewed 2998 related articles extracted from the Science Citation Index-Expanded (SCI-E) database from 1998 to 2018 to analyse and visualize historical trend evolution, current research hotspots, and promising ideas for future research by combining a traditional literature review, bibliometric analysis, and scientific knowledge mapping. The results revealed that the impacts of climate change on water quality mainly included the aggravation of eutrophication, changes in the flow, hydrological and thermal conditions, and the destruction of ecosystems and biodiversity. Further exploration of the influence mechanism of climate change on cyanobacteria is an emerging research topic. Additionally, the water quality conditions of shallow lakes and drinking water are promising future research objects. In the context of climate change, the general rules of water quality management and the scientific planning of land use are of great significance and need to be further studied. This study provides a practical and valuable reference for researchers to help with the selection of future research topics, which may contribute to further development in this field.
C1 [Li, Xia; Li, Yang] Tianjin Univ Technol, Sch Environm Sci & Safety Engn, Tianjin 300384, Peoples R China.
   [Li, Guojin] Tianjin Municipal Engn Design & Res Inst, Tianjin 300392, Peoples R China.
RP Li, X (通讯作者)，Tianjin Univ Technol, Sch Environm Sci & Safety Engn, Tianjin 300384, Peoples R China.
EM tjutlixia@163.com; 173138306@stud.tjut.edu.cn; 18222571677@163.com
OI Li, Xia/0000-0002-8608-9549
FU the National Science Foundation of China [No.51178303] Funding Source:
   Medline; the Tianjin Key Laboratory of Hazardous Waste Safety Disposal
   and Recycling Technology [No] Funding Source: Medline; the Training
   Program for Innovative Research Team in Tianjin Institutions of Higher
   Education [TD13-5021] Funding Source: Medline
CR Abbaspour KC, 2015, J HYDROL, V524, P733, DOI 10.1016/j.jhydrol.2015.03.027
   Adrian R, 2009, LIMNOL OCEANOGR, V54, P2283, DOI 10.4319/lo.2009.54.6_part_2.2283
   Anthony KRN, 2007, LIMNOL OCEANOGR, V52, P716, DOI 10.4319/lo.2007.52.2.0716
   Arnell NW, 1998, CLIMATIC CHANGE, V39, P83, DOI 10.1023/A:1005339412565
   Ateweberhan M, 2013, MAR POLLUT BULL, V74, P526, DOI 10.1016/j.marpolbul.2013.06.011
   Ayres MP, 2000, SCI TOTAL ENVIRON, V262, P263, DOI 10.1016/S0048-9697(00)00528-3
   Bartolini M, 2019, J CLEAN PROD, V226, P242, DOI 10.1016/j.jclepro.2019.04.055
   Beltaos S, 2000, HYDROL PROCESS, V14, P1613, DOI 10.1002/1099-1085(20000630)14:9&lt;1613::AID-HYP73&gt;3.0.CO;2-V
   Beltaos S, 2009, HYDROL PROCESS, V23, P122, DOI 10.1002/hyp.7165
   Bierbaum R, 2002, ESTUARIES, V25, P878, DOI 10.1007/BF02804913
   Boesch DF, 2002, ESTUARIES, V25, P886, DOI 10.1007/BF02804914
   Boesch DF, 2001, J ENVIRON QUAL, V30, P303, DOI 10.2134/jeq2001.302303x
   Boorman DB, 2003, SCI TOTAL ENVIRON, V314, P411, DOI 10.1016/S0048-9697(03)00066-4
   Borah DK, 2004, T ASAE, V47, P789, DOI [10.13031/2013.16110, 10.13031/2013.15644]
   Bosch NS, 2014, J GREAT LAKES RES, V40, P581, DOI 10.1016/j.jglr.2014.04.011
   Bouraoui F, 2002, HYDROL EARTH SYST SC, V6, P197, DOI 10.5194/hess-6-197-2002
   Bragg OM, 2002, SCI TOTAL ENVIRON, V294, P111, DOI 10.1016/S0048-9697(02)00059-1
   Brodie JE, 2012, MAR POLLUT BULL, V65, P81, DOI 10.1016/j.marpolbul.2011.12.012
   Brodie J, 2012, ESTUAR COAST SHELF S, V104, P1, DOI 10.1016/j.ecss.2012.03.012
   Brooks AS, 2002, J GREAT LAKES RES, V28, P597, DOI 10.1016/S0380-1330(02)70608-4
   Broomhall SD, 2004, J APPL ECOL, V41, P105, DOI 10.1111/j.1365-2664.2004.00883.x
   Butcher JB, 2015, CLIMATIC CHANGE, V129, P295, DOI 10.1007/s10584-015-1326-1
   Butler JRA, 2013, AGR ECOSYST ENVIRON, V180, P176, DOI 10.1016/j.agee.2011.08.017
   Caissie D, 2006, FRESHWATER BIOL, V51, P1389, DOI 10.1111/j.1365-2427.2006.01597.x
   Callaway R, 2012, AQUAT CONSERV, V22, P389, DOI 10.1002/aqc.2247
   Caloiero T, 2011, INT J CLIMATOL, V31, P44, DOI 10.1002/joc.2055
   Cardoso PG, 2008, ESTUAR COAST SHELF S, V76, P553, DOI 10.1016/j.ecss.2007.07.026
   Carr J, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001103
   Caruso BS, 2001, HYDROLOG SCI J, V46, P677, DOI 10.1080/02626660109492864
   Chang HJ, 2001, J AM WATER RESOUR AS, V37, P973, DOI 10.1111/j.1752-1688.2001.tb05526.x
   Charron DF, 2004, J TOXICOL ENV HEAL A, V67, P1667, DOI 10.1080/15287390490492313
   Chen CM, 2014, EXPERT OPIN BIOL TH, V14, P1295, DOI 10.1517/14712598.2014.920813
   Choi W, 2008, J ENVIRON MANAGE, V88, P1119, DOI 10.1016/j.jenvman.2007.06.001
   Clarke GKC, 2015, NAT GEOSCI, V8, P372, DOI [10.1038/NGEO2407, 10.1038/ngeo2407]
   Crossman J, 2014, HYDROL EARTH SYST SC, V18, P5125, DOI 10.5194/hess-18-5125-2014
   Crossman J, 2013, J GREAT LAKES RES, V39, P19, DOI 10.1016/j.jglr.2012.11.003
   Daufresne M, 2007, OECOLOGIA, V151, P544, DOI 10.1007/s00442-006-0655-1
   Davies EGR, 2011, ADV WATER RESOUR, V34, P684, DOI 10.1016/j.advwatres.2011.02.010
   Day JW, 2003, BIOTECHNOL ADV, V22, P135, DOI 10.1016/j.biotechadv.2003.08.012
   De'ath G, 2012, P NATL ACAD SCI USA, V109, P17995, DOI 10.1073/pnas.1208909109
   Deal RL, 2012, FOREST POLICY ECON, V17, P69, DOI 10.1016/j.forpol.2011.12.007
   Delpla I, 2009, ENVIRON INT, V35, P1225, DOI 10.1016/j.envint.2009.07.001
   Delucchi MA, 2010, ANN NY ACAD SCI, V1195, P28, DOI 10.1111/j.1749-6632.2010.05457.x
   Deng JM, 2014, FRESHWATER BIOL, V59, P1076, DOI 10.1111/fwb.12330
   Deniz N, 2019, J CLEAN PROD, V225, P697, DOI 10.1016/j.jclepro.2019.03.188
   Dornhofer K, 2016, ECOL INDIC, V64, P105, DOI 10.1016/j.ecolind.2015.12.009
   Ducharne A, 2007, SCI TOTAL ENVIRON, V375, P292, DOI 10.1016/j.scitotenv.2006.12.011
   Duncker P S, 2012, ECOL SOC, V17, P17
   Dupont S, 2010, ECOTOXICOLOGY, V19, P449, DOI 10.1007/s10646-010-0463-6
   Durance I, 2009, FRESHWATER BIOL, V54, P388, DOI 10.1111/j.1365-2427.2008.02112.x
   Dwight RH, 2002, WATER ENVIRON RES, V74, P82, DOI 10.2175/106143002X139776
   Eckhardt K, 2003, J HYDROL, V284, P244, DOI 10.1016/j.jhydrol.2003.08.005
   El-Khoury A, 2015, J ENVIRON MANAGE, V151, P76, DOI 10.1016/j.jenvman.2014.12.012
   Elliott JA, 2006, HYDROBIOLOGIA, V559, P401, DOI 10.1007/s10750-005-1233-y
   Eshel G, 2014, P NATL ACAD SCI USA, V111, P11996, DOI 10.1073/pnas.1402183111
   Etheridge JR, 2014, HYDROL EARTH SYST SC, V18, P1467, DOI 10.5194/hess-18-1467-2014
   Evans CD, 2005, ENVIRON POLLUT, V137, P55, DOI 10.1016/j.envpol.2004.12.031
   Fang X, 1999, ECOL MODEL, V118, P167, DOI 10.1016/S0304-3800(99)00018-6
   Fenner N, 2005, SOIL BIOL BIOCHEM, V37, P1277, DOI 10.1016/j.soilbio.2004.11.024
   Fenner N, 2005, SOIL BIOL BIOCHEM, V37, P1814, DOI 10.1016/j.soilbio.2005.02.032
   Fenner N, 2001, SOIL USE MANAGE, V17, P106, DOI 10.1111/j.1475-2743.2001.tb00015.x
   Ficklin DL, 2009, J HYDROL, V374, P16, DOI 10.1016/j.jhydrol.2009.05.016
   Finn RN, 2007, AQUAT TOXICOL, V81, P337, DOI 10.1016/j.aquatox.2006.12.021
   Fitt WK, 2001, CORAL REEFS, V20, P51, DOI 10.1007/s003380100146
   Fu GB, 2004, CLIMATIC CHANGE, V65, P149, DOI 10.1023/B:CLIM.0000037491.95395.bb
   Fujita M, 2014, CHEMOSPHERE, V95, P628, DOI 10.1016/j.chemosphere.2013.10.023
   Gassman PW, 2007, T ASABE, V50, P1211, DOI 10.13031/2013.23637
   Gelfand I, 2013, NATURE, V493, P514, DOI 10.1038/nature11811
   Gelfand I, 2011, P NATL ACAD SCI USA, V108, P13864, DOI 10.1073/pnas.1017277108
   Glassley WE, 2003, COMPUT GEOSCI-UK, V29, P399, DOI 10.1016/S0098-3004(03)00014-1
   Goldstein JH, 2012, P NATL ACAD SCI USA, V109, P7565, DOI 10.1073/pnas.1201040109
   Graham NAJ, 2013, FRONT ECOL ENVIRON, V11, P541, DOI 10.1890/120305
   Gulati RD, 2002, HYDROBIOLOGIA, V478, P73, DOI 10.1023/A:1021092427559
   Gunn JM, 2002, ENVIRON BIOL FISH, V64, P343, DOI 10.1023/A:1016058606770
   Haapkyla J, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0016893
   Hanak E, 2012, CLIMATIC CHANGE, V111, P17, DOI 10.1007/s10584-011-0241-3
   Hanratty MP, 1998, J ENVIRON QUAL, V27, P1524, DOI 10.2134/jeq1998.00472425002700060032x
   Hassan H, 1998, WATER SCI TECHNOL, V37, P177, DOI 10.1016/S0273-1223(98)00022-5
   Havens KE, 2018, INLAND WATERS, V8, P1, DOI 10.1080/20442041.2018.1428429
   He K, 2019, SCI TOTAL ENVIRON, V670, P236, DOI 10.1016/j.scitotenv.2019.03.184
   Herbert ER, 2015, ECOSPHERE, V6, DOI 10.1890/ES14-00534.1
   Hoegh-Guldberg O, 2007, SCIENCE, V318, P1737, DOI 10.1126/science.1152509
   Hoegh-Guldberg O, 2011, REG ENVIRON CHANGE, V11, pS215, DOI 10.1007/s10113-010-0189-2
   Holdich DM, 2009, KNOWL MANAG AQUAT EC, DOI 10.1051/kmae/2009025
   Howarth RW, 2008, HARMFUL ALGAE, V8, P14, DOI 10.1016/j.hal.2008.08.015
   Hughes TP, 2007, CURR BIOL, V17, P360, DOI 10.1016/j.cub.2006.12.049
   Hughes TP, 2017, NATURE, V543, P373, DOI 10.1038/nature21707
   Interlandi SJ, 2003, WATER RES, V37, P1737, DOI 10.1016/S0043-1354(02)00574-2
   Jackson-Blake LA, 2016, ENVIRON MODELL SOFTW, V83, P356, DOI 10.1016/j.envsoft.2016.05.022
   Jarvie HP, 2013, J ENVIRON QUAL, V42, P295, DOI 10.2134/jeq2012.0085
   Jeppesen E, 2009, J ENVIRON QUAL, V38, P1930, DOI 10.2134/jeq2008.0113
   Jin L, 2013, INLAND WATERS, V3, P207, DOI 10.5268/IW-3.2.520
   Jin L, 2018, SCI TOTAL ENVIRON, V637, P1069, DOI 10.1016/j.scitotenv.2018.04.350
   Jin L, 2018, SCI TOTAL ENVIRON, V637, P907, DOI 10.1016/j.scitotenv.2018.04.349
   Johnson AC, 2009, SCI TOTAL ENVIRON, V407, P4787, DOI 10.1016/j.scitotenv.2009.05.018
   Kadiri M, 2012, RENEW SUST ENERG REV, V16, P329, DOI 10.1016/j.rser.2011.07.160
   Kahya E, 2004, J HYDROL, V289, P128, DOI 10.1016/j.jhydrol.2003.11.006
   Kan HD, 2012, ENVIRON INT, V42, P10, DOI 10.1016/j.envint.2011.03.003
   Kane DD, 2014, J GREAT LAKES RES, V40, P496, DOI 10.1016/j.jglr.2014.04.004
   Kapetsky JM, 2000, AGR FOREST METEOROL, V103, P109, DOI 10.1016/S0168-1923(00)00106-4
   Khaliq MN, 2006, J HYDROL, V329, P534, DOI 10.1016/j.jhydrol.2006.03.004
   Kim K, 2004, AM NAT, V164, pS52, DOI 10.1086/424609
   Ko C, 2004, COMPUT GEOSCI-UK, V30, P379, DOI 10.1016/j.cageo.2003.06.002
   Kokelj SV, 2013, PERMAFROST PERIGLAC, V24, P108, DOI 10.1002/ppp.1779
   Kokelj SV, 2009, PERMAFROST PERIGLAC, V20, P185, DOI 10.1002/ppp.641
   Kokelj SV, 2005, PERMAFROST PERIGLAC, V16, P343, DOI 10.1002/ppp.536
   Komatsu E, 2007, ECOL MODEL, V209, P351, DOI 10.1016/j.ecolmodel.2007.07.021
   Kraemer BM, 2015, GEOPHYS RES LETT, V42, P4981, DOI 10.1002/2015GL064097
   Krysanova V, 2005, HYDROL PROCESS, V19, P763, DOI 10.1002/hyp.5619
   Lal R, 2008, ENERG ENVIRON SCI, V1, P86, DOI 10.1039/b809492f
   Lal R, 2007, SOIL SCI, V172, P943, DOI 10.1097/ss.0b013e31815cc498
   Lal R, 2004, GEODERMA, V123, P1, DOI 10.1016/j.geoderma.2004.01.032
   Lal R, 2011, J SOIL WATER CONSERV, V66, P276, DOI 10.2489/jswc.66.4.276
   Lal R, 2015, SUSTAINABILITY-BASEL, V7, P5875, DOI 10.3390/su7055875
   Lal R, 2010, BIOSCIENCE, V60, P708, DOI 10.1525/bio.2010.60.9.8
   Larsen S, 2011, GLOBAL CHANGE BIOL, V17, P1186, DOI 10.1111/j.1365-2486.2010.02257.x
   Lehman JT, 2002, J GREAT LAKES RES, V28, P583, DOI 10.1016/S0380-1330(02)70607-2
   Lepisto A, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003175
   Li X, 2017, ENVIRON SCI POLLUT R, V24, P17103, DOI 10.1007/s11356-017-9294-9
   Luo YZ, 2013, SCI TOTAL ENVIRON, V450, P72, DOI 10.1016/j.scitotenv.2013.02.004
   Mackay AW, 1998, PHILOS T R SOC B, V353, P1011, DOI 10.1098/rstb.1998.0264
   Mantyka-Pringle CS, 2014, J APPL ECOL, V51, P572, DOI 10.1111/1365-2664.12236
   Marshall E, 2008, CLIMATIC CHANGE, V89, P263, DOI 10.1007/s10584-007-9389-2
   Matthews WJ, 2003, FRESHWATER BIOL, V48, P1232, DOI 10.1046/j.1365-2427.2003.01087.x
   Mccullough DA, 2009, REV FISH SCI, V17, P90, DOI 10.1080/10641260802590152
   Meng F, 2018, WATER RES, V143, P376, DOI 10.1016/j.watres.2018.06.048
   Meyer JL, 1999, J AM WATER RESOUR AS, V35, P1373, DOI 10.1111/j.1752-1688.1999.tb04222.x
   Michelutti N, 2003, GLOBAL PLANET CHANGE, V38, P257, DOI 10.1016/S0921-8181(02)00260-6
   Mitsch WJ, 2012, BIOSCIENCE, V62, P237, DOI 10.1525/bio.2012.62.3.5
   Molina-Navarro E, 2014, J HYDROL, V509, P354, DOI 10.1016/j.jhydrol.2013.11.053
   Moore KA, 2008, J COASTAL RES, P135, DOI 10.2112/SI55-014
   Moore RD, 2009, HYDROL PROCESS, V23, P42, DOI 10.1002/hyp.7162
   Mortsch LD, 1998, CLIMATIC CHANGE, V40, P391, DOI 10.1023/A:1005445709728
   Mosley LM, 2015, EARTH-SCI REV, V140, P203, DOI 10.1016/j.earscirev.2014.11.010
   Mosley LM, 2012, WATER RESOUR MANAG, V26, P3923, DOI 10.1007/s11269-012-0113-2
   Murdoch PS, 2000, J AM WATER RESOUR AS, V36, P347, DOI 10.1111/j.1752-1688.2000.tb04273.x
   Najjar RG, 2010, ESTUAR COAST SHELF S, V86, P1, DOI 10.1016/j.ecss.2009.09.026
   Najjar RG, 2000, CLIMATE RES, V14, P219, DOI 10.3354/cr014219
   Neff R, 2000, CLIMATE RES, V14, P207, DOI 10.3354/cr014207
   Newton A, 2003, CONT SHELF RES, V23, P1945, DOI 10.1016/j.csr.2003.06.008
   Novotny EV, 2007, J HYDROL, V334, P319, DOI 10.1016/j.jhydrol.2006.10.011
   O'Neil JM, 2012, HARMFUL ALGAE, V14, P313, DOI 10.1016/j.hal.2011.10.027
   Olden JD, 2010, FRESHWATER BIOL, V55, P86, DOI 10.1111/j.1365-2427.2009.02179.x
   Orth RJ, 2010, ESTUAR COAST, V33, P139, DOI [10.1007/s12237-010-9311-4, 10.1007/s12237-009-9234-0]
   Ouyang W, 2018, SCI TOTAL ENVIRON, V637, P208, DOI 10.1016/j.scitotenv.2018.04.434
   Ozaki N, 2003, HYDROL PROCESS, V17, P2837, DOI 10.1002/hyp.1437
   Paerl HW, 2016, HARMFUL ALGAE, V54, P213, DOI 10.1016/j.hal.2015.09.009
   Paerl HW, 2012, WATER RES, V46, P1349, DOI 10.1016/j.watres.2011.08.002
   Paerl HW, 2011, SCI TOTAL ENVIRON, V409, P1739, DOI 10.1016/j.scitotenv.2011.02.001
   Paerl HW, 2009, ENV MICROBIOL REP, V1, P27, DOI 10.1111/j.1758-2229.2008.00004.x
   Palm C, 2014, AGR ECOSYST ENVIRON, V187, P87, DOI 10.1016/j.agee.2013.10.010
   Park JH, 2010, ENVIRON INT, V36, P212, DOI 10.1016/j.envint.2009.10.008
   Parry LE, 2014, J ENVIRON MANAGE, V133, P193, DOI 10.1016/j.jenvman.2013.11.033
   Passell HD, 2004, J AM WATER RESOUR AS, V40, P111, DOI 10.1111/j.1752-1688.2004.tb01014.x
   Pathak D, 2018, SCI TOTAL ENVIRON, V631-632, P201, DOI 10.1016/j.scitotenv.2018.03.022
   Pearce DW, 2001, ECOSYST HEALTH, V7, P284, DOI 10.1046/j.1526-0992.2001.01037.x
   Philibert A, 2003, J PALEOLIMNOL, V30, P167, DOI 10.1023/A:1025526200880
   Pilgrim JM, 1998, J AM WATER RESOUR AS, V34, P1109, DOI 10.1111/j.1752-1688.1998.tb04158.x
   Powlson DS, 2005, ANN APPL BIOL, V146, P193, DOI 10.1111/j.1744-7348.2005.040056.x
   Praskievicz S, 2009, PROG PHYS GEOG, V33, P650, DOI 10.1177/0309133309348098
   Pratchett MS, 2015, OCEANOGR MAR BIOL, V53, P215
   Preston BL, 2004, ENVIRON MANAGE, V34, P125, DOI 10.1007/s00267-004-0159-x
   Price K, 2011, PROG PHYS GEOG, V35, P465, DOI 10.1177/0309133311402714
   Prowse TD, 2002, HYDROL PROCESS, V16, P805, DOI 10.1002/hyp.369
   Przeslawski R, 2008, GLOBAL CHANGE BIOL, V14, P2773, DOI 10.1111/j.1365-2486.2008.01693.x
   Qu XL, 2013, WATER RES, V47, P3931, DOI 10.1016/j.watres.2012.09.058
   Qu XL, 2013, ACCOUNTS CHEM RES, V46, P834, DOI 10.1021/ar300029v
   Quinlan R, 2002, J PALEOLIMNOL, V27, P249, DOI 10.1023/A:1014219629845
   Quinn NWT, 2001, ADV ENVIRON RES, V5, P309, DOI 10.1016/S1093-0191(01)00081-8
   Rabalais NN, 2009, ICES J MAR SCI, V66, P1528, DOI 10.1093/icesjms/fsp047
   Reichwaldt ES, 2012, WATER RES, V46, P1372, DOI 10.1016/j.watres.2011.11.052
   Richter A, 2005, REV FISH SCI, V13, P23, DOI 10.1080/10641260590885861
   Rolls RJ, 2012, FRESHW SCI, V31, P1163, DOI 10.1899/12-002.1
   Romero E, 2013, BIOGEOCHEMISTRY, V113, P481, DOI 10.1007/s10533-012-9778-0
   Rose JB, 2000, J AM WATER WORKS ASS, V92, P77
   Ruhland KM, 2015, J PALEOLIMNOL, V54, P1, DOI 10.1007/s10933-015-9837-3
   Ruhland K, 2003, ARCT ANTARCT ALP RES, V35, P110, DOI 10.1657/1523-0430(2003)035[0110:PEFDFR]2.0.CO;2
   Salvadore E, 2015, J HYDROL, V529, P62, DOI 10.1016/j.jhydrol.2015.06.028
   Sandor R, 2002, PHILOS T ROY SOC A, V360, P1889, DOI 10.1098/rsta.2002.1038
   Sandor RL, 2002, PHILOS T ROY SOC A, V360, P1607, DOI 10.1098/rsta.2002.1022
   Santos RMB, 2015, SCI TOTAL ENVIRON, V511, P477, DOI 10.1016/j.scitotenv.2014.12.090
   Saros JE, 2003, ARCT ANTARCT ALP RES, V35, P18, DOI 10.1657/1523-0430(2003)035[0018:RCITDC]2.0.CO;2
   Schmalz B, 2015, ECOHYDROLOGY, V8, P1119, DOI 10.1002/eco.1569
   Schneider C, 2013, HYDROL EARTH SYST SC, V17, P325, DOI 10.5194/hess-17-325-2013
   Schoumans OF, 2015, AMBIO, V44, pS180, DOI 10.1007/s13280-014-0613-9
   Sinha E, 2017, SCIENCE, V357, P405, DOI 10.1126/science.aan2409
   Smol JP, 2010, FRESHWATER BIOL, V55, P43, DOI 10.1111/j.1365-2427.2009.02373.x
   Souty-Grosset C, 2016, LIMNOLOGICA, V58, P78, DOI 10.1016/j.limno.2016.03.003
   Steele-Dunne S, 2008, J HYDROL, V356, P28, DOI 10.1016/j.jhydrol.2008.03.025
   Strecker AL, 2004, LIMNOL OCEANOGR, V49, P1182, DOI 10.4319/lo.2004.49.4.1182
   Sukenik A, 2012, FRONT MICROBIOL, V3, DOI 10.3389/fmicb.2012.00086
   Sun G, 2008, J AM WATER RESOUR AS, V44, P1441, DOI 10.1111/j.1752-1688.2008.00250.x
   Tabari H, 2011, GLOBAL PLANET CHANGE, V79, P1, DOI 10.1016/j.gloplacha.2011.07.008
   Taranu ZE, 2015, ECOL LETT, V18, P375, DOI 10.1111/ele.12420
   Thiel R, 2003, J APPL ICHTHYOL, V19, P330, DOI 10.1046/j.1439-0426.2003.00474.x
   Thomson JR, 2012, GLOBAL CHANGE BIOL, V18, P1582, DOI 10.1111/j.1365-2486.2011.02609.x
   Tisseuil C, 2008, J ENVIRON QUAL, V37, P2155, DOI 10.2134/jeq2007.0507
   Tong STY, 2002, J ENVIRON MANAGE, V66, P377, DOI 10.1006/jema.2002.0593
   Trolle D, 2011, ENVIRON MODELL SOFTW, V26, P354, DOI 10.1016/j.envsoft.2010.08.009
   Tu J, 2009, J HYDROL, V379, P268, DOI 10.1016/j.jhydrol.2009.10.009
   Tuppad P, 2011, T ASABE, V54, P1677, DOI 10.13031/2013.39856
   Unsworth RKF, 2015, MAR POLLUT BULL, V100, P34, DOI 10.1016/j.marpolbul.2015.08.016
   van Katwijk MM, 2016, J APPL ECOL, V53, P567, DOI 10.1111/1365-2664.12562
   van Leeuwen CJ, 2012, WATER RESOUR MANAG, V26, P2177, DOI 10.1007/s11269-012-0009-1
   van Vliet MTH, 2008, J HYDROL, V353, P1, DOI 10.1016/j.jhydrol.2008.01.001
   van Vliet MTH, 2013, GLOBAL ENVIRON CHANG, V23, P450, DOI 10.1016/j.gloenvcha.2012.11.002
   van Woesik R, 2012, ECOL EVOL, V2, P2474, DOI 10.1002/ece3.363
   Varanou E, 2002, J HYDROL ENG, V7, P228, DOI 10.1061/(ASCE)1084-0699(2002)7:3(228)
   Veron JEN, 2009, MAR POLLUT BULL, V58, P1428, DOI 10.1016/j.marpolbul.2009.09.009
   Vichi M, 2003, CLIMATE RES, V24, P141, DOI 10.3354/cr024141
   Visser PM, 2016, HARMFUL ALGAE, V54, P145, DOI 10.1016/j.hal.2015.12.006
   Wade AJ, 2008, SCI TOTAL ENVIRON, V400, P52, DOI 10.1016/j.scitotenv.2008.04.030
   Wade AJ, 2006, J HYDROL, V330, P185, DOI 10.1016/j.jhydrol.2006.04.023
   Wang S, 2012, WATER RES, V46, P2591, DOI 10.1016/j.watres.2012.02.014
   Waycott M, 2009, P NATL ACAD SCI USA, V106, P12377, DOI 10.1073/pnas.0905620106
   Wernberg T, 2010, ECOL LETT, V13, P685, DOI 10.1111/j.1461-0248.2010.01466.x
   Whitehead PG, 2006, SCI TOTAL ENVIRON, V365, P260, DOI 10.1016/j.scitotenv.2006.02.040
   Whitehead PG, 2013, PHILOS T R SOC A, V371, DOI 10.1098/rsta.2012.0413
   Whitehead PG, 2009, HYDROLOG SCI J, V54, P101, DOI 10.1623/hysj.54.1.101
   Whitworth KL, 2012, J HYDROL, V450, P190, DOI 10.1016/j.jhydrol.2012.04.057
   Wilby RL, 2006, J HYDROL, V330, P204, DOI 10.1016/j.jhydrol.2006.04.033
   Wilson CO, 2011, SCI TOTAL ENVIRON, V409, P4387, DOI 10.1016/j.scitotenv.2011.07.001
   Wooldridge SA, 2009, MAR POLLUT BULL, V58, P745, DOI 10.1016/j.marpolbul.2008.12.013
   Worrall F, 2004, BIOGEOCHEMISTRY, V70, P369, DOI 10.1007/s10533-004-8131-7
   Worrall F, 2003, WATER RESOUR RES, V39, P14
   Wu L, 2012, J HYDROL, V475, P26, DOI 10.1016/j.jhydrol.2012.08.022
   Wu YP, 2012, SCI TOTAL ENVIRON, V430, P150, DOI 10.1016/j.scitotenv.2012.04.058
   Yahner RH, 2003, NORTHEAST NAT, V10, P343, DOI 10.1656/1092-6194(2003)010[0343:TVIPSA]2.0.CO;2
   Yu DJ, 2017, INFORM SCIENCES, V418, P619, DOI 10.1016/j.ins.2017.08.031
   Yue S, 2003, THEOR APPL CLIMATOL, V75, P15, DOI 10.1007/s00704-002-0717-1
   Zakkour PD, 2002, ENVIRON TECHNOL, V23, P823, DOI 10.1080/09593332308618375
   Zakkour PD, 2002, J ENVIRON MANAGE, V66, P105, DOI 10.1006/jema.2001.0549
   Zhou QQ, 2014, WATER-SUI, V6, P976, DOI 10.3390/w6040976
NR 233
TC 8
Z9 8
U1 5
U2 85
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
PD MAY
PY 2020
VL 27
IS 13
BP 14322
EP 14341
DI 10.1007/s11356-020-08176-7
PG 20
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA LP3KX
UT WOS:000534218600007
PM 32152856
DA 2022-08-23
ER

PT J
AU Zhan, XL
   Huang, YY
   Dong, X
   Cao, QX
   Liang, XD
AF Zhan, Xunlin
   Huang, Yinya
   Dong, Xiao
   Cao, Qingxing
   Liang, Xiaodan
TI PathReasoner: Explainable reasoning paths for commonsense question
   answering
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Commonsense reasoning; Knowledge graph; Reasoning path; Interpretability
AB Commonsense question answering has attracted increasing attention as a challenging task requiring the human reasoning process of answering questions with the help of abundant commonsense knowledge. Existing methods mostly resort to large pre-trained language models and face many difficulties when dealing with the out-of-scope reasoning target, and are unaware of explainable structured information. In this paper, we explore explicitly incorporate external reasoning paths with structured information to explain and facilitate commonsense QA. For this purpose, we propose a PathReasoner to both extract and learn from such structured information. The proposed PathReasoner consists of two main components, a path finder and a hierarchical path learner. To answer a commonsense question, the path finder first retrieves explainable reasoning paths from a large-scale knowledge graph, then the path learner encodes the paths with hierarchical encoders and uses the path features to predict the answers. The experiments on two typical commonsense QA datasets demonstrate the effectiveness of the PathReasoner. The case study gives insightful findings that the reasoning paths provide explainable information for the question answering through the PathReasoner. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Zhan, Xunlin; Huang, Yinya; Cao, Qingxing; Liang, Xiaodan] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510000, Peoples R China.
   [Dong, Xiao] Sun Yat Sen Univ, Sch Artificial Intelligence, Zhuhai 528478, Peoples R China.
RP Liang, XD (通讯作者)，Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510000, Peoples R China.
EM zhanxlin@mail2.sysu.edu.cn; yinya.el.huang@gmail.com;
   dx.icandoit@gmail.com; caoqx@mail2.sysu.edu.cn; xdliang328@gmail.com
OI Cao, Qingxing/0000-0001-7042-6726; Zhan, Xunlin/0000-0001-5053-7349
FU National Natural Science Foundation of China [61976233, U19A2073];
   Guangdong Provincial Natural Science Foundation of China
   [2019B1515120039]
FX This work is supported by the National Natural Science Foundation of
   China (Grant No. 61976233 and U19A2073), Guangdong Provincial Natural
   Science Foundation of China (Grant No. 2019B1515120039).
CR Asai Akari, 2020, 8 INT C LEARN REPR I
   Assante M, 2019, FUTURE GENER COMP SY, V101, P555, DOI 10.1016/j.future.2019.05.063
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Boratko M, 2020, P 2020 C EMP METH NA, P1122, DOI [10.18653/v1/2020.emnlpmain, DOI 10.18653/V1/2020.EMNLPMAIN]
   Bordes Antoine, 2015, ARXIV150602075
   Bordes J, 2015, ARXIV14103916
   Bosselut N, 2019, EMNLP
   Cai Q., 2013, P 2 JOINT C LEX COMP, P328
   Cao Y, 2019, LONG SHORT PAPERS, V1, P357
   Chen Q., 2020, P 28 INT C COMP LING, P2583, DOI DOI 10.18653/V1/2020.COLINGMAIN.232
   Devlin J., 2019, BERT PRETRAINING DEE, DOI [10.18653/v1/N19-1423, DOI 10.18653/V1/N19-1423]
   Duan N., ABS210503659 CORR, V2021
   Freund Y, 1997, J COMPUT SYST SCI, V55, P119, DOI 10.1006/jcss.1997.1504
   Ge Q., 2014, P INTERSPEECH, P2635, DOI DOI 10.1016/J.CSL.2015.07.001
   Hermann KM, 2015, ADV NEUR IN, V28
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Huang L, 2019, ADV NEUR IN, V32
   Huang Y., 2020, ARXIV PREPRINT ARXIV
   Kingma D. P., 2017, ARXIV
   Kumar V, 2016, INT CONF ADVAN COMPU
   Levesque H., 2012, P 13 INT C PRINCIPLE
   Li RH, 2021, KNOWL-BASED SYST, V220, DOI 10.1016/j.knosys.2021.106936
   Liang X., 2021, DAGN DISCOURSE AWARE
   Lim J, 2020, P 28 INT C COMP LING, P2459, DOI [10.18653/v1/2020.coling-main.222, DOI 10.18653/V1/2020.COLING-MAIN.222]
   Lin Bill Yuchen, 2019, P 2019 C EMP METH NA, P2829, DOI DOI 10.18653/V1/D19-1282
   Liu Y, 2019, ARXIV PREPRINT ARXIV
   LV S, 2020, AAAI
   Miller Alexander, 2016, EMNLP, DOI [10.18653/v1/D16-1147, DOI 10.18653/V1/D16-1147]
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Moghimifar Farhad, 2020, P 28 INT C COMPUTATI, P5347
   Parikh A., 2016, P 2016 C EMP METH NA, P2249, DOI DOI 10.18653/V1/D16-1244
   Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI 10.3115/v1/D14-1162
   Rajani NF, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4932
   Richardson M., 2013, P 2013 C EMP METH NA, V3, P4
   Sap M, 2019, AAAI CONF ARTIF INTE, P3027
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Sukhbaatar S, 2015, ADV NEUR IN, V28
   Talmor Alon, 2019, P 2019 C N AM CHAPT, V1, P4149
   Thomason J, 2019, IEEE INT CONF ROBOT, P6934, DOI 10.1109/ICRA.2019.8794287
   Trischler A., 2017, P 2 WORKSH REPR LEAR, P191, DOI [DOI 10.18653/V1/W17-2623, 10.18653/v1/W17-2623]
   Welbl J., 2018, IEE T ASS COMPUT LIN, V6, P287, DOI 10.1162/tacl_a_00021
   Weston J., 2016, P 4 INT C LEARN REPR
   Yang Z., 2018, P 2018 C EMP METHNAT, P2369, DOI 10.18653/v1/D18-1259
   Yu J, 2020, PATTERN RECOGN, V108, DOI 10.1016/j.patcog.2020.107563
   Zhang HM, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P201, DOI 10.1145/3366423.3380107
   Zhang T., 2020, INT C LEARN REPR
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
   Zhong WJ, 2019, LECT NOTES ARTIF INT, V11838, P16, DOI 10.1007/978-3-030-32233-5_2
   Zhou M, 2019, ACL
NR 49
TC 1
Z9 1
U1 4
U2 7
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD JAN 10
PY 2022
VL 235
AR 107612
DI 10.1016/j.knosys.2021.107612
EA OCT 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WW8BM
UT WOS:000718134800006
DA 2022-08-23
ER

PT J
AU Lu, JZ
   Ma, JD
   Zheng, XC
   Wang, GX
   Li, H
   Kiritsis, D
AF Lu, Jinzhi
   Ma, Junda
   Zheng, Xiaochen
   Wang, Guoxin
   Li, Han
   Kiritsis, Dimitris
TI Design Ontology Supporting Model-Based Systems Engineering Formalisms
SO IEEE SYSTEMS JOURNAL
LA English
DT Article; Early Access
DE Modeling; Ontologies; Unified modeling language; Tools; Systems
   engineering and theory; Semantics; Data models; Formalism;
   interoperability; knowledge graph; model-based systems engineering;
   ontology
ID INTEGRATION
AB Model-based systems engineering (MBSE) provides an important capability for managing the complexities of system development. MBSE empowers the formalism of system architectures for supporting model-based requirement elicitation, specification, design, development, testing, fielding, etc. However, the modeling languages and techniques are heterogeneous, even within the same enterprise system, which leads to difficulties for data interoperability. The discrepancies among data structures and language syntaxes make information exchange among MBSE models more difficult, resulting in considerable information deviations when connecting data flows across the enterprise. Therefore, this article presents an ontology based upon graphs, objects, points, properties, roles, and relationships with extensions (GOPPRRE), providing metamodels that support the various MBSE formalisms across lifecycle stages. In particular, knowledge graph models are developed to support unified model representations to further implement ontological data integration based on GOPPRRE throughout the entire lifecycle. The applicability of the MBSE formalism is verified using quantitative and qualitative approaches. Moreover, the GOPPRRE ontologies are used to create the MBSE formalisms in a domain-specific modeling tool, MetaGraph, for evaluating its availability. The results demonstrate that the proposed ontology supports the formal structures and descriptive logic of the systems engineering lifecycle.
C1 [Lu, Jinzhi; Zheng, Xiaochen; Kiritsis, Dimitris] Ecole Polytech Fed Lausanne, SCI STI DK Grp, CH-1015 Lausanne, Switzerland.
   [Ma, Junda; Wang, Guoxin; Li, Han] Beijing Inst Technol, Beijing 100081, Peoples R China.
RP Wang, GX (通讯作者)，Beijing Inst Technol, Beijing 100081, Peoples R China.
EM jinzhl@kth.se; mjd2015@sina.cn; xiaochen.zheng@epfl.ch;
   wangguoxin@bit.edu.cn; 3120190349@bit.edu.cn; dimitris.kiritsis@epfl.ch
OI Ma, Junda/0000-0003-4559-6349
FU CN National Key Research and Development Plan [2020YFB1708100]; European
   Union (EU) H2020 [825030]; InnoSwiss IMPULSE project on Digital Twins
FX This work was supported in part by the CN National Key Research and
   Development Plan under Grant 2020YFB1708100, in part by European Union
   (EU) H2020 under Project 825030 QU4LITY Digital Reality in Zero Defect
   Manufacturing, and in part by InnoSwiss IMPULSE project on Digital
   Twins.
CR Arp R, 2015, BUILDING ONTOLOGIES WITH BASIC FORMAL ONTOLOGY, P1, DOI 10.7551/mitpress/9780262527811.001.0001
   Bassi L, 2011, IEEE-ASME T MECH, V16, P1049, DOI 10.1109/TMECH.2010.2073480
   Beihoff B., 2010, P INT COUNCIL SYST E, V327, P1183
   Bezivin J, 2001, 16TH ANNUAL INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2001), PROCEEDINGS, P273, DOI 10.1109/ASE.2001.989813
   Chami Mohammad, 2018, INCOSE International Symposium, V28, P1463, DOI 10.1002/j.2334-5837.2018.00561.x
   Chen JJ, 2020, IEEE ACCESS, V8, P214842, DOI 10.1109/ACCESS.2020.3040229
   Dickerson CE, 2013, IEEE SYST J, V7, P581, DOI 10.1109/JSYST.2013.2253034
   Dori D., 2016, MODEL BASED SYSTEMS
   Fernandez A., 2014, REP
   Guo SZ, 2019, IEEE T IND INFORM, V15, P5435, DOI 10.1109/TII.2019.2904092
   Halstenberg FA, 2019, PROC CIRP, V83, P157, DOI 10.1016/j.procir.2019.02.133
   Hao J, 2019, IEEE ACCESS, V7, P116835, DOI 10.1109/ACCESS.2019.2933544
   Hao J, 2014, DECIS SUPPORT SYST, V61, P106, DOI 10.1016/j.dss.2014.02.001
   Hongwei Wang, 2019, New Knowledge in Information Systems and Technologies. Advances in Intelligent Systems and Computing (930), P426, DOI 10.1007/978-3-030-16181-1_40
   Horrocks I., 2004, W3C MEMBER SUBMISSIO, V21, P79
   Jinzhi Lu, 2020, INCOSE International Symposium, V30, P323, DOI 10.1002/j.2334-5837.2020.00725.x
   Jinzhi Lu, 2018, Trends and Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing (745), P813, DOI 10.1007/978-3-319-77703-0_80
   Kelly S., 1996, ADV INFORM SYSTEMS E, V1080
   Kern H, ACM INT C SYST PROGR, P7
   Kharrat M, 2021, IEEE SYST J, V15, P747, DOI 10.1109/JSYST.2020.2980144
   Kulvatunyou B, 2018, IFIP ADV INF COMM TE, V536, P402, DOI 10.1007/978-3-319-99707-0_50
   Lu J., P 5 INT C IND 4 0 MO, P105
   Lu JZ, 2020, IEEE SYST J, V14, P1297, DOI 10.1109/JSYST.2019.2911418
   McDermott Tom, 2020, INSIGHT, V23, P8, DOI 10.1002/inst.12278
   Mierlo S. V., 2017, HUMAN COMPUTER INTER
   Ming ZJ, 2018, J COMPUT INF SCI ENG, V18, DOI 10.1115/1.4037934
   Morales-Trujillo M. E., 2018, P INT C SOFTW ENG KN, P225
   Mordecai Y, 2018, IEEE T SYST MAN CY-S, V48, P637, DOI 10.1109/TSMC.2016.2602543
   Nuzzo P, 2015, P IEEE, V103, P2104, DOI 10.1109/JPROC.2015.2453253
   O'Connor MJ., 2009, CEUR WORKSHOP P, V529, P208
   Papakonstantinou N., 2013, P IEEE 18 C EM TECHN, P1
   Paredis C J J, 2010, INCOSE INT S, V20, P709
   Patti E, 2016, IEEE SYST J, V10, P1137, DOI 10.1109/JSYST.2014.2302750
   Ravikumar G, 2018, IEEE SYST J, V12, P149, DOI 10.1109/JSYST.2016.2535719
   Schluse M, 2018, IEEE T IND INFORM, V14, P1722, DOI 10.1109/TII.2018.2804917
   Seidner C, 2008, IEEE T IND INFORM, V4, P280, DOI 10.1109/TII.2008.2008998
   Sjostedt C.-J, 2007, P 1 INT WORKSH EQ BA, P25
   Walter T, 2014, SOFTW SYST MODEL, V13, P83, DOI 10.1007/s10270-012-0249-9
   Wang R, 2019, J ENG DESIGN, V30, P726, DOI 10.1080/09544828.2019.1668918
   Wang WT, 2019, IEEE T IND INFORM, V15, P3155, DOI 10.1109/TII.2018.2878782
   wikla G. C, 2017, IOP C SERIES MAT SCI, V227
   Yang L, 2021, IEEE T IND INFORM, V17, P1039, DOI 10.1109/TII.2020.2990953
NR 42
TC 6
Z9 6
U1 7
U2 12
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1932-8184
EI 1937-9234
J9 IEEE SYST J
JI IEEE Syst. J.
DI 10.1109/JSYST.2021.3106195
EA SEP 2021
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Operations Research & Management Science; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science;
   Telecommunications
GA XT4AR
UT WOS:000733533000001
OA Bronze, Green Submitted
DA 2022-08-23
ER

PT J
AU Zheng, J
   Hong, W
AF Zheng, Jia
   Hong, Wei
TI Construction of Knowledge Graph of 3D Clothing Design Resources Based on
   Multimodal Clustering Network
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
AB The construction of 3D design model is a hotspot of applied research in the fields of clothing functional design system teaching and display. The simple 3D clothing visualization postprocessing lacks interactive functions, which is a hot issue that needs to be solved urgently at present. Based on analyzing the existing clothing modeling technology, template technology, and fusion technology, and based on the multimodal clustering network theory, this paper proposes a 3D clothing design resource knowledge graph modeling method with multiple fusion of features and templates. The position of each joint point is converted into the coordinate system centered on the torso point in advance and normalized to avoid the problem that the relative position of the camera and the collector cannot be determined, and the shape of different collectors is different. The paper provides a multimodal clustering network intelligence method, illustrates the interoperability of users switching between different design networks in the seamless connection movement, and combines the hybrid intelligence algorithm with the fuzzy logic interpretation algorithm to solve the problems in the field of 3D clothing design service quality. During the simulation process, the research scheme builds a logical multimodal clustering network framework, which integrates compatibility access and global access partition fusion of style templates to achieve information extraction of clothing parts. The experimental results show that the realistic 3D clothing modeling can be achieved by layering the 3D clothing map, contour features, clothing size features, and color texture features with the modeling template. The developed ActiveX control is mounted on MSN, and the system is compatible. The performance and integration rate reached 77.1% and 89.7%, respectively, which effectively strengthened the practical role of the 3D clothing design system.
C1 [Zheng, Jia] Xian Polytech Univ, Sch New Media Art, Xi'an 710048, Shaanxi, Peoples R China.
   [Hong, Wei] Xi An Jiao Tong Univ, Sch Informat & Commun Engn, Xi'an 710049, Shaanxi, Peoples R China.
RP Zheng, J (通讯作者)，Xian Polytech Univ, Sch New Media Art, Xi'an 710048, Shaanxi, Peoples R China.
EM 20040737@xpu.edu.cn; weihong@mail.xjtu.edu.cn
CR Arun PV, 2022, INFORM FUSION, V78, P20, DOI 10.1016/j.inffus.2021.09.004
   Cai Q, 2019, IEEE ACCESS, V7, P133583, DOI 10.1109/ACCESS.2019.2941419
   Callaway EM, 2021, NATURE, V598, P86, DOI 10.1038/s41586-021-03950-0
   Chatzitofis A, 2020, IEEE ACCESS, V8, P176241, DOI 10.1109/ACCESS.2020.3026276
   Ekambaram V, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P3110, DOI 10.1145/3394486.3403362
   Feng D, 2021, IEEE T INTELL TRANSP, V22, P1341, DOI 10.1109/TITS.2020.2972974
   Gao JL, 2022, IEEE ACM T COMPUT BI, V19, P699, DOI 10.1109/TCBB.2021.3083566
   Gaudelet T, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbab159
   Hosseini MP, 2020, ARTIF INTELL MED, V104, DOI 10.1016/j.artmed.2020.101813
   Liao LZ, 2021, SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P675, DOI 10.1145/3404835.3462970
   Liu F, 2019, PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19), P1526, DOI 10.1145/3343031.3350953
   Liu Z., 2021, MULTIMED TOOLS APPL, V81, P1
   Nadon F, 2018, ROBOTICS, V7, DOI 10.3390/robotics7040074
   Partarakis NNP, 2022, ACM J COMPUT CULT HE, V15, DOI 10.1145/3465556
   Perez M, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12187539
   Phoha S., 2019, DISTRIBUTED SENSOR N, P576
   Ramesh R, 2020, COMPANION PUBLICATON OF THE 2020 INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (ICMI '20 COMPANION), P135, DOI 10.1145/3395035.3425641
   Sengupta N, 2018, IEEE T NEUR NET LEAR, V29, P5249, DOI 10.1109/TNNLS.2018.2796023
   Tiddi I., 2020, KNOWLEDGE GRAPHS EXP, V47, P143
   Vidaurre R, 2020, COMPUT GRAPH FORUM, V39, P145, DOI 10.1111/cgf.14109
   Wang Y, 2021, ACM T MULTIM COMPUT, V17, DOI 10.1145/3408317
   Xue B, 2020, IEEE T CYBERNETICS, V50, P4256, DOI 10.1109/TCYB.2019.2933224
   Yadav SK, 2021, KNOWL-BASED SYST, V223, DOI 10.1016/j.knosys.2021.106970
   Yan XQ, 2021, NEUROCOMPUTING, V448, P106, DOI 10.1016/j.neucom.2021.03.090
   Yang WC, 2021, J LIBR INF SCI, DOI 10.1177/09610006211063201
   Yuste R, 2020, NAT NEUROSCI, V23, P1456, DOI 10.1038/s41593-020-0685-8
NR 26
TC 0
Z9 0
U1 1
U2 1
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-5265
EI 1687-5273
J9 COMPUT INTEL NEUROSC
JI Comput. Intell. Neurosci.
PD JUN 2
PY 2022
VL 2022
AR 1168012
DI 10.1155/2022/1168012
PG 12
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 2D1AH
UT WOS:000811287600012
PM 35694580
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Kalaivani, PCD
   Sathishkumar, VE
   Hatamleh, WA
   Haouam, KD
   Venkatesh, B
   Sweidan, D
AF Kalaivani, P. C. D.
   Sathishkumar, V. E.
   Hatamleh, Wesam Atef
   Haouam, Kamel Dine
   Venkatesh, B.
   Sweidan, Dirar
TI Advanced lightweight feature interaction in deep neural networks for
   improving the prediction in click through rate
SO ANNALS OF OPERATIONS RESEARCH
LA English
DT Article; Early Access
DE Click through Rate; Neural Network; Knowledge graph-based convolution
   network; Lightweight; Deep Neural Network
AB Online advertising has expanded to a hundred-dollar billion industry in recent years, with sales growing at faster rate in every year. Prediction of the click-through rate (CTR) is an important role in recommended systems and online ads. Click through rating (CTR) is the newest evolution in the advertising and marketing digital world. It is essential for any online advertising company in real time to display the appropriate ads to the right users in the correct context. A huge amount of research work proposed considers each ad separately and does not takes in the relationship with other ads that may have an impact on Click Through Rate. A Factorization machine, a more generalized predictor like support vector machines (SVM) is not able to estimate reliable parameters under sparsity. The main drawback is that the primary features and existing algorithms considers the large weighted parameters. KGCN (Knowledge graph-based convolution network) overcomes the drawback and works on alternating graphs which creates additional clustering and node comparison with high latency and performance. A new framework DeepLight Weight is proposed to resolve the high server latency and high usage of memory issues in online advertising. This work presents a framework to improve the CTR predictions with an objective to accelerate the model inference, prune redundant parameters and the dense embedding vectors. Field Weighed Factorization machine helps to organize the data features with high structure to improve the accuracy. For clearing latency issues, structural pruning makes the algorithm work with dense matrices by combining and executing the individual matrix values or neural nodes.
C1 [Kalaivani, P. C. D.] Kongu Engn Coll, Dept Comp Sci & Engn, Erode 638060, Tamil Nadu, India.
   [Sathishkumar, V. E.] Sunchon Natl Univ, Dept Informat & Commun Engn, Sunchon, South Korea.
   [Hatamleh, Wesam Atef; Haouam, Kamel Dine] King Saud Univ, Coll Comp & Informat Sci, Dept Comp Sci, POB 51178, Riyadh 11543, Saudi Arabia.
   [Venkatesh, B.] Vignans Fdn Sci Technol & Res Deemed Univ, Dept Comp Sci & Engn, Guntur 522213, Andhra Pradesh, India.
   [Sweidan, Dirar] Univ Skovde, Sch Informat, Hogskolevagen 1, S-54128 Skovde, Sweden.
RP Sathishkumar, VE (通讯作者)，Sunchon Natl Univ, Dept Informat & Commun Engn, Sunchon, South Korea.
EM srisathishkumarve@gmail.com
RI Venkatesh, Dr B/AAA-9179-2020; V E, Sathishkumar/AAA-1176-2020
OI Venkatesh, Dr B/0000-0001-8116-9235; V E,
   Sathishkumar/0000-0002-8271-2022
FU King Saud University, Riyadh, Saudi Arabia [RSP2021/384]
FX The authors extend their appreciation to the Researchers supporting
   project number (RSP2021/384) King Saud University, Riyadh, Saudi Arabia.
CR Chen JH, 2017, 2017 17TH INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS AND INFORMATION TECHNOLOGIES (ISCIT)
   Kalaivaani PCD, 2020, INTELL AUTOM SOFT CO, V26, P71, DOI 10.31209/2019.100000152
   Keerthini N, 2018, J COMPUT THEOR NANOS, V15, P1603, DOI [10.1166/jctn.2018.7348, DOI 10.1166/JCTN.2018.7348]
   Khan G, 2019, INT CONF ADV COMP SC, P80
   Lian JX, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1754, DOI 10.1145/3219819.3220023
   Liu B, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2636, DOI 10.1145/3394486.3403314
   Murali MV, 2019, INT CONF ADVAN COMPU, P550, DOI 10.1109/ICACCS.2019.8728409
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang HW, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P592, DOI 10.1145/3159652.3159666
   Wang TF, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P563
   Xia BH, 2019, 2019 IEEE FIFTH INTERNATIONAL CONFERENCE ON MULTIMEDIA BIG DATA (BIGMM 2019), P162, DOI [10.1109/BigMM.2019.00-29, 10.1109/BigMM.2019.00033]
   Zhou GR, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1059, DOI 10.1145/3219819.3219823
   Zhu J, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1703, DOI 10.1145/3097983.3098059
NR 14
TC 2
Z9 2
U1 5
U2 8
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0254-5330
EI 1572-9338
J9 ANN OPER RES
JI Ann. Oper. Res.
DI 10.1007/s10479-021-04384-7
EA NOV 2021
PG 15
WC Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Operations Research & Management Science
GA WZ8LE
UT WOS:000720212600002
DA 2022-08-23
ER

PT J
AU Ensan, F
   Al-Obeidat, F
AF Ensan, Faezeh
   Al-Obeidat, Feras
TI Relevance-based entity selection for ad hoc retrieval
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Ad hoc retrieval; Semantic search; Entity-based retrieval; Entity
   selection
AB Recent developments have shown that entity-based models that rely on information from the knowledge graph can improve document retrieval performance. However, given the non-transitive nature of relatedness between entities on the knowledge graph, the use of semantic relatedness measures can lead to topic drift. To address this issue, we propose a relevance-based model for entity selection based on pseudo-relevance feedback, which is then used to systematically expand the input query leading to improved retrieval performance. We perform our experiments on the widely used TREC Web corpora and empirically show that our proposed approach to entity selection significantly improves ad hoc document retrieval compared to strong baselines. More concretely, the contributions of this work are as follows: (1) We introduce a graphical probability model that captures dependencies between entities within the query and documents. (2) We propose an unsupervised entity selection method based on the graphical model for query entity expansion and then for ad hoc retrieval. (3) We thoroughly evaluate our method and compare it with the state-of-the-art keyword and entity based retrieval methods. We demonstrate that the proposed retrieval model shows improved performance over all the other baselines on ClueWeb09B and ClueWeb12B, two widely used Web corpora, on the NDCG@20, and ERR@20 metrics. We also show that the proposed method is most effective on the difficult queries. In addition, We compare our proposed entity selection with a state-of-the-art entity selection technique within the context of ad hoc retrieval using a basic query expansion method and illustrate that it provides more effective retrieval for all expansion weights and different number of expansion entities.
C1 [Ensan, Faezeh] Ferdowsi Univ Mashhad, Mashhad, Iran.
   [Al-Obeidat, Feras] Zayed Univ, Dubai, U Arab Emirates.
RP Ensan, F (通讯作者)，Ferdowsi Univ Mashhad, Mashhad, Iran.
EM ensan@um.ac.ir
CR [Anonymous], 2001, P 18 INT C MACHINE L
   Bagheri E, 2018, INFORM PROCESS MANAG, V54, P657, DOI 10.1016/j.ipm.2018.04.007
   Balaneshinkordan Saeid, 2016, Advances in Information Retrieval. 38th European Conference on IR Research, ECIR 2016. Proceedings; LNCS 9626, P761, DOI 10.1007/978-3-319-30671-1_65
   Balog K, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P737
   Balog K, 2009, INFORM PROCESS MANAG, V45, P1, DOI 10.1016/j.ipm.2008.06.003
   Carpineto C, 2012, ACM COMPUT SURV, V44, DOI 10.1145/2071389.2071390
   Chen J, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P737, DOI 10.1145/2911451.2914725
   Dalton J, 2014, SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P365, DOI 10.1145/2600428.2609628
   Demartini G, 2010, LECT NOTES COMPUT SC, V6203, P254, DOI 10.1007/978-3-642-14556-8_26
   Dietz L, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P815, DOI 10.1145/3018661.3022756
   EGOZI O., 2011, ACM T INFORM SYSTEMS, V29, P8, DOI DOI 10.1145/1961209.1961211
   Ensan F, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2059, DOI 10.1145/3132847.3133138
   Ensan F, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P181, DOI 10.1145/3018661.3018692
   Feng Y., 2017, KNOWL ENG REV, P1
   Ferragina P., 2010, P 19 ACM INT C INF K, P1625, DOI DOI 10.1145/1871437.1871689
   Gabrilovich E., 2013, FACC1 FREEBASE ANNOT
   Garigliotti D, 2018, INFORM RETRIEVAL J, P1
   Hasibi F, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1289, DOI 10.1145/3077136.3084149
   Hasibi F, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1265, DOI 10.1145/3077136.3080751
   Jiang YC, 2017, INFORM PROCESS MANAG, V53, P248, DOI 10.1016/j.ipm.2016.09.001
   Kaptein R., 2010, P 19 ACM INT C INF K, P69, DOI DOI 10.1145/1871437.1871451
   Keikha A., 2017, J INTELL INF SYST, P1
   Kohlschutter C., 2010, P 3 ACM INT C WEB SE, P441, DOI DOI 10.1145/1718487.1718542
   Krishnan A, 2018, WORLD WIDE WEB, V21, P1041, DOI 10.1007/s11280-017-0468-7
   Lavrenko V., 2001, SIGIR Forum, P120
   Li H, 2013, FOUND TRENDS INF RET, V7, P345, DOI 10.1561/1500000035
   Li Y, 2016, COLING 2016, V2016, P2678
   Li Y, 2016, IEEE DATA COMPR CONF, P181, DOI 10.1109/DCC.2016.16
   Lin XS, 2018, LECT NOTES COMPUT SC, V10772, P773, DOI 10.1007/978-3-319-76941-7_75
   Liu XT, 2015, INFORM RETRIEVAL J, V18, P473, DOI 10.1007/s10791-015-9267-x
   Liu XT, 2013, INFORM PROCESS MANAG, V49, P995, DOI 10.1016/j.ipm.2013.03.004
   Metzler Donald, 2007, 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P311, DOI 10.1145/1277741.1277796
   Paik JH, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P343
   Payne VL, 2005, COMP MED SY, P479, DOI 10.1109/CBMS.2005.64
   Pound Jeffrey, 2010, P 19 INT C WORLD WID, P771, DOI [10.1145/1772690.1772769, DOI 10.1145/1772690.1772769]
   Raviv H, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P65, DOI 10.1145/2911451.2911508
   Robertson S. E., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, P232
   Schuhmacher M., 2015, P 24 ACM INT C INF K, P1461
   Schuhmacher M, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P543, DOI 10.1145/2556195.2556250
   Serdyukov Pavel, 2008, P 17 ACM C INF KNOWL, P1133
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Sherman G, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1045, DOI 10.1145/3077136.3080716
   Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022
   Strube M, 2006, P AAAI, P1419
   Sutton C, 2012, FOUND TRENDS MACH LE, V4, P267, DOI 10.1561/2200000013
   Wallach HannaM., 2004, TECHNICAL REPORTS CI, V22
   Wang Z, 2014, P C EMP METH NAT LAN, P1591
   Witten I. H, 2008, P AAAI 2008 WORKSH W
   Xiong C, 2015, P 2015 INT C THEOR I, P111, DOI DOI 10.1145/2808194.2809446
   Xiong CY, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P763, DOI 10.1145/3077136.3080768
   Xiong CY, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1271, DOI 10.1145/3038912.3052558
   Xu Y, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P59, DOI 10.1145/1571941.1571954
   Yahya M, 2016, PROCEEDINGS OF THE NINTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'16), P605, DOI 10.1145/2835776.2835795
   Zhiltsov N, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P253, DOI 10.1145/2766462.2767756
NR 54
TC 9
Z9 9
U1 2
U2 19
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD SEP
PY 2019
VL 56
IS 5
BP 1645
EP 1666
DI 10.1016/j.ipm.2019.05.005
PG 22
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA IH5AS
UT WOS:000474504100004
DA 2022-08-23
ER

PT J
AU Yang, JJ
   Gessner, CR
   Duerksen, JL
   Biber, D
   Binder, JL
   Ozturk, M
   Foote, B
   McEntire, R
   Stirling, K
   Ding, Y
   Wild, DJ
AF Yang, Jeremy J.
   Gessner, Christopher R.
   Duerksen, Joel L.
   Biber, Daniel
   Binder, Jessica L.
   Ozturk, Murat
   Foote, Brian
   McEntire, Robin
   Stirling, Kyle
   Ding, Ying
   Wild, David J.
TI Knowledge graph analytics platform with LINCS and IDG for Parkinson's
   disease target illumination
SO BMC BIOINFORMATICS
LA English
DT Article
DE Knowledge graph; Graph analytics; Systems biology; Drug discovery; Drug
   target; Druggable genome; Parkinson's disease
AB Background LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. Results Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG's resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. Conclusions The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.
C1 [Yang, Jeremy J.; Gessner, Christopher R.; Ozturk, Murat; Stirling, Kyle; Ding, Ying; Wild, David J.] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN 47405 USA.
   [Yang, Jeremy J.; Duerksen, Joel L.; Biber, Daniel; Ozturk, Murat; Foote, Brian; McEntire, Robin; Stirling, Kyle; Ding, Ying; Wild, David J.] Data2Discovery Inc, Bloomington, IN 47408 USA.
   [Gessner, Christopher R.] Chem Abstracts Serv Inc, Columbus, OH USA.
   [Yang, Jeremy J.; Binder, Jessica L.] Univ New Mexico, Dept Internal Med, Translat Informat Div, Albuquerque, NM 87131 USA.
   [Ding, Ying] Univ Texas Austin, Sch Informat, Dell Med Sch, Austin, TX 78712 USA.
RP Wild, DJ (通讯作者)，Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN 47405 USA.; Wild, DJ (通讯作者)，Data2Discovery Inc, Bloomington, IN 47408 USA.
EM djwild@indiana.edu
CR Amazon Web Services Inc, AMAZ NEPT
   Avram S, 2021, NUCLEIC ACIDS RES, V49, pD1160, DOI 10.1093/nar/gkaa997
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Callahan Alison, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P200
   Cannon DC, 2017, BIOINFORMATICS, V33, P2601, DOI 10.1093/bioinformatics/btx200
   Chen B, 2017, NAT COMMUN, V8, DOI 10.1038/ncomms16022
   Chen B, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002574
   Chen B, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-255
   Dgraph Labs Inc, DGRAPH
   Digles D, 2018, METHODS MOL BIOL, V1787, P183, DOI 10.1007/978-1-4939-7847-2_14
   Egana LA, 2009, J NEUROSCI, V29, P4592, DOI 10.1523/JNEUROSCI.4559-08.2009
   Greene CS, 2015, NAT GENET, V47, P569, DOI 10.1038/ng.3259
   Have CT, 2013, BIOINFORMATICS, V29, P3107, DOI 10.1093/bioinformatics/btt549
   Himmelstein DS, 2015, PLOS COMPUT BIOL, V11, DOI 10.1371/journal.pcbi.1004259
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Hripcsak G, 2019, J BIOMED INFORM, V96, DOI 10.1016/j.jbi.2019.103253
   Hripcsak G, 2015, STUD HEALTH TECHNOL, V216, P574, DOI 10.3233/978-1-61499-564-7-574
   IDG-KMC (Illuminating the Druggable Genome Knowledge Management Center), IDG KMC TARG CENTR R
   JCC Consulting Inc, ACT BEH UN ASS ISO G
   Keenan AB, 2018, CELL SYST, V6, P13, DOI 10.1016/j.cels.2017.11.001
   Li J, 2013, BMC BIOINFORMATICS, V14, DOI 10.1186/1471-2105-14-S16-S3
   Lysenko A, 2016, BIODATA MIN, V9, DOI 10.1186/s13040-016-0102-8
   Mathias SL, 2013, DATABASE-OXFORD, DOI 10.1093/database/bat044
   Morton K, 2019, BIOINFORMATICS, V35, P5382, DOI 10.1093/bioinformatics/btz604
   Napolitano F, 2016, BIOINFORMATICS, V32, P235, DOI 10.1093/bioinformatics/btv536
   National Institutes of Health U.S. Department of Health and Human Services, NAT I HLTH COMM FUND
   Neo4j Inc, NEO4J LIC OV
   Neo4j Inc, NEO4J PYTH DRIV
   Ochoa D, 2021, NUCLEIC ACIDS RES, V49, pD1302, DOI 10.1093/nar/gkaa1027
   Oprea TI, 2018, NAT REV DRUG DISCOV, V17, P317, DOI [10.1038/nrd.2018.14, 10.1038/nrd.2018.52]
   ROSENTHAL R, 1978, PSYCHOL BULL, V85, P185, DOI 10.1037/0033-2909.85.1.185
   Schmitt M, 2017, SYNAPSE, V71, DOI 10.1002/syn.21980
   Schmitt M, 2016, SYNAPSE, V70, P71, DOI 10.1002/syn.21881
   Sheils Timothy, 2020, Curr Protoc Bioinformatics, V69, pe92, DOI 10.1002/cpbi.92
   Sirota M, 2011, SCI TRANSL MED, V3, DOI 10.1126/scitranslmed.3001318
   Stoker TB., 2019, PARKINSONS DIS PATHO
   Subramanian A, 2017, CELL, V171, P1437, DOI 10.1016/j.cell.2017.10.049
   Szklarczyk D, 2019, NUCLEIC ACIDS RES, V47, pD607, DOI 10.1093/nar/gky1131
   Ursu O, 2019, NUCLEIC ACIDS RES, V47, pD963, DOI 10.1093/nar/gky963
   Yoon Byoung-Ha, 2017, Genomics & Informatics, V15, P19, DOI 10.5808/GI.2017.15.1.19
NR 40
TC 0
Z9 0
U1 4
U2 6
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD JAN 12
PY 2022
VL 23
IS 1
AR 37
DI 10.1186/s12859-021-04530-9
PG 15
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
GA YF7SF
UT WOS:000742001600003
PM 35021991
OA gold, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Pocatilu, P
   Enachescu, MI
   Dita, A
AF Pocatilu, Paul
   Enachescu, Mihaela-Irina
   Dita, Alexandru
TI ASSESSING A CANDIDATE'S SENIORITY LEVEL IN COMPUTER SCIENCE FIELD BY
   INTEGRATING SEMANTIC WEB TECHNOLOGIES WITH AUGMENTED REALITY
SO ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH
LA English
DT Article
DE e-recruitment; semantic web technologies; graphs; ontology; regression;
   Bayesian approach; augmented reality
AB Usually, the process of recruiting employees is very complex, and this requires the allocation of numerous resources within the organization. Several steps of this process can be automated, especially related to Curriculum Vitae (CV) analysis. This paper presents the prototype of an application that combines semantic technologies with augmented reality (AR) in order to enhance the plain text from a CV content with context-aware information in regards to the technical background of the applicant. This approach proves to be more trustworthy than manual selection performed by the HR personnel, since rather than looking on an applicant profile as a collection of skills we try to reveal how all these competences interact with each other and what implicit knowledge the candidate has. We tested our prototype on a set of real CVs, gathered several metrics - number of found skills, number of knowledge graphs, number of significant knowledge graphs, maximum depth of known skills and primary known skills weight - and used them to analyze what influence they have on assessing the candidate's seniority level. We applied logistic regression with Bayesian approach and used the ROC curve in order to estimate the performance of the forecasting model.
C1 [Pocatilu, Paul] Bucharest Univ Econ Studies, Dept Econ Informat & Cybernet, Bucharest, Romania.
   [Enachescu, Mihaela-Irina; Dita, Alexandru] Bucharest Univ Econ Studies, Bucharest, Romania.
RP Pocatilu, P (通讯作者)，Bucharest Univ Econ Studies, Dept Econ Informat & Cybernet, Bucharest, Romania.
EM ppaul@ase.ro; irina.enachescu@csie.ase.ro; alexandru.dita@csie.ase.ro
CR Abd Majid NA, 2015, PROCD SOC BEHV, V176, P111, DOI 10.1016/j.sbspro.2015.01.450
   AltexSoft, 2018, SOFTW ENG QUAL LEV J
   Bajenaru L., 2015, EC COMPUTATION EC CY, V49, P1
   Bloomberg D, 2014, BUILDING SIMPLE TEXT
   Chen P, 2016, J VIS COMMUN IMAGE R, V37, P63, DOI 10.1016/j.jvcir.2015.06.016
   Cunningham H., 2014, DEV LANGUAGE PROCESS
   Dita F.-A, 2016, INFORM EC, V20, P76, DOI [10.12948/issn14531305/20.4.2016.07, DOI 10.12948/ISSN14531305/20.4.2016.07]
   EnaChescu Mihaela-Irina, 2019, Informatica Economica, V23, P51, DOI 10.12948/issn14531305/23.4.2019.05
   Ermilov T, 2014, INT J SEMANT WEB INF, V10, P66, DOI 10.4018/ijswis.2014010103
   Gelder A. B., 2012, LOGISTIC REGRESSION
   Geroimenko V., 2012, 16 INT C INF VIS
   Ghosh H., 2015, MULTIMEDIA ONTOLOGY
   Grace- Martin K., 2018, DIFFERENCE LOGISTIC
   Hahn E.D., 2005, PROBIT LOGIT MODELS
   Han PF, 2015, COMPUT GRAPH-UK, V50, P36, DOI 10.1016/j.cag.2015.05.021
   Hitzler P., 2010, FDN SEMANTIC WEB TEC
   Jamali SS, 2015, PROCD SOC BEHV, V197, P659, DOI 10.1016/j.sbspro.2015.07.054
   Karaman A, 2016, PROCEDIA COMPUT SCI, V83, P300, DOI 10.1016/j.procs.2016.04.129
   Luo X., 2011, AUGMENTED REALITY SO
   Pocatilu P., 2017, INT C CURR EC TRENDS
   Sanchez J., 2013, INT J LATEST RES SCI, V2, P9
   Schoonjans F., 2019, LOGISTIC REGRESSION
   Sicilia, 2014, HDB METADATA SEMANTI
   Sikos L, 2015, MASTERING STRUCTURED
   Thai N., 2019, CONTENT ANAL APACHE
   Viet HT, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16 COMPANION), P195, DOI 10.1145/2872518.2890549
   Weng ENG, 2013, PROCD SOC BEHV, V97, P753, DOI 10.1016/j.sbspro.2013.10.297
NR 27
TC 0
Z9 0
U1 0
U2 3
PU ACAD ECONOMIC STUDIES
PI BUCHAREST
PA 15-17 CALEA DOROBANTI, SECTOR 1, BUCHAREST, 00000, ROMANIA
SN 0424-267X
EI 1842-3264
J9 ECON COMPUT ECON CYB
JI Econ. Comput. Econ. Cybern. Stud.
PY 2020
VL 54
IS 3
BP 231
EP 248
DI 10.24818/18423264/54.3.20.14
PG 18
WC Economics; Mathematics, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Mathematics
GA NT9KE
UT WOS:000573257700014
OA gold
DA 2022-08-23
ER

PT J
AU Bean, DM
   Wu, HH
   Dzahini, O
   Broadbent, M
   Stewart, R
   Dobson, RJB
AF Bean, Daniel M.
   Wu, Honghan
   Dzahini, Olubanke
   Broadbent, Matthew
   Stewart, Robert
   Dobson, Richard J. B.
TI Knowledge graph prediction of unknown adverse drug reactions and
   validation in electronic health records
SO SCIENTIFIC REPORTS
LA English
DT Article
ID AKATHISIA
AB Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials.
C1 [Bean, Daniel M.; Wu, Honghan; Dobson, Richard J. B.] Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Biostat & Hlth Informat, 16 Crespigny Pk, London SE5 8AF, England.
   [Dzahini, Olubanke; Broadbent, Matthew; Stewart, Robert] South London & Maudsley NHS Fdn Trust, Denmark Hill, London SE5 8AZ, England.
   [Dzahini, Olubanke] Kings Coll London, Inst Pharmaceut Sci, 5th Floor,Franklin Wilkins Bldg,150 Stamford St, London SE1 9NH, England.
   [Stewart, Robert] Kings Coll London, Inst Psychiat Psychol & Neurosci, 16 De Crespigny Pk, London SE5 8AF, England.
   [Dobson, Richard J. B.] UCL, Inst Hlth Informat, Farr Inst Hlth Informat Res, London WC1E 6BT, England.
RP Dobson, RJB (通讯作者)，Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Biostat & Hlth Informat, 16 Crespigny Pk, London SE5 8AF, England.; Dobson, RJB (通讯作者)，UCL, Inst Hlth Informat, Farr Inst Hlth Informat Res, London WC1E 6BT, England.
EM richard.j.dobson@kcl.ac.uk
RI Bean, Daniel/AFQ-4601-2022; Wu, Honghan/AAT-6084-2020; Stewart,
   Robert/B-1667-2010; dobson, richard/C-9269-2011
OI Stewart, Robert/0000-0002-4435-6397; dobson,
   richard/0000-0003-4224-9245; Iqbal, Ehtesham/0000-0001-9477-9745;
   Dzahini, Olubanke/0000-0003-3878-2143; Bean, Daniel/0000-0002-8594-7804;
   Wu, Honghan/0000-0002-0213-5668
FU National Institute for Health Research (NIHR) Biomedical Research Centre
   at South London and Maudsley NHS Foundation Trust; King's College
   London; Medical Research Council; Arthritis Research UK; British Heart
   Foundation; Cancer Research UK; Chief Scientist Office; Economic and
   Social Research Council; Engineering and Physical Sciences Research
   Council; National Institute for Health Research; National Institute for
   Social Care and Health Research; Wellcome Trust [MR/K006584/1]
FX This paper represents independent research funded by the National
   Institute for Health Research (NIHR) Biomedical Research Centre at South
   London and Maudsley NHS Foundation Trust and King's College London. The
   views expressed are those of the author(s) and not necessarily those of
   the NHS, the NIHR or the Department of Health. This research was
   supported by researchers at the National Institute for Health Research
   University College London Hospitals Biomedical Research Centre, and by
   awards establishing the Farr Institute of Health Informatics Research at
   UCL Partners, from the Medical Research Council, Arthritis Research UK,
   British Heart Foundation, Cancer Research UK, Chief Scientist Office,
   Economic and Social Research Council, Engineering and Physical Sciences
   Research Council, National Institute for Health Research, National
   Institute for Social Care and Health Research, and Wellcome Trust (grant
   MR/K006584/1).
CR Administration U. S. F. D, 2004, FDA ADV EV REP SYST
   [Anonymous], 2011, J MACH LEARN RES
   Boland MR, 2016, WIRES SYST BIOL MED, V8, P104, DOI 10.1002/wsbm.1323
   Bresso E, 2013, BMC BIOINFORMATICS, V14, DOI 10.1186/1471-2105-14-207
   Cami A, 2011, SCI TRANSL MED, V3, DOI 10.1126/scitranslmed.3002774
   Cherkasov A, 2014, J MED CHEM, V57, P4977, DOI 10.1021/jm4004285
   Duran-Frigola M, 2013, CHEM BIOL, V20, P594, DOI 10.1016/j.chembiol.2013.03.017
   EIKMEIER G, 1988, J NEUROL NEUROSUR PS, V51, P1461, DOI 10.1136/jnnp.51.11.1461
   European Medicines Agency, EUR DAT SUSP ADV DRU
   Fernandes AC, 2013, BMC MED INFORM DECIS, V13, DOI 10.1186/1472-6947-13-71
   Frid AA, 2010, REGUL TOXICOL PHARM, V56, P276, DOI 10.1016/j.yrtph.2009.11.005
   Gill H. S., 1997, J CLIN PSYCHOPHARMAC, V17
   Harpaz R, 2013, J AM MED INFORM ASSN, V20, P413, DOI 10.1136/amiajnl-2012-000930
   Hazell L, 2006, DRUG SAFETY, V29, P385, DOI 10.2165/00002018-200629050-00003
   Ho TB, 2016, CURR PHARM DESIGN, V22, P3498, DOI 10.2174/1381612822666160509125047
   Huang LC, 2011, BMC GENOMICS, V12, DOI 10.1186/1471-2164-12-S5-S11
   Iqbal E, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0134208
   Kuhn M, 2016, NUCLEIC ACIDS RES, V44, pD1075, DOI 10.1093/nar/gkv1075
   Lane RM, 1998, J PSYCHOPHARMACOL, V12, P192, DOI 10.1177/026988119801200212
   Law V, 2014, NUCLEIC ACIDS RES, V42, pD1091, DOI 10.1093/nar/gkt1068
   Lazarou J, 1998, JAMA-J AM MED ASSOC, V279, P1200, DOI 10.1001/jama.279.15.1200
   Liu M., 2012, J BIOMOLEC STRUC DYN, P1
   Liu M, 2012, J AM MED INFORM ASSN, V19, pE28, DOI 10.1136/amiajnl-2011-000699
   Loonen AJM, 2011, CNS SPECTRUMS, V16, P7, DOI 10.1017/S1092852912000107
   Medicines and Healthcare Products Regulatory Agency, YELL CARD SCH
   Pauwels E, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-169
   Perez-Nueno VI, 2015, J CHEM INF MODEL, V55, P1804, DOI 10.1021/acs.jcim.5b00120
   Pirmohamed M, 2004, BMJ-BRIT MED J, V329, P15, DOI 10.1136/bmj.329.7456.15
   Rahmani H, 2016, COMPUT BIOL MED, V68, P101, DOI 10.1016/j.compbiomed.2015.11.005
   Shao Z, 2015, J CHEM INF MODEL, V55, P2519, DOI 10.1021/acs.jcim.5b00376
   Uppsala Monitoring Centre, VIGIBASE
   Vandel P, 1997, EUR NEUROPSYCHOPHARM, V7, P207, DOI 10.1016/S0924-977X(97)00405-7
   Wang ZC, 2016, BIOINFORMATICS, V32, P2338, DOI 10.1093/bioinformatics/btw168
   Whitebread S, 2005, DRUG DISCOV TODAY, V10, P1421, DOI 10.1016/S1359-6446(05)03632-9
   Yamanishi Y, 2012, J CHEM INF MODEL, V52, P3284, DOI 10.1021/ci2005548
NR 35
TC 30
Z9 32
U1 1
U2 25
PU NATURE PUBLISHING GROUP
PI LONDON
PA MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 2045-2322
J9 SCI REP-UK
JI Sci Rep
PD NOV 27
PY 2017
VL 7
AR 16416
DI 10.1038/s41598-017-16674-x
PG 11
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA FN8WQ
UT WOS:000416308200024
PM 29180758
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Johnson, JM
   Narock, T
   Singh-Mohudpur, J
   Fils, D
   Clarke, KC
   Saksena, S
   Shepherd, A
   Arumugam, S
   Yeghiazarian, L
AF Johnson, J. Michael
   Narock, Tom
   Singh-Mohudpur, Justin
   Fils, Doug
   Clarke, Keith C.
   Saksena, Siddharth
   Shepherd, Adam
   Arumugam, Sankar
   Yeghiazarian, Lilit
TI Knowledge graphs to support real-time flood impact evaluation
SO AI MAGAZINE
LA English
DT Article
AB A digital map of the built environment is useful for a range of economic, emergency response, and urban planning exercises such as helping find places in app driven interfaces, helping emergency managers know what locations might be impacted by a flood or fire, and helping city planners proactively identify vulnerabilities and plan for how a city is growing. Since its inception in 2004, OpenStreetMap (OSM) sets the benchmark for open geospatial data and has become a key player in the public, research, and corporate realms. Following the foundations laid by OSM, several open geospatial products describing the built environment have blossomed including the Microsoft USA building footprint layer and the OpenAddress project. Each of these products use different data collection methods ranging from public contributions to artificial intelligence, and if taken together, could provide a comprehensive description of the built environment. Yet, these projects are still siloed, and their variety makes integration and interoperability a major challenge. Here, we document an approach for merging data from these three major open building datasets and outline a workflow that is scalable to the continental United States (CONUS). We show how the results can be structured as a knowledge graph over which machine learning models are built. These models can help propagate and complete unknown quantities that can then be leveraged in disaster management.
C1 [Johnson, J. Michael; Singh-Mohudpur, Justin; Clarke, Keith C.] Univ Calif Santa Barbara, Santa Barbara, CA 93106 USA.
   [Narock, Tom] Goucher Coll, 1021 Dulaney Valley Rd, Baltimore, MD 21204 USA.
   [Fils, Doug] Consortium Ocean Leadership, Washington, DC USA.
   [Saksena, Siddharth] Virginia Polytech Inst & State Univ, Blacksburg, VA 24061 USA.
   [Shepherd, Adam] Woods Hole Oceanog Inst, Falmouth, MA USA.
   [Arumugam, Sankar] North Carolina State Univ, Raleigh, NC USA.
   [Yeghiazarian, Lilit] Univ Cincinnati, Cincinnati, OH 45221 USA.
RP Narock, T (通讯作者)，Goucher Coll, 1021 Dulaney Valley Rd, Baltimore, MD 21204 USA.
EM Thomas.Narock@goucher.edu
RI Saksena, Siddharth/J-2094-2019; Arumugam, Sankarasubramanian/A-1929-2019
OI Saksena, Siddharth/0000-0003-3746-6368; Arumugam,
   Sankarasubramanian/0000-0002-7668-1311
FU NSF [1937099, 2033607]
FX NSF, Grant/Award Numbers: OIA #1937099, #2033607
CR Angles R, 2017, ACM COMPUT SURV, V50, DOI 10.1145/3104031
   Cyganiak R., 2014, W3C RECOMMENDATION
   Hitzler P, 2012, OWL 2 WEB ONTOLOGY L, VSecond
   Hitzler P, 2021, COMMUN ACM, V64, P76, DOI 10.1145/3397512
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   Johnson JM, 2019, NAT HAZARD EARTH SYS, V19, P2405, DOI 10.5194/nhess-19-2405-2019
   Liu YY, 2018, J AM WATER RESOUR AS, V54, P770, DOI 10.1111/1752-1688.12660
   Maidment David., 2009, WORLD ENV WAT RES C
   Maidment DR, 2017, J AM WATER RESOUR AS, V53, P245, DOI 10.1111/1752-1688.12474
   Microsoft, 2018, US BUILD FOOTPR
   Noy N, 2019, COMMUN ACM, V62, P36, DOI 10.1145/3331166
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
NR 12
TC 1
Z9 1
U1 4
U2 4
PU AMER ASSOC ARTIFICIAL INTELL
PI MENLO PK
PA 445 BURGESS DRIVE, MENLO PK, CA 94025-3496 USA
SN 0738-4602
EI 2371-9621
J9 AI MAG
JI AI Mag.
PD MAR
PY 2022
VL 43
IS 1
BP 40
EP 45
DI 10.1002/aaai.12035
PG 6
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0J8FN
UT WOS:000780336400006
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Wang, T
   Guo, JL
   Wu, Z
   Xu, TS
AF Wang, Ting
   Guo, Jiale
   Wu, Zhuang
   Xu, Tiansheng
TI IFTA: Iterative filtering by using TF-AICL algorithm for Chinese
   encyclopedia knowledge refinement
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Knowledge base; Online encyclopedia; Knowledge refining; Iterative
   algorithm; Knowledge graph
ID LARGE-SCALE; WEB; ONTOLOGY; DBPEDIA; BASE
AB The influence of inaccurate knowledge still exists in the Semantic Web. The problem of knowledge inaccuracy in Knowledge Bases (KBs) is one of the largest obstacles that limit the development of Linked Open Data (LOD) and Knowledge Graphs (KGs). To solve the semantic ambiguity and improper classification of knowledge triples in the process of constructing Chinese online encyclopedia KBs, first, a new TF-AICL algorithm is proposed to calculate the concentration level of predicates in each top-category. Second, the predicate which can best represent the features of a top-category is selected, and the related predicate candidate set is extracted. Third, based on the positive and negative examples counting strategy, the predicate candidate set is used as the comparison group to filter each entity. Finally, based on the TF-AICL algorithm, this paper proposes a new iterative filtering method called IFTA. IFTA adopts a new predicate feature extraction method, TF-AICL, which considers the hierarchical features of the predicate. In addition, IFTA can automatically prune, filter and refine large-scale online encyclopedia knowledge in an iterative way. The precision, recall and F-measure results on the BaiduBaike and Hudong datasets indicate that the refining effects on open-domain Chinese encyclopedia KBs by the IFTA method outperform the state-of-the-art methods.
C1 [Wang, Ting; Guo, Jiale; Wu, Zhuang; Xu, Tiansheng] Capital Univ Econ & Business, Sch Management & Engn, Beijing 100070, Peoples R China.
RP Wang, T (通讯作者)，Capital Univ Econ & Business, Sch Management & Engn, Beijing 100070, Peoples R China.
EM wangting@cueb.edu.cn
RI WANG, TING/ACE-5023-2022
OI WANG, TING/0000-0003-2481-2890
FU Scientific Research Project of Beijing Municipal Education Commission
   (General Social Science Project) [SM201910038010]; National Social
   Science Fund of China [19BXW120]; Backup Academic Leaders Grant of
   Capital University of Economics and Business; Special Fund of
   Fundamental Research Expenses of Beijing Municipal University of Capital
   University of Economics and Business
FX This work was supported in part by the Scientific Research Project of
   Beijing Municipal Education Commission (General Social Science Project)
   [grant number SM201910038010]; National Social Science Fund of China
   [grant number 19BXW120]; Backup Academic Leaders Grant of Capital
   University of Economics and Business; and Special Fund of Fundamental
   Research Expenses of Beijing Municipal University of Capital University
   of Economics and Business.
CR Andreev MV, 2016, 2016 2ND INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING, APPLICATIONS AND MANUFACTURING (ICIEAM)
   Berners-Lee T, 2001, SCI AM, V284, P34, DOI 10.1038/scientificamerican0501-34
   Bing L., 2013, P 6 ACM INT C WEB SE, P567
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2011, AAAI
   Che FH, 2020, AAAI CONF ARTIF INTE, V34, P2774
   Chen KW, 2016, EXPERT SYST APPL, V66, P245, DOI 10.1016/j.eswa.2016.09.009
   Chen XJ, 2020, EXPERT SYST APPL, V162, DOI 10.1016/j.eswa.2020.113889
   Cheng X, 2016, J CHINESE INFORM PRO
   Choi Y, 2020, P 30 AAAI C ART INT
   Dalton J, 2014, SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P365, DOI 10.1145/2600428.2609628
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Esuli A, 2006, LECT NOTES COMPUT SC, V4209, P13
   Galarraga L, 2014, P 23 ACM INT C C INF, P1679
   Gardner M., 2015, P 2015 C EMP METH NA, P1488, DOI DOI 10.18653/V1/D15-1173
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   He T, 2020, AAAI CONF ARTIF INTE, V34, P4091
   Hess A, 2004, LECT NOTES COMPUT SC, V3201, P156
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Huang Y, 2017, COMM COM INF SC, V784, P1, DOI 10.1007/978-981-10-7359-5_1
   Isaac A, 2007, LECT NOTES COMPUT SC, V4825, P253
   Jain P, 2010, LECT NOTES COMPUT SC, V6496, P402, DOI 10.1007/978-3-642-17746-0_26
   Jiang F, 2018, PROCEEDINGS OF 2018 THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE (CSAI 2018) / 2018 THE 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND MULTIMEDIA TECHNOLOGY (ICIMT 2018), P521, DOI 10.1145/3297156.3297256
   Jin HL, 2019, DATA INTELLIGENCE, V1, P77, DOI 10.1162/dint_a_00003
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li JY, 2015, LECT NOTES COMPUT SC, V9313, P623, DOI 10.1007/978-3-319-25255-1_51
   Li Y, 2020, J BIOMED INFORM, V106, DOI 10.1016/j.jbi.2020.103435
   Lin L, 2020, APPL INTELL, V50, P2867, DOI 10.1007/s10489-020-01673-9
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu FF, 2020, FRONT COMPUT SCI-CHI, V14, DOI 10.1007/s11704-019-8264-4
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Niu X., 2012, P 21 ACM INT C INF K, P1085, DOI DOI 10.1145/2396761.2398406
   Niu X, 2011, LECT NOTES COMPUT SC, V7032, P205, DOI 10.1007/978-3-642-25093-4_14
   Nizzoli L, 2020, DECIS SUPPORT SYST, V136, DOI 10.1016/j.dss.2020.113346
   Oren E, 2007, LECT NOTES COMPUT SC, V4519, P160
   Papadakis I, 2015, D LIB MAG, V21, DOI [10.1045/may2015-papadakis, DOI 10.1045/MAY2015-PAPADAKIS]
   Romadhony A, 2019, APPL INTELL, V49, P206, DOI 10.1007/s10489-018-1269-0
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Soru T., 2014, P 10 INT C SEM SYST, P41, DOI DOI 10.1145/2660517.2660532
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   Talukdar P, 2020, P 30 AAAI C ART INT
   Ting W, 2016, NOVEL KNOWLEDGE EXTR
   Volz J., 2009, P LDOW APR, V538, P53
   Volz J, 2009, LECT NOTES COMPUT SC, V5823, P650, DOI 10.1007/978-3-642-04930-9_41
   Wang T, 2020, KNOWLEDGE BASE BAIDU
   Wang T, 2020, NEURAL COMPUT APPL, V32, P235, DOI 10.1007/s00521-018-3806-5
   Wang T, 2019, IEEE ACCESS, V7, P107413, DOI 10.1109/ACCESS.2019.2933249
   Wang T, 2017, IEICE T INF SYST, VE100D, P1251, DOI 10.1587/transinf.2016EDP7307
   Wang X, 2015, COMPUT ENG APPL, V51
   [王雪鹏 Wang Xuepeng], 2017, [计算机学报, Chinese Journal of Computers], V40, P701
   Wang YP, 2018, 2018 IEEE/ACIS 16TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING RESEARCH, MANAGEMENT AND APPLICATION (SERA), P19, DOI 10.1109/SERA.2018.8477196
   Wang Z., 2012, P 21 INT C WORLD WID, P459, DOI DOI 10.1145/2187836.2187899
   Wang Z., 2016, IJCAI, P1293
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang ZC, 2012, J ZHEJIANG U-SCI C, V13, P268, DOI 10.1631/jzus.C1101008
   Wang ZC, 2019, LECT NOTES ARTIF INT, V11441, P501, DOI 10.1007/978-3-030-16142-2_39
   Wu F., 2007, P 16 ACM C INF KNOWL, P41, DOI DOI 10.1145/1321440.1321449
   Wu F., 2008, P 17 INT C WORLD WID, P635, DOI DOI 10.1145/1367497.1367583
   Wu TX, 2019, INT J SEMANT WEB INF, V15, P22, DOI 10.4018/IJSWIS.2019040102
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xin Wang, 2012, Web Technologies and Applications. Proceedings of the 14th Asia-Pacific Web Conference, APWeb 2012, P779, DOI 10.1007/978-3-642-29253-8_77
   Xiong CY, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1271, DOI 10.1145/3038912.3052558
   Xu B, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P903, DOI 10.1145/3269206.3271804
   Xu B, 2017, LECT NOTES ARTIF INT, V10351, P428, DOI 10.1007/978-3-319-60045-1_44
   Yidong Chen, 2012, Web Technologies and Applications. Proceedings of the APWeb 2012 International Workshops: SenDe, IDP, IEKB, MBC, P179, DOI 10.1007/978-3-642-29426-6_22
   Zhang F, 2018, APPL INTELL, V48, P220, DOI 10.1007/s10489-017-0965-5
   Zhang X, 2020, IEEE ACCESS, V8, P27257, DOI 10.1109/ACCESS.2020.2971567
   Zhichun Wang, 2012, The Semantic Web. Proceedings Joint International Semantic Technology Conference (JIST 2011), P80, DOI 10.1007/978-3-642-29923-0_6
   Zhou ZL, 2017, IEEE IJCNN, P737, DOI 10.1109/IJCNN.2017.7965925
NR 74
TC 1
Z9 1
U1 7
U2 14
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
PD AUG
PY 2021
VL 51
IS 8
BP 6265
EP 6293
DI 10.1007/s10489-021-02220-w
EA FEB 2021
PG 29
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TG7OE
UT WOS:000620434000002
DA 2022-08-23
ER

PT J
AU Hussain, MJ
   Wasti, SH
   Huang, GJ
   Wei, LN
   Jiang, YC
   Tang, Y
AF Hussain, Muhammad Jawad
   Wasti, Shahbaz Hassan
   Huang, Guangjian
   Wei, Lina
   Jiang, Yuncheng
   Tang, Yong
TI An approach for measuring semantic similarity between Wikipedia concepts
   using multiple inheritances
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Semantic similarity; Multiple inheritance; Information content;
   Wikipedia category graph; Knowledge graph
ID INFORMATION-CONTENT; KNOWLEDGE; RELATEDNESS; WORDNET; REPRESENTATION;
   MODELS
AB Wikipedia provides a huge collaboratively made semi-structured taxonomy called Wikipedia category graph (WCG), which can be utilized as a Knowledge Graph (KG) to measure the semantic similarity (SS) between Wikipedia concepts. Previously, several Most Informative Common Ancestor-based (MICA-based) SS methods have been proposed by intrinsically manipulating the taxonomic structure of WCG. However, some basic structural issues in WCG such as huge size, branching factor and multiple inheritance relations hamper the applicability of traditional MICA-based and multiple inheritance-based approaches in it. Therefore, in this paper, we propose a solution to handle these structural issues and present a new multiple inheritance-based SS approach, called Neighborhood Ancestor Semantic Contribution (NASC). In this approach, firstly, we define the neighborhood of a category (a taxonomic concept in WCG) to define its semantic space. Secondly, we describe the semantic value of a category by aggregating the intrinsic IC-based semantic contribution weights of its semantically relevant multiple ancestors. Thirdly, based on our approach, we propose six different methods to compute the SS between Wikipedia concepts. Finally, we evaluate our methods on gold standard word similarity benchmarks for English, German, Spanish and French languages. The experimental evaluation demonstrates that the proposed NASC-based methods remarkably outperform traditional MICA-based and multiple inheritance-based approaches.
C1 [Hussain, Muhammad Jawad; Wasti, Shahbaz Hassan; Huang, Guangjian; Wei, Lina; Jiang, Yuncheng; Tang, Yong] South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Guangdong, Peoples R China.
RP Jiang, YC (通讯作者)，South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Guangdong, Peoples R China.
EM ycjiang@scnu.edu.cn
OI Hussain, Muhammad Jawad/0000-0002-2798-4375
FU National Natural Science Foundation of China [61772210, U1911201];
   Guangdong Province Universities Pearl River Scholar Funded Scheme
   (2018); Project of Science and Technology in Guangzhou in China
   [201807010043]
FX The works described in this paper are supported by The National Natural
   Science Foundation of China under Grant Nos. 61772210 and U1911201;
   Guangdong Province Universities Pearl River Scholar Funded Scheme
   (2018); The Project of Science and Technology in Guangzhou in China
   under Grant No. 201807010043.
CR Agirre E, 2009, P HUM LANG TECHN 200, P19, DOI DOI 10.3115/1620754.1620758
   Al-Smadi M, 2017, INFORM PROCESS MANAG, V53, P640, DOI 10.1016/j.ipm.2017.01.002
   Batet M, 2013, APPL INTELL, V38, P29, DOI 10.1007/s10489-012-0355-y
   Ben Aouicha M, 2016, ENG APPL ARTIF INTEL, V50, P265, DOI 10.1016/j.engappai.2016.01.033
   Camacho-Collados J, 2016, ARTIF INTELL, V240, P36, DOI 10.1016/j.artint.2016.07.005
   Camacho-Collados J, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (IJCNLP), VOL 2, P1
   Choumane A, 2014, SOC NETW ANAL MIN, V4, DOI 10.1007/s13278-014-0175-7
   Finkelstein L, 2001, WWW, DOI DOI 10.1145/371920.372094
   Formica A, 2002, COMPUT J, V45, P583, DOI 10.1093/comjnl/45.6.583
   Formica A, 2006, INFORM SCIENCES, V176, P2624, DOI 10.1016/j.ins.2005.11.014
   GALIL Z, 1986, COMPUT SURV, V18, P23
   Gurevych I, 2005, LECT NOTES ARTIF INT, V3651, P767
   Harispe S., 2015, SYNTHESIS LECT HUMAN, VVolume 8, P1
   Hill F, 2015, COMPUT LINGUIST, V41, P665, DOI 10.1162/COLI_a_00237
   Jiang YC, 2017, INFORM PROCESS MANAG, V53, P248, DOI 10.1016/j.ipm.2016.09.001
   Jiang YC, 2015, INFORM PROCESS MANAG, V51, P215, DOI 10.1016/j.ipm.2015.01.001
   Joubarne C, 2011, LECT NOTES ARTIF INT, V6657, P216, DOI 10.1007/978-3-642-21043-3_26
   Kaptein R, 2013, ARTIF INTELL, V194, P111, DOI 10.1016/j.artint.2012.06.003
   Leacock C, 1998, LANG SPEECH & COMMUN, P265
   Li P, 2017, ENG APPL ARTIF INTEL, V65, P268, DOI 10.1016/j.engappai.2017.07.027
   Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005
   Lin D, 1998, ELECTRICAL OVERSTRESS/ELECTROSTATIC DISCHARGE SYMPOSIUM PROCEEDINGS, P29
   Liu HZ, 2012, J SYST SOFTWARE, V85, P370, DOI 10.1016/j.jss.2011.08.029
   Medelyan O, 2009, INT J HUM-COMPUT ST, V67, P716, DOI 10.1016/j.ijhcs.2009.05.004
   Mikolov T., 2013, ADV NEURAL INFORM PR
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936
   Mohamed M, 2019, INFORM PROCESS MANAG, V56, P1356, DOI 10.1016/j.ipm.2019.04.003
   Nasir JA, 2019, INFORM PROCESS MANAG, V56, P1605, DOI 10.1016/j.ipm.2019.04.007
   Ponzetto SP, 2007, J ARTIF INTELL RES, V30, P181, DOI 10.1613/jair.2308
   Qu R, 2018, INFORM PROCESS MANAG, V54, P1002, DOI 10.1016/j.ipm.2018.07.002
   Resnik P, 1995, INT JOINT CONF ARTIF, P448
   RUBENSTEIN H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657
   Sanchez D, 2012, EXPERT SYST APPL, V39, P7718, DOI 10.1016/j.eswa.2012.01.082
   Sanchez D, 2011, KNOWL-BASED SYST, V24, P297, DOI 10.1016/j.knosys.2010.10.001
   Schwartz R, 2015, BEST PRACTICES IN ENDODONTICS: A DESK REFERENCE, P258
   Seco N, 2004, FRONT ARTIF INTEL AP, V110, P1089
   SHANNON CE, 1948, BELL SYST TECH J, V27, P623, DOI 10.1002/j.1538-7305.1948.tb00917.x
   Singer P, 2013, INT J SEMANT WEB INF, V9, P41, DOI 10.4018/ijswis.2013100103
   Song XB, 2014, IEEE ACM T COMPUT BI, V11, P468, DOI 10.1109/TCBB.2013.176
   Taieb Mohamed Ali Hadj, 2012, Data and Knowledge Engineering. Third International Conference, ICDKE 2012. Proceedings, P128, DOI 10.1007/978-3-642-34679-8_13
   Taieb MAH, 2013, KNOWL-BASED SYST, V50, P260, DOI 10.1016/j.knosys.2013.06.015
   Wang JZ, 2007, BIOINFORMATICS, V23, P1274, DOI 10.1093/bioinformatics/btm087
   Zhu GG, 2018, EXPERT SYST APPL, V101, P8, DOI 10.1016/j.eswa.2018.02.011
   Zhu GG, 2017, IEEE T KNOWL DATA EN, V29, P72, DOI 10.1109/TKDE.2016.2610428
   Zhu XH, 2019, ADAPT PHYS ACT Q, V36, P492, DOI 10.1123/apaq.2019-0040
   Zili Zhou, 2008, 2008 Second International Conference on Future Generation Communication and Networking Symposia (FGCNS), P85, DOI 10.1109/FGCNS.2008.16
NR 47
TC 15
Z9 15
U1 0
U2 14
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD MAY
PY 2020
VL 57
IS 3
AR 102188
DI 10.1016/j.ipm.2019.102188
PG 19
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA LH1LF
UT WOS:000528550100019
DA 2022-08-23
ER

PT J
AU Fecho, K
   Balhoff, J
   Bizon, C
   Byrd, WE
   Hang, S
   Koslicki, D
   Rensi, SE
   Schmitt, PL
   Wawer, MJ
   Williams, M
   Ahalt, SC
AF Fecho, Karamarie
   Balhoff, James
   Bizon, Chris
   Byrd, William E.
   Hang, Sui
   Koslicki, David
   Rensi, Stefano E.
   Schmitt, Patrick L.
   Wawer, Mathias J.
   Williams, Mark
   Ahalt, Stanley C.
TI Application of MCAT questions as a testing tool and evaluation metric
   for knowledge graph-based reasoning systems
SO CTS-CLINICAL AND TRANSLATIONAL SCIENCE
LA English
DT Article
AB "Knowledge graphs" (KGs) have become a common approach for representing biomedical knowledge. In a KG, multiple biomedical data sets can be linked together as a graph representation, with nodes representing entities, such as "chemical substance" or "genes," and edges representing predicates, such as "causes" or "treats." Reasoning and inference algorithms can then be applied to the KG and used to generate new knowledge. We developed three KG-based question-answering systems as part of the Biomedical Data Translator program. These systems are typically tested and evaluated using traditional software engineering tools and approaches. In this study, we explored a team-based approach to test and evaluate the prototype "Translator Reasoners" through the application of Medical College Admission Test (MCAT) questions. Specifically, we describe three "hackathons," in which the developers of each of the three systems worked together with a moderator to determine whether the applications could be used to solve MCAT questions. The results demonstrate progressive improvement in system performance, with 0% (0/5) correct answers during the first hackathon, 75% (3/4) correct during the second hackathon, and 100% (5/5) correct during the final hackathon. We discuss the technical and sociologic lessons learned and conclude that MCAT questions can be applied successfully in the context of moderated hackathons to test and evaluate prototype KG-based question-answering systems, identify gaps in current capabilities, and improve performance. Finally, we highlight several published clinical and translational science applications of the Translator Reasoners.
C1 [Fecho, Karamarie; Balhoff, James; Bizon, Chris; Schmitt, Patrick L.; Ahalt, Stanley C.] Univ N Carolina, Renaissance Comp Inst, Chapel Hill, NC 27599 USA.
   [Byrd, William E.] Univ Alabama Birmingham, Birmingham, AL USA.
   [Hang, Sui] Inst Syst Biol, Seattle, WA USA.
   [Koslicki, David] Penn State Univ, University Pk, PA 16802 USA.
   [Rensi, Stefano E.] Stanford Univ, Stanford, CA 94305 USA.
   [Wawer, Mathias J.] Broad Inst, Cambridge, MA USA.
   [Williams, Mark] Natl Ctr Adv Translat Sci, Bethesda, MD USA.
RP Fecho, K (通讯作者)，Univ N Carolina, Renaissance Comp Inst, Chapel Hill, NC 27599 USA.
EM kfecho@copperlineprofessionalsolutions.com
FU National Center for Advancing Translational Sciences (NCATS), National
   Institutes of Health; NCATS Biomedical Data Translator program
   [OT3TR002026, OT3TR002020, OT3TR002025]
FX Support for this work was provided by the intramural research program
   within the National Center for Advancing Translational Sciences (NCATS),
   National Institutes of Health, and the NCATS Biomedical Data Translator
   program (awards OT3TR002026, OT3TR002020, OT3TR002025,
CR Austin CP, 2019, CTS-CLIN TRANSL SCI, V12, P85, DOI 10.1111/cts.12595
   Bizon C, 2019, J CHEM INF MODEL, V59, P4968, DOI 10.1021/acs.jcim.9b00683
   Fecho K, 2019, J BIOMED INFORM, V100, DOI 10.1016/j.jbi.2019.103325
   Fecho K, 2019, CTS-CLIN TRANSL SCI, V12, P91, DOI 10.1111/cts.12592
   Fecho K, 2019, CTS-CLIN TRANSL SCI, V12, P86, DOI 10.1111/cts.12591
   Khan Academy, MCAT TEST PREP
   Miller FW., 2020, AMIA 2020 ANN S NOV
   Morton K, 2019, BIOINFORMATICS, V35, P5382, DOI 10.1093/bioinformatics/btz604
   Mungall CM., STANDARDIZED BIOL KN
   Nicholson DN, 2020, COMPUT STRUCT BIOTEC, V18, P1414, DOI 10.1016/j.csbj.2020.05.017
   Prager JM., 2018, QUESTION ANSWERING C
   Shefchek KA, 2020, NUCLEIC ACIDS RES, V48, pD704, DOI 10.1093/nar/gkz997
   Siva V., ARE HACKATHONS GOOD
   Swanner N., DICE
   Tauberer J., 2017, RUN SUCCESSFUL HACKA
NR 15
TC 0
Z9 0
U1 2
U2 8
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1752-8054
EI 1752-8062
J9 CTS-CLIN TRANSL SCI
JI CTS-Clin. Transl. Sci.
PD SEP
PY 2021
VL 14
IS 5
BP 1719
EP 1724
DI 10.1111/cts.13021
EA APR 2021
PG 6
WC Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Research & Experimental Medicine
GA WQ2LD
UT WOS:000638067200001
PM 33742785
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Su, C
   Tong, J
   Zhu, YJ
   Cui, P
   Wang, F
AF Su, Chang
   Tong, Jie
   Zhu, Yongjun
   Cui, Peng
   Wang, Fei
TI Network embedding in biomedical data science
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Review
DE biomedical networks; biomedical knowledge graphs; biomedical
   informatics; network embedding; graph embedding; network-based learning
ID TARGET INTERACTION PREDICTION; MINIMUM CURVILINEARITY; PROTEIN
   INTERACTIONS; KNOWLEDGE GRAPH; LARGE-SCALE; ONTOLOGY; DISEASE; MICRORNA;
   BIOLOGY; REDUCTION
AB Owning to the rapid development of computer technologies, an increasing number of relational data have been emerging in modern biomedical research. Many network-based learning methods have been proposed to perform analysis on such data, which provide people a deep understanding of topology and knowledge behind the biomedical networks and benefit a lot of applications for human healthcare. However, most network-based methods suffer from high computational and space cost. There remain challenges on handling high dimensionality and sparsity of the biomedical networks. The latest advances in network embedding technologies provide new effective paradigms to solve the network analysis problem. It converts network into a low-dimensional space while maximally preserves structural properties. In this way, downstream tasks such as link prediction and node classification can be done by traditional machine learning methods. In this survey, we conduct a comprehensive review of the literature on applying network embedding to advance the biomedical domain. We first briefly introduce the widely used network embedding models. After that, we carefully discuss how the network embedding approaches were performed on biomedical networks as well as how they accelerated the downstream tasks in biomedical science. Finally, we discuss challenges the existing network embedding applications in biomedical domains are faced with and suggest several promising future directions for a better improvement in human healthcare.
C1 [Su, Chang; Wang, Fei] Cornell Univ, Weill Cornell Med, Dept Healthcare Policy & Res, Div Hlth Informat, 425 East 61 St, New York, NY 10065 USA.
   [Tong, Jie] NYU, Dept Mech & Aerosp Engn, New York, NY USA.
   [Zhu, Yongjun] Sungkyunkwan Univ, Dept Lib & Informat Sci, Seoul, South Korea.
   [Cui, Peng] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China.
RP Wang, F (通讯作者)，Cornell Univ, Weill Cornell Med, Dept Healthcare Policy & Res, Div Hlth Informat, 425 East 61 St, New York, NY 10065 USA.
EM few2001@med.cornell.edu
OI Su, Chang/0000-0003-4019-6389
FU Office of Naval Research [N00014-18-1-2585]; National Science Foundation
   [IIS-1716432]
FX Office of Naval Research (N00014-18-1-2585); National Science Foundation
   (IIS-1716432).
CR Abdelaziz I, 2017, J WEB SEMANT, V44, P104, DOI 10.1016/j.websem.2017.06.002
   Ahmed A., 2013, P 22 INT C WORLD WID, P37, DOI 10.1145/2488388.2488393
   Airoldi E.M., 2006, P INT BIOM SOC ANN M P INT BIOM SOC ANN M, VVolume 15
   Airoldi EM, 2008, J MACH LEARN RES, V9, P1981
   Albert R, 2005, J CELL SCI, V118, P4947, DOI 10.1242/jcs.02714
   Alshahrani M, 2017, BIOINFORMATICS, V33, P2723, DOI 10.1093/bioinformatics/btx275
   Alvarez-Garcia I, 2005, DEVELOPMENT, V132, P4653, DOI 10.1242/dev.02073
   Angelelli JB, 2008, ADV DATA ANAL CLASSI, V2, P3, DOI 10.1007/s11634-008-0018-3
   [Anonymous], 2018, IEEE T KNOWL DATA EN
   Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Barabasi AL, 2004, NAT REV GENET, V5, P101, DOI 10.1038/nrg1272
   Barabasi AL, 2007, NEW ENGL J MED, V357, P404, DOI 10.1056/NEJMe078114
   Belkin M., ADV NEURAL INFORM PR, P585
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Bordes Antoine, 2011, AAAI
   Buchanan M, 2012, NETWORKCELL BIOL
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Cannistraci CV, 2013, BIOINFORMATICS, V29, P199, DOI 10.1093/bioinformatics/btt208
   Cannistraci CV, 2010, BIOINFORMATICS, V26, pi531, DOI 10.1093/bioinformatics/btq376
   Cao SS, 2016, AAAI CONF ARTIF INTE, P1145
   Chang SY, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P119, DOI 10.1145/2783258.2783296
   Chen HJ, 2009, BRIEF BIOINFORM, V10, P177, DOI 10.1093/bib/bbp002
   Chen T, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P295, DOI 10.1145/3018661.3018735
   Chen X, 2002, NUCLEIC ACIDS RES, V30, P412, DOI 10.1093/nar/30.1.412
   Cho H, 2015, LECT N BIOINFORMAT, V9029, P62, DOI 10.1007/978-3-319-16706-0_9
   Choi E, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P787, DOI 10.1145/3097983.3098126
   Choi E, 2017, J AM MED INFORM ASSN, V24, P361, DOI 10.1093/jamia/ocw112
   Choi Youngduck, 2016, AMIA Jt Summits Transl Sci Proc, V2016, P41
   Cobanoglu MC, 2013, J CHEM INF MODEL, V53, P3399, DOI 10.1021/ci400219z
   Dai W, 2015, COMPUT MATH METHOD M, V2015, DOI 10.1155/2015/275045
   Defferrard M, P 30 INT C NEUR INF, P3844
   DEJONG S, 1993, CHEMOMETR INTELL LAB, V18, P251, DOI 10.1016/0169-7439(93)85002-X
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Dong YX, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P135, DOI 10.1145/3097983.3098036
   Edwards AM, 2004, DRUG DISCOV TODAY, V9, pS32
   Ezzat A, 2017, METHODS, V129, P81, DOI 10.1016/j.ymeth.2017.05.016
   Ezzat A, 2017, IEEE ACM T COMPUT BI, V14, P646, DOI 10.1109/TCBB.2016.2530062
   Feng J, 2016, FIFTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P557
   Gavin AC, 2002, NATURE, V415, P141, DOI 10.1038/415141a
   Goh KI, 2007, P NATL ACAD SCI USA, V104, P8685, DOI 10.1073/pnas.0701361104
   Goyal P, 2018, KNOWL-BASED SYST, V151, P78, DOI 10.1016/j.knosys.2018.03.022
   Graves A., 2012, 29 INT C MACH LEARN
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Hofmann T., 1995, Advances in Neural Information Processing Systems 7, P459
   Huang EW, 2018, BIOCOMPUT-PAC SYM, P578
   Huang X, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P731, DOI 10.1145/3018661.3018667
   Huang Z., 2017, ARXIV170105291
   Ito T, 2001, P NATL ACAD SCI USA, V98, P4569, DOI 10.1073/pnas.061034498
   James I, 2007, GEN PRACT, V56, P193
   Janjic V, 2012, BRIEF FUNCT GENOMICS, V11, P522, DOI 10.1093/bfgp/els037
   Jenatton R, P 25 INT C NEUR INF, P3167
   Ji G, P 53 ANN M ASS COMP, V1, P687
   Ji G, AAAI C ART INT, P985
   Jiang QH, 2009, NUCLEIC ACIDS RES, V37, pD98, DOI 10.1093/nar/gkn714
   Josifoski M, 2017, P 14 INT C INF INF T, P200
   Kibbe WA, 2015, NUCLEIC ACIDS RES, V43, pD1071, DOI 10.1093/nar/gku1011
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Kito K, INTRO SYSTEMS BIOL, P160
   Klein T. E., 2001, Pharmacogenomics Journal, V1, P167
   Kohler S, 2008, AM J HUM GENET, V82, P949, DOI 10.1016/j.ajhg.2008.02.013
   Kohler S, 2014, NUCLEIC ACIDS RES, V42, pD966, DOI 10.1093/nar/gkt1026
   Krogan NJ, 2006, NATURE, V440, P637, DOI 10.1038/nature04670
   Kuchaiev O, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000454
   Kulmanov M, 2018, BIOINFORMATICS, V34, P660, DOI 10.1093/bioinformatics/btx624
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lei YK, 2012, BMC BIOINFORMATICS, V13, DOI 10.1186/1471-2105-13-S7-S3
   Levy O, P 27 INT C NEUR INF, P2177
   Li GH, 2017, IEEE ACCESS, V5, P24032, DOI 10.1109/ACCESS.2017.2766758
   Li J, 2016, BRIEF BIOINFORM, V17, P2, DOI 10.1093/bib/bbv020
   Li XY, 2017, NUCLEIC ACIDS RES, V45, DOI 10.1093/nar/gkx750
   Liang B, 2015, INT CONF SOFTW ENG, P894, DOI 10.1109/ICSESS.2015.7339198
   Lin Y, AAAI C ART INT, P2181
   Liu CR, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P705, DOI 10.1145/2783258.2783352
   Luo YA, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-00680-8
   Lynam-Lennon N, 2009, BIOL REV, V84, P55, DOI 10.1111/j.1469-185X.2008.00061.x
   Meola Nicola, 2009, Pathogenetics, V2, P7, DOI 10.1186/1755-8417-2-7
   Mikolov T, ARXIV13013781
   Moerchen F, 2017, P 2010 SIAM INT C DA, P315
   Morchen F, 2007, DATA MIN KNOWL DISC, V15, P181, DOI 10.1007/s10618-007-0070-1
   Nickel Maximilian, 2013, Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2013. Proceedings: LNCS 8190, P617, DOI 10.1007/978-3-642-40994-3_40
   Nickel M, P 28 INT C MACH LEAR, P809
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Ou MD, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1105, DOI 10.1145/2939672.2939751
   Perozzi B, 2016, COMPUT RES REPOSITOR
   Perozzi B., 2014, P 20 ACM SIGKDD INT, P701, DOI DOI 10.1145/2623330.2623732
   Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323
   Shang FH, 2016, AAAI CONF ARTIF INTE, P2016
   Shen Z., 2017, COMPLEXITY, V2017, P1
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Stanovsky G, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P142
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319
   Tsuyuzaki K, 2017, 1 INT WORKSH HET NET
   Wang DX, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1225, DOI 10.1145/2939672.2939753
   Wang F, 2013, IEEE T PATTERN ANAL, V35, P272, DOI 10.1109/TPAMI.2012.111
   Wang M, 2017, ARXIV171208875
   Wang M., 2017, ARXIV171005980
   Wang PW, 2017, J ASSOC INF SCI TECH, V68, P2649, DOI 10.1002/asi.23876
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang S, 2017, BIORXIV168450, DOI [10.1101/168450, DOI 10.1101/168450]
   Wang S, 2017, BIOCOMPUT-PAC SYM, P27, DOI 10.1142/9789813207813_0004
   Wang S, 2015, BIOINFORMATICS, V31, P357, DOI 10.1093/bioinformatics/btv260
   Wang Z, AAAI C ART INT, P1112
   Weinshilboum R, 2003, NEW ENGL J MED, V348, P529, DOI 10.1056/NEJMra020021
   Wishart DS, 2006, NUCLEIC ACIDS RES, V34, pD668, DOI 10.1093/nar/gkj067
   WOLD S, 1987, CHEMOMETR INTELL LAB, V2, P37, DOI 10.1016/0169-7439(87)80084-9
   Wu ZK, 2013, MOL BIOSYST, V9, P1268, DOI 10.1039/c3mb25382a
   Xu LC, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P741, DOI 10.1145/3018661.3018723
   Yamanishi Y, 2008, BIOINFORMATICS, V24, pI232, DOI 10.1093/bioinformatics/btn162
   Yamanishi Y, 2014, NUCLEIC ACIDS RES, V42, pW39, DOI 10.1093/nar/gku337
   Yang B, INT C LEARN REPR ICL
   Yang PY, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-S1-S5
   You ZH, 2010, BIOINFORMATICS, V26, P2744, DOI 10.1093/bioinformatics/btq510
   Zeng XX, 2017, BMC MED GENOMICS, V10, DOI 10.1186/s12920-017-0313-y
   Zhang W, 2017, NPJ PRECIS ONCOL, V1, DOI 10.1038/s41698-017-0029-7
   Zhao C, 2018, ARTIF INTELL MED, V87, P49, DOI 10.1016/j.artmed.2018.03.005
   Zhao SD, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3497
   Zheng XD, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P1025
   Zhong G., 2016, J FINANC DATA SCI, V2, P265, DOI [10.1016/j.jfds.2017.05.001., DOI 10.1016/J.JFDS.2017.05.001]
   Zhu L, 2015, IEEE T NANOBIOSCI, V14, P528, DOI 10.1109/TNB.2015.2420754
   Zhu YJ, 2019, BRIEF BIOINFORM, V20, P1308, DOI 10.1093/bib/bbx169
   Zitnik M, 2018, BIOINFORMATICS, DOI [10.1101/258814, DOI 10.1101/258814]
   Zitnik M, 2016, BIOCOMPUT-PAC SYM, P81
   Zong NS, 2017, BIOINFORMATICS, V33, P2337, DOI 10.1093/bioinformatics/btx160
NR 128
TC 63
Z9 63
U1 6
U2 42
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1467-5463
EI 1477-4054
J9 BRIEF BIOINFORM
JI Brief. Bioinform.
PD JAN
PY 2020
VL 21
IS 1
BP 182
EP 197
DI 10.1093/bib/bby117
PG 16
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA KQ0HC
UT WOS:000516610800016
PM 30535359
DA 2022-08-23
ER

PT J
AU Reformat, MZ
   Yager, RR
   Chen, JX
AF Reformat, Marek Z.
   Yager, Ronald R.
   Chen, Jesse X.
TI DEFINITIONS OF CONCEPTS AND IMPRECISION
SO TWMS JOURNAL OF PURE AND APPLIED MATHEMATICS
LA English
DT Article
DE knowledge representation; fuzzy logic; logic of vagueness;
   classification and discrimination; cluster analysis; learning and
   adaptive systems in artificial intelligence
AB Knowledge graphs become an important form of representing data and information. Their intrinsic ability to express semantics via relations enables development of novel methods of processing data and building data models.
   In the paper, we propose a methodology for generating definitions of concepts and constructing their hierarchy. It is a fully data-driven process that uses information about multiple entities represented in a form of a knowledge graph. In this work, we state that a concept is defined via relations between the concept and other concepts. We perform a thorough analysis of relations and determine their levels of importance and degrees of their contributions to the definitions. This allows us to include impression reflecting the dependence of the construction process on the context in which it is performed - a limited amount of available data in our case.
   We provide details of the proposed approach, and illustrate its performance presenting a case study using a set of facts from dbpedia.org. In the study, we construct a structure of concepts and investigate how importance of relations between them changes when levels of concept abstractions change.
C1 [Reformat, Marek Z.; Chen, Jesse X.] Univ Alberta, Edmonton, AB, Canada.
   [Reformat, Marek Z.] Univ Social Sci, Warsaw, Poland.
   [Yager, Ronald R.] King Abdelaziz Univ, Jeddah, Saudi Arabia.
   [Yager, Ronald R.] Iona Coll, New Rochelle, NY USA.
RP Reformat, MZ (通讯作者)，Univ Alberta, Edmonton, AB, Canada.; Reformat, MZ (通讯作者)，Univ Social Sci, Warsaw, Poland.
EM reformat@ualberta.ca; yager@panix.com; jesse.chen@ualberta.ca
CR Berners-Lee T., 2001, SCI AM, V284, P34
   Christodoulou K., 2015, EDBT ICDT WORKSH, V19, P1, DOI [10.1007/978-3-662-46562-2_1, DOI 10.1007/978-3-662-46562-2_1]
   Ferrara A., 2013, EDBT ICDT WORKSH, P75, DOI DOI 10.1145/2457317.2457330
   Giannini S, 2013, LECT NOTES BUS INF P, V160, P220
   Gurrutxaga I, 2009, ICEIS 2009 : PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL AIDSS, P300
   Kim H.G., 2012, ICUIMC 12 P 6 INT C, P1
   Lalithsena S, 2013, 2013 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1, P205, DOI 10.1109/WI-IAT.2013.206
   Szekely GJ, 2005, J CLASSIF, V22, P151, DOI 10.1007/s00357-005-0012-9
   ZADEH LA, 1984, COMMUN ACM, V27, P304, DOI 10.1145/358027.358032
   ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X
   Zadeh PDH, 2013, J AMB INTEL HUM COMP, V4, P515, DOI 10.1007/s12652-012-0154-7
   Zadeh PDH, 2013, INFORM SCIENCES, V250, P21, DOI 10.1016/j.ins.2013.06.056
NR 12
TC 0
Z9 0
U1 0
U2 4
PU INST APPLIED MATHEMATICS
PI BAKU
PA BAKU STATE UNIV, Z KHALILOV, 23, BAKU, AZ1148, AZERBAIJAN
SN 2076-2585
EI 2219-1259
J9 TWMS J PURE APPL MAT
JI TWMS J. Pure Appl. Math.
PY 2021
VL 12
IS 1
SI SI
BP 14
EP 28
PG 15
WC Mathematics, Applied; Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA RU8UZ
UT WOS:000645418000003
DA 2022-08-23
ER

PT J
AU Menon, A
   Krdzavac, NB
   Kraft, M
AF Menon, Angiras
   Krdzavac, Nenad B.
   Kraft, Markus
TI From database to knowledge graph - using data in chemistry
SO CURRENT OPINION IN CHEMICAL ENGINEERING
LA English
DT Review
ID WORLD-WIDE-WEB; CHEMICAL MARKUP; XML VOCABULARY; INFORMATION;
   ENTHALPIES; MOLECULES; UNIVERSE
AB Over the last couple of decades, the scientific community has made large efforts to process and store experimental and computational chemical data and information on the world wide web. This review summarizes several databases and ontologies available on the web for researchers to use. We also discuss briefly the categories of chemistry data that are stored, its main usage and how it can be accessed and understood in the framework of the Semantic Web.
C1 [Menon, Angiras; Krdzavac, Nenad B.; Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, West Site,Philippa Fawcett Dr, Cambridge CB3 0AS, England.
   [Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, West Site,Philippa Fawcett Dr,62 Nanyang Dr, Singapore 637459, Singapore.
   [Menon, Angiras; Krdzavac, Nenad B.; Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore CARES, CREATE Tower,1 Create Way, Singapore 138602, Singapore.
RP Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, West Site,Philippa Fawcett Dr, Cambridge CB3 0AS, England.; Kraft, M (通讯作者)，Nanyang Technol Univ, Sch Chem & Biomed Engn, West Site,Philippa Fawcett Dr,62 Nanyang Dr, Singapore 637459, Singapore.; Kraft, M (通讯作者)，Cambridge Ctr Adv Res & Educ Singapore CARES, CREATE Tower,1 Create Way, Singapore 138602, Singapore.
EM mk306@cam.ac.uk
RI ; Kraft, Markus/D-7243-2016
OI Menon, Angiras/0000-0002-6810-2797; Kraft, Markus/0000-0002-4293-8924
FU Johnson Matthey; Singapore National Research Foundation (NRF) through
   the Campus for Research Excellence and Technological Enterprise (CREATE)
   program; Alexander von Humboldt foundation
FX AM acknowledges Johnson Matthey for financial support. The authors also
   acknowledge the financial support of the Singapore National Research
   Foundation (NRF) through the Campus for Research Excellence and
   Technological Enterprise (CREATE) program. MK gratefully acknowledges
   the support of the Alexander von Humboldt foundation.
CR Adams N, 2008, J CHEM INF MODEL, V48, P2118, DOI 10.1021/ci8002123
   [Anonymous], 2019, CASREACT CAS CHEM RE
   [Anonymous], 2019, CAS REGISTRY DATABAS
   Bishop KJM, 2006, ANGEW CHEM INT EDIT, V45, P5348, DOI 10.1002/anie.200600881
   Chagrov A. V., 1997, OXFORD LOGIC GUIDES, V35
   DAY NE, 2008, CRYSTALEYE
   Farazi F, 2019, LINKING REACTION MEC
   Farazi F., 2019, ONTOKIN ONTOLOGY CHE
   Fialkowski M, 2005, ANGEW CHEM INT EDIT, V44, P7263, DOI 10.1002/anie.200502272
   Frenklach M, 2007, P COMBUST INST, V31, P125, DOI 10.1016/j.proci.2006.08.121
   Ghahremanpour MM, 2018, SCI DATA, V5, DOI 10.1038/sdata.2018.62
   Gkoutos GV, 2001, J CHEM INF COMP SCI, V41, P1124, DOI 10.1021/ci000406v
   Glimm B, 2014, J AUTOM REASONING, V53, P245, DOI 10.1007/s10817-014-9305-1
   Goodman J, 2009, COMPUTER SOFTWARE RE
   Grzybowski BA, 2009, NAT CHEM, V1, P31, DOI [10.1038/NCHEM.136, 10.1038/nchem.136]
   Hait D, 2018, J CHEM THEORY COMPUT, V14, P1969, DOI 10.1021/acs.jctc.7b01252
   Hastings J, 2016, NUCLEIC ACIDS RES, V44, pD1214, DOI 10.1093/nar/gkv1031
   Hastings J, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0025513
   Heller SR, 2015, J CHEMINFORMATICS, V7, DOI 10.1186/s13321-015-0068-4
   Holliday GL, 2006, J CHEM INF MODEL, V46, P145, DOI 10.1021/ci0502698
   Jacob PM, 2018, REACT CHEM ENG, V3, P102, DOI 10.1039/c7re00129k
   Johnson R III, CCCBDB COMPUTATIONAL, V101
   Kim S, 2019, NUCLEIC ACIDS RES, V47, pD1102, DOI 10.1093/nar/gky1033
   Kim S, 2016, J CHEMINFORMATICS, V8, DOI 10.1186/s13321-016-0142-6
   Kraft M, 2010, PHILOS T R SOC A, V368, P3633, DOI 10.1098/rsta.2010.0124
   Krdzavac N, 2019, J CHEM INF MODEL, V59, P3154, DOI 10.1021/acs.jcim.9b00227
   Kuhn S, 2007, J CHEM INF MODEL, V47, P2015, DOI 10.1021/ci600531a
   Lawson AJ, 2014, ACS SYM SER, V1164, P127
   Lopez MF, 1999, IEEE INTELL SYST APP, V14, P37, DOI 10.1109/5254.747904
   Maler E., 2000, EXTENSIBLE MARKUP LA, V5th
   Meloche K. J., INTRIGUING RECORDS C, P21
   Murray-Rust P, 2003, J CHEM INF COMP SCI, V43, P757, DOI 10.1021/ci0256541
   Murray-Rust P, 1999, J CHEM INF COMP SCI, V39, P928, DOI 10.1021/ci990052b
   O'Boyle NM, 2012, J CHEMINFORMATICS, V4, DOI 10.1186/1758-2946-4-22
   Ostlund NS, 2015, GNVC GAINESVILLE COR
   Ostlund NS, 2019, P 6 INT WORKSH SEM W
   Phadungsukanan W, 2012, J CHEMINFORMATICS, V4, DOI 10.1186/1758-2946-4-15
   Ramakrishnan R, 2014, SCI DATA, V1, DOI 10.1038/sdata.2014.22
   Ruddigkeit L, 2012, J CHEM INF MODEL, V52, P2864, DOI 10.1021/ci300415d
   Ruscic B, 2004, J PHYS CHEM A, V108, P9979, DOI 10.1021/jp047912y
   Ruscic B, J PHYS C SERIES, V16, P561
   Simmie JM, 2015, J PHYS CHEM A, V119, P10511, DOI 10.1021/acs.jpca.5b06054
   Smith JS, 2017, SCI DATA, V4, DOI 10.1038/sdata.2017.193
   Steiner S, 2019, SCIENCE, V363, P144, DOI 10.1126/science.aav2211
   Stojanovic N, 2018, J APPL LOG-IFCOLOG, V5, P1605
   Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
   Taylor KR, 2006, J CHEM INF MODEL, V46, P939, DOI 10.1021/ci050378m
   Townsend JA, 2011, J CHEMINFORMATICS, V3, DOI 10.1186/1758-2946-3-39
   WEININGER D, 1988, J CHEM INF COMP SCI, V28, P31, DOI 10.1021/ci00057a005
NR 49
TC 18
Z9 18
U1 3
U2 33
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 2211-3398
J9 CURR OPIN CHEM ENG
JI Curr. Opin. Chem. Eng.
PD DEC
PY 2019
VL 26
BP 33
EP 37
DI 10.1016/j.coche.2019.08.004
PG 5
WC Biotechnology & Applied Microbiology; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biotechnology & Applied Microbiology; Engineering
GA JW7DP
UT WOS:000503208700007
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Papastefanatos, G
   Meimaris, M
   Vassiliadis, P
AF Papastefanatos, George
   Meimaris, Marios
   Vassiliadis, Panos
TI Relational schema optimization for RDF-based knowledge graphs
SO INFORMATION SYSTEMS
LA English
DT Article
DE Schema optimization; Query processing; Knowledge graphs; RDF data
ID SYSTEM; SPARQL
AB Characteristic sets (CS) organize RDF triples based on the set of properties associated with their subject nodes. This concept was recently used in indexing techniques, as it can capture the implicit schema of RDF data. While most CS-based approaches yield significant improvements in space and query performance, they fail to perform well when answering complex query workloads in the presence of schema heterogeneity, i.e., when the number of CSs becomes very large, resulting in a highly partitioned data organization. In this paper, we address this problem by introducing a novel technique, for merging CSs based on their hierarchical structure. Our method employs a lattice to capture the hierarchical relationships between CSs, identifies dense CSs and merges dense CSs with their ancestors. We have implemented our algorithm on top of a relational backbone, where each merged CS is stored in a relational table, and therefore, CS merging results in a smaller number of required tables to host the source triples of a dataset. Moreover, we perform an extensive experimental study to evaluate the performance and impact of merging to the storage and querying of RDF datasets, indicating significant improvements. We also conduct a sensitivity analysis to identify the stability and any possible weaknesses of our algorithm, and report on our results. (C) 2021 The Authors. Published by Elsevier Ltd.
C1 [Papastefanatos, George; Meimaris, Marios] Athena Res Ctr, Athens, Hellas, Greece.
   [Vassiliadis, Panos] Univ Ioannina, Ioannina, Hellas, Greece.
RP Papastefanatos, G (通讯作者)，Athena Res Ctr, Athens, Hellas, Greece.
EM gpapas@athenarc.gr; m.meimaris@athenarc.gr; pvassil@cs.uoi.gr
OI Papastefanatos, George/0000-0002-9273-9843
FU project VisualFacts, Greece (Hellenic Foundation for Research and
   Innovation) [1614]
FX This work is funded by the project VisualFacts, Greece (#1614-1st Call
   of the Hellenic Foundation for Research and Innovation Research Projects
   for the support of post-doctoral researchers) .U
CR Abadi DJ, 2007, P 33 INT C VER LARG, P411
   Aluc G, 2014, LECT NOTES COMPUT SC, V8796, P197, DOI 10.1007/978-3-319-11964-9_13
   Anagnostopoulos I., 2016, P 19 C EXT DAT TECHN, P281, DOI [10. 5441/, 10.5441/002/edbt.2016.27]
   Bornea MA, 2013, SIGMOD, P121, DOI DOI 10.1145/2463676.2463718
   Erling O, 2010, SEMANTIC WEB INFORMATION MANAGEMENT, P501, DOI 10.1007/978-3-642-04329-1_21
   Gubichev A., 2014, EDBT, P439, DOI [10.5441/002/edbt.2014.40, DOI 10.5441/002/EDBT.2014]
   Guo YB, 2005, J WEB SEMANT, V3, P158, DOI 10.1016/j.websem.2005.06.005
   Gupta H, 1997, PROC INT CONF DATA, P208, DOI 10.1109/ICDE.1997.581755
   Janik M, 2005, LECT NOTES COMPUT SC, V3729, P431
   K W., 2006, P 2 INT WORKSH SCAL, P35
   Mailis T, 2019, INT CONF MANAGE DATA, P1499, DOI 10.1145/3299869.3319864
   Meimaris M, 2018, INFORM SYST, V75, P56, DOI 10.1016/j.is.2018.02.010
   Meimaris M, 2017, PROC INT CONF DATA, P497, DOI 10.1109/ICDE.2017.106
   Meimaris M, 2017, PROC INT CONF DATA, P1559, DOI 10.1109/ICDE.2017.227
   Meimaris Marios, 2020, 22 INT WORKSH DES OP, P36
   Meimaris Marios, 2016, P668, DOI 10.5441/002/edbt.2016.78
   Pham MD, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P864, DOI 10.1145/2736277.2741121
   Pham MD, 2016, LECT NOTES COMPUT SC, V9981, P463, DOI 10.1007/978-3-319-46523-4_28
   Montoya G, 2017, LECT NOTES COMPUT SC, V10587, P471, DOI 10.1007/978-3-319-68288-4_28
   Neumann T, 2011, PROC INT CONF DATA, P984, DOI 10.1109/ICDE.2011.5767868
   Neumann T, 2010, VLDB J, V19, P91, DOI 10.1007/s00778-009-0165-y
   Papailiou N, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P909, DOI 10.1145/2588555.2594535
   Schatzle A, 2016, PROC VLDB ENDOW, V9, P804, DOI 10.14778/2977797.2977806
   Schatzle A, 2014, LECT NOTES COMPUT SC, V8796, P164, DOI 10.1007/978-3-319-11964-9_11
   Sidirourgos L, 2008, PROC VLDB ENDOW, V1, P1553
   Weiss C, 2008, PROC VLDB ENDOW, V1, P1008, DOI 10.14778/1453856.1453965
   Wylot M, 2018, ACM COMPUT SURV, V51, DOI 10.1145/3177850
   Yuan PP, 2013, PROC VLDB ENDOW, V6, P517, DOI 10.14778/2536349.2536352
NR 28
TC 0
Z9 0
U1 1
U2 4
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0306-4379
EI 1873-6076
J9 INFORM SYST
JI Inf. Syst.
PD FEB
PY 2022
VL 104
AR 101754
DI 10.1016/j.is.2021.101754
EA NOV 2021
PG 19
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XK8QP
UT WOS:000727723200006
OA hybrid
DA 2022-08-23
ER

PT J
AU Xie, W
   Wang, B
   Li, C
   Xie, DM
   Auclair, J
AF Xie, Wei
   Wang, Bo
   Li, Cheng
   Xie, Dongming
   Auclair, Jared
TI Interpretable biomanufacturing process risk and sensitivity analyses for
   quality-by-design and stability control
SO NAVAL RESEARCH LOGISTICS
LA English
DT Article
DE Bayesian network; bioprocess risk analysis; manufacturing process
   stability control; process causal interdependence; sensitivity analysis
ID PREDICTION; NETWORK; OPTIMIZATION; OPERATIONS; MODEL
AB While biomanufacturing plays a significant role in supporting the economy and ensuring public health, it faces critical challenges, including complexity, high variability, lengthy lead time, and very limited process data, especially for personalized new cell and gene biotherapeutics. Driven by these challenges, we propose an interpretable semantic bioprocess probabilistic knowledge graph and develop a game theory based risk and sensitivity analyses for production process to facilitate quality-by-design and stability control. Specifically, by exploring the causal relationships and interactions of critical process parameters and product quality attributes, we create a Bayesian network based probabilistic knowledge graph characterizing the complex causal interdependencies of all factors. Then, we introduce a Shapley value based sensitivity analysis, which can correctly quantify the variation contribution from each input factor on the outputs (i.e., productivity, product quality). Since the bioprocess model coefficients are learned from limited process observations, we derive the Bayesian posterior distribution to quantify model uncertainty and further develop the Shapley value based sensitivity analysis to evaluate the impact of estimation uncertainty from each set of model coefficients. Therefore, the proposed bioprocess risk and sensitivity analyses can identify the bottlenecks, guide the reliable process specifications and the most informative data collection, and improve production stability.
C1 [Xie, Wei; Wang, Bo] Northeastern Univ, Dept Mech & Ind Engn, Boston, MA 02115 USA.
   [Li, Cheng] Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore, Singapore.
   [Xie, Dongming] Univ Massachusetts, Dept Chem Engn, Lowell, MA USA.
   [Auclair, Jared] Northeastern Univ, Dept Chem & Chem Biol, Boston, MA 02115 USA.
RP Xie, W (通讯作者)，Northeastern Univ, Dept Mech & Ind Engn, Boston, MA 02115 USA.
EM w.xie@northeastern.edu
FU UML-WPI seed grant; Singapore Ministry of Education Academic Research
   Funds Tier 1 [R-155-000-201-114, R-155-000-223-114]
FX Dongming Xie's research was supported by UML-WPI seed grant. Singapore
   Ministry of Education Academic Research Funds Tier 1, Grant/Award
   Numbers: R-155-000-201-114, R-155-000-223-114.
CR Ayech N., 2012, IFAC P VOLUMES, V45, P606, DOI [10.3182/20120829-3-MX-2028.00198, DOI 10.3182/20120829-3-MX-2028.00198]
   de Lira LFB, 2010, FUEL, V89, P405, DOI 10.1016/j.fuel.2009.05.028
   Borchert D, 2019, BIOPROC BIOSYST ENG, V42, P245, DOI 10.1007/s00449-018-2029-6
   Borgonovo E, 2016, EUR J OPER RES, V248, P869, DOI 10.1016/j.ejor.2015.06.032
   Castillo E, 1997, IEEE T SYST MAN CY A, V27, P412, DOI 10.1109/3468.594909
   Castro J, 2009, COMPUT OPER RES, V36, P1726, DOI 10.1016/j.cor.2008.04.004
   Coleman MC, 2006, BIOTECHNOL BIOENG, V95, P412, DOI 10.1002/bit.20961
   Doran P.M, 1995, BIOPROCESS ENG PRINC, DOI [DOI 10.1017/CBO9781107415324.004, 10.1017/CBO9781107415324.004]
   Doran PM, 2013, BIOPROCESS ENGINEERING PRINCIPLES, 2ND EDITION, P1
   Feng N, 2014, INFORM SCIENCES, V256, P57, DOI 10.1016/j.ins.2013.02.036
   Fleischhacker AJ, 2011, EUR J OPER RES, V211, P496, DOI 10.1016/j.ejor.2011.01.004
   Gelman A., 2004, BAYESIAN DATA ANAL, V2nd
   Hassan SS, 2013, BMC SYST BIOL, V7, DOI 10.1186/1752-0509-7-S1-S5
   HELTON JC, 1993, RELIAB ENG SYST SAFE, V42, P327, DOI 10.1016/0951-8320(93)90097-I
   I.H.T. Guideline, 2009, PHARM DEV Q8 R2 CURR, V4
   Kabir G, 2015, EUR J OPER RES, V240, P220, DOI 10.1016/j.ejor.2014.06.033
   Kaminsky P, 2015, PHARM BIOPROCESS, V3, P61, DOI 10.4155/PBP.14.57
   Koller D, 2009, PROBABILISTIC GRAPHI
   Kulkarni NS, 2015, WINT SIMUL C PROC, P2260, DOI 10.1109/WSC.2015.7408338
   Kyriakopoulos S, 2018, BIOTECHNOL J, V13, DOI 10.1002/biot.201700229
   Lakhdar K, 2007, BIOTECHNOL PROGR, V23, P1383, DOI 10.1021/bp0701362
   Leachman RC, 2014, EUR J OPER RES, V238, P327, DOI 10.1016/j.ejor.2014.03.002
   Leland P., 2018, MANUFACTURING SERVIC, V22, P223
   Li YF, 2006, CHEM ENG TECHNOL, V29, P313, DOI 10.1002/ceat.200500182
   Li YF, 2018, J PHARM INNOV, V13, P36, DOI 10.1007/s12247-017-9301-1
   Lim AC, 2004, COMPUT CHEM ENG, V28, P727, DOI 10.1016/j.compchemeng.2004.02.013
   Lu QG, 2018, J PROCESS CONTR, V71, P90, DOI 10.1016/j.jprocont.2018.09.009
   Lundberg S., 2017, ARXIV PREPRINT ARXIV
   Martagan T, 2018, OPER RES, V66, P18, DOI 10.1287/opre.2017.1661
   Martagan T, 2016, IIE TRANS, V48, P440, DOI 10.1080/0740817X.2015.1101523
   Moullec ML, 2013, J MECH DESIGN, V135, DOI 10.1115/1.4023514
   Ojha R, 2018, INT J PROD RES, V56, P5795, DOI 10.1080/00207543.2018.1467059
   Otto R., 2014, SCI OPERATIONS QUEST
   Owen AB, 2014, SIAM-ASA J UNCERTAIN, V2, P245, DOI 10.1137/130936233
   Pharmaceutical Current Good Manufacturing Practices (CGMPs), 2004, TECHN REP
   Prinsloo NM, 2008, APPL CATAL A-GEN, V344, P20, DOI 10.1016/j.apcata.2008.03.041
   Rabitz H, 1999, J MATH CHEM, V25, P197, DOI 10.1023/A:1019188517934
   Rader RA, 2019, SINGLE-USE TECHNOLOGY IN BIOPHARMACEUTICAL MANUFACTURE, 2ND EDITION, P193
   Rathore AS, 2010, ANAL BIOANAL CHEM, V398, P137, DOI 10.1007/s00216-010-3781-x
   Severson K, 2015, COMPUT CHEM ENG, V80, P30, DOI 10.1016/j.compchemeng.2015.05.006
   Shapley LS., 1953, CONTRIBUTIONS THEORY, V2, P307
   Sobol I.M., 1993, MATH MODEL COMPUT EX, V1, P407, DOI DOI 10.18287/0134-2452-2015-39-4-459-461
   Sokolov M, 2018, BIOTECHNOL J, V13, DOI 10.1002/biot.201700461
   Song EH, 2016, SIAM-ASA J UNCERTAIN, V4, P1060, DOI 10.1137/15M1048070
   Steinwandter V, 2019, DRUG DISCOV TODAY, V24, P1795, DOI 10.1016/j.drudis.2019.06.005
   Telenko C, 2014, J MECH DESIGN, V136, DOI 10.1115/1.4027983
   Troyanskaya OG, 2003, P NATL ACAD SCI USA, V100, P8348, DOI 10.1073/pnas.0832373100
   van der Gaag LC, 2007, STUD FUZZ SOFT COMP, V213, P103
   Wagner HM, 1995, OPER RES, V43, P948, DOI 10.1287/opre.43.6.948
   Walsh G., 2013, PHARM BIOTECHNOLOGY
   Wang YB, 2018, J MECH DESIGN, V140, DOI 10.1115/1.4039201
   Wechselberger P, 2012, BIOPROC BIOSYST ENG, V35, P1637, DOI 10.1007/s00449-012-0754-9
   Zhai QQ, 2014, EUR J OPER RES, V239, P449, DOI 10.1016/j.ejor.2014.05.021
   Zi Z, 2011, IET SYST BIOL, V5, P336, DOI 10.1049/iet-syb.2011.0015
NR 54
TC 0
Z9 0
U1 1
U2 3
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0894-069X
EI 1520-6750
J9 NAV RES LOG
JI Nav. Res. Logist.
PD APR
PY 2022
VL 69
IS 3
BP 461
EP 483
DI 10.1002/nav.22019
EA AUG 2021
PG 23
WC Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Operations Research & Management Science
GA ZP7KG
UT WOS:000686008000001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Xie, YX
   Yang, K
   Luo, P
AF Xie, Yuxia
   Yang, Kai
   Luo, Pan
TI LogM: Log Analysis for Multiple Components of Hadoop Platform
SO IEEE ACCESS
LA English
DT Article
DE Anomaly detection; Support vector machines; Big Data; Predictive models;
   Maintenance engineering; Distributed databases; Analytical models; Log
   analysis; Hadoop; anomaly detection; failure diagnosis
ID ANOMALY DETECTION
AB The Hadoop platform provides a powerful software framework for distributed storage and processing of massive amounts of data. It is at the heart of big data processing and has found numerous applications in diverse areas, ranging from environmental monitoring to security analysis. To facilitate the storage and processing of big data, a Hadoop platform typically runs on a cluster of servers and may scale up to process big data over thousands of hardware nodes. However, the growing scale and complexity of the Hadoop platform also make it increasingly challenging to manage and operate. In this paper, we present a framework called LogM that leverages not only the deep learning model, but also the knowledge graph technology for failure prediction and analysis of the Hadoop cluster. In particular, we first develop a CAB net (Convolutional Neural Network (CNN) with attention-based Bi-directional Long Short Term Memory (Bi-LSTM)) architecture to effectively learn the temporal dynamics from the sequential log data, which allows us to predict system failures. We then adopt a knowledge graph approach for failure analysis and diagnosis. Extensive experiments have been carried out to assess the performance of the proposed approach. It is seen that LogM is highly effective in predicting and diagnosing system failures.
C1 [Xie, Yuxia; Yang, Kai; Luo, Pan] Tongji Univ, Dept Comp Sci, Shanghai 201800, Peoples R China.
RP Yang, K (通讯作者)，Tongji Univ, Dept Comp Sci, Shanghai 201800, Peoples R China.
EM kaiyang@tongji.edu.cn
FU National Natural Science Foundation of China [61771013]; Fundamental
   Research Funds for the Central Universities of China; Fundamental
   Research Funds of Shanghai Jiading District
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61771013, in part by the Fundamental
   Research Funds for the Central Universities of China, and in part by the
   Fundamental Research Funds of Shanghai Jiading District.
CR Ankerst M, 1999, SIGMOD RECORD, VOL 28, NO 2 - JUNE 1999, P49
   [Anonymous], 2010, P USENIX ANN TECH C
   [Anonymous], 2014, P SDM WORKSH HET LEA
   Chuah E., 2010, P INT C HIGH PERF CO P INT C HIGH PERF CO, P1
   Chuah E, 2015, 2015 ELEVENTH EUROPEAN DEPENDABLE COMPUTING CONFERENCE (EDCC), P225, DOI 10.1109/EDCC.2015.19
   Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492
   Debnath B, 2018, INT CON DISTR COMP S, P1052, DOI 10.1109/ICDCS.2018.00105
   Du M, 2017, CCS'17: PROCEEDINGS OF THE 2017 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P1285, DOI 10.1145/3133956.3134015
   Fan JQ, 2014, NATL SCI REV, V1, P293, DOI 10.1093/nsr/nwt032
   George L., 2011, HBASE DEFINITIVE GUI
   Han JW, 2000, SIGMOD RECORD, V29, P1
   He S., ARXIV200907237
   He SL, 2018, ESEC/FSE'18: PROCEEDINGS OF THE 2018 26TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, P60, DOI 10.1145/3236024.3236083
   He SL, 2016, PROC INT SYMP SOFTW, P207, DOI 10.1109/ISSRE.2016.21
   Hunt P., 2010, P USENIX ANN TECHNIC, P1
   Keras, PYTH DEEP LEARN LIB
   Khatuya S, 2018, IEEE INFOCOM SER, P2123
   Kim Y., 2014, P 2014 C EMP METH NA, P1746
   Kingma D. P., 2017, ARXIV
   Kobayashi S, 2018, IEEE T NETW SERV MAN, V15, P53, DOI 10.1109/TNSM.2017.2778096
   Le Q.V., 2014, COMPUT RES REPOS COR, P1, DOI [DOI 10.1145/2740908.2742760, 10.1145/2740908.2742760]
   Le SY, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017), P389, DOI 10.1109/ICWS.2017.135
   Li T, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1547, DOI 10.1145/3097983.3098022
   Liang YL, 2007, IEEE DATA MINING, P583, DOI 10.1109/ICDM.2007.46
   Lin QW, 2016, 2016 IEEE/ACM 38TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING COMPANION (ICSE-C), P102, DOI 10.1145/2889160.2889232
   Liu FC, 2019, PROCEEDINGS OF THE 2019 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'19), P1777, DOI 10.1145/3319535.3363224
   Meng W., 2019, P 28 INT JOINT C ART, V7, P4739, DOI DOI 10.24963/IJCAI.2019/658
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Mueller J, 2016, AAAI CONF ARTIF INTE, P2786
   Neville Jennifer, P 9 USENIX C NETW SY, P26
   Oliner A, 2012, COMMUN ACM, V55, P63, DOI 10.1145/2076450.2076466
   Oppenheimer D, 2003, USENIX ASSOCIATION PROCEEDINGS OF THE 4TH USENIX SYMPOSIUM ON INTERNET TECHNOLOGIES AND SYSTEMS (USITS'03), P1
   Platini M., 2021, P INT C DISTR COMP N P INT C DISTR COMP N, P116
   Shang WY, 2013, PROCEEDINGS OF THE 35TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2013), P402, DOI 10.1109/ICSE.2013.6606586
   Shvachko K., 2010, SYMPOSIUM, P1, DOI DOI 10.1109/MSST.2010.5496972
   Vavilapalli Vinod Kumar, 2013, C SOCC, P1, DOI 10.1145/2523616.2523633
   White T., 2012, HADOOP DEFINITIVE GU
   Xia B., 2020, INF SYST FRONTIERS, V7, P1
   Xu W, 2009, SOSP'09: PROCEEDINGS OF THE TWENTY-SECOND ACM SIGOPS SYMPOSIUM ON OPERATING SYSTEMS PRINCIPLES, P117
   Yang RP, 2019, IEEE ACCESS, V7, P181152, DOI 10.1109/ACCESS.2019.2953981
   Yuan Y, 2019, IEEE INT CONF CLOUD, P124, DOI 10.1109/CLOUD.2019.00031
   Zaharia M., 2010, HOTCLOUD, V10, P95
   Zhang K, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), P1291, DOI 10.1109/BigData.2016.7840733
   Zhang X, 2019, ESEC/FSE'2019: PROCEEDINGS OF THE 2019 27TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, P807, DOI 10.1145/3338906.3338931
   Zhang YL, 2019, PROCEEDINGS OF THE TWENTY-SEVENTH ACM SYMPOSIUM ON OPERATING SYSTEMS PRINCIPLES (SOSP '19), P131, DOI 10.1145/3341301.3359650
NR 45
TC 2
Z9 2
U1 12
U2 17
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2021
VL 9
BP 73522
EP 73532
DI 10.1109/ACCESS.2021.3076897
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA TJ5XU
UT WOS:000673555000001
OA gold
DA 2022-08-23
ER

PT J
AU Liu, H
   Li, YF
   Hong, R
   Li, ZM
   Li, M
   Pan, W
   Glowacz, A
   He, H
AF Liu, Hui
   Li, Yifan
   Hong, Rui
   Li, Zhenming
   Li, Ming
   Pan, Wei
   Glowacz, Adam
   He, Hao
TI Knowledge graph analysis and visualization of research trends on driver
   behavior
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Driver behavior; research trends; driving safety; knowledge graph;
   visualization
ID DRIVING BEHAVIORS; ROAD SAFETY; SIMULATION; VEHICLES
AB In order to trace the research trends and hotspots related to the field of driver behavior research both in China and worldwide, a bibliometric analysis was performed to systematically analyze 7208 scientific papers about driver behavior in the Web of Science database from 1957 to 2018, and the results were discussed from the 6 perspectives of growth trends of publications, geographical distribution, subject categories, institutions, authors and keywords distribution. The results show that research on driver behavior has been increasing rapidly since the 1990 s, but research in Europe and the United States started more than 20 years earlier than in China. Currently, transportation, engineering, computer science and behavioral science have replaced the traditional subject categories, becoming the most important domains in this field. Research on driver behavior focuses on safety and systems using simulation models which is interacted with almost everything concerning traffic operation and safety. The current trend is that more researchers are working on understand the driver-vehicle-system interactions under the environment of connected and autonomous vehicle. Further research is needed to focus on unsafe driving behavior based on driver-vehicle-system interactions. This study will provide scholars in related academia and industry with panoramic knowledge of driver behavior research, as well as research hotspots and future research directions.
C1 [Liu, Hui; Li, Yifan; Hong, Rui; He, Hao] China Jiliang Univ, Coll Qual & Safety Engn, Hangzhou 310018, Peoples R China.
   [Li, Zhenming] Zhejiang Univ Technol, Coll Educ, Hangzhou, Peoples R China.
   [Li, Ming; Pan, Wei] Cent S Univ, Sch Resource & Safety Engn, Changsha, Peoples R China.
   [Glowacz, Adam] AGH Univ Sci & Technol, Fac Elect Engn Automat Comp Sci & Biomed Engn, Al A Mickiewicza 30, Krakow, Poland.
RP Liu, H (通讯作者)，China Jiliang Univ, Coll Qual & Safety Engn, Hangzhou 310018, Peoples R China.
EM liuhui2003@126.com
RI Glowacz, Adam/I-1024-2019; Glowacz, Adam/N-6462-2013; liu,
   hui/K-2142-2015
OI Glowacz, Adam/0000-0003-0546-7083; Glowacz, Adam/0000-0003-0546-7083;
   liu, hui/0000-0002-4265-7241
FU National Natural Science Foundation of China [516742 24]; Science and
   Technology Project of Department of Education of Zhejiang Province
   [Y20184 0583]
FX This work was supported in part by the National Natural Science
   Foundation of China (No. 516742 24), the Science and Technology Project
   of Department of Education of Zhejiang Province (No. Y20184 0583).
CR Almahasneh H, 2014, TRANSPORT RES F-TRAF, V26, P218, DOI 10.1016/j.trf.2014.08.001
   Anstey KJ, 2005, CLIN PSYCHOL REV, V25, P45, DOI 10.1016/j.cpr.2004.07.008
   Bell JL, 2017, J SAFETY RES, V60, P125, DOI 10.1016/j.jsr.2016.12.008
   Benson Suzanne G, 2003, J Nurs Manag, V11, P315, DOI 10.1046/j.1365-2834.2003.00409.x
   Blanco-Mesa F, 2017, J INTELL FUZZY SYST, V32, P2033, DOI 10.3233/JIFS-161640
   Boldi P, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1629096.1629097
   Bragazzi NL, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15061121
   Chai C, 2014, ACCIDENT ANAL PREV, V63, P94, DOI 10.1016/j.aap.2013.10.023
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Cobo MJ, 2014, IEEE T INTELL TRANSP, V15, P901, DOI 10.1109/TITS.2013.2284756
   Daniel A, 2016, WIRELESS PERS COMMUN, V87, P461, DOI 10.1007/s11277-015-3078-7
   Engelbrecht J, 2015, IET INTELL TRANSP SY, V9, P924, DOI 10.1049/iet-its.2014.0248
   Graham-Rowe E, 2011, TRANSPORT RES A-POL, V45, P401, DOI 10.1016/j.tra.2011.02.001
   Grunbaum Jo Anne, 2004, Morbidity and Mortality Weekly Report, V53, P1
   Hatfield J, 2018, ACCIDENT ANAL PREV, V119, P1, DOI 10.1016/j.aap.2018.06.012
   Heinrich H.W., 1980, IND ACCIDENT PREVENT, V5th
   International Transport Forum, 2017, ROAD SAF ANN REP 201
   Jamson AH, 2013, TRANSPORT RES C-EMER, V30, P116, DOI 10.1016/j.trc.2013.02.008
   JIN HQ, 1991, IND HEALTH, V29, P23, DOI 10.2486/indhealth.29.23
   Johnson DA, 2011, IEEE INT C INTELL TR, P1609, DOI 10.1109/ITSC.2011.6083078
   Kalaiselvi V., 2018, INT J INNOVATIVE RES, V6
   Kaye SA, 2018, J SAFETY RES, V65, P141, DOI 10.1016/j.jsr.2018.02.012
   Kushwaha Anjani, 2018, INT J INNOVATIVE RES, V6
   Lal SKL, 2001, BIOL PSYCHOL, V55, P173, DOI 10.1016/S0301-0511(00)00085-5
   Lee JD, 2008, HUM FACTORS, V50, P404, DOI 10.1518/001872008X288547
   Li J, 2017, J LOSS PREVENT PROC, V49, P103, DOI 10.1016/j.jlp.2016.06.003
   Liu H, 2018, J MED SYST, V42, DOI 10.1007/s10916-018-1033-3
   Markkula G, 2012, HUM FACTORS, V54, P1117, DOI 10.1177/0018720812448474
   Merat N, 2014, TRANSPORT RES F-TRAF, V27, P274, DOI 10.1016/j.trf.2014.09.005
   National Bureau of Statistics of China, 2017, CHIN STAT YB 2017
   National Bureau of Statistics of China, 2016, CHIN STAT YB 2016
   Orosz G, 2010, PHILOS T R SOC A, V368, P4455, DOI 10.1098/rsta.2010.0205
   Paul A, 2017, IEEE SYST J, V11, P1249, DOI 10.1109/JSYST.2015.2411856
   Porter MM, 2015, TRAFFIC INJ PREV, V16, P24, DOI 10.1080/15389588.2014.894995
   Reason J, 1995, Qual Health Care, V4, P80, DOI 10.1136/qshc.4.2.80
   Shiffrin RM, 2004, P NATL ACAD SCI USA, V101, P5183, DOI 10.1073/pnas.0307852100
   Simons-Morton BG, 2014, J ADOLESCENT HEALTH, V54, pS61, DOI 10.1016/j.jadohealth.2013.11.021
   STACK HJ, 1957, AM J PUBLIC HEALTH N, V47, P446, DOI 10.2105/AJPH.47.4_Pt_1.446
   Standardization Administration of the People's Republic of China, 1986, 64411986 GBT STAND A
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vasvani Chitra Kalpesh, 2018, INT J INNOVATIVE RES, V6
   Wang J., 1983, J CHONGQING JIAOTONG, V2, P77
   Wang P, 2010, TRAFFIC INJ PREV, V11, P425, DOI 10.1080/15389581003754593
   WANG RB, 2003, J HIGHWAY TRANSPORTA, V20, P111
   Wang XS, 2016, IEEE T INTELL TRANSP, V17, P2583, DOI 10.1109/TITS.2016.2528508
   Wang XS, 2015, ACCIDENT ANAL PREV, V76, P110, DOI 10.1016/j.aap.2015.01.003
   Wang XS, 2013, ACCIDENT ANAL PREV, V56, P22, DOI 10.1016/j.aap.2013.02.026
   Wang Y, 2014, TRANSPORT RES F-TRAF, V26, P227, DOI 10.1016/j.trf.2014.08.003
   Wang Y, 2010, ERGONOMICS, V53, P404, DOI 10.1080/00140130903464358
   Wolfe A. W., 1994, CONTEMP SOCIOL, V91, P219
   Young MS, 2011, APPL ERGON, V42, P533, DOI 10.1016/j.apergo.2010.08.012
   Yu RJ, 2013, ACCIDENT ANAL PREV, V50, P371, DOI 10.1016/j.aap.2012.05.011
   Zeng Q, 2016, J SAFETY RES, V59, P105, DOI 10.1016/j.jsr.2016.10.005
   Zheng L, 2017, J INTELL TRANSPORT S, V21, P255, DOI 10.1080/15472450.2016.1254047
   Zou X, 2018, ACCIDENT ANAL PREV, V118, P131, DOI 10.1016/j.aap.2018.06.010
NR 55
TC 5
Z9 6
U1 4
U2 40
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2020
VL 38
IS 1
BP 495
EP 511
DI 10.3233/JIFS-179424
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA KC0DF
UT WOS:000506856200051
DA 2022-08-23
ER

PT J
AU Zheng, R
   Ma, H
   Wang, QY
   Fu, JM
   Jiang, ZW
AF Zheng, Rui
   Ma, Hao
   Wang, Qiuyun
   Fu, Jianming
   Jiang, Zhengwei
TI Assessing the Security of Campus Networks: The Case of Seven
   Universities
SO SENSORS
LA English
DT Article
DE campus network; network assets; threat intelligence; vulnerability
   detection
AB The network security situation of campus networks on CERNET (China Education and Research Network) has received great concern. However, most network managers have no complete picture of the network security because of its special management and the rapid growth of network assets. In this investigation, the security of campus networks belonging to seven universities in Wuhan was investigated. A tool called "WebHunt" was designed for campus networks, and with its help, the network security risks were found. Differently from existing tools for network probing, WebHunt can adopt the network scale and special rules of the campus network. According to the characteristics of campus websites, a series of functions were integrated into WebHunt, including reverse resolution of domain names, active network detection and fingerprint identification for software assets. Besides, WebHunt builds its vulnerability intelligence database with a knowledge graph structure and locates the vulnerabilities through matching knowledge graph information. Security assessments of seven universities presents WebHunt's applicability for campus networks. Besides, it also shows that many security risks are concealed in campus networks, such as non-compliance IP addresses and domain names, system vulnerabilities and so on. The security reports containing risks have been sent to the relevant universities, and positive feedback was received.
C1 [Zheng, Rui; Ma, Hao; Fu, Jianming] Wuhan Univ, Sch Cyber Sci & Engn, Key Lab Aerosp Informat Secur & Trusted Comp, Minist Educ, Wuhan 430072, Peoples R China.
   [Zheng, Rui; Wang, Qiuyun; Jiang, Zhengwei] Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China.
   [Jiang, Zhengwei] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049, Peoples R China.
RP Fu, JM (通讯作者)，Wuhan Univ, Sch Cyber Sci & Engn, Key Lab Aerosp Informat Secur & Trusted Comp, Minist Educ, Wuhan 430072, Peoples R China.
EM zr_12f@whu.edu.cn; 15827331132@163.com; wangqiuyun@iie.ac.cn;
   jmfu@whu.edu.cn; jiangzhengwei@iie.ac.cn
OI Wang, QiuYun/0000-0001-6101-9715; Zheng, Rui/0000-0002-9592-3283
FU National Key R&D Program of China [2016YFB0801004]; NSFC [61972297,
   U1636107]
FX This research was funded by National Key R&D Program of China (number
   2016YFB0801004) and NSFC (numbers 61972297 and U1636107).
CR Barnes J, 2013, 2013 ACM/IEEE SYMPOSIUM ON ARCHITECTURES FOR NETWORKING AND COMMUNICATIONS SYSTEMS (ANCS), P113, DOI 10.1109/ANCS.2013.6665187
   Bartlett G, 2007, IMC'07: PROCEEDINGS OF THE 2007 ACM SIGCOMM INTERNET MEASUREMENT CONFERENCE, P57
   Boranbayev AS, 2009, NONLINEAR ANAL-THEOR, V71, pE1633, DOI 10.1016/j.na.2009.02.002
   Bostock M., 2019, DATA DRIVEN DOCUMENT
   [曹来成 Cao Laicheng], 2016, [中国科学院大学学报, Journal of University of Chinese Academy of Sciences], V33, P562
   CERNIC, 1997, MEAS REG DOM NAM ED
   CERNIC, 2006, CERNET
   CERNIC, 2014, TECHNICAL REPORT
   Cert A., 2017, TECHNICAL REPORT
   Chen S., 2017, WHY CHINAS U ARE SO
   Dunigan T., 2007, CHATTER WIRE LOOK DH, P1
   Durumeric Z., 2013, 22 USENIX SEC S USEN, V8, P605, DOI DOI 10.1145/2663716.2663755
   Fjellskal E., PASSIVE REAL TIME AS
   Genge B, 2016, SECUR COMMUN NETW, V9, P2696, DOI 10.1002/sec.1262
   Gordon L., OFFICIAL NMAP PROJEC
   Graham R., 2013, ERRATA SECURITY BLOG
   Greenbone Networks, OP VULN ASS SYST OPE
   Huang Z, 2015, INT CONF SOFTW ENG, P1042, DOI 10.1109/ICSESS.2015.7339231
   Khademi AF, 2015, IEEE SOFTWARE, V32, P46, DOI 10.1109/MS.2015.77
   Lee D, 2002, 18TH ANNUAL COMPUTER SECURITY APPLICATIONS CONFERENCE, PROCEEDINGS, P321, DOI 10.1109/CSAC.2002.1176304
   Martinez E., 2013, VIRUSTOTAL PASSIVE D
   Matherly J., 2016, COMPLETE GUIDE SHODA
   Michael A.D., P0F PASSIVE OS FINGE
   Montz A. B., 1995, Proceedings Fifth Workshop on Hot Topics in Operating Systems (HotOS-V) (Cat. No.95TH8059), P58, DOI 10.1109/HOTOS.1995.513455
   [南世慧 Nan Shihui], 2018, [计算机科学, Computer Science], V45, P141
   Network C.E, 2015, STAY AW FAK U ED CN
   Panjwani S, 2005, I C DEPEND SYS NETWO, P602, DOI 10.1109/DSN.2005.18
   Rimmer V., 2017, ARXIV170806376
   Rosenblatt J., 2019, WHY HACKERS LOVE U
   Sanguino L.A.B., 2017, ARXIV170505347
   Seba Garcia, DNAMP WIKI
   Simon K, 2017, J UNIVERS COMPUT SCI, V23, P404
   Spiceworks Inc, 2020, PFSENSE
   Spiceworks Inc, SPIC
   Stevens W., 1994, TCP IP ILLUSTRATED, V1
   Tian YX, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON SMART CITY/SOCIALCOM/SUSTAINCOM (SMARTCITY), P153, DOI 10.1109/SmartCity.2015.63
   Walden J., CENSYS BETTER IPV4 S
   Wei D., 2018, J COMMUN, V39, P70
   [闫淑筠 Yan Shujun], 2016, [中国科学院大学学报, Journal of University of Chinese Academy of Sciences], V33, P679
NR 39
TC 3
Z9 3
U1 6
U2 16
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD JAN
PY 2021
VL 21
IS 1
AR 306
DI 10.3390/s21010306
PG 22
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA PQ0FU
UT WOS:000606227900001
PM 33466335
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Chen, YY
   Tao, YY
   Zheng, ZW
   Chen, D
AF Chen, Yuanyi
   Tao, Yanyun
   Zheng, Zengwei
   Chen, Dan
TI Graph-based service recommendation in Social Internet of Things
SO INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
LA English
DT Article
DE Graph embedding; object usage event; service recommendation; Social
   Internet of Things
ID CONTEXT
AB While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user's object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user's preferences. More exactly, we learn user's preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the "like" property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.
C1 [Chen, Yuanyi; Tao, Yanyun; Zheng, Zengwei; Chen, Dan] Zhejiang Univ City Coll, Hangzhou Key Lab IoT Technol & Applicat, 48 Huzhou St, Hangzhou 310015, Zhejiang, Peoples R China.
RP Zheng, ZW (通讯作者)，Zhejiang Univ City Coll, Hangzhou Key Lab IoT Technol & Applicat, 48 Huzhou St, Hangzhou 310015, Zhejiang, Peoples R China.
EM zhengzw@zucc.edu.cn
FU Young Scientists Fund of the National Natural Science Foundation of
   China [61802343]; Zhejiang Provincial Natural Science Foundation of
   China [LGF19F020019, LGN20F020003]; Intelligent Plant Factory of
   Zhejiang Province Engineering Lab
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: This work
   was supported in part by the Young Scientists Fund of the National
   Natural Science Foundation of China (grant no. 61802343), in part by the
   Zhejiang Provincial Natural Science Foundation of China (grant nos.
   LGF19F020019 and LGN20F020003), and in part by the Intelligent Plant
   Factory of Zhejiang Province Engineering Lab.
CR Atzori L, 2012, COMPUT NETW, V56, P3594, DOI 10.1016/j.comnet.2012.07.010
   Atzori L, 2011, IEEE COMMUN LETT, V15, P1193, DOI 10.1109/LCOMM.2011.090911.111340
   Beltran V, 2014, 2014 IEEE WORLD FORUM ON INTERNET OF THINGS (WF-IOT), P283, DOI 10.1109/WF-IoT.2014.6803173
   Cao BQ, 2019, J PARALLEL DISTR COM, V132, P177, DOI 10.1016/j.jpdc.2018.04.002
   Chen IR, 2016, IEEE T SERV COMPUT, V9, P482, DOI 10.1109/TSC.2014.2365797
   Chen YY, 2020, IEEE T SERV COMPUT, V13, P1100, DOI 10.1109/TSC.2017.2755009
   Chen YY, 2020, IEEE T SERV COMPUT, V13, P915, DOI 10.1109/TSC.2017.2715159
   Chen YY, 2020, IEEE INTERNET THINGS, V7, P2014, DOI 10.1109/JIOT.2019.2960822
   Chen YY, 2019, IEEE ACCESS, V7, P67666, DOI 10.1109/ACCESS.2019.2918754
   Chen YY, 2016, TELECOMMUN SYST, V62, P77, DOI 10.1007/s11235-015-9984-x
   Chen ZK, 2016, INT J COMMUN SYST, V29, P694, DOI 10.1002/dac.2930
   Fanizzi N., 2016, SAC, P327
   Felfernig A, 2017, PROCEDIA COMPUT SCI, V110, P504, DOI 10.1016/j.procs.2017.06.135
   Ferrag MA, 2017, IEEE COMMUN SURV TUT, V19, P3015, DOI 10.1109/COMST.2017.2718178
   Gardner M., 2015, P 2015 C EMP METH NA, P1488, DOI DOI 10.18653/V1/D15-1173
   Hussein D, 2015, IEEE CLOUD COMPUT, V2, P10, DOI 10.1109/MCC.2015.117
   Jeong H, 2019, CLUSTER COMPUT, V22, P1837, DOI 10.1007/s10586-017-1078-y
   Khelloufi A, 2021, IEEE INTERNET THINGS, V8, P1859, DOI 10.1109/JIOT.2020.3016659
   Lee JS, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), P276, DOI 10.1109/ICWS.2016.43
   Li J, 2017, IEEE ACCESS, V5, P19154, DOI 10.1109/ACCESS.2017.2756446
   Li WJ, 2020, IEEE INTERNET THINGS, V7, P5882, DOI 10.1109/JIOT.2019.2949352
   Li WJ, 2019, IEEE INTERNET THINGS, V6, P4844, DOI 10.1109/JIOT.2018.2872133
   Mashal I, 2015, INT CONF UBIQ FUTUR, P328, DOI 10.1109/ICUFN.2015.7182559
   Pattar S, 2018, IEEE COMMUN SURV TUT, V20, P2101, DOI 10.1109/COMST.2018.2825231
   Rasch K, 2013, SMART ASSISTANTS SMA
   Saleem Y, 2016, 2016 IEEE 3RD WORLD FORUM ON INTERNET OF THINGS (WF-IOT), P359, DOI 10.1109/WF-IoT.2016.7845500
   Subramaniyaswamy V, 2019, J SUPERCOMPUT, V75, P3184, DOI 10.1007/s11227-018-2331-8
   Tapia EM, 2004, LECT NOTES COMPUT SC, V3001, P158, DOI 10.1007/978-3-540-24646-6_10
   Wang HF, 2019, FUTURE GENER COMP SY, V100, P600, DOI 10.1016/j.future.2019.05.045
   Wang P., 2018, 2018 IEEE 19 WORKSH, P1
   Wang PQ, 2017, LECT NOTES COMPUT SC, V10586, P34, DOI 10.1007/978-3-319-67585-5_4
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wanigasekara N., 2016, P 6 INT C INT THINGS, P121
   Yao L, 2016, ACM T INTERNET TECHN, V16, DOI 10.1145/2837024
   Yao L, 2014, SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P855, DOI 10.1145/2600428.2609458
   Zhang JY, 2020, J INTERNET TECHNOL, V21, P1, DOI 10.3966/160792642020012101001
   Zhang JY, 2018, FRONT COMPUT SCI-CHI, V12, P1090, DOI 10.1007/s11704-017-6349-5
NR 37
TC 1
Z9 1
U1 6
U2 27
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1550-1477
J9 INT J DISTRIB SENS N
JI Int. J. Distrib. Sens. Netw.
PD APR
PY 2021
VL 17
IS 4
AR 15501477211009047
DI 10.1177/15501477211009047
PG 13
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA RR7FV
UT WOS:000643260100001
OA gold
DA 2022-08-23
ER

PT J
AU Kanza, S
   Frey, JG
AF Kanza, Samantha
   Frey, Jeremy Graham
TI A new wave of innovation in Semantic web tools for drug discovery
SO EXPERT OPINION ON DRUG DISCOVERY
LA English
DT Review
DE Drug discovery; semantic web; ontologies; semantic search; knowledge
   graph; linked data; inferencing
ID BIG DATA; ONTOLOGY; DATABASE; VACCINE; DESIGN; FUTURE; QSAR
AB Introduction: The use of semantic web technologies to aid drug discovery has gained momentum over recent years. Researchers in this domain have realized that semantic web technologies are key to dealing with the high levels of data for drug discovery. These technologies enable us to represent the data in a formal, structured, interoperable and comparable way, and to tease out undiscovered links between drug data (be it identifying new drug-targets or relevant compounds, or links between specific drugs and diseases). Areas covered: This review focuses on explaining how semantic web technologies are being used to aid advances in drug discovery. The main types of semantic web technologies are explained, outlining how they work and how they can be used in the drug discovery process, with a consideration of how the use of these technologies has progressed from their initial usage. Expert opinion: The increased availability of shared semantic resources (tools, data and importantly the communities) have enabled the application of semantic web technologies to facilitate semantic (context dependent) search across multiple data sources, which can be used by machine learning to produce better predictions by exploiting the semantic links in knowledge graphs and linked datasets.
C1 [Kanza, Samantha; Frey, Jeremy Graham] Univ Southampton, Dept Chem, Highfield Campus, Southampton, Hants, England.
RP Kanza, S; Frey, JG (通讯作者)，Univ Southampton, Dept Chem, Highfield Campus, Southampton, Hants, England.
EM s.kanza@soton.ac.uk; j.g.frey@soton.ac.uk
RI Frey, Jeremy G/B-8630-2011; Kanza, Samantha/Q-6550-2019; Frey, Jeremy
   G./ABC-1436-2021
OI Frey, Jeremy G/0000-0003-0842-4302; Kanza, Samantha/0000-0002-4831-9489;
   Frey, Jeremy G./0000-0003-0842-4302
FU Web Science Centre for Doctoral Training at the University of
   Southampton - UK Engineering Physical and Sciences Research Council
   (EPSRC) [EP/G036926/1]; Artificial Intelligence and Augmented
   Intelligence for Automated Investigations for Scientific Discovery
   Network+ (AI3SD) - UK Engineering Physical and Sciences Research Council
   (EPSRC) [EP/S000356/1]; EPSRC [EP/S000356/1] Funding Source: UKRI
FX This work was supported in part by the Web Science Centre for Doctoral
   Training at the University of Southampton, funded by the UK Engineering
   Physical and Sciences Research Council (EPSRC) under the Grant No:
   EP/G036926/1, and by the Artificial Intelligence and Augmented
   Intelligence for Automated Investigations for Scientific Discovery
   Network+ (AI3SD), which is also funded by the UK Engineering Physical
   and Sciences Research Council (EPSRC) under the Grant No: EP/S000356/1.
CR Abeyruwan S, 2014, J BIOMED SEMANT, V5, DOI 10.1186/2041-1480-5-S1-S5
   Andronis C, 2011, BRIEF BIOINFORM, V12, P357, DOI 10.1093/bib/bbr005
   Berners-Lee T, 2001, SCI AM, V284, P34, DOI 10.1038/scientificamerican0501-34
   BORKUM MI, 2014, J CHEMINFORMATICS, V6, DOI DOI 10.1186/1758-2946-6-18
   Brown N, 2018, PROGR MED CHEM, V57, P277, DOI 10.1016/bs.pmch.2017.12.003
   Chen B, 2016, CLIN PHARMACOL THER, V99, P285, DOI 10.1002/cpt.318
   CHEN B, 2010, BMC BIOINFORMATICS, V11, DOI DOI 10.1186/1471-2105-11-255
   Chen B, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002574
   Chen HM, 2018, DRUG DISCOV TODAY, V23, P1241, DOI 10.1016/j.drudis.2018.01.039
   Chen HJ, 2010, EXPERT OPIN DRUG DIS, V5, P413, DOI 10.1517/17460441003762709
   Chen L, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0126492
   Cheng FX, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002503
   Chichester C, 2015, DRUG DISCOV TODAY, V20, P399, DOI 10.1016/j.drudis.2014.11.006
   Clark AM, 2016, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.61
   CONSTANDT H, 2013, DRUG DISCOVERY WORLD, V14, P72, DOI DOI 10.3233/JPD-140364
   DASTGHEIB S, ACCELERATING DRUG DI, DOI DOI 10.1111/DOM.13553
   *DATAVERSITY, 2017, SEM WEB SEM TECHN TR, DOI DOI 10.1038/NRD4510
   Degtyarenko K, 2008, NUCLEIC ACIDS RES, V36, pD344, DOI 10.1093/nar/gkm791
   DJOKICPETROVIC M, 2019, INTERNET, V8, DOI DOI 10.1186/S13326-017-0151-Z
   DUNLOP I, 2013, OPEN PHACTS EXPLORER
   EKINS S, 2016, F1000RES, V5, DOI DOI 10.1016/J.BCP.2013.03.009
   Ekseth OK, 2018, IEEE INT C SEMANT CO, P403, DOI 10.1109/ICSC.2018.00080
   Ferrero E, 2018, BIODATA MIN, V11, DOI 10.1186/s13040-018-0171-y
   FOULGER RE, 2015, BMC MICROBIOL, V15, DOI DOI 10.1111/DOM.12932
   Frey JG, 2009, DRUG DISCOV TODAY, V14, P552, DOI 10.1016/j.drudis.2009.03.007
   FU G, 2016, BMC BIOINFORMATICS, V17, DOI DOI 10.1186/S12859-016-1005-X
   Gardner Stephen P, 2005, Drug Discov Today Technol, V2, P235, DOI 10.1016/j.ddtec.2005.08.004
   Gray AJG, 2014, SEMANT WEB, V5, P101, DOI 10.3233/SW-2012-0088
   Guha RV, 2016, COMMUN ACM, V59, P44, DOI 10.1145/2844544
   Han ZJ, 2018, CNS NEUROSCI THER, V24, P1253, DOI 10.1111/cns.13051
   HANNA J, 2013, J BIOMED SEMANT, V4, DOI DOI 10.1186/2041-1480-4-44
   HE Y, 2009, NAT PRECEDINGS, DOI DOI 10.2337/DB13-1609/-/DC1
   Herrero-Zazo M, 2015, J CHEM INF MODEL, V55, P1698, DOI 10.1021/acs.jcim.5b00119
   JING YK, 2018, AAPS J, V20, DOI DOI 10.1208/S12248-018-0210-0
   Katsila T, 2018, EXPERT OPIN DRUG DIS, V13, P791, DOI 10.1080/17460441.2018.1504767
   Katsila T, 2016, COMPUT STRUCT BIOTEC, V14, P177, DOI 10.1016/j.csbj.2016.04.004
   Lavecchia A, 2015, DRUG DISCOV TODAY, V20, P318, DOI 10.1016/j.drudis.2014.10.012
   Li K, 2018, JCI INSIGHT, V3, DOI 10.1172/jci.insight.98942
   LIN Y, 2017, J BIOMED SEMANT, V8, DOI DOI 10.1186/S13326-017-0161-X
   Lopez-Massaguer O, 2018, BIOINFORMATICS, V34, P131, DOI 10.1093/bioinformatics/btx566
   Lu P., 2018, ACCELERATED PATH CUR, P7, DOI [10.1007/978-3-319-73238-1_2, DOI 10.1007/978-3-319-73238-1_2]
   Machado CM, 2015, BRIEF BIOINFORM, V16, P89, DOI 10.1093/bib/bbt079
   MALAS TB, 2017, SEMANTIC KNOWLEDGE G
   Marcos E, 2013, J BIOMED SEMANT, V4, DOI 10.1186/2041-1480-4-40
   Marshall LJ, 2018, DRUG DISCOV TODAY, V23, P1824, DOI 10.1016/j.drudis.2018.05.038
   Miller Ryan A, 2018, F1000Res, V7, P75, DOI 10.12688/f1000research.13197.1
   Neumann EK, 2004, DRUG DISCOV TODAY BI, V2, P228, DOI DOI 10.1016/S1741-8364(04)02420-5
   Olier I, 2018, MACH LEARN, V107, P285, DOI 10.1007/s10994-017-5685-x
   *ONT, ONT PARTN DRUGBANK O, DOI DOI 10.1039/C3NP70052F
   PASSI A, 2018, J CHEMINFORMATICS, V10, DOI DOI 10.1186/S13321-018-0276-9
   PAULHEIM H, DATA MINING BACKGROU, DOI DOI 10.1038/NM.3009
   RAMSAY RR, 2018, CLIN TRANSL MED, V7, DOI DOI 10.1186/S40169-017-0181-2
   Ristoski P, 2016, J WEB SEMANT, V36, P1, DOI 10.1016/j.websem.2016.01.001
   SAHOO SS, 2018, INT J MED INFORM, DOI DOI 10.1007/S11065-013-9224-7
   SANG ST, 2018, BMC BIOINFORMATICS, V19, DOI DOI 10.1186/S12859-018-2167-5
   Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62
   Shen FC, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0160005
   Slenter DN, 2018, NUCLEIC ACIDS RES, V46, pD661, DOI 10.1093/nar/gkx1064
   Taglang G, 2016, GYNECOL ONCOL, V141, P17, DOI 10.1016/j.ygyno.2016.02.022
   Tang J, 2018, CELL CHEM BIOL, V25, P224, DOI 10.1016/j.chembiol.2017.11.009
   Tao C, 2014, J BIOMED SEMANT, V5, DOI 10.1186/2041-1480-5-16
   TETKO IV, 2008, OPEN APPL INFORM J, V2, P18
   Vazquez-Naya JM, 2010, CURR PHARM DESIGN, V16, P2724
   VISSER U, 2011, BMC BIOINFORMATICS, V12, DOI DOI 10.1186/1471-2105-12-257
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Weibel S, 1997, B AM SOC INFORM INF, V24, P9, DOI 10.1002/bult.70
   Wild DJ, 2012, DRUG DISCOV TODAY, V17, P469, DOI 10.1016/j.drudis.2011.12.019
   Wilkinson Mark D, 2016, Sci Data, V3, P160018, DOI 10.1038/sdata.2016.18
   Williams AJ, 2012, DRUG DISCOV TODAY, V17, P1188, DOI 10.1016/j.drudis.2012.05.016
   Xu J, 2002, MOLECULES, V7, P566, DOI 10.3390/70800566
   Zhang L, 2017, DRUG DISCOV TODAY, V22, P1680, DOI 10.1016/j.drudis.2017.08.010
   Zheng P, 2019, SCI ADV, V5, DOI 10.1126/sciadv.aau8317
NR 72
TC 12
Z9 12
U1 2
U2 18
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1746-0441
EI 1746-045X
J9 EXPERT OPIN DRUG DIS
JI Expert. Opin. Drug Discov.
PD MAY 4
PY 2019
VL 14
IS 5
BP 433
EP 444
DI 10.1080/17460441.2019.1586880
EA MAR 2019
PG 12
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA HY0XL
UT WOS:000463440700001
PM 30884989
OA Green Accepted, hybrid
DA 2022-08-23
ER

PT J
AU Yu, TL
   Yang, H
   Luo, XW
   Jiang, YF
   Wu, X
   Gao, JQ
AF Yu, Tianlong
   Yang, Hao
   Luo, Xiaowei
   Jiang, Yifeng
   Wu, Xiang
   Gao, Jingqi
TI Scientometric Analysis of Disaster Risk Perception: 2000-2020
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE disaster risk perception; CiteSpace; web of science; visual analysis;
   knowledge graph
ID DECISION-MAKING; GOOGLE-SCHOLAR; MEDIATING ROLE; SCIENCE; DETERMINANTS;
   ASSOCIATION; SCOPUS; WEB
AB This paper used 1526 works from the literature on disaster risk perception from 2000 to 2020 in the Web of Science core collection database as the research subject. The CiteSpace knowledge graph analysis tool was used to visual analyze the country, author, institution, discipline distribution, keywords, and keyword clustering mapping. The paper drew the following conclusions. Firstly, disaster risk perception research has experienced three stages of steady development, undulating growth, and rapid growth. Secondly, the field of disaster risk perception was mainly concentrated in the disciplines of engineering, natural science, and management science. Thirdly, meteorological disasters, earthquakes, nuclear radiation, and epidemics were the main disasters in the field of disaster risk perception. Residents and adolescents were the main subjects of research in the field of disaster risk perception. Fourthly, research on human risk behavior and risk psychology and research on disaster risk control and emergency management were two major research hotspots in the field of disaster risk perception. Finally, the research field of disaster risk perception is constantly expanding. There is a trend from theory to application and multi-perspective combination, and future research on disaster risk perception will be presented more systematically. The conclusion can provide a reference for disaster risk perception research, as well as directions for future research.
C1 [Yu, Tianlong; Yang, Hao; Wu, Xiang; Gao, Jingqi] China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China.
   [Luo, Xiaowei] City Univ Hong Kong, Dept Architecture & Civil Engn, Hong Kong 999077, Peoples R China.
   [Jiang, Yifeng] China Elect Power Res Inst, Beijing 100192, Peoples R China.
RP Yang, H (通讯作者)，China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China.
EM 1002184115@cugb.edu.cn; yanghao@cugb.edu.cn; xiaowluo@cityu.edu.hk;
   jiangyifeng@epri.sgcc.com.cn; wuxiang@cugb.edu.cn;
   2002190082@cugb.edu.cn
RI luo, xiaowei/GON-4131-2022
OI luo, xiaowei/0000-0002-9360-7308; Wu, Xiang/0000-0001-6317-8832; Yu,
   Tianlong/0000-0003-0759-9987
FU MOE (Ministry of Education in China) Project of Humanities and Social
   Sciences [18YJCZH191]; Fundamental Research Funds for the Central
   Universities of China [2652019073]
FX FundingThis research was funded by the MOE (Ministry of Education in
   China) Project of Humanities and Social Sciences (18YJCZH191) and the
   Fundamental Research Funds for the Central Universities of China
   (2652019073).
CR Allendoerfer K, 2005, INFOVIS 05: IEEE SYMPOSIUM ON INFORMATION VISUALIZATION, PROCEEDINGS, P195, DOI 10.1109/INFVIS.2005.1532147
   [Anonymous], 2020, LANCET, DOI DOI 10.1016/S0140-6736(20)30183-5
   Bailon-Moreno R, 2005, SCIENTOMETRICS, V63, P259, DOI 10.1007/s11192-005-0212-4
   Bauer R.A., 1961, RISK TAKING INFORM H, P389
   Botzen WJW, 2009, WATER RESOUR RES, V45, DOI 10.1029/2009WR007743
   Bubeck P, 2012, RISK ANAL, V32, P1481, DOI 10.1111/j.1539-6924.2011.01783.x
   CALLON M, 1991, SCIENTOMETRICS, V22, P155, DOI 10.1007/BF02019280
   Chatfield AT, 2015, J HOMEL SECUR EMERG, V12, P351, DOI 10.1515/jhsem-2014-0086
   Chen Chaomei, 2005, AMIA Annu Symp Proc, P121
   Cobo MJ, 2011, J AM SOC INF SCI TEC, V62, P1382, DOI 10.1002/asi.21525
   Costa DF, 2017, SCIENTOMETRICS, V111, P1775, DOI 10.1007/s11192-017-2371-5
   Cvetkovic VM, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17114124
   De Masi F, 2021, INT J DISAST RISK RE, V59, DOI 10.1016/j.ijdrr.2021.102251
   Falagas ME, 2008, FASEB J, V22, P338, DOI 10.1096/fj.07-9492LSF
   Fritz CE., 1961, CONT SOCIAL PROBLEMS, P651
   Gandia RM, 2019, TRANSPORT REV, V39, P9, DOI 10.1080/01441647.2018.1518937
   Gao JQ, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18010273
   Garfield E, 2009, J INFORMETR, V3, P173, DOI 10.1016/j.joi.2009.03.009
   Gibson S, 2015, HUM ECOL RISK ASSESS, V21, P205, DOI 10.1080/10807039.2014.902683
   Grothmann T, 2006, NAT HAZARDS, V38, P101, DOI 10.1007/s11069-005-8604-6
   Halpern-Felsher BL, 2001, HEALTH PSYCHOL, V20, P120, DOI 10.1037/0278-6133.20.2.120
   Ho MC, 2008, RISK ANAL, V28, P635, DOI 10.1111/j.1539-6924.2008.01040.x
   Hopf SC, 2015, RURAL REMOTE HEALTH, V15
   Hosseini MR, 2018, AUTOMAT CONSTR, V87, P235, DOI 10.1016/j.autcon.2017.12.002
   Hou L, 2020, INT J CONSTR MANAG, DOI 10.1080/15623599.2020.1768463
   Kunii Y, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0158821
   Li JG, 2017, ROUTL STUD CHIN ECON, P1
   Marshall TM, 2020, INT J DISAST RISK RE, V47, DOI 10.1016/j.ijdrr.2020.101557
   Martin-Martin A, 2018, J INFORMETR, V12, P1160, DOI 10.1016/j.joi.2018.09.002
   Martins VN, 2018, J HOMEL SECUR EMERG, V15, DOI 10.1515/jhsem-2017-0002
   McDermott BM, 2005, CAN J PSYCHIAT, V50, P137, DOI 10.1177/070674370505000302
   Miaomiao Zhang, 2020, International Journal of Information and Education Technology, V10, P394, DOI 10.18178/ijiet.2020.10.5.1396
   Olawumi TO, 2018, J CLEAN PROD, V183, P231, DOI 10.1016/j.jclepro.2018.02.162
   Perry Ronald W., 2005, WHAT IS DISASTER NEW
   Quarantelli EnricoLouis, 1998, WHAT IS DISASTER PER
   Seyedin H, 2019, J EDUC HEALTH PROMOT, V8, DOI 10.4103/jehp.jehp_200_18
   Sherry J, 2018, GLOBAL ENVIRON CHANG, V52, P190, DOI 10.1016/j.gloenvcha.2018.07.007
   SITKIN SB, 1992, ACAD MANAGE REV, V17, P9, DOI 10.2307/258646
   Sitkin SB, 1995, ACAD MANAGE J, V38, P1573, DOI 10.2307/256844
   Sjoberg L, 2000, RISK ANAL, V20, P1, DOI 10.1111/0272-4332.00001
   SLOVIC P, 1987, SCIENCE, V236, P280, DOI 10.1126/science.3563507
   STARR C, 1969, SCIENCE, V165, P1232, DOI 10.1126/science.165.3899.1232
   Steinhauser G, 2014, SCI TOTAL ENVIRON, V470, P800, DOI 10.1016/j.scitotenv.2013.10.029
   [苏飞 Su Fei], 2016, [灾害学, Journal of Catastrophology], V31, P145
   Synnestvedt Marie B, 2005, AMIA Annu Symp Proc, P724
   Trenberth KE, 2011, CLIM RES, V47, P123, DOI 10.3354/cr00953
   Wachinger G, 2013, RISK ANAL, V33, P1049, DOI 10.1111/j.1539-6924.2012.01942.x
   Wallace JW, 2016, J FLOOD RISK MANAG, V9, P125, DOI 10.1111/jfr3.12115
   WEINSTEIN G, 1983, AM POLIT SCI REV, V77, P203, DOI 10.2307/1956029
   Xue KJ, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18042106
   Yang H, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11184908
   Yoshida M, 2020, NURS EDUC TODAY, V94, DOI 10.1016/j.nedt.2020.104552
   You M, 2021, J MED INTERNET RES, V23, DOI 10.2196/24165
NR 53
TC 1
Z9 1
U1 25
U2 35
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD DEC
PY 2021
VL 18
IS 24
AR 13003
DI 10.3390/ijerph182413003
PG 19
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA XZ9HF
UT WOS:000737955900001
PM 34948613
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Lissandrini, M
   Mottin, D
   Palpanas, T
   Papadimitriou, D
   Velegrakis, Y
AF Lissandrini, Matteo
   Mottin, Davide
   Palpanas, Themis
   Papadimitriou, Dimitra
   Velegrakis, Yannis
TI Unleashing the Power of Information Graphs
SO SIGMOD RECORD
LA English
DT Article
AB Information graphs are generic graphs that model different types of information through nodes and edges. Knowledge graphs are the most common type of information graphs in which nodes represent entities and edges represent relationships among them. In this paper, we argue that exploitation of information graphs can lead into novel query answering capabilities that go beyond the existing capabilities of keyword search, and focus on one of them, namely, exemplar queries. Exemplar queries is a recently introduced paradigm that treats a user query as an example from the desired result set. In this paper, we describe the foundations of exemplar queries and the significant role of information graphs, and we present several applications and relevant research directions.
C1 [Lissandrini, Matteo; Mottin, Davide; Papadimitriou, Dimitra; Velegrakis, Yannis] Univ Trento, Trento, Italy.
   [Palpanas, Themis] Paris Descartes Univ, Paris, France.
RP Lissandrini, M (通讯作者)，Univ Trento, Trento, Italy.
EM ml@disi.unitn.eu; mottin@disi.unitn.eu; themis@mi.parisdescartes.fr;
   papadimitriou@disi.unitn.eu; velgias@disi.unitn.eu
OI Mottin, Davide/0000-0001-8256-2258; Velegrakis,
   Yannis/0000-0001-6332-0296; Lissandrini, Matteo/0000-0001-7922-5998
CR Anagnostopoulos A., 2010, WSDM
   Bergamaschi S., 2011, SIGMOD
   Bergamaschi S., 2011, ER
   Bhatia Sumit, 2011, SIGIR
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bodner R. C., 1996, CANADIAN AI
   Boldi P., 2011, IR, V14
   Borodin A., 2012, PODS
   Chakravarthy U. S., 1986, VLDB
   Guo F., 2009, WWW
   Han X., 2011, SIGIR
   Jayaram N., 2013, CORR
   Kasneci Gjergji, 2008, ICDE
   Kefato Z., 2013, KEYWORD QUERY GRAPH
   Khan A., 2011, P 2011 ACM SIGMOD IN
   Lao N., 2010, KDD
   Lin W., 2012, CIKM
   Mishra Chaitanya, 2009, EDBT
   Mottin D., 2014, PVLDB, V7
   Mottin D., 2013, PVLDB, V6
   Mottin D, 2013, INTELL DATA ANAL, V17, P837, DOI 10.3233/IDA-130609
   Mottin Davide, 2014, SIGMOD
   Ngo V. M., 2010, IJIIDS, V283
   Papadimitriou D., 2015, P ICDE
   Pound Jeffrey, 2012, CIKM
   Sarwar B., 2001, WWW
   Yan X., 2004, P ACM SIGMOD
NR 27
TC 6
Z9 6
U1 0
U2 3
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 0163-5808
EI 1943-5835
J9 SIGMOD REC
JI Sigmod Rec.
PD DEC
PY 2014
VL 43
IS 4
BP 21
EP 26
DI 10.1145/2737817.2737822
PG 6
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA CC2AI
UT WOS:000350146400003
DA 2022-08-23
ER

PT J
AU Teng, YP
   Huang, Y
   Yang, S
AF Teng, Youping
   Huang, Yue
   Yang, Shuai
TI Applying Knowledge Graph to Analyze the Historical Landscape Based on
   CiteSpace
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
ID SUSTAINABLE DEVELOPMENT; URBAN
AB The theory of "urban historical landscape" is gradually emerging in cultural heritage protection and urban planning in recent years. It was first proposed and promoted by UNESCO. In this study, the identification and evaluation are taken as the prerequisite for the protection and management of historical landscape. This paper uses CiteSpace to analyze the map of knowledge data to collect and sort out the global research status of urban historical landscape. In addition, the clustering function of knowledge graph software VOSviewer is used to analyze the knowledge clustering in the research field of urban historical landscape, and the research process and interdisciplinary development of urban historical landscape are obtained, to make some guiding suggestions for the future study of urban historical landscape. The results show that the study of urban historical landscape has experienced three stages. The early stage is the introduction and tracing stage, the middle stage is the diversification and enrichment stage, and the recent stage is the practice and construction stage. At present, it has become a multidisciplinary and multiperspective international research. The in-depth study of urban historical landscape undoubtedly opens a door for the traditional thought of urban heritage protection. At the same time, it gradually turned to more active management of urban historical landscape and also promoted the intersection of city, architecture, landscape architecture, anthropology, sociology, economics, and other disciplines from the side, with far-reaching influence. Reviewing and looking forward to studying urban historical landscape is more conducive to sustainable construction of the future. CiteSpace, as an excellent bibliometrics software, can help researchers sort out and display past research tracks in a novel visual way, to conduct future research better.
C1 [Teng, Youping; Yang, Shuai] Zhejiang Univ City Coll, Dept Coll Creat Studies, Hangzhou 310015, Peoples R China.
   [Huang, Yue] City Univ Macau, Fac Innovat & Design, Macau 999078, Peoples R China.
   [Yang, Shuai] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310063, Peoples R China.
RP Yang, S (通讯作者)，Zhejiang Univ City Coll, Dept Coll Creat Studies, Hangzhou 310015, Peoples R China.; Huang, Y (通讯作者)，City Univ Macau, Fac Innovat & Design, Macau 999078, Peoples R China.; Yang, S (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310063, Peoples R China.
EM yangs@zucc.edu.cn; samyang@zju.edu.cn
FU China Postdoctoral Science Foundation [2021M692781]
FX This work is supported by the China Postdoctoral Science Foundation
   (Grant No.: 2021M692781).
CR Bandarin F., 2012, HIST URBAN LANDSCAPE, DOI [10.1002/9781119968115, DOI 10.1002/9781119968115]
   Cao Y.M., 2019, URBAN DEV RES, V26, P13
   Chen Y., 2015, STUDIES SCI SCI, V33, P242
   Gao K, 2015, SCI TECHNOLOGY INFOR
   Girard LF, 2013, SUSTAINABILITY-BASEL, V5, P4329, DOI 10.3390/su5104329
   Heping L., 2002, CHINESE GARDEN, V2, P90
   Huang Y, 2019, APPL ECOL ENV RES, V17, P1093, DOI 10.15666/aeer/1701_10931108
   Soto JLL, 2011, CIUDADES, V14, P15
   Li Z.M., 2019, PLANNER, V35, P5
   Liu Z.Y., 2008, MAPPING SCI KNOWLEDG
   Orse R.V., 2012, CHINESE GARDEN, V28, P16
   Roberts P., 2000, URBAN REGENERATION H
   Rodwell D, 2008, J ARCHIT CONSERV, V14, P83, DOI 10.1080/13556207.2008.10785025
   UNESCO, 2011, PROP DES STAND SETT
   UNESCO, 2013, OP GUID
   UNESCO, 2017, APPL REC HIST URB LA
   UNESCO Culture urban future, 2016, CULT URB FUT GLOB RE
   Van Oers R., 2007, CITY AND TIME, V3, P43
   Veldpaus L, 2013, HIST ENVIRON POLICY, V4, P3, DOI 10.1179/1756750513Z.00000000022
   Zhang S., 2011, J TONGJI U, V22, P29
NR 20
TC 0
Z9 0
U1 15
U2 15
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1530-8669
EI 1530-8677
J9 WIREL COMMUN MOB COM
JI Wirel. Commun. Mob. Comput.
PD JAN 10
PY 2022
VL 2022
AR 3867541
DI 10.1155/2022/3867541
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 0B8HS
UT WOS:000774869900008
OA gold
DA 2022-08-23
ER

PT J
AU Shao, ZZ
   Tan, B
   Guo, Y
   Li, TZ
   Li, XM
   Fang, XY
   Wang, FR
   Zhang, Q
   Wang, HY
AF Shao, Zhuangzhuang
   Tan, Bo
   Guo, Yan
   Li, Tianze
   Li, Xiaomeng
   Fang, Xiyang
   Wang, Feiran
   Zhang, Qing
   Wang, Haiyan
TI Visualization and analysis of mapping knowledge domains for coal pores
   studies
SO FUEL
LA English
DT Article
DE Visualization; Knowledge mapping; Bibliometrics; Coal pores
ID CH4 ADSORPTION CAPACITY; COMPETITIVE ADSORPTION; MOLECULAR SIMULATION;
   GAS-ADSORPTION; POROSITY; METHANE; CO2; RECOMMENDATIONS; TRANSPORT;
   PRESSURE
AB The structure and quantity of coal pores determine the adsorption capacity of CO2, CH4, O-2, N-2 and other multi element mixed gases, which is an important link connecting coal spontaneous combustion and gas disasters. To better grasp developments and trends in research on spontaneous combustion and gas disasters around the world and to promote theoretical research into their prevention and control, research progress on coal pores is reviewed and analyzed using bibliometrics based on a total of 5515 publications collected from the Web of Science Core Collection database. Yearly quantitative distribution of literature, country/region distribution, organization distribution, main source journal distribution, subject category distribution, research knowledge bases, research hotspots and frontiers are all analyzed, CiteSpace and Vosviewer are used to plot knowledge domain maps. The results show that the number of papers on spontaneous combustion studies has grown exponentially, that China, USA, Australia, France, Canada, Russia, Japan, Germany, and Poland are the most active countries in coal pores, but results from USA are the most influential, and that Fuel, E & F, IJCG, JNGSE, JLPPI and FPT are the main sources of published research on coal pores. At present, a framework for the basic theory and research methods is complete, but the research network is too centralized, and there are few frontier branches. Structure and quantity of coal pores research, Intrinsic mechanism of pore development, Influencing factors of pore development and microscopic chemical group evolution law are the main knowledge bases for coal pores studies. Research on coal pores has expanded from the macro level to the micro scale. Molecular simulation, pore structure characterization, methane reservoir and methane adsorption have become research frontiers.
C1 [Shao, Zhuangzhuang; Tan, Bo; Guo, Yan; Li, Tianze; Li, Xiaomeng; Fang, Xiyang; Wang, Feiran; Zhang, Qing] China Univ Min & Technol Beijing, Sch Emergency Management & Safety Engn, Beijing 100083, Peoples R China.
   [Wang, Haiyan] Univ Sci & Technol Beijing, Sch Civil & Resources Engn, Beijing 100083, Peoples R China.
RP Tan, B (通讯作者)，China Univ Min & Technol Beijing, Sch Emergency Management & Safety Engn, Beijing 100083, Peoples R China.
EM tanbo709@126.com; cocacola840612@163.com; Tepp_123@163.com;
   1981701489@qq.com; 2146431@ustb.edu.cn
FU National Natural Science Foundation of China [51804161, 52074156,
   51804355, 51774291, 51864045]; Central University Basic Scientific
   Research Business Expenses Special Funds [2021YJSAQ20]
FX Acknowledgments This work was financially sponsored by the National
   Natural Science Foundation of China (51804161, 52074156, 51804355,
   51774291, and 51864045) . And Central University Basic Scientific
   Research Business Expenses Special Funds (2021YJSAQ20) .
CR Aryadoust Vahid, FRONT PSYCHOL, V2021, P11
   BARRETT EP, 1951, J AM CHEM SOC, V73, P373, DOI 10.1021/ja01145a126
   Brochard L, 2012, LANGMUIR, V28, P2659, DOI 10.1021/la204072d
   Brunauer S, 1938, J AM CHEM SOC, V60, P309, DOI 10.1021/ja01269a023
   Cai YD, 2013, FUEL, V103, P258, DOI 10.1016/j.fuel.2012.06.055
   Chen C, FRONT RES METR ANAL, V2021
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   [程远平 Cheng Yuanping], 2021, [煤炭学报, Journal of China Coal Society], V46, P2933
   CITICS, CHIN EM AR EXP PEAK
   Clarkson CR, 1999, FUEL, V78, P1333, DOI 10.1016/S0016-2361(99)00055-1
   de Boer J. H., 1958, STRUCTURE PROPERTIES, P68
   Ding X, 2020, ELECTRON COMMER RES, DOI 10.1007/s10660-020-09410-7
   Dombrowski RJ, 2001, COLLOID SURFACE A, V187, P23, DOI 10.1016/S0927-7757(01)00618-5
   Dubinin M.M., 1947, P AC SCI PHYS CHEM U, V55, P331
   Dutta P, 2008, FUEL, V87, P2023, DOI 10.1016/j.fuel.2007.12.015
   Dzikowski P, 2018, J BUS RES, V85, P281, DOI 10.1016/j.jbusres.2017.12.054
   Fu XH, 2005, CHINESE SCI BULL, V50, P66, DOI [10.1360/98zk0009, 10.1007/BF03184085]
   GAN H, 1972, FUEL, V51, P272, DOI 10.1016/0016-2361(72)90003-8
   Gao DM, 2020, FUEL, V269, DOI 10.1016/j.fuel.2020.117443
   Giffin S, 2013, INT J COAL GEOL, V114, P85, DOI 10.1016/j.coal.2013.02.009
   Giffin S, 2013, INT J COAL GEOL, P11485
   Hodot BB, 1961, COAL GAS OUTBURST
   Hou JH, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0234347
   Hu B, 2019, ENERG FUEL, V33, P10611, DOI 10.1021/acs.energyfuels.9b02354
   Hui Liu, 2020, FUEL GUILDFORD
   [金智新 Jin Zhixin], 2017, [煤炭学报, Journal of China Coal Society], V42, P1201
   Jun W., 1991, J CHINA COAL SOC, V16, P86
   Karacan CO, 2001, FUEL, V80, P509, DOI 10.1016/S0016-2361(00)00112-5
   Khattak AK, 2000, COLLOID SURFACE A, V162, P99, DOI 10.1016/S0927-7757(99)00218-6
   Li ZT, 2021, J PETROL SCI ENG, V204, DOI 10.1016/j.petrol.2021.108704
   Liu B., 2021, PRICE ISSUE, V11, P87
   Liu Hui, 2020, FUEL
   Long H, 2021, POWDER TECHNOL, V385, P348, DOI 10.1016/j.powtec.2021.03.007
   Long H, 2021, FUEL, V292, DOI 10.1016/j.fuel.2021.120268
   Lu YJ, 2022, J PETROL SCI ENG, V208, DOI 10.1016/j.petrol.2021.109296
   Mahamud MM, 2008, FUEL, V87, P222, DOI 10.1016/j.fuel.2007.04.020
   Mastalerz M, 2012, ENERG FUEL, V26, P5109, DOI 10.1021/ef300735t
   Mohamed A, 2005, EUR J NUCL MED MOL I, V32, P180, DOI 10.1007/s00259-004-1654-2
   Moore TA, 2012, INT J COAL GEOL, V101, P36, DOI 10.1016/j.coal.2012.05.011
   Mou PW, 2021, ENERG FUEL, V35, P7467, DOI 10.1021/acs.energyfuels.1c00344
   Ni Z, 2021, FUEL, V306, DOI 10.1016/j.fuel.2021.121621
   Nie BS, 2015, FUEL, V158, P908, DOI 10.1016/j.fuel.2015.06.050
   Pan ZJ, 2012, INT J COAL GEOL, V92, P1, DOI 10.1016/j.coal.2011.12.009
   Pant LM, 2015, FUEL, V159, P315, DOI 10.1016/j.fuel.2015.06.089
   Pillalamarry M, 2011, INT J COAL GEOL, V86, P342, DOI 10.1016/j.coal.2011.03.007
   Qin Y, 2005, CHINESE SCI BULL, V50, P99, DOI [10.1007/BF03184090, 10.1360/98zk0017]
   Qin Y, 1994, COAL GEOL EXPLOR, V05, P13
   Qiu HH, 2018, ENERGIES, V11, DOI 10.3390/en11051103
   Rodrigues CF, 2002, INT J COAL GEOL, V48, P245, DOI 10.1016/S0166-5162(01)00061-1
   ROUQUEROL J, 1994, PURE APPL CHEM, V66, P1739, DOI 10.1351/pac199466081739
   SAITO A, 1991, AICHE J, V37, P429, DOI 10.1002/aic.690370312
   Shao XH, 2003, ACTA PHYS-CHIM SIN, V19, P538, DOI 10.3866/PKU.WHXB20030612
   Shao ZL, 2021, INT J REMOTE SENS, V42, P672, DOI 10.1080/01431161.2020.1823044
   Shen L, 2020, FUEL, V271, DOI 10.1016/j.fuel.2020.117557
   SING KSW, 1985, PURE APPL CHEM, V57, P603, DOI 10.1351/pac198557040603
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Suggate RP, 2004, INT J COAL GEOL, V57, P1, DOI 10.1016/S0166-5162(03)00116-2
   Sugiyama T, 2010, CEMENT CONCRETE RES, V40, P1265, DOI 10.1016/j.cemconres.2009.10.009
   Sun Y, 2018, INVESTIGATION GAS AD
   Tan B, 2020, J THERM ANAL CALORIM, V140, P295, DOI 10.1007/s10973-019-08771-y
   Tan B, 2019, ENVIRON SCI POLLUT R, V26, P29570, DOI 10.1007/s11356-019-06129-3
   Terzyk AP, 2002, CARBON, V40, P2879, DOI 10.1016/S0008-6223(02)00219-1
   Tian ZJ, 2020, COMBUST SCI TECHNOL, V192, P638, DOI 10.1080/00102202.2019.1590347
   Tu QY, 2022, FUEL, V307, DOI 10.1016/j.fuel.2021.121888
   Van Eck NJ, 2007, STUD CLASS DATA ANAL, P299
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Wang CH, 2021, J NAT GAS SCI ENG, V96, DOI 10.1016/j.jngse.2021.104231
   Wang H, 2021, ENERGIES, V14, DOI 10.3390/en14102944
   Wang Z, 2022, FUEL, V307, DOI 10.1016/j.fuel.2021.121815
   Wang ZY, 2022, FUEL, V309, DOI 10.1016/j.fuel.2021.122120
   Wen ZH, 2021, GEOFLUIDS, V2021, DOI 10.1155/2021/5535321
   White CM, 2005, ENERG FUEL, V19, P659, DOI 10.1021/ef040047w
   Yao YB, 2008, INT J COAL GEOL, V73, P27, DOI 10.1016/j.coal.2007.07.003
   Yao YB, 2010, FUEL, V89, P1371, DOI 10.1016/j.fuel.2009.11.005
   Yue T, 2020, ENVIRON SCI POLLUT R, V27, P17671, DOI 10.1007/s11356-020-08158-9
   Zhang JF, 2015, FUEL, V160, P309, DOI 10.1016/j.fuel.2015.07.092
   Zhang XL, 2022, FUEL, V307, DOI 10.1016/j.fuel.2021.121809
   Zhao YX, 2014, ENERG FUEL, V28, P3704, DOI 10.1021/ef500487d
   Zheng YN, 2020, FUEL PROCESS TECHNOL, V208, DOI 10.1016/j.fuproc.2020.106510
NR 80
TC 1
Z9 1
U1 12
U2 12
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0016-2361
EI 1873-7153
J9 FUEL
JI Fuel
PD JUL 15
PY 2022
VL 320
AR 123761
DI 10.1016/j.fuel.2022.123761
PG 14
WC Energy & Fuels; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels; Engineering
GA 1W5NO
UT WOS:000806820400003
DA 2022-08-23
ER

PT J
AU Liu, SYY
   Guo, DP
   Sun, JW
   Yu, J
   Zhou, DB
AF Liu, Sannyuya
   Guo, Dongpo
   Sun, Jianwen
   Yu, Jie
   Zhou, Dongbo
TI MapOnLearn: The Use of Maps in Online Learning Systems for Education
   Sustainability
SO SUSTAINABILITY
LA English
DT Article
DE education sustainability; online learning system; map usage; learning
   resource organization and visualization; higher education; TAM
ID KNOWLEDGE MAPS; BEHAVIOR; CONSTRUCTION; ACCEPTANCE; MOTIVATION;
   TREEMAPS; PLATFORM; CREATION
AB Online learning and teaching have become the primary forms of education during the global pandemic, and online learning systems, which can provide fair educational opportunities for everyone, are increasingly important for sustainability in education. The amount of time a student spends on online learning systems affects the fairness and persistence of sustainability in education. To support personalized learning opportunities, interactive learning, and easy-to-access resources, we propose a map organization and visualization method called MapOnLearn for online learning systems. First, we converted tree-like hierarchical course units (HCUs) and knowledge points (KPs) into a fundamental two-dimensional (2D) map of hierarchically divided polygons and used the map to form containers to manage all learning resources. Then, we used the zoom feature of the map to express the hierarchical structures of knowledge and formulated corresponding rules for displaying information at different levels. Path analysis was applied to express the learning process, and topological processing was adapted to represent the relationships among HCUs and KPs. We developed maps for a high-level math course, a course on data structures, and an English course at a university in China and investigated 264 students and 27 teachers for a semester by using the technology acceptance model (TAM). We found that the map visualization and organization method had a positive impact on the way teachers and students use online learning systems and improved the online learning experience. To attract more students to spend more time on online learning, we hope our method can promote the sustainable development of education.
C1 [Liu, Sannyuya; Guo, Dongpo] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.
   [Sun, Jianwen; Zhou, Dongbo] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
   [Yu, Jie] Wuhan Univ, Off Sci Res & Dev, Wuhan 490070, Peoples R China.
RP Zhou, DB (通讯作者)，Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
EM lsy5918@ccnu.edu.cn; guodp@mails.ccnu.edu.cn; sunjw@ccnu.edu.cn;
   yujie_gsis@whu.edu.cn; zhoudongbo@ccnu.edu.cn
FU NSFC [41671377, 61937001]; National Key R&D Program of China
   [2017YFB1401303]; Humanity and Social Science Youth Foundation of
   Ministry of Education of China [20YJC880083]; Fundamental Research Funds
   for the Central Universities [CCNU20ZN007]
FX This research was funded by NSFC, grant number 41671377, 61937001, and
   supported by the National Key R&D Program of China (2017YFB1401303), the
   Humanity and Social Science Youth Foundation of Ministry of Education of
   China (20YJC880083), and the Fundamental Research Funds for the Central
   Universities (CCNU20ZN007).
CR Arakawa K, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-31
   Asoodar M, 2016, COMPUT HUM BEHAV, V63, P704, DOI 10.1016/j.chb.2016.05.060
   Auvinen T, 2015, IEEE T LEARN TECHNOL, V8, P261, DOI 10.1109/TLT.2015.2441718
   Balaid A, 2016, INT J INFORM MANAGE, V36, P451, DOI 10.1016/j.ijinfomgt.2016.02.005
   Barak M, 2013, COMPUT EDUC, V62, P159, DOI 10.1016/j.compedu.2012.10.015
   Bargel Bela Andreas, 2012, International Journal of Computer Information Systems and Industrial Management Applications, V4, P228
   Boticki I, 2015, COMPUT EDUC, V86, P120, DOI 10.1016/j.compedu.2015.02.015
   Brown DJ, 2011, COMPUT EDUC, V56, P11, DOI 10.1016/j.compedu.2010.04.014
   Caputi V, 2015, J NETW COMPUT APPL, V53, P115, DOI 10.1016/j.jnca.2015.04.001
   Chen NS, 2008, COMPUT EDUC, V50, P1009, DOI 10.1016/j.compedu.2006.10.001
   Chen YH, 2015, COMPUT EDUC, V86, P55, DOI 10.1016/j.compedu.2015.03.008
   Conijn Rianne, 2017, IEEE Transactions on Learning Technologies, V10, P17, DOI 10.1109/TLT.2016.2616312
   Dang Y, 2011, DECIS SUPPORT SYST, V50, P415, DOI 10.1016/j.dss.2010.10.003
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   de Berg M, 2014, COMP GEOM-THEOR APPL, V47, P683, DOI 10.1016/j.comgeo.2013.12.008
   Evans N, 2000, J HIGH ENERGY PHYS
   Fionda V, 2016, ARTIF INTELL, V239, P143, DOI 10.1016/j.artint.2016.07.003
   Heidrich L, 2018, TELEMAT INFORM, V35, P1593, DOI 10.1016/j.tele.2018.04.007
   Heradio R, 2016, COMPUT EDUC, V98, P14, DOI 10.1016/j.compedu.2016.03.010
   Hone KS, 2016, COMPUT EDUC, V98, P157, DOI 10.1016/j.compedu.2016.03.016
   Jeong H, 2010, COMPUT HUM BEHAV, V26, P84, DOI 10.1016/j.chb.2009.08.001
   Jiang R., 2010, MAN SERV SCI MASS 20, P1
   Jones A, 2004, COMPUT EDUC, V43, P91, DOI 10.1016/j.compedu.2003.12.007
   Lau KH, 2018, COMPUT EDUC, V118, P10, DOI 10.1016/j.compedu.2017.11.005
   Li LY, 2017, COMPUT EDUC, V114, P286, DOI 10.1016/j.compedu.2017.07.007
   Li LY, 2013, COMPUT EDUC, V60, P32, DOI 10.1016/j.compedu.2012.07.010
   Liang M., 2012, P WORKSH SEM AD SOC, P872
   Lin FR, 2006, INFORM PROCESS MANAG, V42, P551, DOI 10.1016/j.ipm.2005.03.026
   Lin FR, 2009, DECIS SUPPORT SYST, V46, P774, DOI 10.1016/j.dss.2008.11.020
   Liu ZC, 2017, IEEE T VIS COMPUT GR, V23, P321, DOI 10.1109/TVCG.2016.2598797
   Luo XF, 2005, LECT NOTES COMPUT SC, V3795, P579
   Nawrot I, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P1077, DOI 10.1145/2567948.2580054
   Ong TH, 2005, DECIS SUPPORT SYST, V39, P583, DOI 10.1016/j.dss.2004.03.008
   Palovics R, 2017, PERVASIVE MOB COMPUT, V38, P490, DOI 10.1016/j.pmcj.2016.06.001
   Raes A, 2016, COMPUT EDUC, V92-93, P125, DOI 10.1016/j.compedu.2015.10.014
   Robertson G. G., 1991, Human Factors in Computing Systems. Reaching Through Technology. CHI '91. Conference Proceedings, P189
   Romero C, 2008, COMPUT EDUC, V51, P368, DOI 10.1016/j.compedu.2007.05.016
   Sanchez-Franco MJ, 2010, COMPUT EDUC, V54, P37, DOI 10.1016/j.compedu.2009.07.005
   Schwendimann BA, 2017, IEEE T LEARN TECHNOL, V10, P30, DOI 10.1109/TLT.2016.2599522
   Skupin A., 2000, P IEEE S INF VIZ 200, V1, P8
   Sobreira P, 2015, COMPUT EDUC, V80, P15, DOI 10.1016/j.compedu.2014.08.002
   Sun JCY, 2016, COMPUT EDUC, V102, P117, DOI 10.1016/j.compedu.2016.08.002
   Thoms B, 2014, COMPUT EDUC, V75, P112, DOI 10.1016/j.compedu.2014.02.002
   Tsai YH, 2018, COMPUT EDUC, V121, P18, DOI 10.1016/j.compedu.2018.02.011
   Tseng SS, 2007, COMPUT EDUC, V49, P691, DOI 10.1016/j.compedu.2005.11.020
   van Hees R, 2017, INFORM SOFTWARE TECH, V87, P242, DOI 10.1016/j.infsof.2016.10.003
   Venkatesh V, 2008, DECISION SCI, V39, P273, DOI 10.1111/j.1540-5915.2008.00192.x
   Wang MH, 2013, COMPUT EDUC, V68, P293, DOI 10.1016/j.compedu.2013.05.004
   Weinerth K, 2014, COMPUT EDUC, V78, P201, DOI 10.1016/j.compedu.2014.06.002
   Wette R, 2017, J SECOND LANG WRIT, V38, P58, DOI 10.1016/j.jslw.2017.09.005
   Yang DQ, 2015, J NETW COMPUT APPL, V55, P170, DOI 10.1016/j.jnca.2015.05.010
   Yang F, 2018, COMPUT EDUC, V123, P97, DOI 10.1016/j.compedu.2018.04.006
   Yu BW, 2017, IEEE T VIS COMPUT GR, V23, P251, DOI 10.1109/TVCG.2016.2598497
   Zhang LS, 2012, IEEE CONF VIS ANAL, P173, DOI 10.1109/VAST.2012.6400554
   Zhao GS, 2020, KNOWL-BASED SYST, V196, DOI 10.1016/j.knosys.2020.105849
NR 55
TC 3
Z9 3
U1 8
U2 22
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD SEP
PY 2020
VL 12
IS 17
AR 7018
DI 10.3390/su12177018
PG 21
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA NP7WX
UT WOS:000570384300001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Magableh, B
   Almiani, M
AF Magableh, Basel
   Almiani, Muder
TI A deep recurrent Q network towards self-adapting distributed
   microservice architecture
SO SOFTWARE-PRACTICE & EXPERIENCE
LA English
DT Article
DE deep Q-learning networks; multiagent environment; policy approximation;
   Q-learning algorithms; recurrent Q-learning networks; reinforcement
   learning; self-adaptive architectures; service-oriented architecture
ID NEURAL-NETWORKS; REINFORCEMENT; SYSTEMS; STATE
AB One desired aspect of microservice architecture is the ability to self-adapt its own architecture and behavior in response to changes in the operational environment. To achieve the desired high levels of self-adaptability, this research implements distributed microservice architecture model running a swarm cluster, as informed by the Monitor, Analyze, Plan, and Execute over a shared Knowledge (MAPE-K) model. The proposed architecture employs multiadaptation agents supported by a centralized controller, which can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q-learning network (DRQN). It is argued that such integration between DRQN and Markov decision process (MDP) agents in a MAPE-K model offers distributed microservice architecture with self-adaptability and high levels of availability and scalability. Integrating DRQN into the adaptation process improves the effectiveness of the adaptation and reduces any adaptation risks, including resource overprovisioning and thrashing. The performance of DRQN is evaluated against deep Q-learning and policy gradient algorithms, including (1) a deep Q-learning network (DQN), (2) a dueling DQN (DDQN), (3) a policy gradient neural network, and (4) deep deterministic policy gradient. The DRQN implementation in this paper manages to outperform the aforementioned algorithms in terms of total reward, less adaptation time, lower error rates, plus faster convergence and training time. We strongly believe that DRQN is more suitable for driving the adaptation in distributed services-oriented architecture and offers better performance than other dynamic decision-making algorithms.
C1 [Magableh, Basel] Technol Univ Dublin, Dublin, Ireland.
   [Almiani, Muder] Al Hussein Bin Talal Univ, Maan, Jordan.
RP Magableh, B (通讯作者)，Technol Univ, Sch Comp Sci, Dublin, Ireland.; Almiani, M (通讯作者)，Al Hussein Bin Talal Univ, Sch Comp Sci, Maan 71111, Jordan.
EM basel.magableh@tudublin.ie; malmiani@my.bridgeport.edu
OI Magableh, Basel/0000-0003-2337-637X; almiani, muder/0000-0003-0021-2364
CR Amoui M, 2008, P 4 INT C AUT AUT SY
   [Anonymous], 2020, REINFORCEMENT LEARNI
   Babaoglu O, 2005, SELF STAR PROPERTIES
   Bailey C, 2011, P IEEE 9 INT C DEP A
   Barbacci M, 2004, SOFTWARE QUALITY ATT
   BELLMAN R, 1957, J MATH MECH, V6, P679, DOI 10.1512/iumj.1957.6.56038
   Bencomo N, 2010, P 32 ACM IEEE INT C
   Busoniu L, 2008, IEEE T SYST MAN CY C, V38, P156, DOI 10.1109/TSMCC.2007.913919
   Caicedo JC, 2015, P IEEE INT C COMP VI
   Cheng B, 2008, DAGST SEM P
   Cheng BHC, 2009, LECT NOTES COMPUT SC, V5525, P1, DOI 10.1007/978-3-642-02161-9_1
   Cheng S-W, 2009, P ICSE WORKSH SOFTW
   Cheung-Foo-Wo D, 2007, P 5 INT WORKSH MIDDL
   Chung J, 2015, P INT C MACH LEARN
   Computing A, 2006, IBM WHITE PAPER, P1
   DAPRATO G, 1995, J FUNCT ANAL, V131, P94, DOI 10.1006/jfan.1995.1084
   Dowling J, 2004, P 1 ACM SIGSOFT WORK
   Dutreilh X, 2011, P 7 INT C AUT AUT SY
   Giese Holger, 2010, SOFTWARE ENG SELF AD, P1, DOI [10.1007/978-3- 642-35813- 5_1, DOI 10.1007/978-3-642-35813-5_1]
   Gupta JK, 2017, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD002006.pub4
   Hausknecht M, 2015, P AAAI FALL S SERIES
   Hirschfeld R, 2008, J OBJECT TECHNOL, V7, P125, DOI 10.5381/jot.2008.7.3.a4
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Horn P, 2001, TECHNICAL REPORT
   Jamshidi P, 2016, P 12 INT ACM SIGSOFT
   Jayaraman D, 2016, LECT NOTES COMPUT SC, V9909, P489, DOI 10.1007/978-3-319-46454-1_30
   Jozefowicz R., 2015, P INT C MACH LEARN
   Kakousis K, 2008, LECT NOTES COMPUT SC, V5331, P657
   Kim D, 2009, P ICSE WORKSH SOFTW
   Kiss T, 2017, FUTURE GENER COMPUT, V94, P937
   Lample G, 2017, P 31 AAAI C ART INT
   Lillicrap T. P., 2016, P INT C LEARN REPR
   Lowe R, 2017, ADV NEUR IN, V30
   Medvidovic N, 1996, P 4 ACM SIGSOFT S FD
   Menasce DA, 2007, P IEEE INT C WEB SER
   Mikalsen M, 2006, P 2006 ACM S APPL CO
   Mnih V, 2016, P INT C MACH LEARN
   MONAHAN GE, 1982, MANAGE SCI, V28, P1, DOI 10.1287/mnsc.28.1.1
   Mou LC, 2017, IEEE T GEOSCI REMOTE, V55, P3639, DOI 10.1109/TGRS.2016.2636241
   Murad A, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17112556
   Nair V., 2010, INT C MACH LEARN ICM, P807
   Omidshafiei S, 2017, P 34 INT C MACH LEAR
   Ongaro D, 2014, CONSENSUS BRIDGING T
   Oreizy P, 1999, IEEE INTELL SYST APP, V14, P54, DOI 10.1109/5254.769885
   Paspallis N, 2009, MIDDLEWARE BASED DEV
   Plappert M., 2016, KERAS RL
   Salehie M, 2005, P 13 IEEE INT WORKSH
   Salehie M, 2009, ACM T AUTON ADAP SYS, V4, DOI 10.1145/1516533.1516538
   Sama M, 2008, P 16 ACM SIGSOFT INT
   Silver D., 2014, P 31 INT C INT C MAC
   Silver D, 2016, NATURE, V529, P484, DOI 10.1038/nature16961
   Strang T., 2004, P INT WORKSH ADV CON
   Sutton Richard S, 2000, P ADV NEUR INF PROC
   Tesauro G, 2007, IEEE INTERNET COMPUT, V11, P22, DOI 10.1109/MIC.2007.21
   Tong Wu, 2018, Software Technologies: Applications and Foundations. STAF 2018 Collocated Workshops. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 11176), P387, DOI 10.1007/978-3-030-04771-9_28
   Van Hasselt H, 2016, P AAAI C ART INT PHO
   van Hasselt H, 2012, ADAPT LEARN OPTIM, V12, P207
   Wai H-T, 2018, P ADV NEUR INF PROC
   Wang Z, 2016, PR INT CONGR SOUND V
   Wei W, 2018, IEEE T SERV COMPUT, V11, P78, DOI 10.1109/TSC.2016.2528246
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Yoo S, 2017, P IEEE C COMP VIS PA
NR 62
TC 2
Z9 2
U1 6
U2 20
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0038-0644
EI 1097-024X
J9 SOFTWARE PRACT EXPER
JI Softw.-Pract. Exp.
PD FEB
PY 2020
VL 50
IS 2
BP 116
EP 135
DI 10.1002/spe.2778
EA NOV 2019
PG 20
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KA5NF
UT WOS:000499643900001
OA Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Lukersmith, S
   Fernandez, A
   Millington, M
   Salvador-Carulla, L
AF Lukersmith, Sue
   Fernandez, Ana
   Millington, Michael
   Salvador-Carulla, Luis
TI The brain injury case management taxonomy (BICM-T); a classification of
   community-based case management interventions
SO DISABILITY AND HEALTH JOURNAL
LA English
DT Article
DE Case management; Care coordination; Taxonomy; Intervention; Quality
   analysis
ID REHABILITATION TREATMENT TAXONOMY; COMPLEX INTERVENTIONS; CARE;
   EPIDEMIOLOGY; MODELS
AB Background: Case management is a complex intervention. Complexity arises from the interaction of different components: the model (theoretical basis), implementation context (service), population and health condition, focus for the intervention (client and/or their family), case manager's actions (interventions) and the target of case management (integrated care and support, client's community participation). There is a lack of understanding and a common language. To our knowledge there is no classification (taxonomy) for community-based case management.
   Objective: To develop a community-based case management in brain injury taxonomy (BICM-T), as a common language and understanding of case management for use in quality analysis, policy, planning and practice.
   Methods: The mixed qualitative methods used multiple sources of knowledge including scoping, framing and a nominal group technique to iteratively develop the Beta version (draft) of the taxonomy. A two part developmental evaluation involving case studies and mapping to international frameworks assessed the applicability and acceptability (feasibility) before finalization of the BICM-T.
   Results: The BICM-T includes a definition of community-based case management, taxonomy trees, tables and a glossary. The interventions domain tree has 9 main actions (parent category): engagement, holistic assessment, planning, education, training and skills development, emotional and motivational support, advising, coordination, monitoring; 17 linked actions (children category); 8 related actions; 63 relevant terms defined in the glossary.
   Conclusions: The BICM-T provides a knowledge map with the definitions and relationships between the core actions (interventions domain). Use of the taxonomy as a common language will benefit practice, quality analysis, evaluation, policy, planning and resource allocation. (c) 2016 The Authors. Published by Elsevier Inc.
C1 [Lukersmith, Sue; Millington, Michael] Univ Sydney, Ctr Disabil Res & Policy, Fac Hlth Sci, Cumberland Campus,75 East St, Lidcombe, NSW 2141, Australia.
   [Lukersmith, Sue] Lifetime Care & Support Author, Sydney, NSW, Australia.
   [Fernandez, Ana; Salvador-Carulla, Luis] Univ Sydney, Brain & Mind Res Inst, Ctr Disabil Res & Policy, Fac Hlth Sci, Lidcombe, NSW 2141, Australia.
RP Lukersmith, S (通讯作者)，Univ Sydney, Ctr Disabil Res & Policy, Fac Hlth Sci, Cumberland Campus,75 East St, Lidcombe, NSW 2141, Australia.
EM sluk3618.uni@sydney.edu.au
RI Fernandez, Ana/H-4545-2012; PRISMA, Grupo/AAI-6753-2021;
   Salvador-Carulla, Luis/K-6512-2013
OI Fernandez, Ana/0000-0002-0333-1345; Salvador-Carulla,
   Luis/0000-0002-5742-9866; Lukersmith, Sue/0000-0001-8202-3828
CR Abraham C, 2008, HEALTH PSYCHOL, V27, P379, DOI 10.1037/0278-6133.27.3.379
   Alli C, 2008, J FAM PRACTICE, V57, P747
   Australian Institute of Health and Welfare (AIHW), 2007, DIS AUSTR ACQ BRAIN, P1
   Bedell JR, 2000, COMMUNITY MENT HLT J, V36, P179, DOI 10.1023/A:1001894311127
   Brombley Karen, 2008, Paediatr Nurs, V20, P24
   Casarin Santina Nunes Alves, 2002, Rev. Latino-Am. Enfermagem, V10, P472, DOI 10.1590/S0104-11692002000400002
   Christensen H, 2008, BMC FAM PRACT, V9, DOI 10.1186/1471-2296-9-25
   Craig P, 2008, BRIT MED J, V337, DOI 10.1136/bmj.a1655
   Dijkers MP, 2014, ARCH PHYS MED REHAB, V95, pS1, DOI 10.1016/j.apmr.2013.09.023
   Economics A, 2009, EC COST SPIN CORD IN
   Fitzsimmons RD, 2003, BRAIN INJURY, V17, P947, DOI 10.1080/0269905031000110409
   Fortune N, 1999, DEFINITION INCIDENCE
   Geonnotti K, 2013, FORMATIVE EVALUATION
   Goffman E., 1986, FRAME ANAL ESSAY ORG
   Holloway F, 2001, INT J SOC PSYCHIATR, V47, P21, DOI 10.1177/002076400104700303
   HORTON JN, 1980, ANAESTHESIA, V35, P811, DOI 10.1111/j.1365-2044.1980.tb03924.x
   Hromco JG, 1997, COMMUNITY MENT HLT J, V33, P415, DOI 10.1023/A:1025074503061
   INHATA and HTAi, 2015, HLTH TECHN ASS HTA G
   JONES J, 1995, BRIT MED J, V311, P376, DOI 10.1136/bmj.311.7001.376
   Khan F, 2003, MED J AUSTRALIA, V178, P290, DOI 10.5694/j.1326-5377.2003.tb05199.x
   Kopke S, 2015, COCHRANE DB SYST REV, V1
   Lamb S, 2011, TRIALS, V12, P8
   Lambe P, 2007, CHANDOS KNOWL MANAGE, P1
   Langlois JA, 2006, J HEAD TRAUMA REHAB, V21, P375, DOI 10.1097/00001199-200609000-00001
   Lannin N, 2012, BRAIN IMPAIR, V13, P228, DOI 10.1017/BrImp.2012.19
   Larizgoitia I, 2013, J PUBLIC HEALTH RES, V2, P168, DOI 10.4081/jphr.2013.e29
   Lifetime Care & Support Authority (LTCSA), 2014, MY PLAN MAN IN PRESS
   (LTCSA), 2012, L C S A CAS MAN REP
   Lukersmith S, 2014, INT J INTEGR CARE, V14
   Mas-Exposito L, 2013, COMMUNITY MENT HLT J, V49, P831, DOI 10.1007/s10597-013-9621-7
   McDonald KM, 2010, AHRQ PUBLICATION, V11-0023-EF
   Medical Research Council, 2000, FRAM DEV EV RCTS COM
   Motor Accidents Authority NSW, 2003, CAS MAN NSW MOT ACC
   Mueser KT, 1998, SCHIZOPHRENIA BULL, V24, P37, DOI 10.1093/oxfordjournals.schbul.a033314
   Peters DH, 2013, BMJ-BRIT MED J, V347, DOI 10.1136/bmj.f6753
   Petticrew M, 2011, EUR J PUBLIC HEALTH, V21, P397, DOI 10.1093/eurpub/ckr084
   Reilly S, 2015, COCHRANE DATABASE SY, V1
   Reynolds HW, 2013, BMC HEALTH SERV RES, V13, DOI 10.1186/1472-6963-13-168
   Salvador-Carulla L, 2001, MENTAL HLTH OUTCOME
   Salvador-Carulla L, 2013, IMPROVING MENTAL HLT
   Salvador-Carulla L, 2014, J EVAL CLIN PRACT, V20, P1045, DOI 10.1111/jep.12286
   Salvador-Carulla L, 2013, BMC HEALTH SERV RES, V13, DOI 10.1186/1472-6963-13-218
   Salvador-Carulla L, 2013, INT J ENV RES PUB HE, V10, P1963, DOI 10.3390/ijerph10051963
   Shepperd S, 2009, PLOS MED, V6, DOI 10.1371/journal.pmed.1000086
   Sinnakaruppan I, 2001, INT J REHABIL RES, V24, P35, DOI 10.1097/00004356-200103000-00005
   Stetler CB, 2006, J GEN INTERN MED, V21, pS1, DOI [10.1111/j.1525-1497.2006.00355.x, 10.1007/s11606-006-0267-9]
   Sykes CR, 2014, ARCH PHYS MED REHAB, V95, pS91, DOI 10.1016/j.apmr.2013.10.008
   Tagliaferri F, 2006, ACTA NEUROCHIR, V148, P255, DOI 10.1007/s00701-005-0651-y
   Tahan HA, 2006, LIPPINCOTTS CASE MAN, V11, P88
   Tahan HA, 2006, PROF CASE MANAG, V11, P71
   United States Centers for Medicare & Medicaid Services, 2015, GLOSSARY
   WHO ICHI Development Project, INT CLASS HLTH INT I
   World Health O, 2001, INT CLASS FUNCT DIS
   Yarmo D, 1998, J Case Manag, V7, P84
   Zeilinger E, 2003, J INTELLECT DISABIL
   Zeilinger E, 2011, QUALITY ASSESSMENT E
NR 56
TC 14
Z9 14
U1 0
U2 6
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 1936-6574
EI 1876-7583
J9 DISABIL HEALTH J
JI Disabil. Health J.
PD APR
PY 2016
VL 9
IS 2
BP 272
EP 280
DI 10.1016/j.dhjo.2015.09.006
PG 9
WC Health Care Sciences & Services; Health Policy & Services; Public,
   Environmental & Occupational Health; Rehabilitation
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Health Care Sciences & Services; Public, Environmental & Occupational
   Health; Rehabilitation
GA DH3WT
UT WOS:000372718200015
PM 26616541
OA hybrid
DA 2022-08-23
ER

PT J
AU Azam, A
   Ahmed, A
   Kamran, MS
   Hai, L
   Zhang, ZT
   Ali, A
AF Azam, Ali
   Ahmed, Ammar
   Kamran, Muhammad Sajid
   Hai, Li
   Zhang, Zutao
   Ali, Asif
TI Knowledge structuring for enhancing mechanical energy harvesting (MEH):
   An in-depth review from 2000 to 2020 using CiteSpace
SO RENEWABLE & SUSTAINABLE ENERGY REVIEWS
LA English
DT Review
DE Mechanical energy; Energy harvesting; bibliometric analysis; Knowledge
   map; CiteSpace
ID SELF-POWERED APPLICATIONS; TRIBOELECTRIC NANOGENERATOR; VIBRATION;
   DESIGN; WIND; SYSTEM; WAVE; GENERATOR; RAILWAY; MOTION
AB Due to swift innovations in mechanical energy harvesting (MEH) during recent years, the findings of MEH research are prolific. The objective of the recent study is to visualise the development trends and the current research status of MEH by conducting a bibliometric analysis using CiteSpace. The original articles published between 2000 and 2020 were collected from the Web of Science core collection (WOSCC) using an advanced keyword search related to MEH. Then, the visualisation networks of author co-authorship, institution co authorship, and country co-authorship analyses were engendered to categorise the productive authors, institutions, and countries, correspondingly. Furthermore, the core journals, the top research articles and the most significant authors were determined by visualising the journal co-citation, document co-citation and the author co-citation networks, respectively. The keywords co-occurrence analysis was performed to highlight the current hot research topics and new research frontiers, whereas cluster analysis showed the knowledge structure and core subject categories. The investigation explored the major contributing bodies of MEH research at micro, meso and macro levels, the degree of collaboration among them and the knowledge sources of MEH. The main research areas, the current knowledge status and hotspots of MEH were detected for future research. Furthermore, the analysis of grants and collaborating countries reveals that the policy support of People R. China, the United States of America (USA), England and South Korea is significantly favourable to promote the development of MEH research. Finally, the comparison analysis was performed for various modes of energy harvesting and the practical applications of energy harvesting technologies in the real world were highlighted.
C1 [Azam, Ali; Hai, Li; Zhang, Zutao; Ali, Asif] Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Peoples R China.
   [Ahmed, Ammar] Northwestern Polytech Univ, Sch Mech Engn, Xian 710072, Peoples R China.
   [Kamran, Muhammad Sajid] Univ Engn & Technol, Dept Mech Engn, Lahore 54890, Pakistan.
RP Zhang, ZT (通讯作者)，Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Peoples R China.
EM zzt@swjtu.edu.cn
RI Azam, Ali/ABA-3756-2020; Ahmed, Ammar/ABA-3630-2020
OI Azam, Ali/0000-0002-0175-3966; Ahmed, Ammar/0000-0002-8978-5638; ZHANG,
   ZUTAO/0000-0003-2641-2049
FU National Natural Foundation of China [51975490, 5177424]; Science and
   Technology Projects of Sichuan [2020YFSY0070, 2021JDRC0096]; Sichuan
   Science and Technology Program [2020JDTD0027]
FX This work was funded by the National Natural Foundation of China under
   Grants Nos. 51975490 and 5177424; and by the Science and Technology
   Projects of Sichuan under Grants Nos. 2020YFSY0070 and 2021JDRC0096; and
   by the Sichuan Science and Technology Program under Grant No.
   2020JDTD0027. The asterisk indicates the corre-sponding author.
CR Abdelkareem MAA, 2020, J ADV RES, V24, P53, DOI 10.1016/j.jare.2020.02.012
   Abdelkareem MAA, 2018, APPL ENERG, V229, P672, DOI 10.1016/j.apenergy.2018.08.030
   Ahmed A, 2020, ENERG CONVERS MANAGE, V211, DOI 10.1016/j.enconman.2020.112748
   Andrews M, 2019, J EXP BOT, V70, P683, DOI 10.1093/jxb/ery371
   Anton SR, 2007, SMART MATER STRUCT, V16, pR1, DOI 10.1088/0964-1726/16/3/R01
   Azam A, 2021, ENERGY, V214, DOI 10.1016/j.energy.2020.118894
   Bailey AG, 2001, J ELECTROSTAT, V51, P82, DOI 10.1016/S0304-3886(01)00106-1
   Beeby S., 2010, ENERGY HARVESTING AU
   Beeby SP, 2007, J MICROMECH MICROENG, V17, P1257, DOI 10.1088/0960-1317/17/7/007
   Beeby SP, 2006, MEAS SCI TECHNOL, V17, pR175, DOI 10.1088/0957-0233/17/12/R01
   Behrend J, 2019, ACCOUNT HIST REV, V29, P103, DOI 10.1080/21552851.2019.1606721
   Chalasani S, 2008, PROCEEDINGS IEEE SOUTHEASTCON 2008, VOLS 1 AND 2, P442
   Chang YW, 2015, SCIENTOMETRICS, V105, P2071, DOI 10.1007/s11192-015-1762-8
   Chen N, 2017, SENSOR ACTUAT A-PHYS, V254, P134, DOI 10.1016/j.sna.2016.12.006
   Chen XY, 2020, TRANSPORT POLICY, V85, P1, DOI 10.1016/j.tranpol.2019.10.004
   Chen Y, 2020, NANO ENERGY, V73, DOI 10.1016/j.nanoen.2020.104765
   Cheng P, 2018, NANO ENERGY, V54, P156, DOI 10.1016/j.nanoen.2018.10.007
   Cottone F, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.080601
   Daqaq MF, 2014, APPL MECH REV, V66, DOI 10.1115/1.4026278
   Dhakar L, 2016, NANO ENERGY, V19, P532, DOI 10.1016/j.nanoen.2015.04.020
   Du SJ, 2020, IEEE SENS J, V20, P277, DOI 10.1109/JSEN.2019.2941180
   Elvin N., 2013, ADV ENERGY HARVESTIN
   Erturk A, 2011, J SOUND VIB, V330, P2339, DOI 10.1016/j.jsv.2010.11.018
   Erturk A, 2009, APPL PHYS LETT, V94, DOI 10.1063/1.3159815
   Erturk A, 2009, SMART MATER STRUCT, V18, DOI 10.1088/0964-1726/18/2/025009
   Fan KQ, 2020, RENEW ENERG, V156, P1028, DOI 10.1016/j.renene.2020.04.117
   Ferreira ACR, 2020, IEEE INT SYM MED MEA
   Gao MY, 2020, APPL ENERG, V257, DOI 10.1016/j.apenergy.2019.113969
   Gao MY, 2019, ENERGY, V180, P315, DOI 10.1016/j.energy.2019.05.087
   Gao MY, 2018, INT J DISTRIB SENS N, V14, DOI 10.1177/1550147718814469
   Gao MY, 2017, UNDERGR SPACE, V2, P210, DOI 10.1016/j.undsp.2017.10.002
   Gu YH, 2020, APPL ENERG, V266, DOI 10.1016/j.apenergy.2020.114846
   Guo HY, 2016, ADV ENERGY MATER, V6, DOI 10.1002/aenm.201501593
   Guo LK, 2017, APPL ENERG, V208, P1071, DOI 10.1016/j.apenergy.2017.09.045
   Halim MA, 2016, SENSOR ACTUAT A-PHYS, V249, P23, DOI 10.1016/j.sna.2016.08.008
   Han MD, 2014, SENSOR ACTUAT A-PHYS, V219, P38, DOI 10.1016/j.sna.2014.08.008
   Hara Y, 2020, J INTEL MAT SYST STR, V31, P2326, DOI 10.1177/1045389X20943944
   Harne RL, 2013, SMART MATER STRUCT, V22, DOI 10.1088/0964-1726/22/2/023001
   He J, 2018, NANO ENERGY, V43, P326, DOI 10.1016/j.nanoen.2017.11.039
   Hoppe T, 2016, ENERGY RES SOC SCI, V21, P12, DOI 10.1016/j.erss.2016.06.006
   Hou WQ, 2020, J CLEAN PROD, V254, DOI 10.1016/j.jclepro.2020.119981
   Hu CP, 2011, SCIENTOMETRICS, V86, P657, DOI 10.1007/s11192-010-0313-6
   Jadhao JS., 2013, INT J ENG INNOV TECH, V2, P93
   Jing TT, 2020, NANO ENERGY, V74, DOI 10.1016/j.nanoen.2020.104856
   Khaligh A, 2010, IEEE T IND ELECTRON, V57, P850, DOI 10.1109/TIE.2009.2024652
   Kim H, 2009, ENERGY HARVESTING TECHNOLOGIES, P3, DOI 10.1007/978-0-387-76464-1_1
   Lacks DJ, 2011, J PHYS D APPL PHYS, V44, DOI 10.1088/0022-3727/44/45/453001
   Lee D, 2013, PROC SPIE, V8728, DOI 10.1117/12.2016238
   Li C, 2014, J SOUND VIB, V333, P3904, DOI 10.1016/j.jsv.2014.04.020
   Li H, 2021, APPL ENERG, V295, DOI 10.1016/j.apenergy.2021.117020
   Li XF, 2021, RENEW ENERG, V163, P910, DOI 10.1016/j.renene.2020.08.113
   Li ZJ, 2013, SMART MATER STRUCT, V22, DOI 10.1088/0964-1726/22/2/025008
   Liang CW, 2017, OCEAN ENG, V136, P190, DOI 10.1016/j.oceaneng.2017.03.024
   Liao HC, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10010166
   Lin T, 2018, MECHATRONICS, V53, P277, DOI 10.1016/j.mechatronics.2018.06.019
   Lin T, 2018, APPL ENERG, V213, P219, DOI 10.1016/j.apenergy.2018.01.032
   Liu JJ, 2020, MICROELECTRON ENG, V231, DOI 10.1016/j.mee.2020.111333
   Liu MY, 2018, APPL ENERG, V212, P966, DOI 10.1016/j.apenergy.2017.12.123
   Liu ZG, 2015, SCIENTOMETRICS, V103, P135, DOI 10.1007/s11192-014-1517-y
   Luo AX, 2020, PROC IEEE MICR ELECT, P536, DOI 10.1109/MEMS46641.2020.9056319
   Maharjan P, 2019, APPL ENERG, V256, DOI 10.1016/j.apenergy.2019.113987
   Mann BP, 2009, J SOUND VIB, V319, P515, DOI 10.1016/j.jsv.2008.06.011
   Maravandi A, 2015, IEEE-ASME T MECH, V20, P2853, DOI 10.1109/TMECH.2015.2395437
   MCCAIN KW, 1991, J AM SOC INFORM SCI, V42, P290, DOI 10.1002/(SICI)1097-4571(199105)42:4<290::AID-ASI5>3.0.CO;2-9
   McCullagh J, 2018, IEEE T CIRCUITS-I, V65, P1758, DOI 10.1109/TCSI.2017.2764878
   Mi J, 2020, APPL ENERG, V279, DOI 10.1016/j.apenergy.2020.115726
   Mitcheson PD, 2008, P IEEE, V96, P1457, DOI 10.1109/JPROC.2008.927494
   Mondal S, 2020, NANO ENERGY, V74, DOI 10.1016/j.nanoen.2020.104870
   Na Y, 2020, APPL ENERG, V264, DOI 10.1016/j.apenergy.2020.114710
   Pan HY, 2021, APPL ENERG, V286, DOI 10.1016/j.apenergy.2021.116518
   Pan HY, 2019, ENERG CONVERS MANAGE, V196, P56, DOI 10.1016/j.enconman.2019.05.115
   Pan Y, 2019, APPL ENERG, V250, P568, DOI 10.1016/j.apenergy.2019.04.182
   Park CS, 2019, NANO ENERGY, V57, P327, DOI 10.1016/j.nanoen.2018.12.026
   Pozzi M, 2011, SMART MATER STRUCT, V20, DOI 10.1088/0964-1726/20/5/055007
   Priya S, 2009, ENERGY HARVESTING TECHNOLOGIES, P1, DOI 10.1007/978-0-387-76464-1
   Pu X, 2017, SCI ADV, V3, DOI 10.1126/sciadv.1700015
   Qi LF, 2018, ENERG CONVERS MANAGE, V165, P748, DOI 10.1016/j.enconman.2018.04.003
   Qian F, 2018, ENERG CONVERS MANAGE, V171, P1352, DOI 10.1016/j.enconman.2018.06.069
   Qian JW, 2019, SCAND J HOSP TOUR, V19, P192, DOI 10.1080/15022250.2018.1526113
   Qiu CK, 2020, NANO ENERGY, V70, DOI 10.1016/j.nanoen.2020.104456
   Ramlan R, 2010, NONLINEAR DYNAM, V59, P545, DOI 10.1007/s11071-009-9561-5
   Rasuo B, 2014, RENEW SUST ENERG REV, V39, P874, DOI 10.1016/j.rser.2014.07.137
   Rodrigues C, 2020, NANO ENERGY, V72, DOI 10.1016/j.nanoen.2020.104682
   Roshan YM, 2015, IEEE T IND ELECTRON, V62, P3052, DOI 10.1109/TIE.2015.2392714
   Salman W, 2018, ENERGY, V159, P361, DOI 10.1016/j.energy.2018.06.152
   Sari I, 2008, SENSOR ACTUAT A-PHYS, V145, P405, DOI 10.1016/j.sna.2007.11.021
   Sarmiento J, 2019, OCEAN ENG, V173, P761, DOI 10.1016/j.oceaneng.2018.12.046
   Seol ML, 2017, NANO ENERGY, V31, P233, DOI 10.1016/j.nanoen.2016.11.038
   Siddiqui S, 2016, NANO ENERGY, V30, P434, DOI 10.1016/j.nanoen.2016.10.034
   Singh P, 2020, MATER TODAY-PROC, V28, P1569, DOI 10.1016/j.matpr.2020.04.842
   Singh U, 2020, RENEW SUST ENERG REV, V117, DOI 10.1016/j.rser.2019.109501
   Sokolov A, 2020, IEEE T CIRCUITS-I, V67, P565, DOI 10.1109/TCSI.2019.2938421
   Song GJ, 2019, APPL ENERG, V247, P221, DOI 10.1016/j.apenergy.2019.04.040
   Sovacool BK, 2012, ENERG ENVIRON SCI, V5, P9157, DOI 10.1039/c2ee22468b
   Stanton SC, 2010, PHYSICA D, V239, P640, DOI 10.1016/j.physd.2010.01.019
   Stordeur M, 1997, PROCEEDINGS ICT'97 - XVI INTERNATIONAL CONFERENCE ON THERMOELECTRICS, P575, DOI 10.1109/ICT.1997.667595
   Tan YS, 2020, ENERG CONVERS MANAGE, V217, DOI 10.1016/j.enconman.2020.112958
   Tang LH, 2010, J INTEL MAT SYST STR, V21, P1867, DOI 10.1177/1045389X10390249
   Tang MF, 2019, SMART MATER STRUCT, V28, DOI 10.1088/1361-665X/ab45fe
   Ting CC, 2012, APPL ENERG, V92, P1, DOI 10.1016/j.apenergy.2011.10.006
   Todaria P, 2015, PROC SPIE, V9435, DOI 10.1117/12.2084371
   Toprak A, 2014, APPL PHYS REV, V1, DOI 10.1063/1.4896166
   Viet NV, 2016, ENERGY, V112, P1219, DOI 10.1016/j.energy.2016.07.019
   Wang HK, 2018, APPL ENERG, V228, P2448, DOI 10.1016/j.apenergy.2018.07.059
   Wang JL, 2019, ENERG CONVERS MANAGE, V181, P645, DOI 10.1016/j.enconman.2018.12.034
   Wang Q, 2020, SCI TOTAL ENVIRON, V728, DOI 10.1016/j.scitotenv.2020.138915
   Wang S, 2020, ENERG CONVERS MANAGE, V207, DOI 10.1016/j.enconman.2020.112571
   Wang ZL, 2017, NANO ENERGY, V39, P9, DOI 10.1016/j.nanoen.2017.06.035
   Wang ZL, 2014, FARADAY DISCUSS, V176, P447, DOI 10.1039/c4fd00159a
   Waterbury AC, 2013, P I MECH ENG C-J MEC, V227, P1187, DOI 10.1177/0954406212457895
   Widen J, 2015, RENEW SUST ENERG REV, V44, P356, DOI 10.1016/j.rser.2014.12.019
   Wu C, 2018, NANO ENERGY, V44, P279, DOI 10.1016/j.nanoen.2017.11.080
   Xie Q, 2020, ENERG CONVERS MANAGE, V204, DOI 10.1016/j.enconman.2019.112286
   Xie YN, 2013, ACS NANO, V7, P7119, DOI 10.1021/nn402477h
   Xu W, 2017, ADV ENERGY MATER, V7, DOI 10.1002/aenm.201601529
   Yaglioglu O., 2002, MODELING DESIGN CONS
   Yan C, 2020, NANO ENERGY, V67, DOI 10.1016/j.nanoen.2019.104235
   Yang J, 2014, ADV ENERGY MATER, V4, DOI 10.1002/aenm.201301322
   Yang WQ, 2013, ACS NANO, V7, P11317, DOI 10.1021/nn405175z
   Yang YQ, 2019, ENERGY, V179, P222, DOI 10.1016/j.energy.2019.05.036
   Yu DJ, 2017, INFORM SCIENCES, V418, P619, DOI 10.1016/j.ins.2017.08.031
   Yu YH, 2015, ADV MATER, V27, P4938, DOI 10.1002/adma.201502546
   Zambrano-Monserrate MA, 2020, SCI TOTAL ENVIRON, V728, DOI 10.1016/j.scitotenv.2020.138813
   Zhang CL, 2020, J SOUND VIB, V487, DOI 10.1016/j.jsv.2020.115616
   Zhang JT, 2017, SENSOR ACTUAT A-PHYS, V262, P123, DOI 10.1016/j.sna.2017.05.027
   Zhang LM, 2016, NANO ENERGY, V22, P87, DOI 10.1016/j.nanoen.2016.01.009
   Zhang XS, 2016, NANO ENERGY, V20, P37, DOI 10.1016/j.nanoen.2015.11.036
   Zhang XT, 2017, APPL ENERG, V204, P1535, DOI 10.1016/j.apenergy.2017.04.064
   Zhang XT, 2016, ENERG CONVERS MANAGE, V118, P287, DOI 10.1016/j.enconman.2016.04.012
   Zhang ZT, 2016, APPL ENERG, V178, P177, DOI 10.1016/j.apenergy.2016.06.054
   Zhang ZT, 2016, APPL ENERG, V164, P152, DOI 10.1016/j.apenergy.2015.11.096
   Zhou SX, 2018, COMMUN NONLINEAR SCI, V61, P271, DOI 10.1016/j.cnsns.2018.02.017
   Zhou SX, 2014, APPL ENERG, V133, P33, DOI 10.1016/j.apenergy.2014.07.077
   Zhu DM, 2020, ENERGY REP, V6, P378, DOI 10.1016/j.egyr.2020.01.011
   Zhu DB, 2010, MEAS SCI TECHNOL, V21, DOI 10.1088/0957-0233/21/2/022001
   Zou HX, 2019, APPL ENERG, V255, DOI 10.1016/j.apenergy.2019.113871
   Zou JY, 2019, MECH SYST SIGNAL PR, V127, P345, DOI 10.1016/j.ymssp.2019.02.047
NR 137
TC 17
Z9 18
U1 69
U2 121
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1364-0321
EI 1879-0690
J9 RENEW SUST ENERG REV
JI Renew. Sust. Energ. Rev.
PD OCT
PY 2021
VL 150
AR 111460
DI 10.1016/j.rser.2021.111460
EA JUL 2021
PG 21
WC Green & Sustainable Science & Technology; Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Energy & Fuels
GA UY0WE
UT WOS:000701252900004
DA 2022-08-23
ER

PT J
AU Zaccaria, KJ
   McClure, PR
AF Zaccaria, Kimberly J.
   McClure, Peter R.
TI Using Immunotoxicity Information to Improve Cancer Risk Assessment for
   Polycyclic Aromatic Hydrocarbon Mixtures
SO INTERNATIONAL JOURNAL OF TOXICOLOGY
LA English
DT Review
DE benzo[a]pyrene; cancer risk assessment; carcinogenicity; immunotoxicity;
   PAH mixtures
ID ENVIRONMENTAL CONTAMINANT BENZO(A)PYRENE; DISCRIMINATE RODENT
   CARCINOGENS; VITRO GENOTOXICITY TESTS; RECEPTOR-MEDIATED INDUCTION;
   HUMAN T-CELLS; IN-VITRO; BONE-MARROW; DEPENDENT INDUCTION;
   COMPLEX-MIXTURES; ORAL-EXPOSURE
AB Estimating cancer risk from environmental mixtures containing polycyclic aromatic hydrocarbons (PAHs) is challenging. Ideally, each mixture would undergo toxicity testing to derive a cancer slope factor (CSF) for use in site-specific cancer risk assessments. However, this whole mixture approach is extremely costly in terms of finances, time, and animal usage. Alternatively, if an untested mixture is sufficiently similar to a well-characterized mixture with a CSF, the surrogate CSF can be used in risk assessments. We propose that similarity between 2 mixtures could be established using an in vitro battery of genotoxic and nongenotoxic tests. An observed association between carcinogenicity and immunosuppression of PAHs suggests that the addition of immune suppression assays may improve this battery. First, using published studies of benzo[a]pyrene (BaP) and other PAHs, we demonstrated a correlation between the derived immune suppression relative potency factors (RPFs) for 9 PAHs and their respective cancer RPFs, confirming observations published previously. Second, we constructed an integrated knowledge map for immune suppression by BaP based on the available mechanistic information. The map illustrates the mechanistic complexities involved in BaP immunosuppression, suggesting that multiple in vitro tests of immune suppression involving different processes, cell types, and tissues will have greater predictive value for immune suppression in vivo than a single test. Based on these observations, research strategies are recommended to validate a battery of in vitro immune suppression tests that, along with tests for genotoxic and other nongenotoxic modes of cancer action, could be used to establish sufficient similarity of 2 mixtures for site-specific cancer risk assessments.
C1 [Zaccaria, Kimberly J.; McClure, Peter R.] SRC Inc, Def & Environm Solut, North Syracuse, NY 13212 USA.
RP McClure, PR (通讯作者)，SRC Inc, Def & Environm Solut, 7502 Round Pond Rd, North Syracuse, NY 13212 USA.
EM mcclure@srcinc.com
FU SRC, Inc
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: Support to
   write this manuscript was provided by internal SRC, Inc funds.
CR Allan LL, 2006, BIOCHEM BIOPH RES CO, V342, P227, DOI 10.1016/j.bbrc.2006.01.131
   [Anonymous], 2005, GUID CARC RISK ASS
   [Anonymous], 2000, EPA630R00002
   [Anonymous], 1993, 420R93007 EPA
   Baken KA, 2008, TOXICOL APPL PHARM, V226, P46, DOI 10.1016/j.taap.2007.08.026
   Baken KA, 2007, METHODS, V41, P132, DOI 10.1016/j.ymeth.2006.07.010
   Beland F., 1998, TECHNICAL REPORT 672
   Belitskaya-Levy I, 2007, J ENVIRON PATHOL TOX, V26, P281, DOI 10.1615/JEnvironPatholToxicolOncol.v26.i4.50
   BICE DE, 1979, DRUG CHEM TOXICOL, V2, P35, DOI 10.3109/01480547908993180
   Biro A, 2002, IMMUNOL LETT, V81, P133, DOI 10.1016/S0165-2478(01)00342-X
   BLANTON RH, 1986, CANCER RES, V46, P2735
   BLANTON RH, 1988, TOXICOL APPL PHARM, V93, P267, DOI 10.1016/0041-008X(88)90126-3
   CalEPA, 1994, BENZ TOX AIR CONT
   Carfi M, 2007, TOXICOLOGY, V229, P11, DOI 10.1016/j.tox.2006.09.003
   Cavallo F, 2011, CANCER IMMUNOL IMMUN, V60, P319, DOI 10.1007/s00262-010-0968-0
   Cizmas L, 2004, ENVIRON SCI TECHNOL, V38, P5127, DOI 10.1021/es035287p
   Culp SJ, 1998, CARCINOGENESIS, V19, P117, DOI 10.1093/carcin/19.1.117
   Davila DR, 1996, TOXICOL APPL PHARM, V139, P333, DOI 10.1006/taap.1996.0173
   De Jong WH, 1999, TOXICOL SCI, V50, P214, DOI 10.1093/toxsci/50.2.214
   DEAN JH, 1983, CLIN EXP IMMUNOL, V52, P199
   Dix DJ, 2007, TOXICOL SCI, V95, P5, DOI 10.1093/toxsci/kfl103
   Dreij K, 2010, CARCINOGENESIS, V31, P1149, DOI 10.1093/carcin/bgq073
   Eastmond DA, 2009, MUTAGENESIS, V24, P341, DOI 10.1093/mutage/gep014
   Eide I, 2004, ENVIRON TOXICOL PHAR, V18, P127, DOI 10.1016/j.etap.2004.01.011
   Eide I, 2002, ENVIRON HEALTH PERSP, V110, P985, DOI 10.1289/ehp.02110s6985
   Fang C, 2010, J PHARMACOL EXP THER, V334, P156, DOI 10.1124/jpet.110.167742
   Fischer A, 2011, TOXICOL IN VITRO, V25, P1966, DOI 10.1016/j.tiv.2011.06.020
   Fujimaki H, 1997, CHEMOSPHERE, V34, P1487, DOI 10.1016/S0045-6535(97)00445-1
   Galbiati V, 2010, J IMMUNOTOXICOL, V7, P255, DOI 10.3109/1547691X.2010.509848
   Galvan N, 2003, TOXICOL APPL PHARM, V193, P84, DOI 10.1016/S0041-008X(03)00338-7
   Galvan N, 2006, TOXICOL APPL PHARM, V213, P105, DOI 10.1016/j.taap.2005.09.018
   Gao J, 2005, TOXICOL SCI, V87, P419, DOI 10.1093/toxsci/kfi265
   Gennari Alessandra, 2005, J Immunotoxicol, V2, P61, DOI 10.1080/15476910590965832
   Halappanavar S, 2011, TOXICOLOGY, V285, P133, DOI 10.1016/j.tox.2011.04.011
   Hanahan D, 2011, CELL, V144, P646, DOI 10.1016/j.cell.2011.02.013
   HARDIN JA, 1992, TOXICOL APPL PHARM, V117, P155, DOI 10.1016/0041-008X(92)90232-H
   HOLLADAY SD, 1995, VET HUM TOXICOL, V37, P99
   HOLLADAY SD, 1994, J TOXICOL ENV HEALTH, V42, P259, DOI 10.1080/15287399409531878
   Hwang JA, 2007, TOXICOL LETT, V169, P82, DOI 10.1016/j.toxlet.2007.01.001
   IARC, 2010, IARC MON EV CARC RIS, V100
   IARC, 2010, IARC MON EV CARC RIS, V83, P83
   Integrated Risk Information System (IRIS), 2003, DIES ENG EXH
   International Agency for Research on Cancer, 2004, IARC MON EV CARC RIS, V83
   IRIS, 1994, 8007452 IRIS CASRN U
   Judson RS, 2010, ENVIRON HEALTH PERSP, V118, P485, DOI 10.1289/ehp.0901392
   Karakaya A, 1999, TOXICOLOGY, V135, P43, DOI 10.1016/S0300-483X(99)00048-7
   Karakaya A, 2004, HUM EXP TOXICOL, V23, P317, DOI 10.1191/0960327104ht455oa
   Kavlock R, 2012, CHEM RES TOXICOL, V25, P1287, DOI 10.1021/tx3000939
   KAWABATA TT, 1987, CANCER RES, V47, P2317
   Kirkland D, 2005, MUTAT RES-GEN TOX EN, V584, P1, DOI 10.1016/j.mrgentox.2005.02.004
   Kirkland D, 2008, MUTAT RES-GEN TOX EN, V654, P114, DOI 10.1016/j.mrgentox.2008.05.002
   Kirkland D, 2006, MUTAT RES-GEN TOX EN, V608, P29, DOI 10.1016/j.mrgentox.2006.04.017
   Koeper LM, 2009, FOOD CHEM TOXICOL, V47, P110, DOI 10.1016/j.fct.2008.10.025
   KONG LY, 1994, AM J RESP CRIT CARE, V150, P1123, DOI 10.1164/ajrccm.150.4.7921446
   KRIEGER JA, 1995, TOXICOL APPL PHARM, V133, P102, DOI 10.1006/taap.1995.1131
   KRIEGER JA, 1994, TOXICOL APPL PHARM, V127, P268, DOI 10.1006/taap.1994.1161
   Kroese ED, 2001, 658603 RIVM NAT I PU
   Labib S, 2012, TOXICOL SCI, V129, P213, DOI 10.1093/toxsci/kfs177
   Laupeze W, 2002, J IMMUNOL, V168, P2652, DOI 10.4049/jimmunol.168.6.2652
   Lecureur V, 2005, FEBS LETT, V579, P1904, DOI 10.1016/j.febslet.2005.01.081
   Lee ME, 2007, IMMUNOPHARM IMMUNOT, V29, P425, DOI 10.1080/08923970701675069
   LEGRAVEREND C, 1983, TOXICOL APPL PHARM, V70, P390, DOI 10.1016/0041-008X(83)90157-6
   Luebke RW, 2006, TOXICOL SCI, V94, P22, DOI 10.1093/toxsci/kfl074
   LUMMUS ZL, 1995, INT J IMMUNOPHARMACO, V17, P339, DOI 10.1016/0192-0561(94)00098-9
   LUSTER MI, 1992, FUND APPL TOXICOL, V18, P200, DOI 10.1016/0272-0590(92)90047-L
   LUSTER MI, 1988, FUND APPL TOXICOL, V10, P2, DOI 10.1016/0272-0590(88)90247-3
   LUSTER MI, 1993, FUND APPL TOXICOL, V21, P71, DOI 10.1006/faat.1993.1074
   LYTE M, 1985, MECH AGEING DEV, V30, P333, DOI 10.1016/0047-6374(85)90122-8
   LYTE M, 1986, INT J IMMUNOPHARMACO, V8, P377, DOI 10.1016/0192-0561(86)90120-7
   LYTE M, 1987, INT J IMMUNOPHARMACO, V9, P307, DOI 10.1016/0192-0561(87)90055-5
   MISHELL RI, 1966, SCIENCE, V153, P1004, DOI 10.1126/science.153.3739.1004
   Monteiro P, 2007, BIOCHEM BIOPH RES CO, V358, P442, DOI 10.1016/j.bbrc.2007.04.111
   Mounho BJ, 1997, TOXICOL APPL PHARM, V145, P323, DOI 10.1006/taap.1997.8182
   Mounho BJ, 1998, TOXICOL APPL PHARM, V149, P80, DOI 10.1006/taap.1997.8345
   MUDZINSKI SP, 1993, TOXICOL APPL PHARM, V119, P166, DOI 10.1006/taap.1993.1057
   Munson AE, 1983, OTSAX11820220 NTIS A, VOTSAX-1182-0220
   Munson AE, 1985, FYIAX04850220 EPA AM
   MYERS MJ, 1987, J PHARMACOL EXP THER, V242, P399
   N'Diaye M, 2006, J BIOL CHEM, V281, P19906, DOI 10.1074/jbc.M601192200
   N'jai AU, 2011, MOL PHARMACOL, V79, P724, DOI 10.1124/mol.110.070631
   Near RI, 1999, P SOC EXP BIOL MED, V221, P242, DOI 10.1046/j.1525-1373.1999.d01-82.x
   Nebert DW, 2004, J BIOL CHEM, V279, P23847, DOI 10.1074/jbc.R400004200
   NEBERT DW, 1980, CLIN TOXICOL, V16, P99, DOI 10.3109/15563658008989927
   NEBERT DW, 1977, ARCH TOXICOL, V39, P109, DOI 10.1007/BF00343280
   Oh E, 2006, ENVIRON TOXICOL PHAR, V21, P108, DOI 10.1016/j.etap.2005.08.004
   Pessah IN, 2001, MOL PHARMACOL, V59, P506, DOI 10.1124/mol.59.3.506
   Pinel-Marie ML, 2011, TOXICOLOGY, V290, P89, DOI 10.1016/j.tox.2011.08.018
   Pinel-Marie ML, 2009, FREE RADICAL BIO MED, V47, P825, DOI 10.1016/j.freeradbiomed.2009.06.025
   Podechard N, 2008, TOXICOL LETT, V177, P130, DOI 10.1016/j.toxlet.2008.01.006
   Popp W, 1997, OCCUP ENVIRON MED, V54, P176, DOI 10.1136/oem.54.3.176
   ROBINSON JR, 1975, MOL PHARMACOL, V11, P850
   Rodriguez JW, 1999, IMMUNOPHARM IMMUNOT, V21, P379, DOI 10.3109/08923979909052769
   Romero DL, 1997, TOXICOL APPL PHARM, V144, P62, DOI 10.1006/taap.1997.8113
   Sagredo C, 2006, TOXICOL LETT, V167, P173, DOI 10.1016/j.toxlet.2006.09.005
   Salas VM, 1998, TOXICOL APPL PHARM, V151, P367, DOI 10.1006/taap.1998.8455
   Schellenberger MT, 2009, TOXICOL APPL PHARM, V240, P37, DOI 10.1016/j.taap.2009.06.019
   SCHNIZLEIN CT, 1987, INT J IMMUNOPHARMACO, V9, P99, DOI 10.1016/0192-0561(87)90115-9
   Shi ZQ, 2010, MOL PHARMACOL, V78, P46, DOI 10.1124/mol.110.063438
   Silkworth JB, 1995, TOXICOLOGY, V105, P375, DOI 10.1016/0300-483X(95)03235-8
   Sparfel L, 2010, TOXICOL SCI, V114, P247, DOI 10.1093/toxsci/kfq007
   STJERNSWARD J, 1966, JNCI-J NATL CANCER I, V36, P1189
   Sul D, 2003, MUTAT RES-GEN TOX EN, V538, P109, DOI 10.1016/S1383-5718(03)00095-0
   SZCZEKLIK A, 1994, ENVIRON HEALTH PERSP, V102, P302, DOI 10.1289/ehp.94102302
   TUCKER AN, 1982, ENVIRON HEALTH PERSP, V43, P123, DOI 10.2307/3429176
   U.S. EPA, 1989, RISK ASS GUID SUP
   U. S. EPA, 2010, EPASAB11004
   U. S. EPA, 2002, EPA635R02005
   Uno S, 2004, MOL PHARMACOL, V65, P1225, DOI 10.1124/mol.65.5.1225
   Uno S, 2006, MOL PHARMACOL, V69, P1103, DOI 10.1124/mol.105.021501
   Urso P, 2008, J IMMUNOTOXICOL, V5, P293, DOI 10.1080/15376510802312324
   URSO P, 1987, IMMUNOPHARMACOLOGY, V14, P1, DOI 10.1016/0162-3109(87)90003-8
   Urso P, 2008, IMMUNOPHARM IMMUNOT, V30, P897, DOI [10.1080/08923970802135898, 10.1080/08923970802135898 ]
   Urso P, 2008, IN VITRO CELL DEV-AN, V44, P273, DOI 10.1007/s11626-008-9112-2
   Urso P, 2010, INHAL TOXICOL, V22, P571, DOI 10.3109/08958371003596561
   *US EPA, 1998, EPA712C98351
   van den Berg FA, 2005, TOXICOLOGY, V211, P107, DOI 10.1016/j.tox.2005.03.003
   van Grevenynghe J, 2005, J PHARMACOL EXP THER, V314, P693, DOI 10.1124/jpet.105.084780
   van Grevenynghe J, 2004, BIOCHEM BIOPH RES CO, V317, P708, DOI 10.1016/j.bbrc.2004.03.104
   van Grevenynghe J, 2003, J IMMUNOL, V170, P2374, DOI 10.4049/jimmunol.170.5.2374
   Waters MD, 2010, MUTAT RES-REV MUTAT, V705, P184, DOI 10.1016/j.mrrev.2010.04.005
   WHITE KL, 1984, CANCER RES, V44, P3388
   WHITE KL, 1985, IMMUNOPHARMACOLOGY, V9, P155, DOI 10.1016/0162-3109(85)90011-6
   Winker N, 1997, J APPL TOXICOL, V17, P23, DOI 10.1002/(SICI)1099-1263(199701)17:1<23::AID-JAT387>3.0.CO;2-O
   WOJDANI A, 1984, TOXICOLOGY, V31, P181, DOI 10.1016/0300-483X(84)90100-8
   WOJDANI A, 1984, CANCER RES, V44, P942
   World Health Organization, 2012, GUID IMM RISK ASS CH
   Wu MT, 2003, J OCCUP ENVIRON MED, V45, P1034, DOI 10.1097/01.jom.0000088876.25970.96
   Zhang HM, 2012, J OCCUP HEALTH, V54, P44, DOI 10.1539/joh.11-0155-OA
NR 128
TC 16
Z9 16
U1 3
U2 37
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1091-5818
EI 1092-874X
J9 INT J TOXICOL
JI Int. J. Toxicol.
PD JUL
PY 2013
VL 32
IS 4
BP 236
EP 250
DI 10.1177/1091581813492829
PG 15
WC Pharmacology & Pharmacy; Toxicology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy; Toxicology
GA 196JW
UT WOS:000322771800002
PM 23922326
DA 2022-08-23
ER

PT J
AU Lin, BQ
   Zhu, Y
   Liang, XD
AF Lin, Bingqian
   Zhu, Yi
   Liang, Xiaodan
TI Atom correlation based graph propagation for scene graph generation
SO PATTERN RECOGNITION
LA English
DT Article
DE Scene graph generation; Long-tailed distribution; Knowledge graph; Atom
   correlation; Category space
AB Long-tailed distribution in the dataset is one of the major problems of the scene graph generation task. Previous methods attempt to alleviate this by introducing human commonsense knowledge in the form of statistical correlations between object pairs. However, the reasoning path they used is usually composable and the prior knowledge they employed is generally image-specific, making the knowledge learning less flexible, stable and holistic. In this paper, we propose Atom Correlation Based Graph Propagation (AC GP) for the scene graph generation task. Specifically, diverse atom correlations between objects and their relationships are explored by separating relationships to form new semantic nodes and decomposing the compound reasoning paths. Based on these atom correlations, the knowledge graphs are introduced for the feature enhancement by information propagating in the global category space. By exploiting atom correlations, the introduced prior knowledge can be more common and easy to learn. Moreover, propagating the knowledge in the global category space enables the model aware of more comprehensive and holistic knowledge. As a result, the model capacity and stability can be effectively im proved to mine infrequent and missed relationships. Experimental results on two benchmark datasets: Visual Relation Detection (VRD) and Visual Genome (VG) show the superiority of the proposed AC-GP over strong baseline methods. (c) 2021 Elsevier Ltd. All rights reserved.
C1 [Lin, Bingqian; Liang, Xiaodan] Sun Yat Sen Univ, Shenzhen Campus, Guangzhou, Peoples R China.
   [Zhu, Yi] Univ Chinese Acad Sci UCAS, Beijing, Peoples R China.
RP Liang, XD (通讯作者)，Sun Yat Sen Univ, Shenzhen Campus, Guangzhou, Peoples R China.
EM linbq6@mail2.sysu.edu.cn; zhuyi215@mails.ucas.ac.cn;
   liangxd9@mail.sysu.edu.cn
FU National Key R&D Program of China [2020AAA0109700]; National Natural
   Science Foundation of China (NSFC) [U19A2073, 61976233]; Guangdong
   Province Basic and Applied Basic Research (Regional Joint Fund-Key)
   [2019B1515120039]; Guangdong Outstanding Youth Fund [2021B1515020061];
   Shenzhen Fundamental Research Program [RCYX20200714114642083,
   JCYJ20190807154211365]; Zhejiang Lab's Open Fund [2020AA3AB14]; CSIG
   Young Fellow Support Fund
FX This work was supported in part by National Key R&D Program of China
   under Grant No. 2020AAA0109700, National Natural Science Foundation of
   China (NSFC) under Grant No.U19A2073 and No.61976233, Guangdong Province
   Basic and Applied Basic Research (Regional Joint Fund-Key) Grant
   No.2019B1515120039, Guangdong Outstanding Youth Fund (Grant No.
   2021B1515020061) , Shenzhen Fundamental Research Program (Project No.
   RCYX20200714114642083, No. JCYJ20190807154211365) , Zhejiang Lab's Open
   Fund (No. 2020AA3AB14) and CSIG Young Fellow Support Fund.
CR Agarwal A., 2020, ARXIV PREPRINT ARXIV
   Ashual O, 2019, IEEE I CONF COMP VIS, P4560, DOI 10.1109/ICCV.2019.00466
   Bruna J., 2014, 2 INT C LEARNING REP
   Chen L, 2019, IEEE I CONF COMP VIS, P4612, DOI 10.1109/ICCV.2019.00471
   Chen TS, 2019, PROC CVPR IEEE, P6156, DOI 10.1109/CVPR.2019.00632
   Cho K., 2014, ARXIV14061078, P1724
   Dai B, 2017, PROC CVPR IEEE, P3298, DOI 10.1109/CVPR.2017.352
   Fang ZW, 2019, PATTERN RECOGN, V90, P404, DOI 10.1016/j.patcog.2019.01.038
   Gu JX, 2019, IEEE I CONF COMP VIS, P10322, DOI 10.1109/ICCV.2019.01042
   Gu JX, 2019, PROC CVPR IEEE, P1969, DOI 10.1109/CVPR.2019.00207
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Johnson J, 2018, PROC CVPR IEEE, P1219, DOI 10.1109/CVPR.2018.00133
   Kipf Thomas N., 2017, 5 INT C LEARN REPR I
   Krishna R, 2017, INT J COMPUT VISION, V123, P32, DOI 10.1007/s11263-016-0981-7
   Lafferty J., 2001, P 18 INT C MACH LEAR
   Li YK, 2018, LECT NOTES COMPUT SC, V11205, P346, DOI 10.1007/978-3-030-01246-5_21
   Li YK, 2017, IEEE I CONF COMP VIS, P1270, DOI 10.1109/ICCV.2017.142
   Li YK, 2017, PROC CVPR IEEE, P7244, DOI 10.1109/CVPR.2017.766
   Lu CW, 2016, LECT NOTES COMPUT SC, V9905, P852, DOI 10.1007/978-3-319-46448-0_51
   Luo J., 2020, PATTERN RECOGN
   Monti F, 2017, PROC CVPR IEEE, P5425, DOI 10.1109/CVPR.2017.576
   Pennington J., 2014, EMNLP, P1532
   Ren SQ, 2015, ADV NEUR IN, V28, DOI 10.1109/TPAMI.2016.2577031
   Simonyan K., 2014, ARXIV
   Speer R., 2013, PEOPLES WEB MEETS NL, P161, DOI DOI 10.1007/978-3-642-35085-6_6
   Tang KH, 2020, PROC CVPR IEEE, P3713, DOI 10.1109/CVPR42600.2020.00377
   Tang KH, 2019, PROC CVPR IEEE, P6612, DOI 10.1109/CVPR.2019.00678
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang JB, 2020, PATTERN RECOGN, V98, DOI 10.1016/j.patcog.2019.107075
   Xiao XY, 2019, PATTERN RECOGN, V90, P285, DOI 10.1016/j.patcog.2019.01.028
   Xie SN, 2017, PROC CVPR IEEE, P5987, DOI 10.1109/CVPR.2017.634
   Xu DF, 2017, PROC CVPR IEEE, P3097, DOI 10.1109/CVPR.2017.330
   Yang JW, 2018, LECT NOTES COMPUT SC, V11205, P690, DOI 10.1007/978-3-030-01246-5_41
   Zellers R, 2018, PROC CVPR IEEE, P5831, DOI 10.1109/CVPR.2018.00611
   Zhang JS, 2019, PROC CVPR IEEE, P10150, DOI 10.1109/CVPR.2019.01040
   Zhou J., 2020, ARXIV PREPRINT ARXIV, V1, P57, DOI 10.1016/j.aiopen.2021.01.001
NR 37
TC 0
Z9 0
U1 5
U2 14
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0031-3203
EI 1873-5142
J9 PATTERN RECOGN
JI Pattern Recognit.
PD FEB
PY 2022
VL 122
AR 108300
DI 10.1016/j.patcog.2021.108300
EA SEP 2021
PG 12
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA US8JY
UT WOS:000697672900002
DA 2022-08-23
ER

PT J
AU Belfi, AM
   Bruss, J
   Karlan, B
   Abel, TJ
   Tranel, D
AF Belfi, Amy M.
   Bruss, Joel
   Karlan, Brett
   Abel, Taylor J.
   Tranel, Daniel
TI Neural Correlates of Recognition and Naming of Musical Instruments
SO NEUROPSYCHOLOGY
LA English
DT Article
DE musical instruments; naming; recognition; brain lesions
ID CATEGORY-SPECIFIC DEFICITS; TEMPORAL POLAR DAMAGE; SELECTIVE IMPAIRMENT;
   CONCEPTUAL KNOWLEDGE; SEMANTIC IMPAIRMENT; LEXICAL RETRIEVAL; CONCRETE
   ENTITIES; ALZHEIMERS TYPE; LESION; BRAIN
AB Objective: Retrieval of lexical (names) and conceptual (semantic) information is frequently impaired in individuals with neurological damage. One category of items that is often affected is musical instruments. However, distinct neuroanatomical correlates underlying lexical and conceptual knowledge for musical instruments have not been identified. Method: We used a neuropsychological approach to explore the neural correlates of knowledge retrieval for musical instruments. A large sample of individuals with focal brain damage (N = 298), viewed pictures of 16 musical instruments and were asked to name and identify each instrument. Neuroanatomical data were analyzed with a proportional MAP-3 method to create voxelwise lesion proportion difference maps. Results: Impaired naming (lexical retrieval) of musical instruments was associated with damage to the left temporal pole and inferior pre- and postcentral gyri. Impaired recognition (conceptual knowledge retrieval) of musical instruments was associated with a more broadly and bilaterally distributed network of regions, including ventromedial prefrontal cortices, occipital cortices, and superior temporal gyrus. Conclusions: The findings extend our understanding of how musical instruments are processed at neural system level, and elucidate factors that may explain why brain damage may or may not produce anomia or agnosia for musical instruments. Our findings also help inform broader understanding of category-related knowledge mapping in the brain, as musical instruments possess several characteristics that are similar to various other categories of items: They are inanimate and highly manipulable (similar to tools), produce characteristic sounds (similar to animals), and require fine-grained visual differentiation between each other (similar to people).
C1 [Belfi, Amy M.] Univ Iowa, Coll Med, Interdisciplinary Grad Program Neurosci, Iowa City, IA 52242 USA.
   [Belfi, Amy M.; Bruss, Joel; Karlan, Brett; Tranel, Daniel] Univ Iowa, Coll Med, Dept Neurol, Iowa City, IA 52242 USA.
   [Abel, Taylor J.] Univ Iowa, Coll Med, Dept Neurosurg, Iowa City, IA 52242 USA.
   [Tranel, Daniel] Univ Iowa, Interdisciplinary Grad Program Neurosci, Iowa City, IA 52242 USA.
   [Tranel, Daniel] Univ Iowa, Dept Psychol, Iowa City, IA 52242 USA.
   [Belfi, Amy M.] NYU, Dept Psychol, 6 Washington Pl Room 275, New York, NY 10003 USA.
RP Belfi, AM (通讯作者)，NYU, Dept Psychol, 6 Washington Pl Room 275, New York, NY 10003 USA.
EM amy.belfi@nyu.edu
RI Belfi, Amy/H-9596-2019
OI Belfi, Amy/0000-0002-1469-4117; Abel, Taylor/0000-0002-5089-460X;
   Tranel, Daniel/0000-0002-1338-1389
FU McDonnell Foundation Collaborative Action [220020387]; NATIONAL
   INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [F32NS087664] Funding
   Source: NIH RePORTER
FX This work was supported by McDonnell Foundation Collaborative Action
   Award #220020387.
CR Abel TJ, 2015, J NEUROSCI, V35, P1513, DOI 10.1523/JNEUROSCI.3387-14.2015
   BASSO A, 1988, J NEUROL NEUROSUR PS, V51, P1201, DOI 10.1136/jnnp.51.9.1201
   Belfi AM, 2014, NEUROPSYCHOLOGY, V28, P429, DOI 10.1037/neu0000051
   CAPITANI E, 1994, NEUROPSYCHOLOGIA, V32, P1453, DOI 10.1016/0028-3932(94)90117-1
   Capitani E, 2003, COGN NEUROPSYCHOL, V20, P213, DOI 10.1080/02643290244000266
   COLLINS DL, 1994, J COMPUT ASSIST TOMO, V18, P192, DOI 10.1097/00004728-199403000-00005
   Coppens P, 2005, BRAIN LANG, V94, P61, DOI 10.1016/j.bandl.2004.11.008
   Corina DP, 2010, BRAIN LANG, V115, P101, DOI 10.1016/j.bandl.2010.04.001
   Crutch SJ, 2003, COGN NEUROPSYCHOL, V20, P355, DOI 10.1080/02643290244000220
   DAMASIO AR, 1990, TRENDS NEUROSCI, V13, P95, DOI 10.1016/0166-2236(90)90184-C
   Damasio H, 2004, COGNITION, V92, P179, DOI 10.1016/j.cognition.2002.07.001
   Damasio H, 1996, NATURE, V380, P499, DOI 10.1038/380499a0
   DERENZI E, 1994, CORTEX, V30, P3, DOI 10.1016/S0010-9452(13)80322-X
   Dixon MJ, 2000, BRAIN COGNITION, V43, P158
   Downing PE, 2006, CEREB CORTEX, V16, P1453, DOI 10.1093/cercor/bhj086
   Drane DL, 2008, NEUROPSYCHOLOGIA, V46, P1242, DOI 10.1016/j.neuropsychologia.2007.11.034
   EVANS AC, 1991, P SOC PHOTO-OPT INS, V1445, P236, DOI 10.1117/12.45221
   Fiez JA, 2000, HUM BRAIN MAPP, V9, P192, DOI 10.1002/(SICI)1097-0193(200004)9:4<192::AID-HBM2>3.0.CO;2-Y
   Frank RJ, 1997, NEUROIMAGE, V5, P13, DOI 10.1006/nimg.1996.0250
   Gainotti G, 2005, NEUROPSYCHOLOGIA, V43, P1633, DOI 10.1016/j.neuropsychologia.2005.01.016
   Gainotti G, 1996, COGN NEUROPSYCHOL, V13, P357, DOI 10.1080/026432996381944
   Gale TM, 2001, COGNITIVE NEUROPSYCH, V18, P509, DOI 10.1080/02643290125912
   Garrard P, 1998, BRAIN, V121, P633, DOI 10.1093/brain/121.4.633
   Garrard P, 2001, J COGNITIVE NEUROSCI, V13, P892, DOI 10.1162/089892901753165818
   Gorno-Tempini ML, 2001, BRAIN, V124, P2087, DOI 10.1093/brain/124.10.2087
   Grabowski TJ, 1998, NEUROIMAGE, V7, P232, DOI 10.1006/nimg.1998.0324
   HANLEY JR, 1989, COGNITIVE NEUROPSYCH, V6, P179, DOI 10.1080/02643298908253418
   HART J, 1985, NATURE, V316, P439, DOI 10.1038/316439a0
   HILLIS AE, 1991, BRAIN, V114, P2081, DOI 10.1093/brain/114.5.2081
   Hoenig K, 2011, NEUROIMAGE, V56, P1714, DOI 10.1016/j.neuroimage.2011.02.065
   Johnson H., 2009, INSIGHT J, P1
   Kemmerer D, 2012, CORTEX, V48, P826, DOI 10.1016/j.cortex.2010.11.001
   Kolinsky R, 2002, COGN NEUROPSYCHOL, V19, P301, DOI 10.1080/02643290143000196
   Laws KR, 1999, CORTEX, V35, P729, DOI 10.1016/S0010-9452(08)70831-1
   Masullo C, 2012, BRAIN COGNITION, V80, P155, DOI 10.1016/j.bandc.2012.06.002
   Mazziotta J, 2001, PHILOS T R SOC B, V356, P1293, DOI 10.1098/rstb.2001.0915
   Omar R, 2010, BRAIN, V133, P1200, DOI 10.1093/brain/awp345
   PERETZ I, 1994, BRAIN, V117, P1283, DOI 10.1093/brain/117.6.1283
   Rudrauf D, 2008, NEUROIMAGE, V41, P970, DOI 10.1016/j.neuroimage.2007.12.033
   Shapiro KA, 2001, J COGNITIVE NEUROSCI, V13, P713, DOI 10.1162/08989290152541386
   SNODGRASS JG, 1980, J EXP PSYCHOL-HUM L, V6, P174, DOI 10.1037/0278-7393.6.2.174
   STEWART F, 1992, Q J EXP PSYCHOL-A, V44, P261, DOI 10.1080/02724989243000037
   Tranel D, 2003, COGN NEUROPSYCHOL, V20, P409, DOI 10.1080/02643290244000248
   Tranel D, 2006, NEUROPSYCHOLOGY, V20, P1, DOI 10.1037/0894-4105.20.1.1
   Tranel D, 2005, J COGNITIVE NEUROSCI, V17, P1293, DOI 10.1162/0898929055002508
   Tranel D, 1997, NEUROPSYCHOLOGIA, V35, P1329, DOI 10.1016/S0028-3932(97)00086-9
   Tranel D, 2009, APHASIOLOGY, V23, P867, DOI 10.1080/02687030802586498
   Waldron Eric J, 2014, Front Biosci (Schol Ed), V6, P50
   WARRINGTON EK, 1984, BRAIN, V107, P829, DOI 10.1093/brain/107.3.829
   Wechsler D, 2008, WECHSLER ADULT ILTEL
NR 50
TC 3
Z9 3
U1 0
U2 4
PU AMER PSYCHOLOGICAL ASSOC
PI WASHINGTON
PA 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA
SN 0894-4105
EI 1931-1559
J9 NEUROPSYCHOLOGY
JI Neuropsychology
PD OCT
PY 2016
VL 30
IS 7
BP 860
EP 868
DI 10.1037/neu0000273
PG 9
WC Psychology, Clinical; Neurosciences; Psychology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Psychology; Neurosciences & Neurology
GA DY9FX
UT WOS:000385440400010
PM 26901170
DA 2022-08-23
ER

PT J
AU Fei, XL
   Chen, PY
   Wei, L
   Huang, Y
   Xin, Y
   Li, J
AF Fei, Xiaolu
   Chen, Pengyu
   Wei, Lan
   Huang, Yue
   Xin, Yi
   Li, Jia
TI Quality Management of Pulmonary Nodule Radiology Reports Based on
   Natural Language Processing
SO BIOENGINEERING-BASEL
LA English
DT Article
DE natural language processing; radiology report; quality management;
   knowledge graph; pulmonary nodule
AB To investigate the feasibility of automated follow-up recommendations based on findings in radiology reports, this paper proposed a Natural Language Processing model specific for Pulmonary Nodule Radiology Reports. Unstructured findings used to describe pulmonary nodules in 48,091 radiology reports were processed in this study. We established an NLP model to extract information entities from findings of radiology reports, using deep learning and conditional random-field algorithms. Subsequently, we constructed a knowledge graph comprising 168 entities and four relationships, based on the export recommendations of the internationally renowned Fleischner Society for pulmonary nodules. These were employed in combination with rule templates to automatically generate follow-up recommendations. The automatically generated recommendations were then compared to the impression part of the reports to evaluate the matching rate of proper follow ups in the current situation. The NLP model identified eight types of entities with a recognition accuracy of up to 94.22%. A total of 43,898 out of 48,091 clinical reports were judged to contain appropriate follow-up recommendations, corresponding to the matching rate of 91.28%. The results show that NLP can be used on Chinese radiology reports to extract structured information at the content level, thereby realizing the prompt and intelligent follow-up suggestion generation or post-quality management of follow-up recommendations.
C1 [Fei, Xiaolu; Chen, Pengyu; Wei, Lan; Huang, Yue; Li, Jia] Capital Med Univ, Xuanwu Hosp, Informat Ctr, Beijing 100053, Peoples R China.
   [Xin, Yi] Beijing Inst Technol, Sch Life Sci, Beijing 100081, Peoples R China.
RP Li, J (通讯作者)，Capital Med Univ, Xuanwu Hosp, Informat Ctr, Beijing 100053, Peoples R China.
EM feixiaolu@xwh.ccmu.edu.cn; pengmc_ce@163.com; weilan@xwhosp.org;
   huangyue@xwhosp.org; ameko@bit.edu.cn; lij@xwhosp.org
FU Beijing Natural Science Foundation-Haidian Original Innovation Joint
   Fund [L192047]
FX This research was funded by Beijing Natural Science Foundation-Haidian
   Original Innovation Joint Fund, number L192047.
CR Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
   acr, LUNG RADS VERS
   Kshatriya BSA, 2021, BMC MED INFORM DECIS, V21, DOI 10.1186/s12911-021-01633-4
   [Anonymous], ACR APPR CRIT
   Arnaud E, 2021, HEALTHINF: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES - VOL 5: HEALTHINF, P835, DOI 10.5220/0011012800003123
   Bankier AA, 2017, RADIOLOGY, V285, P584, DOI 10.1148/radiol.2017162894
   Brook OR, 2015, RADIOLOGY, V274, P464, DOI 10.1148/radiol.14140206
   Bursztein E., 2015, KERAS
   Chen Z., 2019, CHIN J MOD MED, V29, P127, DOI [10.3969/j.issn.1005-8982.2019.11.026, DOI 10.3969/J.ISSN.1005-8982.2019.11.026]
   Duszak R, 2012, J AM COLL RADIOL, V9, P403, DOI 10.1016/j.jacr.2012.01.006
   ESR, 2013, INSIGHTS IMAGING, V4, P143, DOI 10.1007/s13244-013-0218-z
   ESR, 2011, INSIGHTS IMAGING, V2, P93, DOI 10.1007/s13244-011-0066-7
   Feng Q., 2017, CHIN CONTIN MED ED, V9, P1, DOI [10.3969/j.issn.1674-9308.2017.29.001, DOI 10.3969/J.ISSN.1674-9308.2017.29.001]
   Gao H., 2017, J PUBLIC MANAG, V14, P139
   Gershanik Esteban F, 2011, AMIA Annu Symp Proc, V2011, P465
   Grant EG, 2015, J AM COLL RADIOL, V12, P1272, DOI 10.1016/j.jacr.2015.07.011
   Jiang SY, 2022, TSINGHUA SCI TECHNOL, V27, P709, DOI 10.26599/TST.2021.9010060
   Karn S.K., 2022, P 60 ANN M ASS COMPU, V1, P1542
   Kohli M, 2016, J DIGIT IMAGING, V29, P297, DOI 10.1007/s10278-015-9835-z
   Lakhani P, 2006, J DIGIT IMAGING, V19, P52, DOI 10.1007/s10278-005-7667-y
   Lee Hyun Gi, 2021, Proc Conf, V2021, P4533, DOI 10.18653/v1/2021.naacl-main.358
   Lv W., 2017, INT J MED RADIOL, V40, P699, DOI [10.19300/j.2017.B5573, DOI 10.19300/J.2017.B5573]
   MacMahon H, 2017, RADIOLOGY, V284, P228, DOI 10.1148/radiol.2017161659
   Marcovici PA, 2014, AM J ROENTGENOL, V203, P1265, DOI 10.2214/AJR.14.12636
   Margolies LR, 2016, AM J ROENTGENOL, V206, P259, DOI 10.2214/AJR.15.15396
   Morgan TA, 2014, RADIOLOGY, V273, P642, DOI 10.1148/radiol.14141227
   Nobel JM, 2020, J DIGIT IMAGING, V33, P1002, DOI 10.1007/s10278-020-00327-z
   Pathak S, 2020, IEEE ACM T COMPUT BI, V17, P1883, DOI 10.1109/TCBB.2019.2914678
   Sahni VA, 2015, AM J ROENTGENOL, V205, P584, DOI 10.2214/AJR.14.14053
   Schoeppe F, 2018, EUR RADIOL, V28, P308, DOI 10.1007/s00330-017-4971-0
   Wang H., 2017, RADIOL PRACT, V32, P1109, DOI [10.13609/j.cnki.1000-0313.2017.11.002, DOI 10.13609/J.CNKI.1000-0313.2017.11.002]
   Wang S., 2022, ARXIV
   Wang Z., 2013, CHIN MED EQUIP, V10, P37, DOI [10.3969/J.ISSN.1672-8270.2013.08.012, DOI 10.3969/J.ISSN.1672-8270.2013.08.012]
   Zaveri A, 2016, SEMANT WEB, V7, P63, DOI 10.3233/SW-150175
   Zhou Qinghua, 2016, Zhongguo Fei Ai Za Zhi, V19, P793
NR 35
TC 0
Z9 0
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2306-5354
J9 BIOENGINEERING-BASEL
JI Bioengineering-Basel
PD JUN
PY 2022
VL 9
IS 6
AR 244
DI 10.3390/bioengineering9060244
PG 11
WC Biotechnology & Applied Microbiology; Engineering, Biomedical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biotechnology & Applied Microbiology; Engineering
GA 2J8VA
UT WOS:000815926200001
PM 35735487
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Jeong, J
   Lee, N
   Shin, Y
   Shin, D
AF Jeong, Joonsoo
   Lee, Nagyeong
   Shin, Yongbeom
   Shin, Dongil
TI Intelligent generation of optimal synthetic pathways based on knowledge
   graph inference and retrosynthetic predictions using reaction big data
SO JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS
LA English
DT Article
DE Chemical reaction big data; Synthetic path generation; Knowledge base
   inference; Decision support system; Knowledge graph, ML-based
   retrosynthetic prediction
AB The selection and design of suitable synthetic paths are important issues that affect the economics and productivity of chemical processes including reactions and the discovery of new chemicals. However, exploration of reaction information is difficult even with reaction databases, causing path explosion that occurs due to the huge search space and conflicting constrains such as economics, safety, efficiency, etc. In this study, we propose an intelligent system ASICS (Advanced System for Intelligent Chemical Synthesis), which supports synthetic path design at the basic stages of research and process design, based on the hybrid generative exploration and exploitation of reaction knowledge base, encoding big data of patented reactions, and machine learning-based retrosynthetic prediction. Based on the pseudo A* search, ASICS generates optimal synthetic paths minimizing scores of the synthetic reaction value function, composed of the synthetic accessibility score, likelihood score and similarity score. The preference in searching between confirmed reaction spaces and unexplored reaction spaces through prediction can be selected by the user. The suggested hybrid approach, combining the reaction knowledge base with the retrosynthetic prediction model, generates feasible and low-cost synthetic paths beyond the accumulated information in patented reactions. (C) 2021 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
C1 [Jeong, Joonsoo; Lee, Nagyeong; Shin, Yongbeom; Shin, Dongil] Myongji Univ, Dept Chem Engn, Yongin 17058, Gyeonggi, South Korea.
RP Shin, D (通讯作者)，Myongji Univ, Dept Chem Engn, Yongin 17058, Gyeonggi, South Korea.
EM dongil@mju.ac.kr
OI Shin, Yongbeom/0000-0003-4484-6596
FU Korea Institute for Advancement of Technology (KIAT) - Korea Government
   (MOTIE) [G02N06240000204]
FX This work was supported by Korea Institute for Advancement of Technology
   (KIAT) grant funded by the Korea Government (MOTIE) (G02N06240000204,
   Virtual Engineering Platform Program).
CR [Anonymous], SMARTS LANG DESCR MO
   Chen BH, 2020, PR MACH LEARN RES, V119
   Ertl P, 2009, J CHEMINFORMATICS, V1, DOI 10.1186/1758-2946-1-8
   GELERNTER H, 1990, J CHEM INF COMP SCI, V30, P492, DOI 10.1021/ci00068a023
   Genheden S, 2020, J CHEMINFORMATICS, V12, DOI 10.1186/s13321-020-00472-1
   Heller SR, 2015, J CHEMINFORMATICS, V7, DOI 10.1186/s13321-015-0068-4
   Landrum G, 2019, RDKIT DOCUMENTATION, P1
   Lowe D.M., 2012, EXTRACTION CHEM STRU
   Pensak D. A., 1977, COMPUTER ASSISTED OR, V61, P1, DOI DOI 10.1021/BK-1977-0061.CH001
   Segler MHS, 2018, NATURE, V555, P604, DOI 10.1038/nature25978
   Soua R, 2018, GLOB INFORM INFRAS
   Szymkuc S, 2016, ANGEW CHEM INT EDIT, V55, P5904, DOI 10.1002/anie.201506101
   WEININGER D, 1989, J CHEM INF COMP SCI, V29, P97, DOI 10.1021/ci00062a008
NR 13
TC 0
Z9 0
U1 6
U2 6
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1876-1070
EI 1876-1089
J9 J TAIWAN INST CHEM E
JI J. Taiwan Inst. Chem. Eng.
PD JAN
PY 2022
VL 130
AR 103982
DI 10.1016/j.jtice.2021.07.015
PG 9
WC Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA YP7JT
UT WOS:000748798500009
OA Bronze
DA 2022-08-23
ER

PT J
AU Wang, M
   Chen, KF
   Xiao, G
   Zhang, XY
   Chen, HX
   Wang, S
AF Wang, Meng
   Chen, Kefei
   Xiao, Gang
   Zhang, Xinyue
   Chen, Hongxu
   Wang, Sen
TI Explaining similarity for SPARQL queries
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Explainability; Similarity model; Regression; Explainable machine
   learning
ID SEARCH
AB Knowledge graph has gained significant popularity in recent years. As one of the W3C standards, SPARQL has become the de facto standard query language to retrieve the desired data from various knowledge graphs on the Web. Therefore, accurately measuring the similarity between different SPARQL queries is an important and fundamental task for many query-based applications, such as query suggestion, query rewriting, and query relaxation. However, conventional SPARQL similarity computation models only provide poorly-interpretable results, i,e., simple similarity scores for pairs of queries. Explaining the computed similarity scores will lead to an outcome of explaining why a specific computation model offers such scores. This helps users and machines understand the result of similarity measures in different query scenarios and can be used in many downstream tasks. We thus focus on providing explanations for typical SPARQL similarity measures in this paper. Specifically, given similarity scores of existing measures, we implement four explainable models based on Linear Regression, Support Vector Regression, Ridge Regression, and Random Forest Regression to provide quantitative weights to different dimensional SPARQL features, i.e., our models are able to explain different kinds of SPARQL similarity computation models by presenting the weights of different dimensional SPARQL features captured by them. Deep insight analysis and extensive experiments on real-world datasets are conducted to illustrate the effectiveness of our explainable models.
C1 [Wang, Meng; Chen, Kefei] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Wang, Meng] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China.
   [Xiao, Gang] AMS, Inst Syst Engn, Beijing, Peoples R China.
   [Zhang, Xinyue] Southeast Univ, Nanjing, Peoples R China.
   [Chen, Hongxu] Univ Technol Sydney, Ultimo, Australia.
   [Wang, Sen] Univ Queensland, Brisbane, Qld, Australia.
RP Wang, M (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.; Wang, M (通讯作者)，Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China.
EM meng.wang@seu.edu.cn; 220201941@seu.edu.cn; searchware@qq.com;
   zhangxy216@seu.edu.cn; hongxu.chen@uts.edu.au; sen.wang@uq.edu.au
OI Wang, Sen/0000-0002-5414-8276; Chen, Hongxu/0000-0001-7963-8813
FU National Natural Science Foundation of China [61906037]; CCF-Tencent
   Open Fund; CCF-Baidu Open Fund [CCF BAIDU OF2020003]
FX This work was supported by National Natural Science Foundation of China
   with Grant No. 61906037; CCF-Tencent Open Fund; CCF-Baidu Open Fund with
   No. CCF BAIDU OF2020003.
CR ALLOCCA C, 2016, EUROPEAN SEMANTIC WE, P128
   Bielefeldt A.., 2018, P WWW2018 WORKSHOP L
   Bonifati A, 2017, PROC VLDB ENDOW, V11, P149, DOI 10.14778/3149193.3149196
   BREIMAN L, 2001, MACH LEARN, V0045
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dividino RQ, 2013, LD4IE ISWC
   Goldstein A, 2015, J COMPUT GRAPH STAT, V24, P44, DOI 10.1080/10618600.2014.907095
   Guidotti R, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3236009
   Harris S, 2013, W3C RECOMMENDATION
   HOERL AE, 1970, TECHNOMETRICS, V12, P55, DOI 10.1080/00401706.1970.10488634
   Hogan Aidan, 2012, The Semantic Web: Research and Applications. Proceedings 9th Extended Semantic Web Conference (ESWC 2012), P687, DOI 10.1007/978-3-642-30284-8_53
   Hurtado CA, 2008, LECT NOTES COMPUT SC, V4900, P31
   Khan A, 2013, PROC VLDB ENDOW, V6, P181, DOI 10.14778/2535569.2448952
   Kiefer C, 2007, LECT NOTES COMPUT SC, V4825, P295
   Le WC, 2012, PROC INT CONF DATA, P666, DOI 10.1109/ICDE.2012.37
   Lehmann J, 2011, LECT NOTES COMPUT SC, V6643, P63, DOI 10.1007/978-3-642-21034-1_5
   Lorey Johannes, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P124
   Molnar C., 2020, INTERPRETABLE MACHIN
   Morsey M, 2011, LECT NOTES COMPUT SC, V7031, P454, DOI 10.1007/978-3-642-25073-6_29
   Ngomo, 2012, AAAI
   Peake G, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2060, DOI 10.1145/3219819.3220072
   Quinlan J. R., 1986, Machine Learning, V1, P81, DOI 10.1007/BF00116251
   Raghuveer A, 2012, P INT WORKSH US AN W
   Reddy B.R.K., 2010, URSW, V654, P37
   Ribeiro MT, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1135, DOI 10.1145/2939672.2939778
   Saleem M, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1623, DOI 10.1145/3308558.3313556
   Saleem M, 2015, LECT NOTES COMPUT SC, V9367, P261, DOI 10.1007/978-3-319-25010-6_15
   Seber G.A., 2012, LINEAR REGRESSION AN, V329
   Smola AJ, 2004, STAT COMPUT, V14, P199, DOI 10.1023/B:STCO.0000035301.49549.88
   Taylor K, 2016, P 31 ANN ACM S APPL, P362
   Torre-Bastida AI, 2019, SEMANT WEB, V10, P529, DOI 10.3233/SW-180311
   Wang CP, 2020, WWW'20: COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2020, P65, DOI 10.1145/3366424.3382699
   Wang M, 2018, LECT NOTES COMPUT SC, V11136, P513, DOI 10.1007/978-3-030-00671-6_30
   Zeng J, 2017, J R STAT SOC A STAT, V180, P689, DOI 10.1111/rssa.12227
   Zhang XY, 2020, LECT NOTES COMPUT SC, V12506, P672, DOI 10.1007/978-3-030-62419-4_38
   Zheng WG, 2016, PROC VLDB ENDOW, V9, P840, DOI 10.14778/2983200.2983201
NR 36
TC 0
Z9 0
U1 1
U2 6
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
PD SEP
PY 2021
VL 24
IS 5
SI SI
BP 1813
EP 1835
DI 10.1007/s11280-021-00886-3
EA JUN 2021
PG 23
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WJ2YV
UT WOS:000660818300002
DA 2022-08-23
ER

PT J
AU Yang, DQ
   Shen, CL
   Liu, BC
   Xue, LX
   Xiao, YH
AF Yang, Deqing
   Shen, Chenlu
   Liu, Baichuan
   Xue, Lyuxin
   Xiao, Yanghua
TI Generating Knowledge-Based Attentive User Representations for Sparse
   Interaction Recommendation
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Motion pictures; Adaptation models; Knowledge based systems; Recommender
   systems; Feature extraction; Computational modeling; Predictive models;
   Recommender system; knowledge graph; sparse interactions; cold-start;
   attention
AB Deep neural networks (DNNs) have been widely imported into collaborative-filtering (CF) based recommender systems and yielded remarkable superiority over traditional recommendation models. However, most deep CF-based models perform weakly when observed user-item interactions are sparse since user preferences and item characteristics are inferred mainly based on observed (historical) interactions. To address this problem, we propose a deep knowledge-enhanced recommendation model in this paper. Specifically, to augment user/item representations in the scenario of sparse historical user-item interactions, we first incorporate the knowledge from open knowledge graphs and personal information of users as side information, from which sufficient features of users and items are extracted. Second, to well capture shifted user preferences, we leverage a memory component constituted by recently interacted items rather than all historical ones. Third, attentive user representations are generated by attention mechanism to capture the diversity of user preferences. Furthermore, we build a convolutional neural network to pool the latent features in user representations for better user modeling, which enhances recommendation performance further. Our extensive experiments conducted against two real-world datasets, i.e., Douban movie and NetEase music, demonstrate our model's remarkable superiority over the state-of-the-art deep recommendation models.
C1 [Yang, Deqing; Shen, Chenlu; Liu, Baichuan; Xue, Lyuxin] Fudan Univ, Sch Data Sci, Shanghai 200433, Peoples R China.
   [Xiao, Yanghua] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China.
   [Xiao, Yanghua] Shanghai Inst Intelligent, Shanghai 200433, Peoples R China.
RP Yang, DQ (通讯作者)，Fudan Univ, Sch Data Sci, Shanghai 200433, Peoples R China.
EM yangdeqing@fudan.edu.cn; clshen17@fudan.edu.cn; bcliu20@fudan.edu.cn;
   lxxue19@fudan.edu.cn; shawyh@fudan.edu.cn
FU National NSF of China [U1636207]; Shanghai Science and Technology
   Innovation Action Plan [19511120400]
FX This paper was supported by National NSF of China No. U1636207, Shanghai
   Science and Technology Innovation Action Plan No. 19511120400.
CR Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   Bigdeli E, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON MANAGEMENT OF INNOVATION AND TECHNOLOGY, VOLS 1-3, P469, DOI 10.1109/ICMIT.2008.4654410
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Castells Pablo, 2015, RECOMMENDER SYSTEMS, P881, DOI DOI 10.1007/978-1-4899-7637-6_26
   Chen JY, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P335, DOI 10.1145/3077136.3080797
   Cheng H.T., 2016, P 1 WORKSH DEEP LEAR, P7
   Cremonesi P., 2010, 4 ACM C REC SYST, P39, DOI DOI 10.1145/1864708.1864721
   Donkers T, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P152, DOI 10.1145/3109859.3109877
   Duchi J, 2011, J MACH LEARN RES, V12, P2121
   Friedman JH, 2002, COMPUT STAT DATA AN, V38, P367, DOI 10.1016/S0167-9473(01)00065-2
   Glorot X., 2011, J MACH LEARN RES, V15, P315, DOI DOI 10.1002/ECS2.1832
   Guo HF, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1725
   Guo Q., IEEE T KNOWL DATA EN, P1
   He XN, 2018, IEEE T KNOWL DATA EN, V30, P2354, DOI 10.1109/TKDE.2018.2831682
   He XN, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P355, DOI 10.1145/3077136.3080777
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   Hidasi B., 2016, PROC INT C LEARN REP
   Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774
   Hofmann T., 2003, P 26 ANN INT ACM SIG, P259, DOI DOI 10.1145/860435.860483
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Huang J, 2018, ACM/SIGIR PROCEEDINGS 2018, P505, DOI 10.1145/3209978.3210017
   Kabbur S, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P659
   Kelly D., 2003, SIGIR Forum, V37, P18, DOI 10.1145/959258.959260
   Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Krishnan A, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P1753, DOI 10.1145/3357384.3357898
   Lathia N, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P210
   Mnih V, 2014, ADV NEURAL INF PROCE, V27, P2204
   Morin Frederic, 2005, P AISTATS, V5, P246
   Oard D. W., 1998, P AAAI WORKSH REC SY, V83, P81
   Rendle Steffen, 2010, Proceedings 2010 10th IEEE International Conference on Data Mining (ICDM 2010), P995, DOI 10.1109/ICDM.2010.127
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Tang JX, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P565, DOI 10.1145/3159652.3159656
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang HW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1835, DOI 10.1145/3178876.3186175
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang RX, 2017, ADKDD'17: 23RD ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD 2017), DOI 10.1145/3124749.3124754
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wu W, 2012, ACM SIGMOD, DOI DOI 10.1145/2213836.2213891
   Xiao J, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3119
   Xu B, 2017, LECT NOTES ARTIF INT, V10351, P428, DOI 10.1007/978-3-319-60045-1_44
   Yang DQ, 2018, IEEE DATA MINING, P1368, DOI 10.1109/ICDM.2018.00187
   Yu WH, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P649, DOI 10.1145/3178876.3186146
   Yu X, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P283, DOI 10.1145/2556195.2556259
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhao H, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P635, DOI 10.1145/3097983.3098063
   Zhou GR, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1059, DOI 10.1145/3219819.3219823
NR 50
TC 0
Z9 0
U1 0
U2 0
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD SEPT 1
PY 2022
VL 34
IS 9
BP 4270
EP 4284
DI 10.1109/TKDE.2020.3037029
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 3R1DR
UT WOS:000838660100001
DA 2022-08-23
ER

PT J
AU Nikishina, I
   Tikhomirov, M
   Logacheva, V
   Nazarov, Y
   Panchenko, A
   Loukachevitch, N
AF Nikishina, Irina
   Tikhomirov, Mikhail
   Logacheva, Varvara
   Nazarov, Yuriy
   Panchenko, Alexander
   Loukachevitch, Natalia
TI Taxonomy enrichment with text and graph vector representations
SO SEMANTIC WEB
LA English
DT Article
DE Taxonomy enrichment; graph vector representations; word embeddings;
   graph convolutional auto-encoder
ID WEB
AB Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a taxonomic backbone that allows the arrangement and structuring of various concepts in accordance with hypo-hypernym ("class-subclass") relationship. With the rapid growth of lexical resources for specific domains, the problem of automatic extension of the existing knowledge bases with new words is becoming more and more widespread. In this paper, we address the problem of taxonomy enrichment which aims at adding new words to the existing taxonomy.
   We present a new method which allows achieving high results on this task with little effort. It uses the resources which exist for the majority of languages, making the method universal. We extend our method by incorporating deep representations of graph structures like node2vec, Poincare embeddings, GCN etc. that have recently demonstrated promising results on various NLP tasks. Furthermore, combining these representations with word embeddings allows us to beat the state of the art.
   We conduct a comprehensive study of the existing approaches to taxonomy enrichment based on word and graph vector representations and their fusion approaches. We also explore the ways of using deep learning architectures to extend taxonomic backbones of knowledge graphs. We create a number of datasets for taxonomy extension for English and Russian. We achieve state-of-the-art results across different datasets and provide an in-depth error analysis of mistakes.
C1 [Nikishina, Irina; Logacheva, Varvara; Panchenko, Alexander] Skolkovo Inst Sci & Technol, Moscow, Russia.
   [Tikhomirov, Mikhail; Nazarov, Yuriy; Loukachevitch, Natalia] Lomonosov Moscow State Univ, Res Comp Ctr, Moscow, Russia.
RP Nikishina, I (通讯作者)，Skolkovo Inst Sci & Technol, Moscow, Russia.
EM irina.nikishina@skoltech.ru; tikhomirov.mm@gmail.com;
   v.logacheva@skoltech.ru; nazarov.yuriy.pavlovich@gmail.com;
   a.panchenko@skoltech.ru; louk_nat@mail.ru
FU RFBR [19-37-90119]; Russian Science Foundation [20-11-20166]
FX The participation of Mikhail Tikhomirov in the reported study
   (experiments with meta-embeddings) was funded by RFBR, project number
   19-37-90119. The work of Natalia Loukachevitch (preparation of RuWordNet
   data for the experiments, mappings of the RuWordNet synsets to English
   WordNet Linked Open Data (LOD) InterLingual Index (ILI)) is supported by
   the Russian Science Foundation (project 20-11-20166). The work of
   Alexander Panchenko, Varvara Logacheva, and Irina Nikishina was
   conducted in the framework of joint MTS-Skoltech laboratory.
CR Aly R, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4811
   Arefyev N, 2020, COMPUT LINGUIST
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Bansal M, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P809
   Berend G., 2018, P 12 INT WORKSH SEM, P928, DOI [10.18653/v1/S18-1152, DOI 10.18653/V1/S18-1152]
   Berners-Lee T, 2001, SCI AM, V284, P34, DOI 10.1038/scientificamerican0501-34
   Bernier-Colborne G., 2018, P 12 INT WORKSH SEM, P725, DOI 10.18653/v1/S18-1116
   Bojanowski P., 2017, T ASSOC COMPUT LING, V5, P135, DOI [DOI 10.1162/TACL_A_00051, 10.1162/tacl_a_00051]
   Bollegala D., 2018, P 27 INT C COMP LING, P1650
   Bond F., 2016, P 8 GLOB WORDNET C R, P50
   Bordea G., 2015, P 9 INT WORKSH SEM E, V452, P902, DOI DOI 10.18653/V1/S15-2151
   Bordea G., 2016, P 10 INT WORKSH SEM, P1081, DOI DOI 10.18653/V1/S16-1168
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Camacho-Collados J., 2018, P 12 INT WORKSH SEM, P712, DOI 10.18653/ v1/ S18- 1115
   Cho Y, 2020, P 28 INT C COMP LING, P3007, DOI [10.18653/v1/2020.coling-main.268, DOI 10.18653/V1/2020.COLING-MAIN.268]
   Chowdhury S, 2019, 191006456 ARXIV
   Cimiano P, 2005, LECT NOTES COMPUT SC, V3513, P227
   Coates J, 2018, 180405262 ARXIV
   Dale D., 2020, COMPUT LINGUIST
   De Giacomo G, 1996, TBOX ABOX REASONING, P37
   Dessi D, 2021, FUTURE GENER COMP SY, V116, P253, DOI 10.1016/j.future.2020.10.026
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Espinosa-Anke L., 2016, P 10 INT WORKSH SEM, P1332, DOI [10.18653/v1/S16-1208, DOI 10.18653/V1/S16-1208]
   Goel R, 2020, AAAI CONF ARTIF INTE, V34, P3988
   Gomez-Perez A, 2002, IEEE INTELL SYST, V17, P54, DOI 10.1109/5254.988453
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Hamilton WL, 2017, ADV NEUR IN, V30
   Han K, 2020, ADBIS TPDL EDA 2020, P207, DOI 10.1007/978-3-030-55814-7_17
   Jurgens D, 2016, P 10 INT WORKSH SEM, P1092, DOI [10.18653/v1/S16-1169, DOI 10.18653/V1/S16-1169]
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Kipf T. N., 2016, ARXIV161107308, P1
   Kipf T.N., 2017, SEMISUPERVISED CLASS
   Kunilovskaya M., 2020, COMPUT LINGUIST
   Kutuzov A., 2017, P 58 C SIM MOD LINK, P271
   Kutuzov A, 2017, COMM COM INF SC, V661, P155, DOI 10.1007/978-3-319-52920-2_15
   Lacroix T, 2020, P 8 INT C LEARN REPR
   Lacroix T, 2018, PR MACH LEARN RES, V80
   Li N, 2019, LECT NOTES COMPUT SC, V11778, P435, DOI 10.1007/978-3-030-30793-6_25
   Liu NH, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1812, DOI 10.1145/3219819.3220001
   Loukachevitch N. V., 2016, COMPUT LINGUIST, P405
   Luan Y., 2018, P 2018 C EMPIRICAL M, P3219, DOI 10.18653/v1/D18-1360
   Makarov I, 2021, PEERJ COMPUT SCI, V7
   Maldonado A., 2018, P 12 INT WORKSH SEM, P924, DOI [10.18653/v1/, DOI 10.18653/V1]
   McGuinness DL, 1995, INT JOINT CONF ARTIF, P816
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Mikolov T., 2013, ADV NEURAL INFORM PR
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Neill J.O, 2018, 180905886 ARXIV
   Nickel M, 2017, ADV NEUR IN, V30
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Nikishina I., 2021, P 11 GLOB WORDN C GL, P126
   Nikishina I, 2020, P 28 INT C COMP LING
   Nikishina I., 2020, COMPUT LINGUIST
   Osborne F, 2015, LECT NOTES COMPUT SC, V9366, P408, DOI 10.1007/978-3-319-25007-6_24
   Ou MD, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1105, DOI 10.1145/2939672.2939751
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Pennington J., 2014, EMNLP, P1532
   Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
   Peters M.E., 2018, P 2018 C N AM CHAPT, P2227, DOI 10.18653/v1/N18-1202
   Pocostales J., 2016, P 10 INT WORKSH SEM, P1298, DOI DOI 10.18653/V1/S16-1202
   Pujary D., 2020, P SAI INT SYST C, P209
   Razavi A., 2019, 180509786 ARXIV
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Ryabinin M., 2010, 201002598 ARXIV
   Schlechtweg D., 2020, P 14 WORKSH SEM EV, P1
   Shang C, 2019, AAAI CONF ARTIF INTE, P3060
   Shen JM, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P486, DOI 10.1145/3366423.3380132
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Shwartz V, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2389
   Straka M, 2017, P CONLL 2017 SHARED, P88, DOI [10.18653/v1/K17-3009, DOI 10.18653/V1/K17-3009]
   Sutskever Ilya, 2014, ADV NEURAL INFORM PR, V27, P3104
   Tanev H, 2016, P 10 INT WORKSH SEM, P1342, DOI [10.18653/v1/S16-1210, DOI 10.18653/V1/S16-1210]
   Thang Luong, 2015, P 2015 C EMP METH NA, P1412, DOI 10.18653/v1/D15- 1166
   Tikhomirov M, 2020, COMPUT LINGUIST
   Tikhomirov M, 2021, COMPUT LINGUIST
   Vaswani A, 2017, ADV NEUR IN, V30
   Velardi P, 2013, COMPUT LINGUIST, V39, DOI 10.1162/COLI_a_00146
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Winata GI, 2019, 4TH WORKSHOP ON REPRESENTATION LEARNING FOR NLP (REPL4NLP-2019), P181
   Yang C, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P2111
   Yin WP, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1351
   Yu WC, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2663, DOI 10.1145/3219819.3220000
   Zettlemoyer L., 2020, P 58 ANN M ASS COMP, P8440, DOI DOI 10.18653/V1/2020.ACL-MAIN.747
NR 84
TC 1
Z9 1
U1 1
U2 1
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2022
VL 13
IS 3
BP 441
EP 475
DI 10.3233/SW-212955
PG 35
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 0J3YH
UT WOS:000780041500008
OA Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Wu, HY
   Cheng, KM
   Tong, LJ
   Wang, YL
   Yang, WG
   Sun, ZM
AF Wu, Haiyang
   Cheng, Kunming
   Tong, Linjian
   Wang, Yulin
   Yang, Weiguang
   Sun, Zhiming
TI Knowledge structure and emerging trends on osteonecrosis of the femoral
   head: a bibliometric and visualized study
SO JOURNAL OF ORTHOPAEDIC SURGERY AND RESEARCH
LA English
DT Article
DE Osteonecrosis of the femoral head; Bibliometric analysis; Hotspots;
   VOSviewer; CiteSpace
ID NONTRAUMATIC OSTEONECROSIS
AB Background Osteonecrosis of the femoral head (ONFH) is a common disabling disease with considerable social and economic impacts. Although extensive studies related to ONFH have been conducted in recent years, a specific bibliometric analysis on this topic has not yet been performed. Our study attempted to summarize the comprehensive knowledge map, development landscape, and future directions of ONFH research with the bibliometric approach. Methods All publications concerning ONFH published from 2001 to 2020 were identified from Web of Science Core Collection. Key bibliometric indicators were calculated and evaluated using CiteSpace, VOSviewer, and the online bibliometric analysis platform. Results A total of 2594 publications were included. Our analysis revealed a significant exponential growth trend in the annual number of publications over the past 20 years (R-2 = 0.9663). China, the USA, and Japan were the major contributors both from the quality and quantity points of view. Correlation analysis indicated that there was a high positive correlation between the number of publications and gross domestic product (r = 0.774), and a moderate positive correlation between publications and demographic factor (r = 0.673). All keywords were categorized into four clusters including Cluster 1 (etiology and risk factors study); Cluster 2 (basic research and stem cell therapy); cluster 3 (hip-preserving study); and Cluster 4 (hip replacement study). Stem cell therapy-related research has been recognized as an important research hotspot in this field. Several topics including exosomes, autophagy, biomarkers, osteogenic differentiation, microRNAs, steroid-induced osteonecrosis, mesenchymal stem cells, double-blind, early-stage osteonecrosis, and asymptomatic osteonecrosis were considered as research focuses in the near future. Conclusion Over the past two decades, increasing attention has been paid to global ONFH-related research. Our bibliometric findings provide valuable information for researchers to understand the basic knowledge structure, identify the current research hotspots, potential collaborators, and future research frontiers in this field.
C1 [Wu, Haiyang; Tong, Linjian; Wang, Yulin; Yang, Weiguang; Sun, Zhiming] Tianjin Med Univ, Grad Sch, 22 Qixiangtai Rd, Tianjin 300070, Peoples R China.
   [Cheng, Kunming] Zhengzhou Univ, Dept Intens Care Unit, Affiliated Hosp 2, Zhengzhou 450014, Henan, Peoples R China.
   [Sun, Zhiming] Tianjin Huanhu Hosp, Dept Orthopaed Surg, 6 Jizhao Rd, Tianjin 300350, Peoples R China.
RP Wu, HY; Sun, ZM (通讯作者)，Tianjin Med Univ, Grad Sch, 22 Qixiangtai Rd, Tianjin 300070, Peoples R China.; Sun, ZM (通讯作者)，Tianjin Huanhu Hosp, Dept Orthopaed Surg, 6 Jizhao Rd, Tianjin 300350, Peoples R China.
EM nfykdxwhy@126.com; szhm618@163.com
FU Tianjin Municipal Health Bureau [14KG115]; Key Program of the Natural
   Science Foundation of Tianjin [20JCZDJC00730]
FX This work was supported by the Tianjin Municipal Health Bureau (Grant
   Number 14KG115) and Key Program of the Natural Science Foundation of
   Tianjin (Grant Number 20JCZDJC00730).
CR Cooper C, 2010, OSTEOPOROSIS INT, V21, P569, DOI 10.1007/s00198-009-1003-1
   Galvez-Romero JL, 2021, J INTERN MED, V289, P906, DOI 10.1111/joim.13223
   Gao JX, 2020, J ORTHOP SURG RES, V15, DOI 10.1186/s13018-020-01707-5
   Giannoudis PV, 2021, INT ORTHOP, V45, P1663, DOI 10.1007/s00264-021-05020-6
   Guo YQ, 2020, J MED INTERNET RES, V22, DOI 10.2196/18228
   Hao C, 2016, SCI REP-UK, V6, DOI 10.1038/srep22599
   Hauzeur JP, 2018, INT ORTHOP, V42, P1429, DOI 10.1007/s00264-017-3650-8
   Hernigou P, 2015, BONE, V70, P102, DOI 10.1016/j.bone.2014.04.034
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Houdek MT, 2016, J ARTHROPLASTY, V31, P893, DOI 10.1016/j.arth.2015.08.017
   Hua KC, 2019, J ORTHOP SURG RES, V14, DOI 10.1186/s13018-019-1359-7
   Huang C, 2021, FRONT CELL DEV BIOL, V9, DOI 10.3389/fcell.2021.777697
   Johnson AJ, 2014, CLIN ORTHOP RELAT R, V472, P617, DOI 10.1007/s11999-013-3220-3
   Li C, 2021, BMC MUSCULOSKEL DIS, V22, DOI 10.1186/s12891-021-04714-7
   Li C, 2020, J ORTHOP SURG RES, V15, DOI 10.1186/s13018-020-01757-9
   Liu LH, 2018, INT ORTHOP, V42, P1575, DOI 10.1007/s00264-018-3918-7
   Liu XL, 2017, INT J BIOL SCI, V13, P232, DOI 10.7150/ijbs.16951
   Mao LH, 2020, STEM CELL RES THER, V11, DOI 10.1186/s13287-020-01956-5
   Mont MA, 2006, J BONE JOINT SURG AM, V88A, P1117, DOI 10.2106/JBJS.E.01041
   Mont MA, 2015, J BONE JOINT SURG AM, V97A, P1604, DOI 10.2106/JBJS.O.00071
   Mont MA, 2010, J BONE JOINT SURG AM, V92A, P2165, DOI 10.2106/JBJS.I.00575
   Moya-Angeler J, 2015, WORLD J ORTHOP, V6, P590, DOI 10.5312/wjo.v6.i8.590
   Sodhi N, 2020, BONE JOINT J, V102B, P122, DOI 10.1302/0301-620X.102B7.BJJ-2019-1611.R1
   Synnestvedt Marie B, 2005, AMIA Annu Symp Proc, P724
   Tan BA, 2021, ORTHOP SURG, V13, P153, DOI 10.1111/os.12857
   Tang F, 2021, J ARTHROPLASTY, V36, P2698, DOI 10.1016/j.arth.2021.03.019
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Wei QS, 2019, J ORTHOP TRANSL, V18, P65, DOI 10.1016/j.jot.2018.11.002
   Wu CT, 2019, INT ORTHOP, V43, P579, DOI 10.1007/s00264-018-4013-9
   Wu HY, 2021, FRONT SURG, V8, DOI 10.3389/fsurg.2021.671403
   Wu HY, 2021, ARCH OSTEOPOROS, V16, DOI 10.1007/s11657-021-01016-2
   Wu HY, 2021, FRONT NEUROSCI-SWITZ, V15, DOI 10.3389/fnins.2021.706105
   Wu HY, 2021, FRONT PHARMACOL, V12, DOI 10.3389/fphar.2021.646626
   Wu HY, 2021, ARCH OSTEOPOROS, V16, DOI 10.1007/s11657-021-00929-2
   Wu HY, 2020, ORTHOP SURG, V12, P488, DOI 10.1111/os.12648
   Yuan HF, 2015, EXP MOL PATHOL, V98, P158, DOI 10.1016/j.yexmp.2015.01.002
   Yue JA, 2020, J ORTHOP SURG RES, V15, DOI 10.1186/s13018-020-01730-6
   Zhang CQ, 2011, MICROSURG, V31, P417, DOI 10.1002/micr.20904
   Zhang SQ, 2021, DRUG DES DEV THER, V15, P983, DOI 10.2147/DDDT.S298691
   Zhang WC, 2019, J ORTHOP SURG RES, V14, DOI 10.1186/s13018-019-1476-3
   Zhao DW, 2015, CHINESE MED J-PEKING, V128, P2843, DOI 10.4103/0366-6999.168017
   Zhao DW, 2020, J ORTHOP TRANSL, V21, P100, DOI 10.1016/j.jot.2019.12.004
   Zu GA, 2010, WORLD J GASTROENTERO, V16, P2707, DOI 10.3748/wjg.v16.i22.2707
NR 43
TC 2
Z9 2
U1 14
U2 14
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1749-799X
J9 J ORTHOP SURG RES
JI J. Orthop. Surg. Res.
PD MAR 28
PY 2022
VL 17
IS 1
AR 194
DI 10.1186/s13018-022-03068-7
PG 14
WC Orthopedics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Orthopedics
GA 0A4XD
UT WOS:000773958500006
PM 35346273
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Lin, YC
   Li, Y
   Lu, KM
   Ma, C
   Zhao, P
   Gao, DQ
   Fan, ZH
   Cheng, ZJ
   Wang, ZY
   Yu, S
AF Lin, Yucong
   Li, Yang
   Lu, Keming
   Ma, Cheng
   Zhao, Peng
   Gao, Daiqi
   Fan, Zihao
   Cheng, Zijie
   Wang, Zheyu
   Yu, Sheng
TI Long-distance disorder-disorder relation extraction with bootstrapped
   noisy data
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Relation extraction; Distant supervision; Article structure embedding;
   Graph information
AB Objective: Artificial intelligence in healthcare increasingly relies on relations in knowledge graphs for algorithm development. However, many important relations are not well covered in existing knowledge graphs. We aim to develop a novel long-distance relation extraction algorithm that leverages the article section structure and is trained with bootstrapped noisy data to identify important relations for diagnosis, including may cause, may be caused by, and differential diagnosis.
   Methods: Known relations were extracted from semistructured web pages and a relational database and were paired with sentences containing corresponding medical concepts to form training data. The sentence form was extended to allow one concept to be in the title. An attention mechanism was applied to reduce the effect of noisily labeled sentences. Section structure embedding was added to provide additional context for relation expressions. Graph information was further incorporated into the model to differentiate the target relations whose expressions were often similar and interwoven.
   Results: The extended sentence form allowed 1.75 times as many relations and 2.17 times as many sentences to be found compared to the conventional form. The various components of the proposed model all added to the accuracy. Overall, the positive sample accuracy of the proposed model was 9 percentage points higher than baseline deep learning models and 13 percentage points higher than naive Bayes and support vector machines.
   Conclusion: Our bootstrap data preparation method and the extended sentence form could form a large training dataset to enable algorithm development and data mining efforts. Section structure embedding and graph information significantly increased prediction accuracy.
C1 [Lin, Yucong; Yu, Sheng] Tsinghua Univ, Ctr Stat Sci, Weiqinglou Rm 209, Beijing 100084, Peoples R China.
   [Lin, Yucong; Zhao, Peng; Gao, Daiqi; Yu, Sheng] Tsinghua Univ, Dept Ind Engn, Beijing, Peoples R China.
   [Li, Yang; Ma, Cheng] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA.
   [Lu, Keming; Wang, Zheyu] Tsinghua Univ, Dept Automat, Beijing, Peoples R China.
   [Fan, Zihao] Univ Calif Berkeley, Sch Informat, Berkeley, CA 94720 USA.
   [Cheng, Zijie] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China.
   [Yu, Sheng] Tsinghua Univ, Inst Data Sci, Beijing, Peoples R China.
RP Yu, S (通讯作者)，Tsinghua Univ, Ctr Stat Sci, Weiqinglou Rm 209, Beijing 100084, Peoples R China.
EM syu@tsinghua.edu.cn
OI Yu, Sheng/0000-0002-6347-0507
FU National Natural Science Foundation of China [11801301]; National Key
   Research and Development Program of China [2018YFC0910404]; Natural
   Science Foundation of Beijing Municipality [Z190024]; Tsinghua
   University Initiative Scientific Research Program
FX This work was supported by the National Natural Science Foundation of
   China (No. 11801301), the National Key Research and Development Program
   of China (No. 2018YFC0910404), the Natural Science Foundation of Beijing
   Municipality (No. Z190024), and the Tsinghua University Initiative
   Scientific Research Program.
CR Alt C., 2019, ARXIV190603088
   Bahdanau D., 2014, 3 INT C LEARN REPR M
   BERNER ES, 1994, NEW ENGL J MED, V330, P1792, DOI 10.1056/NEJM199406233302506
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bunescu R.C., 2005, P C HUM LANG TECHN E, P724, DOI DOI 10.3115/1220575.1220666
   Cai R, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P756
   Chorowski J, 2015, ADV NEUR IN, V28
   Clark M.L., 2017, KUMAR CLARKS CLIN ME
   Culotta A., 2004, P 42 ANN M ASS COMP, P423, DOI [10.3115/1218955.1219009, DOI 10.3115/1218955.1219009]
   Devlin J, 2018, P 2019 C N AM CHAPT
   Goodwin T, 2013, IEEE INT C SEMANT CO, P363, DOI 10.1109/ICSC.2013.68
   Humes H.D., 2000, KELLEYS TXB INTERNAL
   Jameson J. N., 2012, JAMA-J AM MED ASSOC, V308, P1813, DOI DOI 10.1001/jama.308.17.1813-b
   Kilicoglu H, 2008, P 3 INT S SEM MIN BI, P69
   Lin YK, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2124
   Lin Y, 2019, GEOFLUIDS, DOI 10.1155/2019/7320536
   LINDBERG DAB, 1993, METHOD INFORM MED, V32, P281, DOI 10.1055/s-0038-1634945
   Liu C, 2013, INTERNATIONAL CONFERENCE ON COMPLEX SCIENCE MANAGEMENT AND EDUCATION SCIENCE (CSMES 2013), P231
   Liu Y, 2015, ARXIV150704646
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Mikolov T., 2013, ARXIV PREPRINT ARXIV, P1
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Miwa M, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1105
   Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI 10.3115/v1/D14-1162
   Rindflesch Thomas C., 2011, Information Services & Use, V31, P15, DOI 10.3233/ISU-2011-0627
   Robinson PN, 2008, AM J HUM GENET, V83, P610, DOI 10.1016/j.ajhg.2008.09.017
   Sabiston DC., 2001, SABISTON TXB SURG BI
   Schuster M, 1997, IEEE T SIGNAL PROCES, V45, P2673, DOI 10.1109/78.650093
   SCHUYLER PL, 1993, B MED LIBR ASSOC, V81, P217
   Sentis L, 2009, PROC IEEE INT SYMP, P1035
   Shi P., 2019, ARXIV190405255
   Socher R., 2012, P 2012 JOINT C EMP M, P1201, DOI [10.5555/2390948.2391084, DOI 10.1162/153244303322533223]
   Turian J, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P384
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   WEISS SM, 1978, ARTIF INTELL, V11, P145, DOI 10.1016/0004-3702(78)90015-2
   Xu Y, 2015, P 2015 C EMP METH NA, P1785, DOI DOI 10.18653/V1/D15-1206
   Xue K., 2019, ARXIV190807721
   Yamada S., 1993, COMPUTER AIDED DIAGN
   Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205
   Zeng D., 2014, P COLING, P2335
   Zhang D., 2015, ARXIV150801006
   Zhou P, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2, P207, DOI 10.18653/v1/p16-2034
NR 42
TC 0
Z9 0
U1 4
U2 6
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD SEP
PY 2020
VL 109
AR 103529
DI 10.1016/j.jbi.2020.103529
PG 7
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA NW5SP
UT WOS:000575072400001
PM 32771539
OA Bronze
DA 2022-08-23
ER

PT J
AU Li, YS
   Kong, DY
   Zhang, YJ
   Tan, YH
   Chen, L
AF Li, Yansheng
   Kong, Deyu
   Zhang, Yongjun
   Tan, Yihua
   Chen, Ling
TI Robust deep alignment network with remote sensing knowledge graph for
   zero-shot and generalized zero-shot remote sensing image scene
   classification
SO ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
LA English
DT Article
DE Robust deep alignment network (DAN); Remote sensing knowledge graph
   (RSKG); Remote sensing image scene classification; Zero-shot learning
   (ZSL); Generalized zero-shot learning (GZSL)
ID WEB
AB Although deep learning has revolutionized remote sensing (RS) image scene classification, current deep learningbased approaches highly depend on the massive supervision of predetermined scene categories and have disappointingly poor performance on new categories that go beyond predetermined scene categories. In reality, the classification task often has to be extended along with the emergence of new applications that inevitably involve new categories of RS image scenes, so how to make the deep learning model own the inference ability to recognize the RS image scenes from unseen categories, which do not overlap the predetermined scene categories in the training stage, becomes incredibly important. By fully exploiting the RS domain characteristics, this paper constructs a new remote sensing knowledge graph (RSKG) from scratch to support the inference recognition of unseen RS image scenes. To improve the semantic representation ability of RS-oriented scene categories, this paper proposes to generate a Semantic Representation of scene categories by representation learning of RSKG (SR-RSKG). To pursue robust cross-modal matching between visual features and semantic representations, this paper proposes a novel deep alignment network (DAN) with a series of well-designed optimization constraints, which can simultaneously address zero-shot and generalized zero-shot RS image scene classification. Extensive experiments on one merged RS image scene dataset, which is the integration of multiple publicly open datasets, show that the recommended SR-RSKG obviously outperforms the traditional knowledge types (e.g., natural language processing models and manually annotated attribute vectors), and our proposed DAN shows better performance compared with the state-of-the-art methods under both the zero-shot and generalized zero-shot RS image scene classification settings. The constructed RSKG will be made publicly available along with this paper (https://github.com/kdy2021/SR-RSKG).
C1 [Li, Yansheng; Kong, Deyu; Zhang, Yongjun] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.
   [Tan, Yihua] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China.
   [Tan, Yihua] Shenzhen Huazhong Univ Sci & Technol, Res Inst, Shenzhen 518000, Peoples R China.
   [Chen, Ling] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
RP Zhang, YJ (通讯作者)，Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.; Tan, YH (通讯作者)，Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China.; Tan, YH (通讯作者)，Shenzhen Huazhong Univ Sci & Technol, Res Inst, Shenzhen 518000, Peoples R China.; Chen, L (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
EM zhangyj@whu.edu.cn; yhtan@hust.edu.cn; lingchen@cs.zju.edu.cn
FU National Key Research and Development Program of China [2018YFB0505003];
   National Natural Science Foundation of China [41971284]; State Key
   Program of the National Natural Science Foundation of China [42030102,
   92038301]; Foundation for Innovative Research Groups of the Natural
   Science Foundation of Hubei Province [2020CFA003]; China Postdoctoral
   Science Foundation [2016M590716, 2017T100581]; Shenzhen Basic Research
   Program [JCYJ20180305180708546]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2018YFB0505003; the National
   Natural Science Foundation of China under Grant 41971284; the State Key
   Program of the National Natural Science Foundation of China under Grants
   42030102 and 92038301; the Foundation for Innovative Research Groups of
   the Natural Science Foundation of Hubei Province under Grant 2020CFA003;
   the China Postdoctoral Science Foundation under Grants 2016M590716 and
   2017T100581; the Shenzhen Basic Research Program under Grant
   JCYJ20180305180708546.
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Blaschke T, 2010, ISPRS J PHOTOGRAMM, V65, P2, DOI 10.1016/j.isprsjprs.2009.06.004
   Bojanowski P., 2017, T ASSOC COMPUT LING, V5, P135, DOI [DOI 10.1162/TACL_A_00051, 10.1162/tacl_a_00051]
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen T., 2020, P 37 INT C MACH LEAR, P1597
   Cheng G, 2017, P IEEE, V105, P1865, DOI 10.1109/JPROC.2017.2675998
   Cheng G, 2016, ISPRS J PHOTOGRAMM, V117, P11, DOI 10.1016/j.isprsjprs.2016.03.014
   Cheng G, 2013, INT J REMOTE SENS, V34, P45, DOI 10.1080/01431161.2012.705443
   Chi MM, 2016, P IEEE, V104, P2207, DOI 10.1109/JPROC.2016.2598228
   Clementini E, 2019, INF TECHNOL CONTROL, V48, P5, DOI 10.5755/j01.itc.48.1.22246
   Demir B, 2016, IEEE T GEOSCI REMOTE, V54, P892, DOI 10.1109/TGRS.2015.2469138
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Elhoseiny M, 2019, IEEE I CONF COMP VIS, P5783, DOI 10.1109/ICCV.2019.00588
   Erxleben F, 2014, LECT NOTES COMPUT SC, V8796, P50, DOI 10.1007/978-3-319-11964-9_4
   Gerke M, 2014, ISPRS J PHOTOGRAMM, V87, P78, DOI 10.1016/j.isprsjprs.2013.10.011
   Gu YT, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9102110
   Guo WZ, 2019, IEEE ACCESS, V7, P63373, DOI 10.1109/ACCESS.2019.2916887
   He K., 2020, P IEEE CVF C COMP VI, DOI DOI 10.1109/CVPR42600.2020.00975
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Hua YS, 2020, IEEE T GEOSCI REMOTE, V58, P4558, DOI 10.1109/TGRS.2019.2963364
   Ji Z, 2020, IEEE T IMAGE PROCESS, V29, P6549, DOI 10.1109/TIP.2020.2991527
   Kingma Diederik P, 2013, AUTOENCODING VARIATI
   Kodirov E, 2017, PROC CVPR IEEE, P4447, DOI 10.1109/CVPR.2017.473
   Lampert CH, 2009, PROC CVPR IEEE, P951, DOI 10.1109/CVPRW.2009.5206594
   Larochelle Hugo, 2008, AAAI, P646
   Lazaridou A, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P270
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Li AX, 2017, IEEE T GEOSCI REMOTE, V55, P4157, DOI 10.1109/TGRS.2017.2689071
   Li H, 2017, ADV NEUR IN, V30
   Li YN, 2017, PROC CVPR IEEE, P5207, DOI 10.1109/CVPR.2017.553
   Li YS, 2021, ISPRS J PHOTOGRAMM, V175, P20, DOI 10.1016/j.isprsjprs.2021.02.009
   Li YS, 2020, REMOTE SENS ENVIRON, V250, DOI 10.1016/j.rse.2020.112045
   Li YS, 2021, IEEE T CYBERNETICS, V51, P1756, DOI 10.1109/TCYB.2020.2989241
   Li YS, 2018, IEEE T GEOSCI REMOTE, V56, P6521, DOI 10.1109/TGRS.2018.2839705
   Li YS, 2016, IEEE GEOSCI REMOTE S, V13, P157, DOI 10.1109/LGRS.2015.2503142
   Liang YZ, 2019, IEEE I CONF COMP VIS, P10402, DOI 10.1109/ICCV.2019.01050
   Lobry S, 2020, IEEE T GEOSCI REMOTE, V58, P8555, DOI 10.1109/TGRS.2020.2988782
   Long Y, 2017, PROC CVPR IEEE, P6165, DOI 10.1109/CVPR.2017.653
   Marcos D, 2018, ISPRS J PHOTOGRAMM, V145, P96, DOI 10.1016/j.isprsjprs.2018.01.021
   Mikolov T., 2013, EFFICIENT ESTIMATION
   Misra I, 2020, PROC CVPR IEEE, P6706, DOI 10.1109/CVPR42600.2020.00674
   Palatucci M., 2009, P ADV NEUR INF PROC, P1410, DOI DOI 10.1007/116694874
   Quan JC, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS AND COMMUNICATION ENGINEERING (ICECE 2018), P17, DOI 10.1109/ICECOME.2018.8645056
   Schonfeld E, 2019, PROC CVPR IEEE, P8239, DOI 10.1109/CVPR.2019.00844
   Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62
   Shen JW, 2017, ISPRS INT J GEO-INF, V6, DOI 10.3390/ijgi6020037
   Shigeto Y, 2015, LECT NOTES ARTIF INT, V9284, P135, DOI 10.1007/978-3-319-23528-8_9
   Sumbul G, 2018, IEEE T GEOSCI REMOTE, V56, P770, DOI 10.1109/TGRS.2017.2754648
   TAN M, 2019, INT C MACH LEARN, P6105
   Tao C, 2019, ISPRS J PHOTOGRAMM, V158, P155, DOI 10.1016/j.isprsjprs.2019.10.001
   Tao C, 2019, IEEE T GEOSCI REMOTE, V57, P7339, DOI 10.1109/TGRS.2019.2912985
   Tempelmeier N, 2021, FUTURE GENER COMP SY, V116, P349, DOI 10.1016/j.future.2020.11.003
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Velickovi P., 2017, ARXIV171010903
   Wang Y.-R, 2017, P BMVC
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xia GS, 2017, IEEE T GEOSCI REMOTE, V55, P3965, DOI 10.1109/TGRS.2017.2685945
   Xian YQ, 2018, PROC CVPR IEEE, P5542, DOI 10.1109/CVPR.2018.00581
   Xian YQ, 2019, IEEE T PATTERN ANAL, V41, P2251, DOI 10.1109/TPAMI.2018.2857768
   Yang Y, 2011, IEEE I CONF COMP VIS, P1465, DOI 10.1109/ICCV.2011.6126403
   Zhang, 2021, IEEE T GEOSCI REMOTE
   Zhang F, 2016, IEEE T GEOSCI REMOTE, V54, P1793, DOI 10.1109/TGRS.2015.2488681
   Zhang XY, 2018, REMOTE SENS ENVIRON, V212, P231, DOI 10.1016/j.rse.2018.05.006
   Zhou WX, 2018, ISPRS J PHOTOGRAMM, V145, P197, DOI 10.1016/j.isprsjprs.2018.01.004
NR 67
TC 21
Z9 21
U1 18
U2 33
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0924-2716
EI 1872-8235
J9 ISPRS J PHOTOGRAMM
JI ISPRS-J. Photogramm. Remote Sens.
PD SEP
PY 2021
VL 179
BP 145
EP 158
DI 10.1016/j.isprsjprs.2021.08.001
EA AUG 2021
PG 14
WC Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science & Photographic Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physical Geography; Geology; Remote Sensing; Imaging Science &
   Photographic Technology
GA UC2UJ
UT WOS:000686386900011
DA 2022-08-23
ER

PT J
AU Xie, ZW
   Zhu, RJ
   Zhao, KS
   Liu, J
   Zhou, GY
   Huang, JXJ
AF Xie, Zhiwen
   Zhu, Runjie
   Zhao, Kunsong
   Liu, Jin
   Zhou, Guangyou
   Huang, Jimmy Xiangji
TI Dual Gated Graph Attention Networks with Dynamic Iterative Training for
   Cross-Lingual Entity Alignment
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph; cross graph attention; entity alignment; iterative
ID LARGE-SCALE; KNOWLEDGE; DBPEDIA
AB Cross-lingual entity alignment has attracted considerable attention in recent years. Past studies using conventional approaches to match entities share the common problem of missing important structural information beyond entities in the modeling process. This allows graph neural network models to step in. Most existing graph neural network approaches model individual knowledge graphs (KGs) separately with a small amount of pre-aligned entities served as anchors to connect different KG embedding spaces. However, this characteristic can cause several major problems, including performance restraint due to the insufficiency of available seed alignments and ignorance of pre-aligned links that are useful in contextual information in-between nodes. In this article, we propose DuGa-DIT, a dual gated graph attention network with dynamic iterative training, to address these problems in a unified model. The DuGa-DIT model captures neighborhood and cross-KG alignment features by using intra-KG attention and cross-KG attention layers. With the dynamic iterative process, we can dynamically update the cross-KG attention score matrices, which enables our model to capture more cross-KG information. We conduct extensive experiments on two benchmark datasets and a case study in cross-lingual personalized search. Our experimental results demonstrate that DuGa-DIT outperforms state-of-the-art methods.
C1 [Xie, Zhiwen; Zhao, Kunsong; Liu, Jin] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
   [Zhu, Runjie] York Univ, Lassonde Sch Engn, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON, Canada.
   [Zhu, Runjie] AI Singapore, Singapore, Singapore.
   [Zhou, Guangyou] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
   [Huang, Jimmy Xiangji] York Univ, Sch Informat Technol, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON, Canada.
RP Liu, J (通讯作者)，Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.; Zhou, GY (通讯作者)，Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
EM xiezhiwen@whu.edu.cn; sherryzh@cse.yorku.ca; kszhao@whu.edu.cn;
   jinliu@whu.edu.cn; gyzhou@mail.ccnu.edu.cn; jhuang@yorku.ca
OI Huang, Jimmy Xiangji/0000-0003-1292-1491
FU National Natural Science Foundation of China [61972290, 61972173];
   National Key R&D Program of China [2018YFC1604000]; Natural Sciences and
   Engineering Research Council (NSERC) of Canada; York Research Chairs
   (YRC) program; National Research Foundation, Singapore
FX This work was supported by the National Natural Science Foundation of
   China under grants 61972290 and 61972173, and supported by the National
   Key R&D Program of China under grant 2018YFC1604000. This research was
   also supported in part by a research grant from the Natural Sciences and
   Engineering Research Council (NSERC) of Canada and York Research Chairs
   (YRC) program. In addition, this research was supported by the National
   Research Foundation, Singapore under its AI Singapore program.
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bansal T, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4387
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Cao YX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1452
   Chen B, 2020, LECT NOTES ARTIF INT, V12084, P845, DOI 10.1007/978-3-030-47426-3_65
   Chen D., 2013, ARXIV13013618
   Chen MH, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3998
   Chen MH, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1511
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Du XY, 2020, MM '20: PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, P3469, DOI 10.1145/3394171.3413628
   Feng J, 2016, FIFTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P557
   Garcca-Duran Alberto, 2018, ARXIV170904676
   Garcia-Duran A., 2015, P 2015 C EMP METH NA, P286, DOI DOI 10.18653/V1/D15-1034
   Glorot X., 2011, J MACH LEARN RES, V15, P315, DOI DOI 10.1002/ECS2.1832
   Guo LB, 2019, PR MACH LEARN RES, V97
   Hao YC, 2016, COMM COM INF SC, V650, P3, DOI 10.1007/978-981-10-3168-7_1
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Jin HL, 2019, DATA INTELLIGENCE, V1, P77, DOI 10.1162/dint_a_00003
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kingma D. P., 2017, ARXIV
   Kipf T., 2016, 5 INT C LEARN REPR, P1
   Klein Patrick, 2017, P EACL, P516
   Kristiadi Agustinus, 2019, ARXIV180200934
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lei Meng, 2020, MM '20: Proceedings of the 28th ACM International Conference on Multimedia, P3460, DOI 10.1145/3394171.3413598
   Li Chengjiang, 2019, P 2019 C EMPIRICAL M, P2723
   Li JZ, 2009, IEEE T KNOWL DATA EN, V21, P1218, DOI 10.1109/TKDE.2008.202
   Lin, 2018, ARXIV180803752
   Lin XX, 2019, IEEE DATA MINING, P429, DOI 10.1109/ICDM.2019.00053
   Lin Y., 2016, P 25 INT JOINT C ART, P2866
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu Zhiyuan, 2020, P 2020 C EMP METH NA, P6355
   Maas A. L., 2013, P ICML, V30, P3
   Mahdisoltani F., 2015, CIDR 2015
   Mao X, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P420, DOI 10.1145/3336191.3371804
   Mccallum A., 2004, ADV NEURAL INFORM PR, V17, P905, DOI [10.5555/2976040.2976154, DOI 10.5555/2976040.2976154]
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Navigli R, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P216
   Nguyen D.Q., 2019, P NAACL HLT 2019, P2180, DOI DOI 10.18653/V1/N19-1226
   Nguyen D. Q., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Nguyen D.Q., 2016, P 20 SIGNLL C COMP N, P40, DOI DOI 10.18653/V1/K16-1005
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Nie H, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3658
   Oh B, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P257, DOI 10.1145/3269206.3271769
   Pei SC, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2175, DOI 10.1145/3394486.3403268
   Pei SC, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3231
   Pei SC, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3130, DOI 10.1145/3308558.3313646
   Qian W, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4286
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Shang C, 2019, AAAI CONF ARTIF INTE, P3060
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Shi Xiaofei, 2019, EMNLP IJCNLP, P813
   Singhal A., 2012, INTRO KNOWLEDGE GRAP
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   Sun ZQ, 2020, AAAI CONF ARTIF INTE, V34, P222
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Sun Zequn, 2020, EMNLP
   Sun Zhiqing, 2019, P ICLR
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vashishth S, 2020, AAAI CONF ARTIF INTE, V34, P3009
   Velickovi P., 2017, ARXIV171010903
   Vrandecic D., 2012, P 21 INT C WORLD WID, P1063, DOI [DOI 10.1145/2187980.2188242, 10.1145/2187980.2188242]
   Wang Q., 2019, ARXIV PREPRINT ARXIV
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Wei YW, 2020, MM '20: PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, P3541, DOI 10.1145/3394171.3413556
   Wu Peng, 2019, P ACL, P6130, DOI [10.18653/v1/p19- 1616, DOI 10.18653/V1/P19-1616]
   Wu YR, 2018, REPRESENTATION LEARNING FOR NLP, P132
   Wu YT, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5278
   Wu Yuting, 2019, EMNLP IJCNLP, P240
   Wu Yuting, 2020, ACL, P6477
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xie Z, 2020, P 58 ANN M ASS COMP, P5929
   Xie Zhiwen, 2020, P COLING, P5918, DOI [10.18653/v1/2020.coling-main.520, DOI 10.18653/V1/2020.COLING-MAIN.520]
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Xu K, 2020, AAAI CONF ARTIF INTE, V34, P9354
   Xu K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3156
   Yan ZH, 2020, NEUROCOMPUTING, V410, P419, DOI 10.1016/j.neucom.2020.06.054
   Yang B., 2015, P INT C LEARN REPR I
   Yang Hsiu-Wei, 2019, P 2019 C EMP METH NA, P4430
   Yang SH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1935
   Zhang S, 2019, ADV NEUR IN, V32
   Zhang YM, 2018, INT CONF SYST INFORM, P1056, DOI 10.1109/ICSAI.2018.8599288
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
   Zhou D, 2018, INFORM SCIENCES, V430, P200, DOI 10.1016/j.ins.2017.11.044
   Zhu H, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4258
   Zhu QN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1943
NR 99
TC 0
Z9 0
U1 6
U2 6
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1046-8188
EI 1558-2868
J9 ACM T INFORM SYST
JI ACM Trans. Inf. Syst.
PD JUL
PY 2022
VL 40
IS 3
AR 44
DI 10.1145/3471165
PG 30
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0E1NE
UT WOS:000776450500003
DA 2022-08-23
ER

PT J
AU Han, YM
   Chen, GF
   Li, ZK
   Geng, ZQ
   Li, F
   Ma, B
AF Han, Yongming
   Chen, Guofei
   Li, Zhongkun
   Geng, Zhiqiang
   Li, Fang
   Ma, Bo
TI An asymmetric knowledge representation learning in manifold space
SO INFORMATION SCIENCES
LA English
DT Article
DE Knowledge representation; Complex relations; Embedding vectors; Manifold
   space
AB The knowledge graph is constructed to acquire a large amount of structured knowledge that can be read by computers. To effectively complete the knowledge graph, embedding representation models that encode entities and relations in real number tensor space are proposed, such as the TransE model and its extension models, namely, the flexible translation model (FT), dynamic translation model (DT) and manifold-based representation model (OrbitE). These above baselines are proposed to alleviate the problem of inaccurate triple representation in complex relations and have achieved good results. However, with the improvement of the effect, the number of parameters increases. Therefore, an asymmetric knowledge representation learning model in manifold space (MAKR) is proposed in this paper. The position of the golden triple is expanded to the manifold based on the OrbitE model. Then, the embedded representations of the head and tail entities are separately weighted by the corresponding different embedded relations, which are the same manifold space in the same triples instead of the same points. The MAKR alleviates the asymmetry and imbalance of relations and the unsatisfactory precise prediction. Moreover, the time and space complexities of the proposed MAKR are low. Finally, the effectiveness of the MAKR is verified by four datasets: FB15K, WN18, FB13 and WN11. Compared to other baselines, the MAKR has achieved better performance in both triple classification and link prediction tasks. (c) 2020 Elsevier Inc. All rights reserved.
C1 [Han, Yongming; Chen, Guofei; Li, Zhongkun; Geng, Zhiqiang; Li, Fang] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China.
   [Han, Yongming; Chen, Guofei; Li, Zhongkun; Geng, Zhiqiang; Li, Fang] Minist Educ China, Engn Res Ctr Intelligent PSE, Beijing 100029, Peoples R China.
   [Ma, Bo] Beijing Univ Chem Technol, Key Lab, Minist Educ Engine Hlth Monitoring & Networking, Beijing 100029, Peoples R China.
RP Geng, ZQ (通讯作者)，Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China.; Geng, ZQ (通讯作者)，Minist Educ China, Engn Res Ctr Intelligent PSE, Beijing 100029, Peoples R China.
EM gengzhiqiang@mail.buct.edu.cn; mabo@mail.buct.edu.cn
OI Han, Yongming/0000-0003-3209-725X
FU National Key Research and Development Program of China [2017YFC1601800];
   National Natural Science Foundation of China [61673046, 61603025];
   Science and Technology Major Project of Guizhou Province (Guizhou
   Branch) [[2018]3002]; Fundamental Re376 search Funds for the Central
   Universities [XK1802-4]
FX This work is partly financial supported by the National Key Research and
   Development Program of China (2017YFC1601800), the National Natural
   Science Foundation of China (61673046 and 61603025), Science and
   Technology Major Project of Guizhou Province (Guizhou Branch [2018]3002)
   and the Fundamental Re376 search Funds for the Central Universities
   (XK1802-4).
CR Bian Q, 2016, 2016 IEEE CHINESE GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC), P182, DOI 10.1109/CGNCC.2016.7828780
   Bishan Y., 2015, P ICLR
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., P INT C ART INT STAT, P127
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Bordes Antoine, 2011, AAAI
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Chang L, 2017, IEEE ACCESS, V5, P20898, DOI 10.1109/ACCESS.2017.2759139
   Corcoglioniti F, 2016, LECT NOTES COMPUT SC, V9678, P317, DOI 10.1007/978-3-319-34129-3_20
   Di Iorio A, 2018, INFORM SCIENCES, V432, P1, DOI 10.1016/j.ins.2017.12.009
   Fan M., 2017, IEEE WIC ACM INT C W, P42
   Feng J., 2018, P AAAI
   Feng J, 2016, FIFTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P557
   Garcia-Duran A., 2015, P 2015 C EMP METH NA, P286, DOI DOI 10.18653/V1/D15-1034
   Guo S, 2017, IEEE T KNOWL DATA EN, V29, P884, DOI 10.1109/TKDE.2016.2638425
   Hayashi K, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2, P554, DOI 10.18653/v1/P17-2088
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Hu S., 2018, IEEE T KNOWL DATA EN, P1
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nguyen D.Q., 2016, P 2016 C N AM CHAPT, P460, DOI DOI 10.18653/V1/N16-1054
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Socher R., P NIPS, P926
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Tan Z, 2018, IEEE ACCESS, V6, P8232, DOI 10.1109/ACCESS.2018.2797876
   Tan Z, 2016, INT CONF DAT MIN WOR, P684, DOI [10.1109/ICDMW.2016.31, 10.1109/ICDMW.2016.0102]
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang WY, 2015, MACH LEARN, V100, P101, DOI 10.1007/s10994-015-5488-x
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao H., 2016, P 25 INT JOINT C ART, P1315
   Yu, 2017, J INFORM HIDING MULT, V8, P1119
   Zheng WG, 2019, INFORM SCIENCES, V481, P141, DOI 10.1016/j.ins.2018.12.032
NR 39
TC 12
Z9 12
U1 3
U2 23
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD AUG
PY 2020
VL 531
BP 1
EP 12
DI 10.1016/j.ins.2020.04.036
PG 12
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LU2ZD
UT WOS:000537628800001
DA 2022-08-23
ER

PT J
AU Hasan, Z
   Key, S
   Habib, AR
   Wong, E
   Aweidah, L
   Kumar, A
   Sacks, R
   Singh, N
AF Hasan, Zubair
   Key, Seraphina
   Habib, Al-Rahim
   Wong, Eugene
   Aweidah, Layal
   Kumar, Ashnil
   Sacks, Raymond
   Singh, Narinder
TI Convolutional Neural Networks in ENT Radiology: Systematic Review of the
   Literature
SO ANNALS OF OTOLOGY RHINOLOGY AND LARYNGOLOGY
LA English
DT Review; Early Access
DE radiographic image interpretation; artificial intelligence; neural
   networks; otolaryngology
ID THYROID-NODULES; DISTINCTION; MTANN; MODEL
AB Introduction: Convolutional neural networks (CNNs) represent a state-of-the-art methodological technique in AI and deep learning, and were specifically created for image classification and computer vision tasks. CNNs have been applied in radiology in a number of different disciplines, mostly outside otolaryngology, potentially due to a lack of familiarity with this technology within the otolaryngology community. CNNs have the potential to revolutionize clinical practice by reducing the time required to perform manual tasks. This literature search aims to present a comprehensive systematic review of the published literature with regard to CNNs and their utility to date in ENT radiology. Methods: Data were extracted from a variety of databases including PubMED, Proquest, MEDLINE Open Knowledge Maps, and Gale OneFile Computer Science. Medical subject headings (MeSH) terms and keywords were used to extract related literature from each databases inception to October 2020. Inclusion criteria were studies where CNNs were used as the main intervention and CNNs focusing on radiology relevant to ENT. Titles and abstracts were reviewed followed by the contents. Once the final list of articles was obtained, their reference lists were also searched to identify further articles. Results: Thirty articles were identified for inclusion in this study. Studies utilizing CNNs in most ENT subspecialties were identified. Studies utilized CNNs for a number of tasks including identification of structures, presence of pathology, and segmentation of tumors for radiotherapy planning. All studies reported a high degree of accuracy of CNNs in performing the chosen task. Conclusion: This study provides a better understanding of CNN methodology used in ENT radiology demonstrating a myriad of potential uses for this exciting technology including nodule and tumor identification, identification of anatomical variation, and segmentation of tumors. It is anticipated that this field will continue to evolve and these technologies and methodologies will become more entrenched in our everyday practice.
C1 [Hasan, Zubair; Habib, Al-Rahim; Sacks, Raymond; Singh, Narinder] Univ Sydney, Fac Med & Hlth, Camperdown, NSW, Australia.
   [Hasan, Zubair; Habib, Al-Rahim; Wong, Eugene; Singh, Narinder] Westmead Hosp, Dept Otolaryngol Head & Neck Surg, Corner Hawkesbury Rd & Darcy Rd, Westmead, NSW 2145, Australia.
   [Key, Seraphina] Monash Univ, Fac Med Nursing & Hlth Sci, Clayton, Vic, Australia.
   [Habib, Al-Rahim] Princess Alexandra Hosp, Dept Otolaryngol Head & Neck Surg, Woolloongabba, Qld, Australia.
   [Aweidah, Layal] Univ Notre Dame, Fac Med, Darlinghurst, NSW, Australia.
   [Kumar, Ashnil] Univ Sydney, Fac Engn, Sch Biomed Engn, Darlington, NSW, Australia.
   [Sacks, Raymond] Concord Hosp, Dept Otolaryngol Head & Neck Surg, Concord, NSW, Australia.
RP Hasan, Z (通讯作者)，Westmead Hosp, Dept Otolaryngol Head & Neck Surg, Corner Hawkesbury Rd & Darcy Rd, Westmead, NSW 2145, Australia.
EM zhas3802@uni.sydney.edu.au
OI Hasan, Zubair/0000-0002-5346-578X; Key, Seraphina/0000-0002-1019-5986
CR Abdolali F, 2020, COMPUT BIOL MED, V122, DOI 10.1016/j.compbiomed.2020.103871
   [Anonymous], 2018, UNDERSTANDING DIGITA
   Benjamens S, 2020, NPJ DIGIT MED, V3, DOI 10.1038/s41746-020-00324-0
   Bluemke DA., 2020, ASSESSING RADIOLOGY
   Bur AM, 2019, OTOLARYNG HEAD NECK, V160, P603, DOI 10.1177/0194599819827507
   Cardenas CE, 2018, PHYS MED BIOL, V63, DOI 10.1088/1361-6560/aae8a9
   Cardenas CE, 2018, INT J RADIAT ONCOL, V101, P468, DOI 10.1016/j.ijrobp.2018.01.114
   Cho YS, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-63887-8
   Chowdhury NI, 2019, INT FORUM ALLERGY RH, V9, P46, DOI 10.1002/alr.22196
   Daoud B, 2019, COMPUT MED IMAG GRAP, V77, DOI 10.1016/j.compmedimag.2019.101644
   Dey S, 2020, PYTHON IMAGE PROCESS
   Fu J, 2019, 3 INT C COMPUTER ENG
   FUKUSHIMA K, 1980, BIOL CYBERN, V36, P193, DOI 10.1007/BF00344251
   He K, 2015, PROC CVPR IEEE, DOI [10.1109/CVPR.2016.90, DOI 10.1109/CVPR.2016.90]
   Huang J, 2020, J LARYNGOL OTOL, V134, P52, DOI 10.1017/S0022215119002536
   HUBEL DH, 1968, J PHYSIOL-LONDON, V195, P215, DOI 10.1113/jphysiol.1968.sp008455
   Kim Y, 2019, INVEST RADIOL, V54, P7, DOI 10.1097/RLI.0000000000000503
   Krizhevsky A, 2017, COMMUN ACM, V60, P84, DOI 10.1145/3065386
   Kumar A, 2020, BIOCHAR APPLICATIONS IN AGRICULTURE AND ENVIRONMENT MANAGEMENT, P167, DOI 10.1007/978-3-030-40997-5_8
   Li HL, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-25005-7
   Li QL, 2018, BIOMED RES INT, V2018, DOI 10.1155/2018/9128527
   Li SH, 2019, TECHNOL CANCER RES T, V18, DOI 10.1177/1533033819884561
   Li XC, 2019, LANCET ONCOL, V20, P193, DOI 10.1016/S1470-2045(18)30762-9
   Li YH, 2019, PHYS MED BIOL, V64, DOI 10.1088/1361-6560/ab2770
   Liang SJ, 2019, EUR RADIOL, V29, P1961, DOI 10.1007/s00330-018-5748-9
   Lin L, 2019, RADIOLOGY, V291, P677, DOI 10.1148/radiol.2019182012
   Liu TJ, 2019, MED IMAGE ANAL, V58, DOI 10.1016/j.media.2019.101555
   Ma J., 2020, COMPUTATIONAL INTELL
   Ma JL, 2017, INT J COMPUT ASS RAD, V12, P1895, DOI 10.1007/s11548-017-1649-7
   Ma JL, 2017, MED PHYS, V44, P1678, DOI 10.1002/mp.12134
   Ma JL, 2017, ULTRASONICS, V73, P221, DOI 10.1016/j.ultras.2016.09.011
   Ma ZQ, 2019, PHYS MED BIOL, V64, DOI 10.1088/1361-6560/aaf5da
   Murata M, 2019, ORAL RADIOL, V35, P301, DOI 10.1007/s11282-018-0363-7
   Parmar P, 2020, J LARYNGOL OTOL, V134, P328, DOI 10.1017/S0022215120000444
   Peng H, 2019, CLIN CANCER RES, V25, P4271, DOI 10.1158/1078-0432.CCR-18-3065
   Ronneberger O., 2015, P INT C MED IM COMP, V56, P62
   ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519
   Russakovsky O, 2015, INT J COMPUT VISION, V115, P211, DOI 10.1007/s11263-015-0816-y
   Simonyan K., 2014, ARXIV
   Sun HJ, 2020, MATH PROBL ENG, V2020, DOI 10.1155/2020/6147037
   Suzuki K, 2006, IEEE T MED IMAGING, V25, P406, DOI 10.1109/TMI.2006.871549
   Suzuki K, 2005, IEEE T MED IMAGING, V24, P1138, DOI 10.1109/TMI.2005.852048
   Suzuki K, 2005, ACAD RADIOL, V12, P1333, DOI 10.1016/j.acra.2005.06.017
   Suzuki K, 2003, MED PHYS, V30, P1602, DOI 10.1118/1.1580485
   Suzuki K, 2017, RADIOL PHYS TECHNOL, V10, P257, DOI 10.1007/s12194-017-0406-5
   Szegedy C., 2016, RETHINKING INCEPTION
   Wang L, 2019, WORLD J SURG ONCOL, V17, DOI 10.1186/s12957-019-1558-z
   Wang YM, 2020, EAR HEARING, V41, P669, DOI 10.1097/AUD.0000000000000794
   Whiting PF, 2011, ANN INTERN MED, V155, P529, DOI 10.7326/0003-4819-155-8-201110180-00009
   Yamashita R., 2018, INSIGHTS IMAGING, V9, P611, DOI [10.1007/s13244-018-0639-9, DOI 10.1007/s13244-018-0639-9]
   Yu X, 2020, CURR MED IMAGING, V16, P174, DOI 10.2174/1573405615666191023104751
   Zhang DQ, 2020, MED IMAGE ANAL, V61, DOI 10.1016/j.media.2020.101659
   Zhao LJ, 2019, J DIGIT IMAGING, V32, P462, DOI 10.1007/s10278-018-00173-0
NR 53
TC 0
Z9 0
U1 0
U2 0
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0003-4894
EI 1943-572X
J9 ANN OTO RHINOL LARYN
JI Ann. Otol. Rhinol. Laryngol.
AR 00034894221095899
DI 10.1177/00034894221095899
EA JUN 2022
PG 14
WC Otorhinolaryngology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Otorhinolaryngology
GA 1W5RN
UT WOS:000806830800001
PM 35651308
DA 2022-08-23
ER

PT J
AU Li, X
   Xiang, P
   Liang, JF
   Deng, YF
   Du, JY
AF Li, Xiang
   Xiang, Ping
   Liang, Jianfen
   Deng, Yifan
   Du, Jingyi
TI Global Trends and Hotspots in Esketamine Research: A Bibliometric
   Analysis of Past and Estimation of Future Trends
SO DRUG DESIGN DEVELOPMENT AND THERAPY
LA English
DT Review
DE ketamine; esketamine; bibliometrics; research trends; depression
AB Background: Being the S-enantiomer of racemic ketamine, esketamine is found to be effective for sedation, analgesia, and treating depression. However, there is no comprehensive bibliometric analysis about esketamine research. In this study, we aimed to determine the scientific output and emerging topics related to esketamine. Methods: Esketamine-related articles and reviews that published between 2000 and 2020 were obtained from the Web of Science Core Collection database, using key word search of "esketamine" "esketamine hydrochloride", "s-ketamine", "S(+)-ketamine", "(S)ketamine", or "(-)-ketamine". Various bibliographic elements were collected, including the annual number of publications, citation frequency, countries/regions, institutions, authors, journals, and keywords. Two sorts of scientometric software, namely VOS viewer and CiteSpace, were used to conduct bibliometric and knowledge-map analyses. Results: A total of 683 publications were included in the current study. We found the number of publications in esketamine research field had increased annually since 2016. The United States was the leader in this field, with the highest publications number (162, 23.72%), total citations (3504/9713, 36.08%) and H-index (40). The most productive institution was Chiba University in Japan, and esketamine-related papers were mainly published in the journal Anesthesia & Analgesia. The keyword co-occurrence analysis showed that keywords relevant to depression were the most frequent. Moreover, all identified keywords could be divided into four clusters, with the research focus gradually shifting from cluster of "anesthesia and analgesia detection" to "depression treatment effect.". Conclusion: The past two decades have shown a marked increase in esketamine research. The United States maintained a top position worldwide, making the most significant contributions in the field of esketamine research. The contributions and collaborations of Asian countries have continuously increased and is a strong area of growth as well as development in recent years. Additionally, the emerging hotspots of esketamine research concentrate on clarifying its depression treatment effect.
C1 [Li, Xiang; Liang, Jianfen; Deng, Yifan; Du, Jingyi] Sun Yat Sen Univ, Affiliated Hosp 3, Dept Anesthesiol, Guangzhou 510630, Peoples R China.
   [Xiang, Ping] Southern Med Univ, Nanfang Hosp, Dept Med Qual Management, Guangzhou 510515, Peoples R China.
RP Li, X (通讯作者)，Sun Yat Sen Univ, Affiliated Hosp 3, Dept Anesthesiol, Guangzhou 510630, Peoples R China.
EM lixiang27@mail.sysu.edu.cn
OI Li, Xiang/0000-0003-4997-9339
FU Precision Medicine Foundation of Guangdong Province Hospital Association
   [2020-10]
FX This work was supported by the Precision Medicine Foundation of
   Guangdong Province Hospital Association (grant number 2020-10).
CR Bahji A, 2021, J AFFECT DISORDERS, V278, P542, DOI 10.1016/j.jad.2020.09.071
   Capuzzi E, 2021, INT J MOL SCI, V22, DOI 10.3390/ijms22179338
   Chen CM, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0223994
   Chen P, 2021, ANN TRANSL MED, V9, DOI 10.21037/atm-21-3924
   Choudhri AF, 2015, RADIOGRAPHICS, V35, P736, DOI 10.1148/rg.2015140036
   Eberl S, 2020, EUR J ANAESTH, V37, P394, DOI 10.1097/EJA.0000000000001134
   Engqvist L, 2008, TRENDS ECOL EVOL, V23, P250, DOI 10.1016/j.tree.2008.01.009
   Gantenbein L, 2021, J EUR ACAD DERMATOL, V35, P1424, DOI 10.1111/jdv.17196
   Jonkman K, 2017, ANESTHESIOLOGY, V127, P675, DOI 10.1097/ALN.0000000000001798
   Ke LX, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.00842
   Kodonas K, 2021, INT ENDOD J, V54, P220, DOI 10.1111/iej.13422
   Liu T, 2021, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.720787
   Molero P, 2018, CNS DRUGS, V32, P411, DOI 10.1007/s40263-018-0519-3
   Ng J, 2021, J AFFECT DISORDERS, V293, P285, DOI 10.1016/j.jad.2021.06.032
   Perianes-Rodriguez A, 2016, J INFORMETR, V10, P1178, DOI 10.1016/j.joi.2016.10.006
   Qin Y, 2020, BRAIN RES BULL, V156, P15, DOI 10.1016/j.brainresbull.2019.12.004
   Sapkota A, 2021, CUREUS, V13, DOI 10.7759/cureus.17352
   Swainson J, 2019, EXPERT REV NEUROTHER, DOI 10.1080/14737175.2019.1640604
   Sweileh WM, 2021, GLOBALIZATION HEALTH, V17, DOI 10.1186/s12992-021-00766-5
   Wang XM, 2021, J CLIN ANESTH, V68, DOI 10.1016/j.jclinane.2020.110071
   Wang ZQ, 2021, ENVIRON SCI POLLUT R, V28, P54403, DOI 10.1007/s11356-021-15974-0
   Wu HY, 2021, FRONT PHARMACOL, V12, DOI 10.3389/fphar.2021.646626
   Xiao FJ, 2017, FRONT CHEM, V5, DOI 10.3389/fchem.2017.00067
   Yeung AWK, 2017, FRONT NEUROSCI-SWITZ, V11, DOI 10.3389/fnins.2017.00120
   Zhang HT, 2020, J ORTHOP SURG RES, V15, DOI 10.1186/s13018-020-01991-1
   Zhang J, 2021, FRONT ONCOL, V11, DOI 10.3389/fonc.2021.686726
   Zhang MM, 2021, FRONT CELL DEV BIOL, V9, DOI 10.3389/fcell.2021.609452
   Zhao H, 2021, TISSUE ENG PART B-RE, DOI [10.1089/ten.TEB.2021.0080, 10.1089/ten.teb.2021.0080]
NR 28
TC 1
Z9 1
U1 5
U2 5
PU DOVE MEDICAL PRESS LTD
PI ALBANY
PA PO BOX 300-008, ALBANY, AUCKLAND 0752, NEW ZEALAND
SN 1177-8881
J9 DRUG DES DEV THER
JI Drug Des. Dev. Ther.
PY 2022
VL 16
BP 1131
EP 1142
DI 10.2147/DDDT.S356284
PG 12
WC Chemistry, Medicinal; Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA 1A2YV
UT WOS:000791629700001
PM 35478936
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Huang, L
   Zhou, M
   Lv, J
   Chen, K
AF Huang, Li
   Zhou, Mi
   Lv, Jie
   Chen, Ke
TI Trends in global research in forest carbon sequestration: A bibliometric
   analysis
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Article
DE Bibliometric analysis; Forest; Carbon sequestration; Climate change;
   CiteSpace; Knowledge mapping
ID CLIMATE-CHANGE IMPACTS; TEMPERATE FORESTS; PROTECTION PROGRAM; DECIDUOUS
   FOREST; CHINA FORESTS; STOCKS; STORAGE; BIOMASS; BOREAL; SOIL
AB Based on the Web of Science Core Collection databases from 1990 to 2018, a scientometric analysis of 1,284 academic works related to forest carbon sequestration is carried out to characterize the intellectual landscape by identifying and revealing the basic characteristics, research power, intellectual base and research hotspots in this field. The results of this work show that: (1) the number of publications in forest carbon sequestration research has increased rapidly and the research in this field is in its "growth stage"; Forest Ecology and Management is the most productive journal and Forestry is the most popular subject category; (2) the most productive authors and institutions in this subject area are in the USA, China and Canada, with the Chinese Academy of Sciences being the key institution performing such research; (3) in the sample, 9 papers have played a key role in the evolution of the field and laid a solid foundation for future research; (4) Keyword clustering analysis showed that the main research topics in the domain of forest carbon sequestration could be summarized as: (a) temperate forest; (b) forest management; (c) uncertainty analysis; (d) forest floor; (e) REDD; (f) net primary productivity. Meanwhile, keyword bursts analysis showed that the new research hotspots or research frontiers mainly concentrated on changes in the carbon storage and carbon sequestration potential of secondary forests and tropical forests, plant litter carbon storage and contribution to total ecosystem carbon storage, and new national forest inventories (NFIs). Through in-depth analysis of forest carbon sequestration research, this paper provides a better understanding of development trends that have emerged in this field over the past 29 years, which can also offer reference for future research. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Huang, Li; Zhou, Mi; Lv, Jie; Chen, Ke] Shenyang Agr Univ, Coll Econ & Management, Shenyang 110866, Liaoning, Peoples R China.
RP Zhou, M (通讯作者)，Shenyang Agr Univ, Coll Econ & Management, Shenyang 110866, Liaoning, Peoples R China.
EM lilyhuang@syau.edu.cn; zhoumi2011@syau.edu.cn; jieluesy@syau.edu.cn;
   chenkeyaya@syau.edu.cn
RI Huang, Li/AAY-8487-2020
FU National Natural Science Foundation of China [71573179, 71903133,
   71973100];  [2020lslktjdyb-030]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article:
   Preparation of this manuscript was supported by Grant Nos. 71573179,
   71903133 and 71973100 from the National Natural Science Foundation of
   China; Research Project on Economic and Social Development of Liaoning
   Province (2020lslktjdyb-030).
CR Aleixandre-Benavent R, 2017, J CLEAN PROD, V147, P406, DOI 10.1016/j.jclepro.2017.01.112
   Ali A, 2017, TROP ECOL, V58, P1
   Amin MR, 2015, SUSTAINABILITY-BASEL, V7, P898, DOI 10.3390/su7010898
   [Anonymous], ENV EARTH SCI
   [Anonymous], CHIN GEOGR SCI
   [Anonymous], PLOS ONE
   ARMENTANO TV, 1984, ENVIRON MANAGE, V8, P529, DOI 10.1007/BF01871578
   Bartholomee O, 2018, ECOL INDIC, V95, P331, DOI 10.1016/j.ecolind.2018.07.054
   Betts RA, 2000, NATURE, V408, P187, DOI 10.1038/35041545
   Bottalico F, 2016, ENVIRON RES, V144, P72, DOI 10.1016/j.envres.2015.10.025
   Bradshaw CJA, 2015, GLOBAL PLANET CHANGE, V128, P24, DOI 10.1016/j.gloplacha.2015.02.004
   Briceno-Elizondo E, 2006, ENVIRON SCI POLICY, V9, P237, DOI 10.1016/j.envsci.2005.12.003
   Chen C., 2017, TEXT MINING VISUALIZ, V2
   Chen CM, 2008, DATA KNOWL ENG, V67, P234, DOI 10.1016/j.datak.2008.05.004
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen KH, 2011, J INFORMETR, V5, P233, DOI 10.1016/j.joi.2010.10.007
   Chen YQ, 2015, FORESTS, V6, P794, DOI 10.3390/f6030794
   [陈悦 Chen Yue], 2008, [科学学研究, Studies in Science of Science], V26, P449
   Cienciala E, 2008, ANN FOREST SCI, V65, DOI 10.1051/forest:2008043
   Clemmensen KE, 2013, SCIENCE, V339, P1615, DOI 10.1126/science.1231923
   COOPER CF, 1983, CAN J FOREST RES, V13, P155, DOI 10.1139/x83-022
   d'Albertas F, 2018, FOREST ECOL MANAG, V407, P57, DOI 10.1016/j.foreco.2017.09.042
   Dai LM, 2013, FOREST ECOL MANAG, V300, P106, DOI 10.1016/j.foreco.2012.06.046
   Deng L, 2013, CATENA, V110, P1, DOI 10.1016/j.catena.2013.06.016
   Dieleman WIJ, 2013, GEODERMA, V204, P59, DOI 10.1016/j.geoderma.2013.04.005
   DIXON RK, 1994, SCIENCE, V263, P185, DOI 10.1126/science.263.5144.185
   Doblas-Miranda E, 2013, BIOGEOSCIENCES, V10, P8353, DOI 10.5194/bg-10-8353-2013
   Domke GM, 2016, SCI TOTAL ENVIRON, V557, P469, DOI 10.1016/j.scitotenv.2016.03.090
   Domke GM, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0059949
   Domke GM, 2012, FOREST ECOL MANAG, V270, P108, DOI 10.1016/j.foreco.2012.01.022
   Du E, 2018, ENVIRON POLLUT, V242, P1476, DOI 10.1016/j.envpol.2018.08.041
   Eckert S, 2011, FOREST ECOL MANAG, V262, P1996, DOI 10.1016/j.foreco.2011.08.041
   Ekundayo TC, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0207655
   Fang HY, 2018, PLANT SOIL, V433, P363, DOI 10.1007/s11104-018-3847-7
   Fang JY, 2001, SCIENCE, V292, P2320, DOI 10.1126/science.1058629
   Fang Y, 2018, J SUSTAIN TOUR, V26, P108, DOI 10.1080/09669582.2017.1329310
   Fedrigo M, 2014, FOREST ECOL MANAG, V334, P129, DOI 10.1016/j.foreco.2014.08.025
   FREEMAN LC, 1979, SOC NETWORKS, V1, P215, DOI 10.1016/0378-8733(78)90021-7
   Fu LP, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16122077
   Garcia-Gonzalo J, 2007, CLIMATIC CHANGE, V81, P431, DOI 10.1007/s10584-006-9149-8
   Glanzel W, 2003, SCIENTOMETRICS, V57, P197, DOI 10.1023/A:1024185601555
   Goh AHX, 2011, ECOL ECON, V71, P221, DOI 10.1016/j.ecolecon.2011.07.024
   Gren IM, 2016, FOREST POLICY ECON, V70, P128, DOI 10.1016/j.forpol.2016.06.008
   Guo ZD, 2014, BIOGEOSCIENCES, V11, P4115, DOI 10.5194/bg-11-4115-2014
   Guo ZD, 2010, FOREST ECOL MANAG, V259, P1225, DOI 10.1016/j.foreco.2009.09.047
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Hisano M, 2018, BIOL REV, V93, P439, DOI 10.1111/brv.12351
   Hu HF, 2015, SCI REP-UK, V5, DOI 10.1038/srep11203
   HUNTINGTON TG, 1995, SOIL SCI SOC AM J, V59, P1459, DOI 10.2136/sssaj1995.03615995005900050036x
   Hurteau M, 2009, FRONT ECOL ENVIRON, V7, P409, DOI 10.1890/080049
   Indrajaya Y, 2016, FOREST POLICY ECON, V71, P1, DOI 10.1016/j.forpol.2016.06.032
   Islam MT, 2017, J ENVIRON MANAGE, V200, P347, DOI 10.1016/j.jenvman.2017.05.092
   Ji Q, 2014, SCIENTOMETRICS, V101, P1925, DOI 10.1007/s11192-014-1332-5
   Jia S, 2005, AGR FOREST METEOROL, V134, P70, DOI 10.1016/j.agrformet.2005.08.014
   Jiang LH, 2018, J FORESTRY RES, V29, P1187, DOI 10.1007/s11676-017-0542-0
   Keles S, 2010, INT J SUST DEV WORLD, V17, P468, DOI 10.1080/13504509.2010.519574
   Lafforgue G, 2008, ECOL ECON, V67, P589, DOI 10.1016/j.ecolecon.2008.01.008
   Lal R, 2004, SCIENCE, V304, P1623, DOI 10.1126/science.1097396
   Law BE, 2001, GLOBAL CHANGE BIOL, V7, P755, DOI 10.1046/j.1354-1013.2001.00439.x
   Leighty WW, 2006, ECOSYSTEMS, V9, P1051, DOI 10.1007/s10021-005-0028-3
   Lewis SL, 2009, NATURE, V457, P1003, DOI 10.1038/nature07771
   Lian XC, 2015, IEEE IMAGE PROC, P1995, DOI 10.1109/ICIP.2015.7351150
   Lin MY, 2017, ECOL RES, V32, P845, DOI 10.1007/s11284-017-1497-5
   Lindsell JA, 2013, FOREST ECOL MANAG, V289, P10, DOI 10.1016/j.foreco.2012.09.045
   Liski J, 2002, FOREST ECOL MANAG, V169, P159, DOI 10.1016/S0378-1127(02)00306-7
   Liu WJ, 2019, ECOL ECON, V156, P293, DOI 10.1016/j.ecolecon.2018.09.001
   Usuga JCL, 2010, FOREST ECOL MANAG, V260, P1906, DOI 10.1016/j.foreco.2010.08.040
   Lou JN, 2019, CURR POLLUT REP, V5, P144, DOI 10.1007/s40726-019-00112-9
   Mandal RA, 2016, TROP ECOL, V57, P655
   Masera OR, 2003, ECOL MODEL, V164, P177, DOI 10.1016/S0304-3800(02)00419-2
   Mendelsohn R, 2006, ENVIRON DEV ECON, V11, P159, DOI 10.1017/S1355770X05002755
   Mertz O, 2018, J LAND USE SCI, V13, P1, DOI [10.1080/1747423X.2017.1410242, 10.1080/1747423x.2017.1410242]
   Michinaka T, 2015, INT FOREST REV, V17, P66, DOI 10.1505/146554815814725103
   Musa I, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10103414
   Myneni RB, 2001, P NATL ACAD SCI USA, V98, P14784, DOI 10.1073/pnas.261555198
   NABUURS GJ, 1995, CAN J FOREST RES, V25, P1157, DOI 10.1139/x95-128
   Nader A.E., 2013, INT ED STUD, V6, P93, DOI [DOI 10.5539/IES.V6N11P93, 10.6084/m9.figshare.848520.v1, DOI 10.6084/M9.FIGSHARE.848520.V1]
   Nelson GC, 2014, P NATL ACAD SCI USA, V111, P3274, DOI 10.1073/pnas.1222465110
   Norby RJ, 2002, ECOL APPL, V12, P1261, DOI 10.1890/1051-0761(2002)012[1261:NPPOAC]2.0.CO;2
   North MP, 2011, FOREST ECOL MANAG, V261, P1115, DOI 10.1016/j.foreco.2010.12.039
   Nyirambangutse B, 2017, BIOGEOSCIENCES, V14, P1285, DOI 10.5194/bg-14-1285-2017
   PERSSON O, 1994, J AM SOC INFORM SCI, V45, P31, DOI 10.1002/(SICI)1097-4571(199401)45:1<31::AID-ASI4>3.0.CO;2-G
   Piao SL, 2005, J GEOPHYS RES-BIOGEO, V110, DOI 10.1029/2005JG000014
   Pussinen A, 2002, FOREST ECOL MANAG, V158, P103, DOI 10.1016/S0378-1127(00)00675-7
   Qiu H, 2009, SCIENTOMETRICS, V81, P601, DOI 10.1007/s11192-008-2207-4
   Rappaport DI, 2018, ENVIRON RES LETT, V13, DOI 10.1088/1748-9326/aac331
   Ren Y, 2011, PLANT SOIL, V345, P125, DOI 10.1007/s11104-011-0766-2
   Ruiz-Peinado R, 2017, FOREST SYST, V26, DOI 10.5424/fs/2017262-11205
   Rutishauser E, 2013, FOREST ECOL MANAG, V307, P219, DOI 10.1016/j.foreco.2013.07.013
   SCHIFFMAN PM, 1989, CAN J FOREST RES, V19, P69, DOI 10.1139/x89-010
   SCHUBERT A, 1986, SCIENTOMETRICS, V9, P281, DOI 10.1007/BF02017249
   Schulp CJE, 2008, FOREST ECOL MANAG, V256, P482, DOI 10.1016/j.foreco.2008.05.007
   Sharma CM, 2011, J BIOSCIENCES, V36, P701, DOI 10.1007/s12038-011-9103-4
   SHARPE DM, 1981, J ENVIRON MANAGE, V12, P221
   Shi YL, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11133716
   Shi YJ, 2018, FOREST ECOL MANAG, V429, P406, DOI 10.1016/j.foreco.2018.07.035
   Shiffrin RM, 2004, P NATL ACAD SCI USA, V101, P5183, DOI 10.1073/pnas.0307852100
   Sitch S, 2007, NATURE, V448, P791, DOI 10.1038/nature06059
   Song JB, 2016, SCIENTOMETRICS, V107, P1111, DOI 10.1007/s11192-016-1918-1
   Sonwa DJ, 2016, INT FOREST REV, V18, P119, DOI 10.1505/146554816819683753
   Sun YQ, 2019, TRENDS FOOD SCI TECH, V83, P86, DOI 10.1016/j.tifs.2018.11.015
   Tate KR, 1995, J BIOGEOGR, V22, P695, DOI 10.2307/2845972
   Thurner M, 2014, GLOBAL ECOL BIOGEOGR, V23, P297, DOI 10.1111/geb.12125
   Tian X, 2018, RESOUR CONSERV RECY, V131, P148, DOI 10.1016/j.resconrec.2018.01.002
   Ueyama M, 2011, ECOL MODEL, V222, P3216, DOI 10.1016/j.ecolmodel.2011.05.006
   Goetz RU, 2013, ECOL ECON, V88, P86, DOI 10.1016/j.ecolecon.2013.01.012
   van Bellen S, 2010, FIRE ECOL, V6, P16, DOI 10.4996/fireecology.0603016
   Vieilledent G, 2012, ECOL APPL, V22, P572, DOI 10.1890/11-0039.1
   Wang ZH, 2018, J CLEAN PROD, V199, P1072, DOI 10.1016/j.jclepro.2018.06.183
   Wei YW, 2014, CHINESE GEOGR SCI, V24, P397, DOI 10.1007/s11769-014-0703-4
   Wei YW, 2013, PLOS ONE, V8, DOI [10.1371/journal.pone.0072201, 10.1371/journal.pone.0060044]
   Wheeler T, 2013, SCIENCE, V341, P508, DOI 10.1126/science.1239402
   Woodall CW, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0073222
   Woodall CW, 2012, FORESTRY, V85, P125, DOI 10.1093/forestry/cpr065
   Wu SN, 2018, J FORESTRY RES, V29, P415, DOI 10.1007/s11676-017-0462-z
   Xiao FJ, 2017, FRONT CHEM, V5, DOI 10.3389/fchem.2017.00067
   Xu B, 2010, SCI CHINA LIFE SCI, V53, P776, DOI 10.1007/s11427-010-4030-4
   Xu L, 2018, SCI TOTAL ENVIRON, V631-632, P619, DOI 10.1016/j.scitotenv.2018.03.080
   Xu L, 2017, FORESTS, V8, DOI 10.3390/f8100371
   Yang YH, 2014, GLOBAL CHANGE BIOL, V20, P2687, DOI 10.1111/gcb.12536
   Yoshikane F, 2013, SCIENTOMETRICS, V96, P365, DOI 10.1007/s11192-013-0953-4
   Yu DP, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0089572
   Yu DJ, 2017, RENEW SUST ENERG REV, V74, P1314, DOI 10.1016/j.rser.2016.11.144
   Zhong W., 2008, J INTELL-BASEL, V27, P118
   Zhou WM, 2014, CHINESE GEOGR SCI, V24, P406, DOI 10.1007/s11769-014-0702-5
   Zhou W, 2018, ENERGY REP, V4, P724, DOI 10.1016/j.egyr.2018.10.012
   Zhu BA, 2010, J PLANT RES, V123, P439, DOI 10.1007/s10265-009-0301-1
   Zhu JX, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-00207-1
   Zhu JX, 2017, FORESTS, V8, DOI 10.3390/f8060191
   Zhu JX, 2015, SCI CHINA LIFE SCI, V58, P1135, DOI 10.1007/s11427-015-4935-z
NR 130
TC 50
Z9 53
U1 45
U2 246
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0959-6526
EI 1879-1786
J9 J CLEAN PROD
JI J. Clean Prod.
PD APR 10
PY 2020
VL 252
AR 119908
DI 10.1016/j.jclepro.2019.119908
PG 17
WC Green & Sustainable Science & Technology; Engineering, Environmental;
   Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Engineering; Environmental Sciences
   & Ecology
GA KQ2SA
UT WOS:000516777200124
DA 2022-08-23
ER

PT J
AU Smirnova, A
   Cudre-Mauroux, P
AF Smirnova, Alisa
   Cudre-Mauroux, Philippe
TI Relation Extraction Using Distant Supervision: A Survey
SO ACM COMPUTING SURVEYS
LA English
DT Article
DE Relation extraction; distant supervision; knowledge graph
ID WEB
AB Relation extraction is a subtask of information extraction where semantic relationships are extracted from natural language text and then classified. In essence, it allows us to acquire structured knowledge from unstructured text. In this article, we present a survey of relation extraction methods that leverage pre-existing structured or semi-structured data to guide the extraction process. We introduce a taxonomy of existing methods and describe distant supervision approaches in detail. We describe, in addition, the evaluation methodologies and the datasets commonly used for quality assessment. Finally, we give a high-level outlook on the field, highlighting open problems as well as the most promising research directions.
C1 [Smirnova, Alisa; Cudre-Mauroux, Philippe] Univ Fribourg Switzerland, eXascale Infolab, Boullevard Perolles 90, CH-1700 Fribourg, Switzerland.
RP Smirnova, A (通讯作者)，Univ Fribourg Switzerland, eXascale Infolab, Boullevard Perolles 90, CH-1700 Fribourg, Switzerland.
EM alisa.smirnova@unifr.ch; philippe.cudre-mauroux@unifr.ch
OI Cudre-Mauroux, Philippe/0000-0003-2588-4212; Smirnova,
   Alisa/0000-0002-7108-9917
FU European Research Council (ERC) under the European Union's Horizon 2020
   research and innovation programme [683253/GraphInt]
FX This project has received funding from the European Research Council
   (ERC) under the European Union's Horizon 2020 research and innovation
   programme (Grant Agreement No. 683253/GraphInt).
CR Agichtein E., 2000, ACM 2000. Digital Libraries. Proceedings of the Fifth ACM Conference on Digital Libraries, P85
   Akbik Alan, 2014, 25 INT C COMP LING, P2087
   Alfonseca E., 2012, P 50 ANN M ASS COMP, P54
   [Anonymous], 2014, P 2014 C EMP METH NA, P1556, DOI [DOI 10.3115/V1/D14-1164, 10.3115/v1/d14-1164]
   Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   Augenstein I, 2016, SEMANT WEB, V7, P335, DOI 10.3233/SW-150180
   Bach N, 2007, SURVEY RELATION EXTR
   Banko M, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2670
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Brin S., 1998, INT WORKSH WORLD WID, P172, DOI DOI 10.1007/10704656\_11
   Bunescu R., 2006, ADV NEURAL INFORM PR, V18, P171
   Ce Zhang, 2015, DEEPDIVE DATA MANAGE
   Chang K., 2014, PROC C EMPIRICAL MET, P1568
   Craven M, 1999, Proc Int Conf Intell Syst Mol Biol, P77
   Demartini G., 2012, WWW, P469
   Dienstmann R, 2015, CANCER DISCOV, V5, P118, DOI 10.1158/2159-8290.CD-14-1118
   Ernst P, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1013, DOI 10.1145/3178876.3186000
   Etzioni O, 2005, ARTIF INTELL, V165, P91, DOI 10.1016/j.artint.2005.03.001
   Etzioni O., 2011, IJCAI, V11, P3, DOI DOI 10.5591/978-1-57735-516-8/IJCAI11-012
   Etzioni O, 2008, COMMUN ACM, V51, P68, DOI 10.1145/1409360.1409378
   Fan M, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P839
   Han XP, 2016, AAAI CONF ARTIF INTE, P2950
   Han XP, 2011, PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), P765
   Heng Ji, 2011, P TEXT AN C
   Heng Ji, 2010, P TEXT AN C
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Kim JC, 2009, BRIT J NUTR, V102, P350, DOI 10.1017/S0007114508191231
   Kim Y., 2014, PROC C EMPIRICAL MET, P1746, DOI [10.3115/v1/d14-1181, 10.3115/v1/D14-1181]
   Kirschnick J, 2014, LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P2071
   Koch Mitchell, 2014, P EMNLP 2014, P1891
   Lee H, 2013, COMPUT LINGUIST, V39, P885, DOI 10.1162/COLI_a_00152
   Lewis DD, 2004, J MACH LEARN RES, V5, P361
   Ma SQ, 2011, MATH PROGRAM, V128, P321, DOI 10.1007/s10107-009-0306-5
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Milne D., 2008, P 17 ACM C INF KNOWL, P509, DOI DOI 10.1145/1458082.1458150
   Min B., 2013, P 2013 C N AM CHAPT, P777, DOI DOI 10.1186/S40537-016-0043-6
   Min Bonan, 2012, P 5 TEXT AN C TAC 12
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Moro A., 2014, TACL, V2, P231, DOI DOI 10.1162/TACL_A_00179
   Nguyen TH, 2015, P 1 WORKSH VECT SPAC, P39, DOI DOI 10.3115/V1/W15-1506
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nivre J, 2006, TEXT SPEECH LANG TEC, V34, pVII
   Peng Nanyun, 2017, T ASS COMPUT LINGUIS
   Pershina M, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P732
   Pin-Shan Chen P., 1976, ACM Transactions on Database Systems, V1, P9, DOI 10.1145/320434.320440
   Poon H, 2015, BIOCOMPUT-PAC SYM, P120
   Ravichandran D, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P41
   Riedel S., 2013, P 2013 C N AM CHAPT, P74
   Riedel S, 2010, LECT NOTES ARTIF INT, V6323, P148, DOI 10.1007/978-3-642-15939-8_10
   Ritter A., 2013, T ASSOC COMPUT LING, V1, P367, DOI DOI 10.1162/tacl_a_00234
   Rocktaschel T., 2015, P 2015 C N AM CHAPT, P1119
   Roth B., 2013, P 2013 C EMP METH NA, P24
   Roth B, 2013, P 2013 WORKSH AUT KN, P73
   Roth B, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1181, DOI 10.1145/2505515.2507850
   Sandhaus Evan, 2008, P LING DAT CONS
   Schaefer CF, 2009, NUCLEIC ACIDS RES, V37, pD674, DOI 10.1093/nar/gkn653
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Shinyama Y., 2006, P MAIN C HUM LANG TE, P304, DOI DOI 10.3115/1220835.1220874
   Surdeanu M., 2012, P 2012 JOINT C EMP M, P455
   Takamatsu S., 2012, P 50 ANN M ASS COMP, P721
   Tonon A, 2013, LECT NOTES COMPUT SC, V8218, P640, DOI 10.1007/978-3-642-41335-3_40
   Toutanova K., 2015, P 2015 C EMP METH NA, P1499, DOI DOI 10.18653/V1/D15-1174
   Wang C., 2011, P C EMP METH NAT LAN, P1426
   Weston J., 2013, ARXIV PREPRINT ARXIV, P1366
   Wick Michael, 2009, P WORKSH ADV RANK NE
   Wu F., 2007, P 16 ACM C INF KNOWL, P41, DOI DOI 10.1145/1321440.1321449
   Wu F, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P118
   Xu W., 2013, P 51 ANN M ASS COMP, P665
   Yankai Lin, 2016, P 54 ANN M ASS COMP
   Yao L., 2011, P C EMP METH NAT LAN, P1456
   Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205
   Zeng D., 2014, P COLING, P2335
   Zeng D, 2015, P 2015 C EMP METH NA, P1753, DOI DOI 10.18653/V1/D15-1203
NR 74
TC 37
Z9 40
U1 4
U2 53
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 0360-0300
EI 1557-7341
J9 ACM COMPUT SURV
JI ACM Comput. Surv.
PD JAN
PY 2019
VL 51
IS 5
AR 106
DI 10.1145/3241741
PG 35
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HJ4CM
UT WOS:000457121600019
DA 2022-08-23
ER

PT J
AU Cai, M
   Chen, S
   Du, YH
   Li, SQ
   Dong, PH
   Wang, LY
   Yang, RY
   Hu, JY
   Zhu, L
AF Cai, Ming
   Chen, Si
   Du, Yihong
   Li, Siqi
   Dong, Peihai
   Wang, Liyan
   Yang, Ruoyu
   Hu, Jingyun
   Zhu, Lei
TI The Role of Blood-Brain Barrier Dysfunction in Mild Cognitive
   Impairment: a Scientometric and Visualization Analysis from 2000 to 2021
SO JOURNAL OF MOLECULAR NEUROSCIENCE
LA English
DT Article; Early Access
DE Mild cognitive impairment; Blood-brain barrier; Bibliometric analysis;
   Web of Science; VOSviewer; CiteSpace
ID ALZHEIMERS-DISEASE; HIPPOCAMPUS; DIAGNOSIS; DEMENTIA; HEALTH; STATE;
   FLOW; MCI; MRI
AB Mild cognitive impairment (MCI) is a pathological stage between normal cognitive aging and dementia. The blood-brain barrier (BBB) breakdown is emerging as an early biomarker of MCI. This study aimed to visually analyze the literatures related to MCI caused by BBB dysfunction in recent 20 years, systematically identify collaboration networks, track research trends, highlight current hot spots, and predict future frontiers in this field. The related literatures published from 2000 to 2021 were obtained from the Web of Science with the search term "mild cognitive impairment and (blood-brain barrier or neurovascular unit or neurovascular coupling)". VOSviewer software was used to present knowledge map, CiteSpace software was used to extract literature information and make tables, including the top most influential countries, authors, institutions, periodicals, keywords, and references. A total of 333 literatures were used for visual analysis. After 2013, the literatures in the field of BBB dysfunction-induced MCI showed an increased trend in terms of year of publication and quantity of material, with more than 40 publications published each year. USA, England, China, and Sweden cooperated closely. In terms of institutions, Harvard Med Sch ranked first in the number of papers published, followed by Mstricht Univ and Univ Washington. In terms of journals, three of the top five co-cited journals belonged to USA, the other two journals were Neurobiol Aging and J Alzheimers DIS, which were from England and Netherlands respectively. A total of 1752 authors were identified, with Abhay P Sagare the most published and Zlokovic BV the most cited. Keyword emergence detection analysis showed that inflammation, oxidative stress, and memory were new research hot spot in this field. Overall, the research on BBB dysfunction-induced MCI is booming. In the future, cooperation and communication between different countries and institutions should be strengthened.
C1 [Cai, Ming] Shanghai Univ Med & Hlth Sci, Affiliated Zhoupu Hosp, 1500 Zhouyuan Rd, Shanghai 201318, Peoples R China.
   [Cai, Ming] Shanghai Jiao Tong Univ, Bio X Inst, Shanghai 200030, Peoples R China.
   [Chen, Si; Li, Siqi; Dong, Peihai; Zhu, Lei] Qufu Normal Univ, Coll Sport Sci, Qufu 273165, Shandong, Peoples R China.
   [Du, Yihong] East China Jiaotong Univ, Coll Phys & Hlth, Nanchang, Jiangxi, Peoples R China.
   [Wang, Liyan; Yang, Ruoyu] Shanghai Univ Med & Hlth Sci, Coll Rehabil Sci, Shanghai 201318, Peoples R China.
   [Hu, Jingyun] Shanghai Pudong New Area Peoples Hosp, Cent Lab, 490 Chuanhuan South Rd, Shanghai 201299, Peoples R China.
RP Zhu, L (通讯作者)，Qufu Normal Univ, Coll Sport Sci, Qufu 273165, Shandong, Peoples R China.; Hu, JY (通讯作者)，Shanghai Pudong New Area Peoples Hosp, Cent Lab, 490 Chuanhuan South Rd, Shanghai 201299, Peoples R China.
EM jingyunhu121@126.com; zhulei316@126.com
FU Youth Fund Project of Research Planning Foundation on Humanities and
   Social Sciences of the Ministry of Education [20YJCZH001]; China
   Postdoctoral Science Foundation [2021M702128]; Scientific Research
   Foundation of SUMHS [SSF-21-03-008]; Shandong Provincial Department of
   Education [SDYAL20103]; Shanghai Sailing Program [22YF1441600]
FX This work was sponsored by the grant of the funding of the Youth Fund
   Project of Research Planning Foundation on Humanities and Social
   Sciences of the Ministry of Education (20YJCZH001), the China
   Postdoctoral Science Foundation (2021M702128), the Scientific Research
   Foundation of SUMHS (SSF-21-03-008), the Fund Project of Shandong
   Provincial Department of Education (SDYAL20103), and the Shanghai
   Sailing Program (22YF1441600).
CR Abbott NJ, 2010, NEUROBIOL DIS, V37, P13, DOI 10.1016/j.nbd.2009.07.030
   Akhtar A, 2020, NEUROCHEM INT, V135, DOI 10.1016/j.neuint.2020.104707
   Alahmari Abeer, 2021, Neural Plast, V2021, P6564585, DOI 10.1155/2021/6564585
   Albert MS, 2011, ALZHEIMERS DEMENT, V7, P270, DOI 10.1016/j.jalz.2011.03.008
   Anand KS, 2012, ANN INDIAN ACAD NEUR, V15, P239, DOI 10.4103/0972-2327.104323
   Anderson ND, 2019, CNS SPECTRUMS, V24, P78, DOI 10.1017/S1092852918001347
   Arvanitakis Z, 2019, JAMA-J AM MED ASSOC, V322, P1589, DOI 10.1001/jama.2019.4782
   Ball HA, 2020, BRAIN, V143, P2895, DOI 10.1093/brain/awaa224
   Behl T, 2021, INT J MOL SCI, V22, DOI 10.3390/ijms22031413
   Benedictus MR, 2017, EUR RADIOL, V27, P1169, DOI 10.1007/s00330-016-4450-z
   Bettio LEB, 2017, NEUROSCI BIOBEHAV R, V79, P66, DOI 10.1016/j.neubiorev.2017.04.030
   Boese AC, 2020, EXP NEUROL, V324, DOI 10.1016/j.expneurol.2019.113112
   Brandt JS, 2019, JAMA NETW OPEN, V2, DOI 10.1001/jamanetworkopen.2019.18007
   Cai ZY, 2018, J ALZHEIMERS DIS, V63, P1223, DOI 10.3233/JAD-180098
   Cash A, 2020, INT J MOL SCI, V21, DOI 10.3390/ijms21093344
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Cui MY, 2018, NEURAL PLAST, V2018, DOI 10.1155/2018/9234105
   Daneman R, 2015, CSH PERSPECT BIOL, V7, DOI 10.1101/cshperspect.a020412
   Gorelick PB, 2011, STROKE, V42, P2672, DOI 10.1161/STR.0b013e3182299496
   Guler AT, 2016, SCIENTOMETRICS, V107, P385, DOI 10.1007/s11192-016-1885-6
   Hendrix SB, 2012, ALZHEIMERS RES THER, V4, DOI 10.1186/alzrt127
   Iadecola C, 2013, NEURON, V80, P844, DOI 10.1016/j.neuron.2013.10.008
   Kasper S, 2020, WORLD J BIOL PSYCHIA, V21, P579, DOI 10.1080/15622975.2019.1696473
   Ke LX, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.00842
   Ma D, 2021, FRONT IMMUNOL, V12, DOI 10.3389/fimmu.2021.731933
   Mawuenyega KG, 2010, SCIENCE, V330, P1774, DOI 10.1126/science.1197623
   Montagne A, 2017, J EXP MED, V214, P3151, DOI 10.1084/jem.20171406
   Montagne A, 2015, NEURON, V85, P296, DOI 10.1016/j.neuron.2014.12.032
   Nation DA, 2019, NAT MED, V25, P270, DOI 10.1038/s41591-018-0297-y
   Nehra G, 2022, PHARMACOL THERAPEUT, V234, DOI 10.1016/j.pharmthera.2022.108119
   O'Brien J, 2003, INT PSYCHOGERIATR, V15, P133, DOI 10.1017/S1041610203009098
   Rajeev V, 2022, THERANOSTICS, V12, P1639, DOI 10.7150/thno.68304
   Rasmussen MK, 2018, LANCET NEUROL, V17, P1016, DOI 10.1016/S1474-4422(18)30318-1
   Roberts R, 2013, CLIN GERIATR MED, V29, P753, DOI 10.1016/j.cger.2013.07.003
   ROMAN GC, 1993, NEUROLOGY, V43, P250, DOI 10.1212/WNL.43.2.250
   Sachs-Ericsson N, 2015, AGING MENT HEALTH, V19, P2, DOI 10.1080/13607863.2014.920303
   Solfrizzi V, 2004, NEUROLOGY, V63, P1882, DOI 10.1212/01.WNL.0000144281.38555.E3
   Sweeney MD, 2019, PHYSIOL REV, V99, P21, DOI 10.1152/physrev.00050.2017
   Uprety A, 2021, ARCH PHARM RES, V44, P487, DOI 10.1007/s12272-021-01332-8
   van der Flier WM, 2018, NAT REV DIS PRIMERS, V4, DOI 10.1038/nrdp.2018.3
   Wang HL, 2006, J MAGN RESON IMAGING, V24, P695, DOI 10.1002/jmri.20669
   Xie L, 2021, CNS NEUROSCI THER, V27, P743, DOI 10.1111/cns.13636
   Yamazaki Y, 2017, INT J MOL SCI, V18, DOI 10.3390/ijms18091965
   Yang AC, 2022, NATURE, V603, P885, DOI 10.1038/s41586-021-04369-3
   Zhang N, 2017, NEUROSCI BIOBEHAV R, V72, P168, DOI 10.1016/j.neubiorev.2016.11.023
   Zlokovic BV, 2011, NAT REV NEUROSCI, V12, P723, DOI 10.1038/nrn3114
NR 46
TC 0
Z9 0
U1 4
U2 4
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 0895-8696
EI 1559-1166
J9 J MOL NEUROSCI
JI J. Mol. Neurosci.
DI 10.1007/s12031-022-02052-4
EA JUL 2022
PG 13
WC Biochemistry & Molecular Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Neurosciences & Neurology
GA 3E8OH
UT WOS:000830237600001
PM 35861948
DA 2022-08-23
ER

PT J
AU Liu, CL
   Li, WL
   Xu, J
   Zhou, HK
   Li, CH
   Wang, WY
AF Liu, Chenli
   Li, Wenlong
   Xu, Jing
   Zhou, Huakun
   Li, Changhui
   Wang, Wenying
TI Global trends and characteristics of ecological security research in the
   early 21st century: A literature review and bibliometric analysis
SO ECOLOGICAL INDICATORS
LA English
DT Review
DE Ecological security; Knowledge mapping; Research hotspots; Bibliometric
   analysis; VOSviewer
ID TIANJIN-HEBEI REGION; PEARL RIVER DELTA; ECOSYSTEM SERVICES;
   ECO-SECURITY; FOOTPRINT; SUSTAINABILITY; CITIES; AREAS; MODEL; GIS
AB With the worsening of global and local environmental problems, ecological security has a vital role to play in sustainable development, and has become a worldwide topic of concern in the 21st century. This study used bibliometric analysis to comprehensively review the progress of global research in ecological security from 2000 to 2020 to provide an overview for future directions. We analyzed 761 publications related to ecological security from this period, covering 34 countries, 370 journals, 631 institutions, 585 funding agencies, and 2680 authors. The results showed that the majority of publications (71.75%) appeared from 2014 to 2020, with research conducted mostly in China, followed by the USA and Russia. The top 10 active institutions were all Chinese; the Chinese Academy of Sciences was the most productive institution, and also had the highest number of citations. China was at the center of the international cooperation network, with the highest collaboration with the USA, Australia, and Canada. Funding from the National Natural Science Foundation of China (NSFC) has supported the highest number of publications. The top two productive journals in this area were Sustainability and Ecological Indicators. Keyword co-occurrence and frequency analysis showed that "ecological security patterns," "ecosystem services," "ecological security assessment," "sustainable development," and "land use change" were the main topics, whereas "GIS," "PSR model," "ecological footprint," "remote sensing," "MCR model," and "DPSIR model" were the most commonly used research technologies and models. Our findings showed that the focal topics have switched from ecological security assessment to ecological security patterns. We believe that these results would contribute to practitioners' and researchers' better understanding of worldwide ecological security research and facilitate future investigations.
C1 [Liu, Chenli; Li, Wenlong] Lanzhou Univ, Coll Pastoral Agr Sci & Technol, State Key Lab Grassland Agroecosystems, Key Lab Grassland Livestock Ind Innovat, Lanzhou, Peoples R China.
   [Xu, Jing] Lanzhou Univ Finance & Econ, Sch Agr & Forestry Econ & Management, Lanzhou, Peoples R China.
   [Zhou, Huakun] Chinese Acad Sci, Northwest Inst Plateau Biol, Key Lab Cold Reg Restorat Ecol, Xining, Qinghai, Peoples R China.
   [Li, Changhui] Qinghai Univ, Agr & Anim Husb Collage, Xining, Qinghai, Peoples R China.
   [Wang, Wenying] Qinghai Normal Univ, Dept Life Sci, Xining, Peoples R China.
RP Li, WL (通讯作者)，Lanzhou Univ, Coll Pastoral Agr Sci & Technol, State Key Lab Grassland Agroecosystems, Key Lab Grassland Livestock Ind Innovat, Lanzhou, Peoples R China.; Xu, J (通讯作者)，Lanzhou Univ Finance & Econ, Sch Agr & Forestry Econ & Management, Lanzhou, Peoples R China.
EM wllee@lzu.edu.cn; lattice630@sina.cn
OI Liu, Chenli/0000-0001-6389-8792
FU National Natural Science Foundation of China [31170430, 41471450];
   earmarked fund for China Agriculture Research System [CARS-34];
   Fundamental Research Funds for the Central Universities
   [lzujbky-2021-ct11]; funds for investigation and monitoring of typical
   forest, shrub, and meadow ecosystems in the vertical distribution zone
   of the Haibei region of the National Park [QHXH-2021-017]; Protection
   and Restoration of Muli Alpine Wetland in Tianjun County [QHTM-2021-003]
FX We thank Dr. Hong Wang from Lanzhou University for her help in the
   revision of the paper. This work was supported by the National Natural
   Science Foundation of China (31170430, 41471450); the earmarked fund for
   China Agriculture Research System (CARS-34); the Fundamental Research
   Funds for the Central Universities (lzujbky-2021-ct11); the funds for
   investigation and monitoring of typical forest, shrub, and meadow
   ecosystems in the vertical distribution zone of the Haibei region of the
   National Park (QHXH-2021-017); Protection and Restoration of Muli Alpine
   Wetland in Tianjun County (QHTM-2021-003).
CR Araujo AG, 2020, J CLEAN PROD, V256, DOI 10.1016/j.jclepro.2020.120350
   Baranyi G, 2011, ECOL INDIC, V11, P1301, DOI 10.1016/j.ecolind.2011.02.003
   Bartolini M, 2019, J CLEAN PROD, V226, P242, DOI 10.1016/j.jclepro.2019.04.055
   Bi ML, 2020, ECOL INDIC, V116
   BURTON I, 1987, ENVIRONMENT, V29, P25, DOI 10.1080/00139157.1987.9928891
   CAS (Chinese Academy of Sciences, 2021, INTRODUCTION
   Cen XT, 2015, SUSTAINABILITY-BASEL, V7, P1459, DOI 10.3390/su7021459
   Chang Q, 2015, ACTA ECOL SIN, V35, P111, DOI DOI 10.1016/J.CHNAES.2015.07.001
   Chen C, 2021, J CLEAN PROD, V279, DOI 10.1016/j.jclepro.2020.123583
   Chen W, 2020, ENVIRON SCI POLLUT R, V27, P23503, DOI 10.1007/s11356-020-08760-x
   Chen Y., 2020, ECOL INDIC, V111, DOI [DOI 10.1016/j.ecolind.2019.106000, 10.1016/j.ecolind.2019.106000]
   Chu X, 2017, PHYS CHEM EARTH, V101, P43, DOI 10.1016/j.pce.2017.05.001
   Costanza R, 1997, NATURE, V387, P253, DOI 10.1038/387253a0
   Dai FQ, 2010, INT J SUST DEV WORLD, V17, P390, DOI 10.1080/13504509.2010.500074
   Dai L, 2021, SCI TOTAL ENVIRON, V754, DOI 10.1016/j.scitotenv.2020.141868
   De las Heras A, 2021, J CLEAN PROD, V291, DOI 10.1016/j.jclepro.2020.125227
   Dong JQ, 2021, LANDSCAPE ECOL, V36, P2151, DOI 10.1007/s10980-021-01233-7
   EEA, 1999, 25 EEA
   Ertz M, 2018, J CLEAN PROD, V196, P1073, DOI 10.1016/j.jclepro.2018.06.095
   Ezeonu FC, 2000, ENVIRONMENTALIST, V20, P41, DOI DOI 10.1023/A:1006651927333
   Fan FF, 2021, LANDSCAPE ECOL, V36, P2175, DOI 10.1007/s10980-020-01044-2
   Fan YP, 2020, ECOL INDIC, V117, DOI 10.1016/j.ecolind.2020.106659
   Feng YJ, 2018, SCI TOTAL ENVIRON, V633, P1469, DOI 10.1016/j.scitotenv.2018.03.272
   Feng YJ, 2017, STOCH ENV RES RISK A, V31, P2235, DOI 10.1007/s00477-016-1330-7
   Fu YJ, 2020, ECOL INDIC, V112, DOI 10.1016/j.ecolind.2019.106030
   Geng Y, 2017, J CLEAN PROD, V159, P301, DOI 10.1016/j.jclepro.2017.05.091
   Giri R, 2019, ASLIB J INFORM MANAG, V71, P90, DOI 10.1108/AJIM-07-2017-0170
   Gong JZ, 2009, ECOL MODEL, V220, P3612, DOI 10.1016/j.ecolmodel.2009.10.018
   Guo SS, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16234805
   Han BL, 2015, ECOL MODEL, V318, P217, DOI 10.1016/j.ecolmodel.2014.12.015
   He G, 2018, ENVIRON TECHNOL, V39, P3013, DOI 10.1080/09593330.2017.1371250
   Hodson M, 2009, INT J URBAN REGIONAL, V33, P193, DOI 10.1111/j.1468-2427.2009.00832.x
   Hu MM, 2019, ECOL INDIC, V104, P439, DOI 10.1016/j.ecolind.2019.04.081
   Huang JP, 2020, ECOL INDIC, V117, DOI 10.1016/j.ecolind.2020.106651
   Huang L, 2020, J CLEAN PROD, V252, DOI 10.1016/j.jclepro.2019.119908
   Huang Q, 2007, RESOUR CONSERV RECY, V51, P24, DOI 10.1016/j.resconrec.2006.07.004
   Jiang X.F., 2000, ENV PROT, V10, P23
   Ju Chang-hua, 2020, Journal of Ecology and Rural Environment, V36, P626, DOI 10.19741/j.issn.1673-4831.2019.0170
   Kandel P, 2021, ECOL INDIC, V121, DOI 10.1016/j.ecolind.2020.107152
   Lenzen M, 2001, ECOL ECON, V37, P229, DOI 10.1016/S0921-8009(00)00275-5
   Li JX, 2019, J CLEAN PROD, V235, P664, DOI 10.1016/j.jclepro.2019.07.005
   Li J, 2021, SAFETY SCI, V134, DOI 10.1016/j.ssci.2020.105093
   Li J, 2020, J LOSS PREVENT PROC, V65, DOI 10.1016/j.jlp.2020.104141
   Li XB, 2014, ECOL INDIC, V39, P153, DOI 10.1016/j.ecolind.2013.12.014
   Li YF, 2010, ECOL MODEL, V221, P2251, DOI 10.1016/j.ecolmodel.2010.04.016
   Li ZT, 2019, ECOL INDIC, V101, P595, DOI 10.1016/j.ecolind.2019.01.067
   Lin Nai-feng, 2018, Journal of Ecology and Rural Environment, V34, P1057, DOI 10.11934/j.issn.1673-4831.2018.12.001
   Liu CX, 2019, APPL GEOGR, V105, P1, DOI 10.1016/j.apgeog.2019.02.009
   Liu T, 2021, ECOL INDIC, V126, DOI 10.1016/j.ecolind.2021.107636
   Liu XJ, 2011, BIODIVERS CONSERV, V20, P807, DOI 10.1007/s10531-010-9981-z
   Liu YX, 2019, J ENVIRON MANAGE, V249, DOI 10.1016/j.jenvman.2019.109315
   Liu YY, 2021, ECOL INDIC, V132, DOI 10.1016/j.ecolind.2021.108295
   Lu C.A., 2016, CAPITAL NAT SOCIAL, V27, P27
   Lu SS, 2018, ECOL INDIC, V91, P664, DOI 10.1016/j.ecolind.2018.03.088
   Lu XL, 2019, J DESTIN MARK MANAGE, V13, P73, DOI 10.1016/j.jdmm.2019.05.004
   [马克明 Ma Keming], 2004, [生态学报, Acta Ecologica Sinica], V24, P761
   Mao GZ, 2018, SCI TOTAL ENVIRON, V635, P1081, DOI 10.1016/j.scitotenv.2018.04.173
   Mora L, 2017, J URBAN TECHNOL, V24, P3, DOI 10.1080/10630732.2017.1285123
   Mourao PR, 2020, J CLEAN PROD, V256, DOI 10.1016/j.jclepro.2020.120413
   Musa I, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10103414
   Nathwani J, 2019, SCI TOTAL ENVIRON, V672, P51, DOI 10.1016/j.scitotenv.2019.03.322
   OECD Environmental Indicators, 1993, OECD COR SET IND ENV, V83
   Olawumi TO, 2018, J CLEAN PROD, V183, P231, DOI 10.1016/j.jclepro.2018.02.162
   Peng J, 2019, ENVIRON MODELL SOFTW, V117, P214, DOI 10.1016/j.envsoft.2019.03.017
   Peng J, 2018, SCI TOTAL ENVIRON, V644, P781, DOI 10.1016/j.scitotenv.2018.06.292
   Peng J, 2018, HABITAT INT, V71, P110, DOI 10.1016/j.habitatint.2017.11.010
   Ruan WQ, 2019, TOURISM MANAGE, V75, P609, DOI 10.1016/j.tourman.2019.06.021
   Salvati L, 2014, J ENVIRON MANAGE, V145, P299, DOI 10.1016/j.jenvman.2014.07.006
   Shi XQ, 2006, J SYST SOFTWARE, V79, P246, DOI 10.1016/j.jss.2005.04.031
   Si HY, 2019, J CLEAN PROD, V213, P415, DOI 10.1016/j.jclepro.2018.12.157
   Solovjova NV, 1999, ECOL MODEL, V124, P1, DOI 10.1016/S0304-3800(99)00122-2
   Su SL, 2011, STOCH ENV RES RISK A, V25, P737, DOI 10.1007/s00477-011-0457-9
   Su SL, 2010, STOCH ENV RES RISK A, V24, P639, DOI 10.1007/s00477-009-0351-x
   Su YX, 2016, URBAN FOR URBAN GREE, V19, P35, DOI 10.1016/j.ufug.2016.06.013
   Tang CC, 2018, J CLEAN PROD, V197, P999, DOI 10.1016/j.jclepro.2018.06.232
   Tao J, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121041
   Tian JY, 2012, ENRGY PROCED, V16, P1180, DOI 10.1016/j.egypro.2012.01.188
   Tsou JY, 2017, REMOTE SENS-BASEL, V9, DOI 10.3390/rs9060529
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Wackernagel M, 2006, ENVIRON URBAN, V18, P103, DOI 10.1177/0956247806063978
   Waltman L., 2020, VOSVIEWER MANUAL
   Wang BJ, 2021, ECOL INDIC, V125, DOI 10.1016/j.ecolind.2021.107449
   Wang CX, 2020, SCI TOTAL ENVIRON, V740, DOI 10.1016/j.scitotenv.2020.140051
   Wang L, 2012, APPL MECH MATER, V178-181, P337, DOI 10.4028/www.scientific.net/AMM.178-181.337
   Wang MX, 2018, J CLEAN PROD, V174, P315, DOI 10.1016/j.jclepro.2017.10.328
   Wang XD, 2010, J ENVIRON MANAGE, V91, P1981, DOI 10.1016/j.jenvman.2010.05.006
   Wang ZH, 2018, RESOUR CONSERV RECY, V132, P301, DOI 10.1016/j.resconrec.2017.05.003
   Wei SM, 2020, HUM ECOL RISK ASSESS, V26, P782, DOI 10.1080/10807039.2018.1536521
   Wen JF, 2021, ECOL INDIC, V127, DOI 10.1016/j.ecolind.2021.107797
   Wu X, 2019, ECOL INDIC, V102, P469, DOI 10.1016/j.ecolind.2019.02.057
   Xiao Duning, 2002, Yingyong Shengtai Xuebao, V13, P354
   Xie GD, 2017, ECOSYST SERV, V26, P146, DOI 10.1016/j.ecoser.2017.06.010
   Xie HL, 2015, ECOL INDIC, V51, P3, DOI 10.1016/j.ecolind.2014.08.042
   Xu CY, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8080816
   Yang YF, 2020, J CLEAN PROD, V263, DOI 10.1016/j.jclepro.2020.121433
   Yu GM, 2014, STOCH ENV RES RISK A, V28, P307, DOI 10.1007/s00477-013-0750-x
   Zeng JJ, 2021, J CLEAN PROD, V295, DOI 10.1016/j.jclepro.2021.126468
   Zhang LQ, 2017, URBAN ECOSYST, V20, P701, DOI 10.1007/s11252-016-0629-y
   Zhao YZ, 2006, J ENVIRON MANAGE, V80, P120, DOI 10.1016/j.jenvman.2005.08.019
   Zhou KH, 2014, CITIES, V41, P141, DOI 10.1016/j.cities.2014.06.010
NR 100
TC 5
Z9 5
U1 56
U2 56
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1470-160X
EI 1872-7034
J9 ECOL INDIC
JI Ecol. Indic.
PD APR
PY 2022
VL 137
AR 108734
DI 10.1016/j.ecolind.2022.108734
PG 16
WC Biodiversity Conservation; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biodiversity & Conservation; Environmental Sciences & Ecology
GA ZZ9DC
UT WOS:000773562400001
OA gold
DA 2022-08-23
ER

PT J
AU Khatami, SG
   Mubeen, S
   Hofmann-Apitius, M
AF Khatami, Sepehr Golriz
   Mubeen, Sarah
   Hofmann-Apitius, Martin
TI Data science in neurodegenerative disease: its capabilities,
   limitations, and perspectives
SO CURRENT OPINION IN NEUROLOGY
LA English
DT Review
DE Artificial intelligence; data-driven models; knowledge-driven models;
   neurodegenerative disease; virtual patient cohort
ID ALZHEIMERS-DISEASE; HYPOTHETICAL MODEL; PROGRESSION; BIOMARKERS;
   NETWORKS; COHORT; TOOL; MRI
AB Purpose of review With the advancement of computational approaches and abundance of biomedical data, a broad range of neurodegenerative disease models have been developed. In this review, we argue that computational models can be both relevant and useful in neurodegenerative disease research and although the current established models have limitations in clinical practice, artificial intelligence has the potential to overcome deficiencies encountered by these models, which in turn can improve our understanding of disease. Recent findings In recent years, diverse computational approaches have been used to shed light on different aspects of neurodegenerative disease models. For example, linear and nonlinear mixed models, self-modeling regression, differential equation models, and event-based models have been applied to provide a better understanding of disease progression patterns and biomarker trajectories. Additionally, the Cox-regression technique, Bayesian network models, and deep-learning-based approaches have been used to predict the probability of future incidence of disease, whereas nonnegative matrix factorization, nonhierarchical cluster analysis, hierarchical agglomerative clustering, and deep-learning-based approaches have been employed to stratify patients based on their disease subtypes. Furthermore, the interpretation of neurodegenerative disease data is possible through knowledge-based models which use prior knowledge to complement data-driven analyses. These knowledge-based models can include pathway-centric approaches to establish pathways perturbed in a given condition, as well as disease-specific knowledge maps, which elucidate the mechanisms involved in a given disease. Collectively, these established models have revealed high granular details and insights into neurodegenerative disease models. In conjunction with increasingly advanced computational approaches, a wide spectrum of neurodegenerative disease models, which can be broadly categorized into data-driven and knowledge-driven, have been developed. We review the state of the art data and knowledge-driven models and discuss the necessary steps which are vital to bring them into clinical application.
C1 [Khatami, Sepehr Golriz; Mubeen, Sarah; Hofmann-Apitius, Martin] Fraunhofer Inst Algorithms & Sci Comp, Dept Bioinformat, St Augustin, Germany.
   [Khatami, Sepehr Golriz; Hofmann-Apitius, Martin] Rheinische Friedrich Wilhelm Univ Bonn, Bonn Aachen Int Ctr IT, Bonn, Germany.
RP Khatami, SG (通讯作者)，Fraunhofer Inst Algorithms & Sci Comp SCAI, Schloss Birlinghoven, D-53754 St Augustin, Germany.
EM sepehr.golriz.khatami@scai-extern.fraunhofer.de
OI Mubeen, Sarah/0000-0002-9858-3755; Golriz Khatami,
   Sepehr/0000-0002-7606-5228
FU IMI project AETIONOMY within the Seventh Framework Program of the
   European Union [115568]
FX The authors receive funding fromthe IMI project AETIONOMY
   (https://www.aetionomy.eu/en/vision.html) within the Seventh Framework
   Program of the European Union under grant agreement No. 115568.
CR Agus F, 2019, BMC MED GENOMICS, V12, DOI 10.1186/s12920-019-0581-9
   Alexiou A, 2017, FRONT AGING NEUROSCI, V9, DOI 10.3389/fnagi.2017.00077
   Arneson D, 2018, J GENET, V97, P795, DOI 10.1007/s12041-018-0963-3
   Arora P, 2019, VALUE HEALTH, V22, P439, DOI 10.1016/j.jval.2019.01.006
   Barnes DE, 2014, ALZHEIMERS DEMENT, V10, P646, DOI 10.1016/j.jalz.2013.12.014
   Carson E., 2013, MODELING METHODOLOGY
   Chase JG, 2016, ANN UPDATE INTENSIVE
   de Jong J, 2019, GIGASCIENCE, V8, DOI 10.1093/gigascience/giz134
   Delor I., 2013, PHARM SYST PHARM, V2, P1
   Domingo-Fernandez D, 2017, BIOINFORMATICS, V33, P3679, DOI 10.1093/bioinformatics/btx399
   Donohue MC, 2014, ALZHEIMERS DEMENT, V10, pS400, DOI 10.1016/j.jalz.2013.10.003
   Erro R, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0070244
   Fisher CK, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-49656-2
   Fonteijn HM, 2012, NEUROIMAGE, V60, P1880, DOI 10.1016/j.neuroimage.2012.01.062
   Frisoni GB, 2010, NAT REV NEUROL, V6, P67, DOI 10.1038/nrneurol.2009.215
   Fujita KA, 2014, MOL NEUROBIOL, V49, P88, DOI 10.1007/s12035-013-8489-4
   Gomez-Cabrero D, 2014, BMC SYST BIOL, V8, DOI 10.1186/1752-0509-8-S2-I1
   Gootjes-Dreesbach L, 2019, VARIATIONAL AUTOENCO
   Gupta Y, 2019, J HEALTHC ENG, V2019, DOI 10.1155/2019/2492719
   Heemels MT, 2016, NATURE, V539, P179, DOI 10.1038/539179a
   Hu YS, 2017, ALZHEIMERS RES THER, V9, DOI 10.1186/s13195-017-0252-z
   Huang XY, 2018, BMC NEUROL, V18, DOI [10.1186/S12883-017-1010-3, 10.1186/s12883-017-1010-3]
   Hwang Jihye, 2016, Alzheimers Dement (Amst), V2, P58, DOI 10.1016/j.dadm.2015.11.008
   Ibrahim JG, 2012, J CLIN ONCOL, V30, P3297, DOI 10.1200/JCO.2011.38.7589
   Jack CR, 2013, LANCET NEUROL, V12, P207, DOI 10.1016/S1474-4422(12)70291-0
   Jack CR, 2012, ARCH NEUROL-CHICAGO, V69, P856, DOI 10.1001/archneurol.2011.3405
   Jack CR, 2010, LANCET NEUROL, V9, P119, DOI 10.1016/S1474-4422(09)70299-6
   Kang H, 2013, KOREAN J ANESTHESIOL, V64, P402, DOI 10.4097/kjae.2013.64.5.402
   Keener J.P., 1998, MATH PHYSL, V1
   Khanna S, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-29433-3
   Khatri P, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002375
   Laake P, 2015, ACAD PRESS
   Labadorf A, 2018, FRONT MOL NEUROSCI, V10, DOI 10.3389/fnmol.2017.00430
   Lama RK, 2017, J HEALTHC ENG, V2017, DOI 10.1155/2017/5485080
   Lauwers O, 2017, IEEE CONTR SYST LETT, V1, P286, DOI 10.1109/LCSYS.2017.2715399
   Lawrence E, 2017, J ALZHEIMERS DIS, V59, P1359, DOI 10.3233/JAD-170261
   Lee G, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-018-37769-z
   Li K, 2017, J ALZHEIMERS DIS, V58, P360, DOI 10.3233/JAD-161201
   Li MD, 2014, ACTA NEUROPATHOL COM, V2, DOI 10.1186/s40478-014-0093-y
   Li Shanshan, 2013, Am J Alzheimers Dis (Columbia), V2, P12
   Li XZ, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-35789-3
   Liang H, 2017, MATH PROBL ENG, V2017, P1
   Lim SS, 2017, PATIENT PREFER ADHER, V11, P1295, DOI 10.2147/PPA.S137416
   Manjon JV, 2018, COMPUT MED IMAG GRAP, V69, P43, DOI 10.1016/j.compmedimag.2018.05.001
   Mazein A, 2018, NPJ SYST BIOL APPL, V4, DOI 10.1038/s41540-018-0059-y
   Moss DJH, 2017, SCI REP-UK, V7, DOI 10.1038/srep44849
   Mu J, 2017, FRONT AGING NEUROSCI, V9, DOI 10.3389/fnagi.2017.00301
   Mueller SG, 2015, NEUROIMAG CLIN N AM, V15, P869
   Nettiksimmons J, 2014, ALZHEIMERS DEMENT, V10, P511, DOI 10.1016/j.jalz.2013.09.003
   Ogishima S, 2016, SYSTEMS BIOL ALZHEIM
   Oxtoby NP, 2018, BRAIN, V141, P1529, DOI 10.1093/brain/awy050
   Oxtoby NP, 2017, CURR OPIN NEUROL, V30, P371, DOI 10.1097/WCO.0000000000000460
   Patel H, 2019, J ALZHEIMERS DIS, V68, P1635, DOI 10.3233/JAD-181085
   Reimand J, 2019, NAT PROTOC, V14, P482, DOI 10.1038/s41596-018-0103-9
   Saqi M, 2019, BRIEF BIOINFORM, V20, P609, DOI 10.1093/bib/bby025
   Scheltens NME, 2017, ALZHEIMERS DEMENT, V13, P1226, DOI 10.1016/j.jalz.2017.03.002
   Vermunt L, 2018, ALZHEIMERS DEMENT, V14, P837, DOI 10.1016/j.jalz.2018.02.010
   Villemagne VL, 2013, LANCET NEUROL, V12, P357, DOI 10.1016/S1474-4422(13)70044-9
   Wang YA, 2017, HUM GENOMICS, V11, DOI 10.1186/s40246-017-0108-4
   Whitwell JL, 2017, MOVEMENT DISORD, V32, P955, DOI 10.1002/mds.27038
   Yang E, 2011, J ALZHEIMERS DIS, V26, P745, DOI 10.3233/JAD-2011-110551
   Young AL, 2014, ADV CLIN NEUROSCI RE, V14, P6
   Young Alexandra L, 2015, Inf Process Med Imaging, V24, P711, DOI 10.1007/978-3-319-19992-4_56
   Young AL, 2014, BRAIN, V137, P2564, DOI 10.1093/brain/awu176
   Zhang B, 2013, CELL, V153, P707, DOI 10.1016/j.cell.2013.03.030
NR 65
TC 6
Z9 6
U1 1
U2 11
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 1350-7540
EI 1473-6551
J9 CURR OPIN NEUROL
JI Curr. Opin. Neurol.
PD APR
PY 2020
VL 33
IS 2
BP 249
EP 254
DI 10.1097/WCO.0000000000000795
PG 6
WC Clinical Neurology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA LB1LE
UT WOS:000524396400014
PM 32073441
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Canadas, DC
   Perales, AB
   Belmonte, MDC
   Martinez, RG
   Carreno, TP
AF Cristobal Canadas, Delia
   Bonillo Perales, Antonio
   Casado Belmonte, Maria del Pilar
   Galera Martinez, Rafael
   Parron Carreno, Tesifon
TI Kangaroo mother care and skin-to-skin care in preterm infants in the
   neonatal intensive care unit: A bibliometric analysis
SO ARCHIVES DE PEDIATRIE
LA English
DT Review
DE Kangaroo mother care; Skin to skin; Infant; Preterm; NICU
ID OUTCOMES; CONTACT; SCIENCE; PAIN; RECOMMENDATIONS; IMPACT; TRIAL; WEB
AB Background: The desire to understand and the growing interest in research on the effects of kangaroo mother care in preterm infants in the neonatal intensive care unit have led to a significant increase in the number of manuscripts published in this field over the past decade. It is therefore necessary to regularly review the state of knowledge on this phenomenon in order to identify progress and constraints, to stimulate reflection, and to encourage progress in future research. Purpose: The aim of this study was to determine the current state of scientific production in relation to kangaroo mother care in preterm infants in the neonatal intensive care unit through bibliometric analysis. Data sources: This study presents a review of 212 published papers from the Scopus database (1990-2019). Data extraction: Two processing software applications were used: VOSviewer and SciMAT. In addition, through a keyword analysis, this study established the hot spot research trends to be developed in future work. The study adhered to the PRISMA-ScR guidelines for quality improvement studies as part of the EQUATOR network. Results: Our results show that research in this field is going through a time of high productivity and we could sort this growing body of work into different periods, highlighting the most important topics.The analysis shows that most research in this field is focused on five motor topics. These are: prematurity, male, psychology, intensive care neonatal, and major clinical study. The analysis also allowed us to identify four basic and cross-disciplinary topics that need to be developed and that emerge as future research directions: preterm infants, child-parent relations, child development, and skin-to-skin contact. Implications for practice and research: The main contribution of this work is the creation of a knowledge map on the research in this field. This article provides information on how we can shape the future to provide optimal care for these infants and their parents. (c) 2021 French Society of Pediatrics. Published by Elsevier Masson SAS. All rights reserved.
C1 [Cristobal Canadas, Delia] Torrecardenas Univ Hosp, Paediat Intens Care Unit, C Hermandad Donantes Sangre S-N, Almeria 04009, Spain.
   [Bonillo Perales, Antonio; Galera Martinez, Rafael] Torrecardenas Univ Hosp, Pediat Dept, Almeria, Spain.
   [Casado Belmonte, Maria del Pilar] Univ Almeria, Dept Econ & Business, Almeria, Spain.
   [Parron Carreno, Tesifon] Univ Almeria, Dept Nursing, Physiotherapy & Med, Almeria, Spain.
   [Parron Carreno, Tesifon] Andalusian Council Hlth Almeria Prov, Almeria, Spain.
RP Canadas, DC (通讯作者)，Torrecardenas Univ Hosp, Paediat Intens Care Unit, C Hermandad Donantes Sangre S-N, Almeria 04009, Spain.
EM deliacristobal@gmail.com
CR Anderson GC, 2003, JOGNN, V32, P604, DOI 10.1177/0884217503256616
   Archambault E, 2009, J AM SOC INF SCI TEC, V60, P1320, DOI 10.1002/asi.21062
   Badiee Zohreh, 2014, Adv Biomed Res, V3, P214, DOI 10.4103/2277-9175.143262
   Buil A, 2016, ARCH PEDIATRIE, V23, P913, DOI 10.1016/j.arcped.2016.04.023
   Campbell-Yeo M, 2013, BMC PEDIATR, V13, DOI 10.1186/1471-2431-13-182
   Casper C, 2018, ARCH PEDIATRIE, V25, P469, DOI 10.1016/j.arcped.2018.09.008
   Chan GJ, 2016, J GLOB HEALTH, V6, DOI 10.7189/jogh.06.010701
   Charpak N, 2005, ACTA PAEDIATR, V94, P514, DOI 10.1080/08035250510027381
   Closa Monasterolo R, 1998, An Esp Pediatr, V49, P495
   Cobo MJ, 2015, KNOWL-BASED SYST, V80, P3, DOI 10.1016/j.knosys.2014.12.035
   Cobo MJ, 2011, J AM SOC INF SCI TEC, V62, P1382, DOI 10.1002/asi.21525
   Conde-Agudelo A, 2016, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD002771.pub4
   Deng QQ, 2018, TRIALS, V19, DOI 10.1186/s13063-018-3060-2
   Evereklian M, 2017, J PEDIATR NURS, V34, pE10, DOI 10.1016/j.pedn.2017.02.006
   Feldman R, 2002, PEDIATRICS, V110, P16, DOI 10.1542/peds.110.1.16
   Franck LS, 2019, BIRTH DEFECTS RES, V111, P1044, DOI 10.1002/bdr2.1521
   Glass HC, 2015, ANESTH ANALG, V120, P1337, DOI 10.1213/ANE.0000000000000705
   Griffiths N, 2019, EARLY HUM DEV, V139, DOI 10.1016/j.earlhumdev.2019.104840
   Guan WJ, 2020, EUR RESPIR J, V55, DOI 10.1183/13993003.00547-2020
   Huppertz-Kessler CJ, 2010, KLIN PADIATR, V222, pE1, DOI 10.1055/s-0030-1254096
   Ismail FY, 2017, EUR J PAEDIATR NEURO, V21, P23, DOI 10.1016/j.ejpn.2016.07.007
   Johnston CC, 2003, ARCH PEDIAT ADOL MED, V157, P1084, DOI 10.1001/archpedi.157.11.1084
   Kashaninia Z, 2008, J SPEC PEDIATR NURS, V13, P275, DOI 10.1111/j.1744-6155.2008.00165.x
   Knudsen EI, 2004, J COGNITIVE NEUROSCI, V16, P1412, DOI 10.1162/0898929042304796
   Kostandy RR, 2019, BIRTH DEFECTS RES, V111, P1032, DOI 10.1002/bdr2.1565
   Silva VVM, 2019, PUBLICATIONS, V7, DOI 10.3390/publications7040060
   Miles R, 2006, EARLY HUM DEV, V82, P447, DOI 10.1016/j.earlhumdev.2005.11.008
   Mirnia K, 2017, IRAN J PEDIATR, V27, DOI 10.5812/ijp.8151
   Mongeon P, 2016, SCIENTOMETRICS, V106, P213, DOI 10.1007/s11192-015-1765-5
   Moran M, 1999, MCN Am J Matern Child Nurs, V24, P74, DOI 10.1097/00005721-199903000-00006
   Morelius E, 2005, PEDIATRICS, V116, P1105, DOI 10.1542/peds.2004-2440
   Nyqvist KH, 2010, ACTA PAEDIATR, V99, P812, DOI 10.1111/j.1651-2227.2010.01794.x
   Nyqvist KH, 2012, J HUM LACT, V28, P289, DOI 10.1177/0890334412441862
   Parsa Parisa, 2018, Pan Afr Med J, V30, P89, DOI 10.11604/pamj.2018.30.89.14428
   Santos J, 2015, CURR OPIN PEDIATR, V27, P254, DOI 10.1097/MOP.0000000000000190
   Stensvold HJ, 2017, PEDIATRICS, V139, DOI 10.1542/peds.2016-1821
   Torres-Salinas D, 2009, SCIENTOMETRICS, V80, P761, DOI 10.1007/s11192-008-2113-9
   Tricco AC, 2018, ANN INTERN MED, V169, P467, DOI 10.7326/M18-0850
   Vogel JP, 2015, LANCET GLOB HEALTH, V3, pE589, DOI 10.1016/S2214-109X(15)00183-7
   Wheeler J L, 1999, Breastfeed Rev, V7, P15
   WHITELAW A, 1985, LANCET, V1, P1206
   World Health Organization, 2003, DEP REPR HLTH RES KA
   Xie J, 2019, J NERV MENT DIS, V207, P360, DOI 10.1097/NMD.0000000000000971
NR 43
TC 0
Z9 0
U1 8
U2 8
PU ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
PI ISSY-LES-MOULINEAUX
PA 65 RUE CAMILLE DESMOULINS, CS50083, 92442 ISSY-LES-MOULINEAUX, FRANCE
SN 0929-693X
EI 1769-664X
J9 ARCH PEDIATRIE
JI Arch. Pediatr.
PD FEB
PY 2022
VL 29
IS 2
BP 90
EP 99
DI 10.1016/j.arcped.2021.11.007
PG 10
WC Pediatrics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Pediatrics
GA YV1BN
UT WOS:000752467300002
PM 34955302
DA 2022-08-23
ER

PT J
AU Qi, SJ
   Hua, FR
   Xu, SY
   Zhou, Z
   Liu, F
AF Qi, Shaojie
   Hua, Fengrui
   Xu, Shengyuan
   Zhou, Zheng
   Liu, Feng
TI Trends of global health literacy research (1995-2020): Analysis of
   mapping knowledge domains based on citation data mining
SO PLOS ONE
LA English
DT Article
ID EMERGING TRENDS; INFORMATION; EVOLUTION; COMMUNITY; COVID-19; SCIENCE;
   PEOPLE
AB Background During uncertainties associated with the COVID-19 pandemic, effectively improving people's health literacy is more important than ever. Drawing knowledge maps of health literacy research through data mining and visualized measurement technology helps systematically present the research status and development trends in global academic circles. Methods This paper uses CiteSpace to carry out a metric analysis of 9,492 health literacy papers included in Web of Science through mapping knowledge domains. First, based on the production theory of scientific knowledge and the data mining of citations, the main bodies (country, institution and author) that produce health literacy knowledge as well as their mutual cooperation (collaboration network) are both clarified. Additionally, based on the quantitative framework of cocitation analysis, this paper introduces the interdisciplinary features, development trends and hot topics of the field. Finally, by using burst detection technology in the literature, it further reveals the research frontiers of health literacy. Results The results of the BC measures of the global health literacy research collaboration network show that the United States, Australia and the United Kingdom are the major forces in the current international collaboration network on health literacy. There are still relatively very few transnational collaborations between Eastern and Western research institutions. Collaborations in public environmental occupational health, health care science services, nursing and health policy services have been active in the past five years. Research topics in health literacy research evolve over time, mental health has been the most active research field in recent years. Conclusions A systematic approach is needed to address the challenges of health literacy, and the network framework of cooperation on health literacy at regional, national and global levels should be strengthened to further promote the application of health literacy research. In the future, we anticipate that this research field will expand in two directions, namely, mental health literacy and eHealth literacy, both of which are closely linked to social development and issues. The results of this study provide references for future applied research in health literacy.
C1 [Qi, Shaojie; Hua, Fengrui; Zhou, Zheng] Southwestern Univ Finance & Econ, Res Inst Social Dev, Chengdu, Peoples R China.
   [Xu, Shengyuan] Tohoku Univ, Grad Sch Informat Sci, Sendai, Miyagi, Japan.
   [Liu, Feng] Huaiyin Normal Univ, Sch Foreign Language, Huaian, Peoples R China.
RP Liu, F (通讯作者)，Huaiyin Normal Univ, Sch Foreign Language, Huaian, Peoples R China.
EM connieliufeng@163.com
RI xu, shengyuan/AAB-8108-2022
CR Alexander J, 2013, PORTL INT CONF MANAG, P2130
   Altin SV, 2014, BMC PUBLIC HEALTH, V14, DOI 10.1186/1471-2458-14-1207
   Batterham RW, 2016, PUBLIC HEALTH, V132, P3, DOI 10.1016/j.puhe.2016.01.001
   Berkman ND, 2011, ANN INTERN MED, V155, P97, DOI 10.7326/0003-4819-155-2-201107190-00005
   Brors G, 2020, EUR J CARDIOVASC NUR, V19, P458, DOI 10.1177/1474515120941307
   Burmaoglu S, 2017, SCIENTOMETRICS, V112, P1419, DOI 10.1007/s11192-017-2431-x
   Burns JR, 2006, J ADOLESCENCE, V29, P225, DOI 10.1016/j.adolescence.2005.05.004
   Cangussu LR, 2020, REV ASSOC MED BRAS, V66, P31, DOI [10.1590/1806-9282.66.S2.31, 10.1590/1806-9282.66.s2.31]
   Chen CM, 2014, EXPERT OPIN BIOL TH, V14, P1295, DOI 10.1517/14712598.2014.920813
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen CM, 2016, COMPLEX ADAPT SYST M, V23, DOI [10.1186/s40294-016-0036-5, DOI 10.1186/S40294-016-0036-5]
   Chen YB, 2019, EVID-BASED COMPL ALT, V2019, DOI 10.1155/2019/3961395
   Cho H, 2018, J ADV NURS, V74, P1618, DOI 10.1111/jan.13575
   Choi NG, 2013, J MED INTERNET RES, V15, DOI 10.2196/jmir.2645
   Coulter N, 1998, J AM SOC INFORM SCI, V49, P1206, DOI 10.1002/(SICI)1097-4571(1998)49:13<1206::AID-ASI7>3.3.CO;2-6
   Cui Y, 2018, ELECTRON COMMER RES, V18, P837, DOI 10.1007/s10660-018-9288-9
   Dunning T., 1993, Computational Linguistics, V19, P61
   Fortunato S, 2018, SCIENCE, V359, DOI 10.1126/science.aao0185
   FREEMAN LC, 1977, SOCIOMETRY, V40, P35, DOI 10.2307/3033543
   Freeman LC, 2014, ENCY SOCIAL NETWORK
   Gazmararian JA, 1999, OBSTET GYNECOL, V93, P239, DOI 10.1016/S0029-7844(98)00431-1
   Glanzel W, 2012, SCIENTOMETRICS, V91, P399, DOI 10.1007/s11192-011-0591-7
   Han HR, 2018, JMIR PUBLIC HLTH SUR, V4, P68, DOI 10.2196/publichealth.9687
   Haun J, 2012, J HEALTH COMMUN, V17, P141, DOI 10.1080/10810730.2012.712615
   Hessels LK, 2008, RES POLICY, V37, P740, DOI 10.1016/j.respol.2008.01.008
   Hicks D, 2013, HANDBOOK ON THE THEORY AND PRACTICE OF PROGRAM EVALUATION, P323
   Hu YZ, 2020, MEDICINE, V99, DOI 10.1097/MD.0000000000020338
   Jorm AF, 2003, PSYCHOL MED, V33, P1071, DOI 10.1017/S0033291703008079
   Jorm AF, 2012, AM PSYCHOL, V67, P231, DOI 10.1037/a0025957
   Keim DA, 2002, IEEE T VIS COMPUT GR, V8, P1, DOI 10.1109/2945.981847
   Kickbusch I., 2013, HLTH LIT SOL FACTS
   Kim SP, 2001, CANCER INVEST, V19, P684, DOI 10.1081/CNV-100106143
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   Kosir U, 2020, PERSPECT PUBLIC HEAL, DOI 10.1177/1757913920936717
   Kutcher S, 2016, CAN J PSYCHIAT, V61, P154, DOI 10.1177/0706743715616609
   Li J, 2020, ENVIRON HEALTH PREV, V25, DOI 10.1186/s12199-020-00896-z
   Ma XM, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16050842
   Mancuso JM, 2009, NURS HEALTH SCI, V11, P77, DOI 10.1111/j.1442-2018.2008.00408.x
   Mubbashar MH, 2001, BRIT J PSYCHIAT, V179, P75, DOI 10.1192/bjp.179.1.75-a
   Newman MEJ, 2004, PHYS REV E, V69, DOI [10.1103/PhysRevE.70.056131, 10.1103/PhysRevE.69.026113]
   Norman CD, 2006, J MED INTERNET RES, V8, DOI 10.2196/jmir.8.2.e9
   Ohniwa RL, 2019, SCIENTOMETRICS, V121, P1549, DOI 10.1007/s11192-019-03248-z
   Paakkari L, 2020, LANCET PUBLIC HEALTH, V5, pE249, DOI 10.1016/S2468-2667(20)30086-4
   Paek HJ, 2012, HEALTH COMMUN, V27, P727, DOI 10.1080/10410236.2011.616627
   Porter AL, 2009, J NANOPART RES, V11, P1023, DOI 10.1007/s11051-009-9607-0
   Qi SJ, 2022, APPL RES QUAL LIFE, V17, P421, DOI 10.1007/s11482-020-09878-3
   Roundtable on Health Literacy Board on Population Health and Public Health Practice Institute of Medicine., 2013, APP A HLTH LIT WOR 1
   Sansom-Daly UM, 2016, J ADOLESC YOUNG ADUL, V5, P106, DOI 10.1089/jayao.2015.0059
   Seedaket S, 2020, TROP MED INT HEALTH, V25, P1055, DOI 10.1111/tmi.13449
   Sentell T, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17093010
   Sheridan SL, 2011, J HEALTH COMMUN, V16, P30, DOI 10.1080/10810730.2011.604391
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Small H, 1999, J AM SOC INFORM SCI, V50, P799, DOI 10.1002/(SICI)1097-4571(1999)50:9<799::AID-ASI9>3.0.CO;2-G
   Small H, 2014, RES POLICY, V43, P1450, DOI 10.1016/j.respol.2014.02.005
   Small H, 2009, SCIENTOMETRICS, V79, P365, DOI 10.1007/s11192-009-0424-0
   Sorensen K, 2015, EUR J PUBLIC HEALTH, V25, P1053, DOI 10.1093/eurpub/ckv043
   Spring H, 2020, HEALTH INFO LIBR J, V37, P171, DOI 10.1111/hir.12322
   Wang L, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15102181
   Wang MX, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16234654
   Wang ZY, 2020, ISPRS INT J GEO-INF, V9, DOI 10.3390/ijgi9110632
   World Health Organization, 2013, SOLID FACTS HLTH LIT
   World Health Organization (WHO), 2017, 9 GLOBAL C HLTH PROM
NR 69
TC 7
Z9 7
U1 20
U2 55
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1932-6203
J9 PLOS ONE
JI PLoS One
PD AUG 9
PY 2021
VL 16
IS 8
AR e0254988
DI 10.1371/journal.pone.0254988
PG 23
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA UA6JQ
UT WOS:000685266800035
PM 34370749
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Spies, M
   Zuberi, M
   Mahlis, M
   Zakirova, A
   Alff, H
   Raab, C
AF Spies, Michael
   Zuberi, Mehwish
   Maehlis, Madlen
   Zakirova, Aksana
   Alff, Henryk
   Raab, Christoph
TI Towards a participatory systems approach to managing complex bioeconomy
   interventions in the agrarian sector
SO SUSTAINABLE PRODUCTION AND CONSUMPTION
LA English
DT Article
DE Bio-based economy; Participatory methods; Social-ecological systems;
   Intensi fication; Agriculture
ID ECOLOGICAL KNOWLEDGE; GREEN-REVOLUTION; TRANSDISCIPLINARY;
   SUSTAINABILITY; BIODIVERSITY; MANAGEMENT; BIOSPHERE
AB Calls for a "sustainable bioeconomy" have recently begun to proliferate in academic and political circles as an answer to global challenges such as the rapidly increasing world population and changing environmental conditions due to climate change. To build economies that rely on various sources of biomass rather than on fossil based resources, bioeconomy policies highlight a need to explore ways in which productivity in agriculture can be increased. While the focus on productivity growth has largely been inspired by technology optimism and modernisation thinking, the local knowledge of farmers and other rural stakeholders - as a means of contributing to socially and ecologically more sustainable bioeconomy pathways - has frequently been sidelined. This perspective article proposes and discusses a participatory approach to strategy formulation and evaluation in agricultural transformation and bioeconomy development that is based on the methodological toolbox MARISCO (Adaptive MAnagement of vulnerability and RISk at COnservation sites). By offering a means of developing a comprehensive knowledge map, bringing together both insights and perspectives from diverse local stakeholders, this approach enables a systemic evaluation of the social and ecological effects of intervention strategies on the ground. The article outlines the benefits - and challenges - of such a systemic and participatory approach and briefly sketches how the results can be utilised in more sustainable governance processes in the development of bioeconomy strategies. We conclude that our adapted MARISCO methodology offers a viable tool to make more visible the perspectives and knowledge of biomass producers for policymakers and to contribute to a much-needed discursive shift in bioeconomy debates towards more holistic and inclusive perspectives.(c) 2022 The Authors. Published by Elsevier Ltd on behalf of Institution of Chemical Engineers. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C1 [Spies, Michael; Zuberi, Mehwish; Maehlis, Madlen; Zakirova, Aksana; Alff, Henryk; Raab, Christoph] Eberswalde Univ Sustainable Dev, Ctr Econ & Ecosyst Management, Schwappachweg 3, D-16625 Eberswalde, Germany.
RP Spies, M (通讯作者)，Eberswalde Univ Sustainable Dev, Ctr Econ & Ecosyst Management, Schwappachweg 3, D-16625 Eberswalde, Germany.
EM michael.spies@hnee.de
FU German Federal Ministry of Education and Research in the frame of the
   junior research group "TRANSECT -Agrarian Transformation and
   Social-Ecological Complexities; Local Bioeconomy Scenarios in Central
   and South Asia" [031B0753]
FX Thework for this paperwas funded by the German Federal Ministry of
   Education and Research in the frame of the junior research group
   "TRANSECT -Agrarian Transformation and Social-Ecological Complexities.
   Local Bioeconomy Scenarios in Central and South Asia" (2019-2024, Grant
   Number 031B0753).
CR Alff Henryk, 2020, PERIPHERIE, V159, P334, DOI [10.3224/peripherie.v40i3-4.06, DOI 10.3224/PERIPHERIE.V40I3-4.06]
   Anderson Colin, EVERYDAY EXPERTS PEO
   Anderson CR., 2021, AGROECOLOGY NOW TRAN, DOI 10.1007/978-3-030-61315-0_1
   Audouin E, 2019, AGROECOLOGICAL TRANSITIONS, P177, DOI 10.1007/978-3-030-01953-2_9
   Backhouse Maria, 2017, 1 FRIEDRICH SCHILLER
   Backhouse Maria, 2021, BIOECONOMY GLOBAL IN, P25
   Backhouse Maria, 2021, BIOECONOMY GLOBAL IN
   Bergez JE, 2019, AGROECOLOGICAL TRANSITIONS, P1, DOI 10.1007/978-3-030-01953-2
   Berkes F, 2000, ECOL APPL, V10, P1251, DOI 10.1890/1051-0761(2000)010[1251:ROTEKA]2.0.CO;2
   BioSTEP, 2017, PROJECT REPORT
   Blumroeder JS, 2018, CHALL SUSTAIN, V6, P20, DOI 10.12924/cis2018.06010020
   BMBF BMEL, 2020, NAT BIOOK STRAT KAB
   BYERLEE D, 1994, WORLD DEV, V22, P1345, DOI 10.1016/0305-750X(94)90008-6
   Conservation Measures Partnership, 2013, OPEN STAND PRACT CON
   Dangelico R, 2010, J BUS ETHICS, V95, P471, DOI 10.1007/s10551-010-0434-0
   Diaz-Reviriego I, 2019, NAT SUSTAIN, V2, P457, DOI 10.1038/s41893-019-0290-6
   Dieken S, 2021, SUSTAIN PROD CONSUMP, V27, P1703, DOI 10.1016/j.spc.2021.04.006
   Dietz T, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093190
   Ebel R, 2020, CHALL SUSTAIN, V8, P17, DOI 10.12924/cis2020.08010017
   European Commission, 2018, SUST BIOEC EUR STREN
   Friedrich J, 2022, SUSTAIN PROD CONSUMP, V30, P584, DOI 10.1016/j.spc.2021.12.026
   Gerdes Holger, 2018, PROJECT REPORT
   Gerten D, 2020, NAT SUSTAIN, V3, P200, DOI 10.1038/s41893-019-0465-1
   Giampietro M, 2019, ECOL ECON, V162, P143, DOI 10.1016/j.ecolecon.2019.05.001
   GIZ Conservation Measures Partnership, 2020, CONS STAND APPL EC B
   Gottwald Franz-Theo, 2014, IRRWEG BIOOKONOMIE K
   Gunderson L. H., 2002, Panarchy: understanding transformations in human and natural systems
   Ibisch Pierre L., 2014, GUIDEBOOK RISK ROBUS
   Jiao YM, 2012, ECOL RES, V27, P247, DOI 10.1007/s11284-011-0895-3
   Kimpeler Simone, 2021, BIO FUTURES, P25, DOI [10.1007/978-3-030-64969-2_2, DOI 10.1007/978-3-030-64969-2_2]
   Koch Philipp, 2020, CRITIQUE GROWTH HIGH
   Lehmann Rosa, 2021, BIOECONOMY GLOBAL IN, P107, DOI [10.1007/978-3-030-68944-5_6, DOI 10.1007/978-3-030-68944-5_6]
   Levin SA, 1998, ECOSYSTEMS, V1, P431, DOI 10.1007/s100219900037
   Lhmann M., 2021, BIOECONOMY GLOBAL IN, P287
   Marsden T, 2015, SUSTAIN SCI, V10, P331, DOI 10.1007/s11625-014-0287-z
   Mendez VE, 2013, AGROECOL SUST FOOD, V37, P3, DOI 10.1080/10440046.2012.736926
   Murgai R, 2001, WORLD BANK RES OBSER, V16, P199, DOI 10.1093/wbro/16.2.199
   Niazi Tarique, 2012, INT J CONTEMP SOCIOL, V49, P177
   OECD, 2018, M POL CHALL SUST BIO
   Oteros-Rozas E, 2015, ECOL SOC, V20, DOI 10.5751/ES-07985-200432
   Padoch C., 2013, Unasylva (English ed.), V64, P3
   Pimbert MP, 2021, AGR HUM VALUES, V38, P1097, DOI 10.1007/s10460-021-10221-1
   Pissarskoi E., PERIPHERIE, V159, P258, DOI [10. 3224/peripherie.v40i3-4, DOI 10.3224/PERIPHERIE.V40I3-4]
   Reed MS, 2013, J ENVIRON MANAGE, V128, P345, DOI 10.1016/j.jenvman.2013.05.016
   Reed MS, 2009, J ENVIRON MANAGE, V90, P1933, DOI 10.1016/j.jenvman.2009.01.001
   Reed MG, 2018, SOC NATUR RESOUR, V31, P39, DOI 10.1080/08941920.2017.1383545
   Rodriguez Fabricio, 2021, BIOECONOMY GLOBAL IN, P265, DOI 10.1007/
   Roue Marie, 2018, INT ENCY ANTHR, V1st, P1, DOI [DOI 10.1002/9781118924396.WBIEA2215, 10. 1002/9781118924396, DOI 10.1002/9781118924396]
   Schick A, 2019, ECOL COMPLEX, V38, P98, DOI 10.1016/j.ecocom.2019.04.001
   Schick A, 2018, ENVIRON MANAGE, V62, P858, DOI 10.1007/s00267-018-1093-7
   Schick A, 2017, ECOSYST HEALTH SUST, V3, DOI 10.1002/ehs2.1267
   Schroter M, 2014, CONSERV LETT, V7, P514, DOI 10.1111/conl.12091
   Shiva V., 1991, VIOLENCE GREEN REVOL
   South Dakota Soybean Association, 2016, INN US SOYB PART GRO
   Spies M, 2020, GEOGR COMPASS, V14, DOI 10.1111/gec3.12534
   Spies Michael, 2021, FRONT SUSTAIN FOOD S, V5, DOI [10. 3389/fsufs, DOI 10.3389/FSUFS]
   Spies Michael, 2019, NO PAKISTAN HIGH MOU
   Steier F., 2015, SAGE HDB ACTION RES, P211, DOI DOI 10.4135/9781473921290.N21
   Sterling EJ, 2019, ELEMENTA-SCI ANTHROP, V7, DOI 10.1525/elementa.347
   Tokede O, 2020, INT J LIFE CYCLE ASS, V25, P1910, DOI 10.1007/s11367-020-01814-9
   Wakeford Tom, 2017, EVERYDAY EXPERTS PEO, P55
NR 61
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-5509
J9 SUSTAIN PROD CONSUMP
JI Sustain. Prod. Consump.
PD MAY
PY 2022
VL 31
BP 557
EP 568
DI 10.1016/j.spc.2022.03.020
PG 12
WC Green & Sustainable Science & Technology; Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA 0W4PX
UT WOS:000789011800019
OA hybrid
DA 2022-08-23
ER

PT J
AU Saran, S
   Chaudhary, SK
   Singh, P
   Tiwari, A
   Kumar, V
AF Saran, Sameer
   Chaudhary, Sumit Kumar
   Singh, Priyanka
   Tiwari, Amrapali
   Kumar, Vishal
TI A comprehensive review on biodiversity information portals
SO BIODIVERSITY AND CONSERVATION
LA English
DT Review
DE Biodiversity information portals; Biodiversity conservation; Citizen
   science; Sustainable development goal (SDG); Spatial data analytics
ID ATLAS
AB Biodiversity information and precise knowledge are critical for its conservation and management for sustainable development. With the advancement of information technology, various efforts have been made by the scientific and internet community to digitize the biodiversity information and put it on an open-source platform equipped with structured information, compiled knowledge, mapping, and spatial analysis tools. These portals assist researchers and conservation managers in a variety of ways, including providing open access bio-resource-related data and analysis tools, which has accelerated biodiversity and conservation research and management practices in recent years. The dissemination of knowledge about various aspect of these portals will expedite the current scenario. This paper is intended to highlight the various aspects of these portals. In this paper, globally recognized portals with rich biodiversity information, global coverage, and powerful spatial analysis tools are reviewed to characterize their vocabulary differences, strengths, and limitations. In addition to the global database, some country-specific initiatives that use biodiversity data in a geospatial context are included. It is observed that many portals have been solely developed for the purpose of creating an open access digital database of bio-resources, with the geographical aspect being ignored. Some of the global and regional initiatives such as Atlas of Living Australia (ALA), Global Biodiversity Information Facility (GBIF), etc. have attempted to fill this void by developing a variety of tools and integrating biodiversity data with geographic locations and other location-based parameters. Despite significant efforts, some issues remain unaddressed, viz., digitization of georeferenced information from natural history from across the world, the inclusion of more advanced web-GIS based tools for spatial analysis, development of agreed data standard, and so on, which could be the primary set of actions for future portals. The effort to address these issues is underway, and we anticipate that these will be resolved in the near future.
C1 [Saran, Sameer; Chaudhary, Sumit Kumar; Singh, Priyanka; Tiwari, Amrapali; Kumar, Vishal] Indian Space Res Org, Geoinformat Dept, Indian Inst Remote Sensing, Dehra Dun 248001, Uttarakhand, India.
RP Saran, S (通讯作者)，Indian Space Res Org, Geoinformat Dept, Indian Inst Remote Sensing, Dehra Dun 248001, Uttarakhand, India.
EM sameer@iirs.gov.in
RI Chaudhary, Sumit Kumar/AAO-6347-2020
OI Chaudhary, Sumit Kumar/0000-0001-6553-0983
FU Department of Biotechnology, Government of India
FX The authors would like to acknowledge the Department of Biotechnology,
   Government of India for the financial support under the Indian
   Bio-Resource Information Network (IBIN) project. IBIN project is a
   multi-institutional project executed by Indian Institute of Remote
   Sensing, Indian Space Research Collaboration (ISRO), Dehradun, India .
CR Allen KE, 2021, BIODIVERS CONSERV, V30, P2361, DOI 10.1007/s10531-021-02198-4
   Alluvium, 2016, ASS ATL LIV AUSTR IM, P50
   Amano T, 2016, BIOSCIENCE, V66, P393, DOI 10.1093/biosci/biw022
   [Anonymous], 2021, IND BIOD PORT TECHN
   Aoki D, 2021, ECOL EVOL, V11, P6066, DOI 10.1002/ece3.7387
   Assogba D, 2022, J ARID ENVIRON, V198, DOI 10.1016/j.jaridenv.2021.104692
   Belbin, 2011, P ENV INF MAN C, P28
   Belbin L, 2014, 19 ANN WEEDS C HOB, P4
   Belbin L, 2021, BIODIVERS DATA J, V9, DOI 10.3897/BDJ.9.e65023
   Belbin L, 2016, INT J GEOGR INF SCI, V30, P108, DOI 10.1080/13658816.2015.1077962
   Boakes EH, 2010, PLOS BIOL, V8, DOI 10.1371/journal.pbio.1000385
   Booth Trevor H., 2012, Ecological Management & Restoration, V13, P274, DOI 10.1111/emr.12000
   Cavi?re F., 2016, CTR CITIZEN SCI, V4, P87
   Ceballos G, 2015, SCI ADV, V1, DOI 10.1126/sciadv.1400253
   Davidson Sarah C., 2021, Animal Migration, V8, P56, DOI 10.1515/ami-2020-0114
   de Jong Y, 2015, BIODIVERS DATA J, V3, DOI 10.3897/BDJ.3.e5848
   Egri G, 2014, PROCEDIA COMPUT SCI, V36, P335, DOI 10.1016/j.procs.2014.09.102
   Ellwood ER, 2015, BIOSCIENCE, V65, P383, DOI 10.1093/biosci/biv005
   Fetterplace LC., 2016, ANIMAL BIOTELEMETRY, DOI [10.1186/s40317-016-0107-6, DOI 10.1186/S40317-016-0107-6]
   Flemons P, 2007, ECOL INFORM, V2, P49, DOI 10.1016/j.ecoinf.2007.03.004
   GBIF, 2021, BASE MAP TILES BASE
   Goel AK, 2009, AI EDAM, V23, P23, DOI 10.1017/S0890060409000080
   Graham CH, 2004, TRENDS ECOL EVOL, V19, P497, DOI 10.1016/j.tree.2004.07.006
   Guiry MD, 2014, CRYPTOGAMIE ALGOL, V35, P105, DOI 10.7872/crya.v35.iss2.2014.105
   Guralnick Robert, 2005, Biodiversity Informatics, V2, P56
   He P, 2021, ECOL INDIC, V133, DOI 10.1016/j.ecolind.2021.108399
   Hortal J, 2015, ANNU REV ECOL EVOL S, V46, P523, DOI 10.1146/annurev-ecolsys-112414-054400
   Jadhav RJ., 2011, INT J SCI ENG RES, V2, P1
   Pagad S, 2015, MANAG BIOL INVASION, V6, P127, DOI 10.3391/mbi.2015.6.2.03
   Pimm SL, 2014, SCIENCE, V344, P987, DOI 10.1126/science.1246752
   POWO, 2021, PLANTS WORLD
   Ramesh B.-R., 1997, ATLAS ENDEMICS W GHA
   Razis DV, 2010, J ROY SOC MED, V103, P473, DOI 10.1258/jrsm.2010.010090
   Reichman OJ, 2011, SCIENCE, V331, P703, DOI 10.1126/science.1197962
   Roy PS, 2000, BIOL CONSERV, V95, P95, DOI 10.1016/S0006-3207(99)00151-2
   Roy PS., 2004, GEOCARTO INT, V19, P73, DOI [10.1080/10106040408542320, DOI 10.1080/10106040408542320]
   Sequeira AMM, 2021, METHODS ECOL EVOL, V12, P996, DOI 10.1111/2041-210X.13593
   Silvertown J, 2009, TRENDS ECOL EVOL, V24, P467, DOI 10.1016/j.tree.2009.03.017
   Sritharan MS, 2021, ECOL EVOL, V11, P4854, DOI 10.1002/ece3.7392
   Staude IR, 2022, ECOL LETT, V25, P466, DOI 10.1111/ele.13937
   Sullivan BL, 2017, BIOL CONSERV, V208, P5, DOI 10.1016/j.biocon.2016.04.031
   Tann J., 2008, ATLAS LIVING AUSTR, V2008, P309
   Uieda L, 2017, GEOPHYS J INT, V208, P162, DOI 10.1093/gji/ggw390
   United Nations, 1992, UN C ENV DEV RIO DE, P1
   United Nations (UN), 2015, RES AD GEN ASS
   Vattakaven T, 2016, BIODIVERS DATA J, V4, DOI 10.3897/BDJ.4.e10279
   Walker J, 2020, AVIAN CONSERV ECOL, V15, DOI 10.5751/ACE-01671-150210
   Whittaker RJ, 2005, DIVERS DISTRIB, V11, P3, DOI 10.1111/j.1366-9516.2005.00143.x
   Wood C, 2011, PLOS BIOL, V9, DOI 10.1371/journal.pbio.1001220
NR 49
TC 0
Z9 0
U1 13
U2 13
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0960-3115
EI 1572-9710
J9 BIODIVERS CONSERV
JI Biodivers. Conserv.
PD MAY
PY 2022
VL 31
IS 5-6
BP 1445
EP 1468
DI 10.1007/s10531-022-02420-x
EA APR 2022
PG 24
WC Biodiversity Conservation; Ecology; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biodiversity & Conservation; Environmental Sciences & Ecology
GA 2E8YC
UT WOS:000788476500001
DA 2022-08-23
ER

PT J
AU Zhang, M
   Su, HH
   Wen, JH
AF Zhang, Mei
   Su, Huihui
   Wen, Jinghua
TI Analysis and Mining of Internet Public Opinion Based on LDA Subject
   Classification
SO JOURNAL OF WEB ENGINEERING
LA English
DT Article
DE Online public opinion; LDA; co-occurrence knowledge graphs;
   visualization
AB This paper uses Python, R language, Gephi and other software to crawl and classify the comment content of Weibo hot search events. Using word cloud, co-occurrence social network graphs, LDA topic classification visualization methods, this paper regularizes and integrates public opinions of hot events. Through this research, we can get the influence of public opinion mediators, public opinion objects, and government forces on the network public opinion and put forward corresponding improvement suggestions. We hope to contribute to the government's governance and prevention of online public opinion during the spread of COVID-19 and other public hot events.
C1 [Zhang, Mei; Su, Huihui; Wen, Jinghua] Guizhou Univ Financial & Econ, Informat Inst, Guiyang, Guizhou, Peoples R China.
RP Zhang, M (通讯作者)，Guizhou Univ Financial & Econ, Informat Inst, Guiyang, Guizhou, Peoples R China.
EM zm_gy@sina.com
FU Science and Technology Program of Guizhou Provincial Science and
   Technology Agency [Qian KeHe Basic[2020]1Y156]; Guizhou University of
   Finance and Economics [2019XZD02]
FX The authors would like to thank the project supported by Science and
   Technology Program of Guizhou Provincial Science and Technology Agency
   (Contract number:Qian KeHe Basic[2020]1Y156) and the major project
   supported by Guizhou University of Finance and Economics (Project
   approval number: 2019XZD02).
CR Chakraborty Anup, 2012, INT C PUBL ADM 25 HY
   Cobb WNW, 2015, SPACE POLICY, V32, P11, DOI 10.1016/j.spacepol.2015.02.008
   Li Wanlian, 2020, J INFORM, V39, P94
   Ma Mei, 2016, INFORM SCIENTIST, V34, P25
   Mou Dongmei, 2020, LIB INFORM SERVICE, V64, P111
   Peng Hao, 2015, Telecommunication Engineering, V55, P611, DOI 10.3969/j.issn.1001-893x.2015.06.005
   Sun Wenbo, 2017, J CHANGSHA U, P41
   Wang Ying, 2017, INFORM SCIENTIST, V35, P37
   Xia Sheng, 2016, ELECT TECHNOLOGY SOF, P187
   Xing Pengfei, 2020, J INFORM, V39, P67
   Zeng Runxi, 2010, CHINESE ADM, P16
   Zeng Runxi, 2009, LIB INFORM SERVICE, V53, P79
NR 12
TC 1
Z9 1
U1 45
U2 54
PU RIVER PUBLISHERS
PI GISTRUP
PA ALSBJERGVEJ 10, GISTRUP, 9260, DENMARK
SN 1540-9589
EI 1544-5976
J9 J WEB ENG
JI J. Web Eng.
PY 2021
VL 20
IS 8
BP 2457
EP 2472
DI 10.13052/jwe1540-9589.20811
PG 16
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XC4TG
UT WOS:000722006700011
OA Bronze
DA 2022-08-23
ER

PT J
AU Mayers, M
   Tu, R
   Steinecke, D
   Li, TS
   Queralt-Rosinach, N
   Su, AI
AF Mayers, Michael
   Tu, Roger
   Steinecke, Dylan
   Li, Tong Shu
   Queralt-Rosinach, Nuria
   Su, Andrew, I
TI Design and application of a knowledge network for automatic
   prioritization of drug mechanisms
SO BIOINFORMATICS
LA English
DT Article
ID DISTAL CONVOLUTED TUBULE; THIAZIDE DIURETICS; MINERAL-CONTENT;
   VITAMIN-D; C-KIT; BONE; COTRANSPORTER; RESOURCE; ONTOLOGY; CELL
AB Motivation: Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug's mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications.
   Results: In this work, we examined the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrated 18 data sources to produce a heterogeneous knowledge graph, MechRepoNet, capable of capturing the information in these paths. We applied the Rephetio repurposing algorithm to MechRepoNet using only a subset of relationships known to be mechanistic in nature and found adequate predictive ability on an evaluation set with AUROC value of 0.83. The resulting repurposing model allowed us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We found that DrugMechDB paths, when present in the network were rated highly among predicted mechanisms. We then demonstrated MechRepoNet's ability to use mechanistic insight to identify a drug's mechanistic target, with a mean reciprocal rank of 0.525 on a test set of known drug-target interactions. Finally, we walked through repurposing examples of the anti-cancer drug imatinib for use in the treatment of asthma, and metolazone for use in the treatment of osteoporosis, to demonstrate this method's utility in providing mechanistic insight into repurposing predictions it provides.
C1 [Mayers, Michael; Tu, Roger; Steinecke, Dylan; Li, Tong Shu; Queralt-Rosinach, Nuria; Su, Andrew, I] Scripps Res Inst, Dept Struct & Computat Biol, La Jolla, CA 92037 USA.
RP Su, AI (通讯作者)，Scripps Res Inst, Dept Struct & Computat Biol, La Jolla, CA 92037 USA.
EM asu@scripps.edu
RI Rosinach, Núria Queralt/B-7613-2016
OI Rosinach, Núria Queralt/0000-0003-0169-8159; Mayers,
   Michael/0000-0002-7792-0150; Su, Andrew I./0000-0002-9859-4104;
   Steinecke, Dylan/0000-0002-0469-0868
FU National Institutes of Health's National Institute on Aging [R01
   AG066750]; National Center for Advancing Translational Sciences [OT2
   TR003427]
FX This work was supported by the National Institutes of Health's National
   Institute on Aging (R01 AG066750) and National Center for Advancing
   Translational Sciences (OT2 TR003427).
CR Amin K, 2012, RESP MED, V106, P9, DOI 10.1016/j.rmed.2011.09.007
   Ashburn TT, 2004, NAT REV DRUG DISCOV, V3, P673, DOI 10.1038/nrd1468
   Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Bergstra J., 2013, INT C MACHINE LEARNI, P115
   Bokrantz T, 2017, J HYPERTENS, V35, P188, DOI 10.1097/HJH.0000000000001124
   Bradding P, 2006, J ALLERGY CLIN IMMUN, V117, P1277, DOI 10.1016/j.jaci.2006.02.039
   Brown AS, 2018, BRIEF BIOINFORM, V19, P174, DOI 10.1093/bib/bbw110
   Cahill KN, 2017, NEW ENGL J MED, V376, P1911, DOI 10.1056/NEJMoa1613125
   Chou CH, 2018, NUCLEIC ACIDS RES, V46, pD296, DOI 10.1093/nar/gkx1067
   Croft D, 2014, NUCLEIC ACIDS RES, V42, pD472, DOI 10.1093/nar/gkt1102
   Cunningham F, 2019, NUCLEIC ACIDS RES, V47, pD745, DOI 10.1093/nar/gky1113
   Davis AP, 2019, NUCLEIC ACIDS RES, V47, pD948, DOI 10.1093/nar/gky868
   Diehl AD, 2016, J BIOMED SEMANT, V7, DOI 10.1186/s13326-016-0088-7
   Dutta D, 2021, AM J HUM GENET, V108, P669, DOI 10.1016/j.ajhg.2021.02.016
   Dvorak MM, 2007, J AM SOC NEPHROL, V18, P2509, DOI 10.1681/ASN.2007030348
   Edling CE, 2007, INT J BIOCHEM CELL B, V39, P1995, DOI 10.1016/j.biocel.2006.12.005
   Emig D, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0060618
   Gari M, 1999, BRIT J HAEMATOL, V105, P894, DOI 10.1046/j.1365-2141.1999.01449.x
   GESEK FA, 1992, J CLIN INVEST, V90, P429, DOI 10.1172/JCI115878
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Humbert M, 2009, ALLERGY, V64, P1194, DOI 10.1111/j.1398-9995.2009.02122.x
   Kohler S, 2019, NUCLEIC ACIDS RES, V47, pD1018, DOI 10.1093/nar/gky1105
   LACROIX AZ, 1990, NEW ENGL J MED, V322, P286, DOI 10.1056/NEJM199002013220502
   Li J, 2016, BRIEF BIOINFORM, V17, P2, DOI 10.1093/bib/bbv020
   Li Jiao, 2012, Proceedings (IEEE Int Conf Bioinformatics Biomed), V2012, P1
   Lips P, 2011, BEST PRACT RES CL EN, V25, P585, DOI 10.1016/j.beem.2011.05.002
   Luo HM, 2018, BIOINFORMATICS, V34, P1904, DOI 10.1093/bioinformatics/bty013
   Malas TB, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-42806-6
   Mayers M, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-3297-0
   Meldal BHM, 2015, NUCLEIC ACIDS RES, V43, pD479, DOI 10.1093/nar/gku975
   Mitchell AL, 2019, NUCLEIC ACIDS RES, V47, pD351, DOI 10.1093/nar/gky1100
   Morgat A, 2017, NUCLEIC ACIDS RES, V45, pD415, DOI 10.1093/nar/gkw990
   Mungall CJ, 2012, GENOME BIOL, V13, DOI 10.1186/gb-2012-13-1-r5
   Natale DA, 2017, NUCLEIC ACIDS RES, V45, pD339, DOI 10.1093/nar/gkw1075
   Ohyama Y, 2004, FRONT BIOSCI-LANDMRK, V9, P3007, DOI 10.2741/1455
   Oprea TI, 2015, ASSAY DRUG DEV TECHN, V13, P299, DOI 10.1089/adt.2015.29011.tiodrrr
   Prosser DE, 2004, TRENDS BIOCHEM SCI, V29, P664, DOI 10.1016/j.tibs.2004.10.005
   Pushpakom S, 2019, NAT REV DRUG DISCOV, V18, P41, DOI 10.1038/nrd.2018.168
   Schriml LM, 2019, NUCLEIC ACIDS RES, V47, pD955, DOI 10.1093/nar/gky1032
   Shameer K, 2018, BRIEF BIOINFORM, V19, P656, DOI 10.1093/bib/bbw136
   Ursu O, 2017, NUCLEIC ACIDS RES, V45, pD932, DOI 10.1093/nar/gkw993
   Verlander JW, 1998, J CLIN INVEST, V101, P1661, DOI 10.1172/JCI601
   Vrandecic D., 2012, P 21 INT C WORLD WID, P1063, DOI [DOI 10.1145/2187980.2188242, 10.1145/2187980.2188242]
   Wang B, 2004, DRUG METAB DISPOS, V32, P1209, DOI 10.1124/dmd.104.000182
   WASNICH R, 1990, BRIT MED J, V301, P1303, DOI 10.1136/bmj.301.6764.1303
   WASNICH RD, 1986, OBSTET GYNECOL, V67, P457
   WASNICH RD, 1983, NEW ENGL J MED, V309, P344, DOI 10.1056/NEJM198308113090605
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD608, DOI 10.1093/nar/gkx1089
   Xin JW, 2016, GENOME BIOL, V17, DOI 10.1186/s13059-016-0953-9
   Zhu YJ, 2020, HEALTH INFORM J, V26, P2737, DOI 10.1177/1460458220937101
   Zou H, 2005, J R STAT SOC B, V67, P301, DOI 10.1111/j.1467-9868.2005.00503.x
NR 51
TC 0
Z9 0
U1 2
U2 2
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD APR 20
PY 2022
VL 38
IS 10
BP 2880
EP 2891
DI 10.1093/bioinformatics/btac205
EA APR 2022
PG 12
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA 1F5KB
UT WOS:000785793400001
PM 35561182
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Fionda, V
   Pirro, G
   Consens, MP
AF Fionda, Valeria
   Pirro, Giuseppe
   Consens, Mariano P.
TI Querying knowledge graphs with extended property paths
SO SEMANTIC WEB
LA English
DT Article
DE SPARQL; Property Paths; navigational languages; query-based reasoning;
   expressive power; translation
ID RELATIVE EXPRESSIVE POWER; WEB; LANGUAGE; QUERIES
AB The increasing number of Knowledge Graphs (KGs) available today calls for powerful query languages that can strike a balance between expressiveness and complexity of query evaluation, and that can be easily integrated into existing query processing infrastructures. We present Extended Property Paths (EPPs), a significant enhancement of Property Paths (PPs), the navigational core included in the SPARQL query language. We introduce the EPPs syntax, which allows to capture in a succinct way a larger class of navigational queries than PPs and other navigational extensions of SPARQL, and provide formal semantics. We describe a translation from non-recursive EPPs (nEPPs) into SPARQL queries and provide novel expressiveness results about the capability of SPARQL sub-languages to express navigational queries. We prove that the language of EPPs is more expressive than that of PPs; using EPPs within SPARQL allows to express things that cannot be expressed when only using PPs. We also study the expressiveness of SPARQL with EPPs in terms of reasoning capabilities. We show that SPARQL with EPPs is expressive enough to capture the main RDFS reasoning functionalities and describe how a query can be rewritten into another query enhanced with reasoning capabilities. We complement our contributions with an implementation of EPPs as the SPARQL-independent iEPPs language and an implementation of the translation of nEPPs into SPARQL queries. What sets our approach apart from previous research on querying KGs is the possibility to evaluate both nEPPs and SPARQL with nEPPs queries under the RDFS entailment regime on existing query processors. We report on an experimental evaluation on a variety of real KGs.
C1 [Fionda, Valeria] Univ Calabria, DeMaCS, Arcavacata Di Rende, CS, Italy.
   [Pirro, Giuseppe] Sapienza Univ Rome, Dept Comp Sci, Rome, Italy.
   [Consens, Mariano P.] Univ Toronto, MIE, Toronto, ON, Canada.
RP Pirro, G (通讯作者)，Sapienza Univ Rome, Dept Comp Sci, Rome, Italy.
EM fionda@mat.unical.it; pirro@di.uniroma1.it; consens@cs.toronto.edu
RI Pirrò, Giuseppe/AAZ-4500-2020; Pirrò, Giuseppe/AAF-1993-2019
OI Pirrò, Giuseppe/0000-0002-7499-5798
CR Acosta M, 2015, LECT NOTES COMPUT SC, V9366, P111, DOI 10.1007/978-3-319-25007-6_7
   Aho A. V., 1972, SIAM Journal on Computing, V1, P131, DOI 10.1137/0201008
   Alkhateeb F, 2014, INT J WEB INF SYST, V10, P24, DOI 10.1108/IJWIS-05-2013-0013
   Angles R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1322432.1322433
   Angles R, 2018, INT CONF MANAGE DATA, P1421, DOI 10.1145/3183713.3190654
   Angles R, 2016, LECT NOTES COMPUT SC, V9981, P20, DOI 10.1007/978-3-319-46523-4_2
   Arenas M, 2012, P 21 INT C WORLD WID, P629, DOI DOI 10.1145/2187836.2187922
   Arenas M, 2014, PODS'14: PROCEEDINGS OF THE 33RD ACM SIGMOD-SIGACT-SIGART SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS, P14, DOI 10.1145/2594538.2594555
   Barcelo P., 2013, P 32 ACM SIGMOD SIGA, P175, DOI DOI 10.1145/2463664.2465216
   Barcelo P, 2012, ACM T DATABASE SYST, V37, DOI 10.1145/2389241.2389250
   Berglund A., 2010, XML PATH LANGUAGE XP
   Bischof S, 2014, LECT NOTES COMPUT SC, V8796, P584, DOI 10.1007/978-3-319-11964-9_37
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Calvanese D, 2008, ACM T COMPUT LOG, V9, DOI 10.1145/1352582.1352590
   Chekol MW, 2016, LECT NOTES COMPUT SC, V9981, P86, DOI 10.1007/978-3-319-46523-4_6
   CONSENS MP, 1990, PROCEEDINGS OF THE NINTH ACM SIGACT-SIGMOD-SIGART SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS, P404, DOI 10.1145/298514.298591
   Cyganiak R., RDF 1 1 CONCEPTS ABS, P2014
   Fionda V, 2015, AAAI CONF ARTIF INTE, P102
   Fionda V, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P929, DOI 10.1145/2505515.2505758
   Fionda V, 2015, ACM T WEB, V9, DOI 10.1145/2697393
   Fletcher GHL, 2015, INFORM SCIENCES, V298, P390, DOI 10.1016/j.ins.2014.11.031
   Franconi E, 2013, LECT NOTES COMPUT SC, V8218, P101, DOI 10.1007/978-3-642-41335-3_7
   Glimm B, 2011, LECT NOTES COMPUT SC, V6848, P137, DOI 10.1007/978-3-642-23032-5_3
   Gutierrez C., 2004, P 23 ACM SIGMOD SIGA, P95, DOI DOI 10.1145/1055558.1055573
   Harris S., 2013, SPARQL 1 1 QUERY LAN
   Hartig O, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1155, DOI 10.1145/3178876.3186014
   Hartig O, 2016, J WEB SEMANT, V41, P9, DOI 10.1016/j.websem.2016.10.001
   Hartig O, 2015, LECT NOTES COMPUT SC, V9088, P71, DOI 10.1007/978-3-319-18818-8_5
   Heath T., 2011, SYNTHESIS LECT SEMAN, DOI [DOI 10.2200/S00334ED1V01Y201102WBE001, 10.2200/s00334ed1v01y201102wbe001]
   Kim J, 2015, PROC VLDB ENDOW, V8, P1238, DOI 10.14778/2809974.2809985
   Kontchakov R, 2014, LECT NOTES COMPUT SC, V8796, P552, DOI 10.1007/978-3-319-11964-9_35
   Kostylev EV, 2015, LECT NOTES COMPUT SC, V9366, P3, DOI 10.1007/978-3-319-25007-6_1
   Krotzsch M, 2016, J WEB SEMANT, V37-38, P53, DOI 10.1016/j.websem.2016.04.002
   Libkin L., 2013, P 16 INT C DAT THEOR, P129, DOI DOI 10.1145/2448496.2448513
   Libkin L, 2016, J ACM, V63, DOI 10.1145/2850413
   Libkin Leonid, 2013, PODS, P201
   Losemann Katja, 2012, P 31 ACM SIGMOD SIGA, P101, DOI DOI 10.1145/2213556
   MENDELZON AO, 1995, SIAM J COMPUT, V24, P1235, DOI 10.1137/S009753979122370X
   Munoz S, 2009, J WEB SEMANT, V7, P220, DOI 10.1016/j.websem.2009.07.003
   Perez J, 2010, J WEB SEMANT, V8, P255, DOI 10.1016/j.websem.2010.01.002
   Perez J, 2009, ACM T DATABASE SYST, V34, DOI 10.1145/1567274.1567278
   Prud'hommeaux E., 2013, SPARQL 1 1 QUERY LAN
   Przyjaciel-Zablocki M, 2012, LECT NOTES COMPUT SC, V7117, P50, DOI 10.1007/978-3-642-25953-1_5
   Reutter JL, 2015, LECT NOTES COMPUT SC, V9366, P19, DOI 10.1007/978-3-319-25007-6_2
   Rudolph RM, 2013, ETH GLOB POL, P151
   Schaffert Sebastian, 2012, I SEMANTICS, P25, DOI DOI 10.1145/2362499.2362504
   Schmidt Michael, 2010, P 13 INT C DAT THEOR, P4, DOI DOI 10.1145/1804669.1804675
   Stefanoni G, 2014, J ARTIF INTELL RES, V51, P645, DOI 10.1613/jair.4457
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Surinx D, 2015, LOG J IGPL, V23, P759, DOI 10.1093/jigpal/jzv028
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wood PT, 2012, SIGMOD REC, V41, P50, DOI 10.1145/2206869.2206879
   Yakovets N, 2016, SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1875, DOI 10.1145/2882903.2882944
   Zauner H, 2010, LECT NOTES COMPUT SC, V6333, P251, DOI 10.1007/978-3-642-15918-3_25
   Zhang XW, 2015, COMPUT J, V58, P2841, DOI 10.1093/comjnl/bxu128
NR 55
TC 2
Z9 2
U1 1
U2 6
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2019
VL 10
IS 6
BP 1127
EP 1168
DI 10.3233/SW-190365
PG 42
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JH5WI
UT WOS:000492839300009
DA 2022-08-23
ER

PT J
AU Vink, S
   van Tartwijk, J
   Verloop, N
   Gosselink, M
   Driessen, E
   Bolk, J
AF Vink, Sylvia
   van Tartwijk, Jan
   Verloop, Nico
   Gosselink, Manon
   Driessen, Erik
   Bolk, Jan
TI The articulation of integration of clinical and basic sciences in
   concept maps: differences between experienced and resident groups
SO ADVANCES IN HEALTH SCIENCES EDUCATION
LA English
DT Article
DE Concept mapping; Curriculum development; Teacher learning; Integration
   of clinical and basic sciences; Expertise differences; Knowledge
   elicitation
ID CONCEPT MAPPING ASSESSMENT; MEDICAL-EDUCATION; KNOWLEDGE MAPS;
   CURRICULUM; STUDENTS; EXPERTISE; TOOL; PHYSIOLOGY; FAILURE; WORK
AB To determine the content of integrated curricula, clinical concepts and the underlying basic science concepts need to be made explicit. Preconstructed concept maps are recommended for this purpose. They are mainly constructed by experts. However, concept maps constructed by residents are hypothesized to be less complex, to reveal more tacit basic science concepts and these basic science concepts are expected to be used for the organization of the maps. These hypotheses are derived from studies about knowledge development of individuals. However, integrated curricula require a high degree of cooperation between clinicians and basic scientists. This study examined whether there are consistent variations regarding the articulation of integration when groups of experienced clinicians and basic scientists and groups of residents and basic scientists-in-training construct concept maps. Seven groups of three clinicians and basic scientists on experienced level and seven such groups on resident level constructed concept maps illuminating clinical problems. They were guided by instructions that focused them on articulation of integration. The concept maps were analysed by features that described integration. Descriptive statistics showed consistent variations between the two expertise levels. The concept maps of the resident groups exceeded those of the experienced groups in articulated integration. First, they used significantly more links between clinical and basic science concepts. Second, these links connected basic science concepts with a greater variety of clinical concepts than the experienced groups. Third, although residents did not use significantly more basic science concepts, they used them significantly more frequent to organize the clinical concepts. The conclusion was drawn that not all hypotheses could be confirmed and that the resident concept maps were more elaborate than expected. This article discusses the implications for the role that residents and basic scientists-in-training might play in the construction of preconstructed concept maps and the development of integrated curricula.
C1 [Vink, Sylvia; Verloop, Nico] Leiden Univ, ICLON, Wassenaarseweg 62A, NL-2333 AL Leiden, Netherlands.
   [van Tartwijk, Jan] Univ Utrecht, Ctr Teaching & Learning Educ Dev & Training, Fac Social & Behav Sci, POB 80-140, NL-3508 TC Utrecht, Netherlands.
   [Gosselink, Manon] PSYVEL, Straatweg 94, NL-3051 BL Rotterdam, Netherlands.
   [Driessen, Erik] Maastricht Univ, Dept Educ Dev & Res, Tongersestr 53, NL-6211 LM Maastricht, Netherlands.
   [Bolk, Jan] Leiden Univ, Med Ctr, POB 9600, NL-2300 RC Leiden, Netherlands.
RP Vink, S (通讯作者)，Leiden Univ, ICLON, Wassenaarseweg 62A, NL-2333 AL Leiden, Netherlands.
EM svink@iclon.leidenuniv.nl; j.vantartwijk@uu.nl;
   verloop@iclon.leidenuniv.nl; mjgosselink@live.nl;
   e.driessen@maastrichtuniversity.nl; j.h.bolk@lumc.nl
RI Gosselink, Martijn Pieter/D-1564-2009
OI Gosselink, Martijn Pieter/0000-0002-6396-3977
CR Blissett S, 2012, MED EDUC, V46, P815, DOI 10.1111/j.1365-2923.2012.04311.x
   Boshuizen HPA, 2000, CLINICAL REASONING IN THE HEALTH PROFESSIONS, 2ND EDITION, P15
   Cutrer WB, 2011, MED TEACH, V33, P1018, DOI 10.3109/0142159X.2010.531159
   Dahle LO, 2002, MED TEACH, V24, P280, DOI 10.1080/01421590220134097
   Daley BJ, 2010, MED EDUC, V44, P440, DOI 10.1111/j.1365-2923.2010.03628.x
   EDMONDSON KM, 1994, ACAD MED, V69, P108, DOI 10.1097/00001888-199402000-00004
   Edmondson KM, 1998, TEACH LEARN MED, V10, P21, DOI 10.1207/S15328015TLM1001_5
   Eraut M, 2000, BRIT J EDUC PSYCHOL, V70, P113, DOI 10.1348/000709900158001
   Eva KW, 2002, ADV PSY RES, V8, P41
   Eva KW, 2002, TEACH LEARN MED, V14, P257, DOI 10.1207/S15328015TLM1404_10
   Feltovich P. J., 1993, TEST THEORY NEW GENE, P181
   Feltovich PJ, 2006, CAMBRIDGE HANDBOOK OF EXPERTISE AND EXPERT PERFORMANCE, P41
   GENBERG V, 1992, TEACH TEACH EDUC, V8, P485, DOI 10.1016/0742-051X(92)90053-6
   Gonzalez HL, 2008, ADV PHYSIOL EDUC, V32, P312, DOI 10.1152/advan.00021.2007
   Harden RM, 2000, MED EDUC, V34, P551
   Harden RM, 2001, MED TEACH, V23, P123, DOI 10.1080/01421590120036547
   Hoffman RR, 2006, CAMBRIDGE HANDBOOK OF EXPERTISE AND EXPERT PERFORMANCE, P203
   Kassab SE, 2010, MED TEACH, V32, P926, DOI 10.3109/0142159X.2010.497824
   Kinchin IM, 2008, LEARNING HLTH SOCIAL, V7, P93, DOI DOI 10.1111/j.1473-6861.2008.00174.x
   Kirschner PA, 2006, EDUC PSYCHOL-US, V41, P75, DOI 10.1207/s15326985ep4102_1
   Mandin H, 1997, ACAD MED, V72, P173, DOI 10.1097/00001888-199703000-00009
   McGaghie WC, 2000, ADV PHYSIOL EDUC, V23, P72
   Nesbit JC, 2006, REV EDUC RES, V76, P413, DOI 10.3102/00346543076003413
   Norman G, 2013, ACAD MED, V88, P151, DOI [10.1097/ACM.0b013e31827c953e, 10.1097/ACM.0b013e31827c9465, 10.1097/ACM.0b013e3182a45def]
   Novak JD, 2002, SCI EDUC, V86, P548, DOI 10.1002/sce.10032
   O'Donnell AM, 2002, EDUC PSYCHOL REV, V14, P71, DOI 10.1023/A:1013132527007
   Prince KJA, 2004, PROFESSIONAL LEARNIN, P121
   Rendas AB, 2006, ADV PHYSIOL EDUC, V30, P23, DOI 10.1152/advan.00036.2005
   Schraw G, 2006, HDB ED PSYCHOL, P245
   van de Wiel MWJ, 2000, EUR J COGN PSYCHOL, V12, P323, DOI 10.1080/09541440050114543
   Vink SC, 2015, BMC MED EDUC, V15, DOI 10.1186/s12909-015-0299-0
   Weiss LB, 2000, ACAD MED, V75, P1081, DOI 10.1097/00001888-200011000-00012
   West DC, 2002, MED EDUC, V36, P820, DOI 10.1046/j.1365-2923.2002.01292.x
   West DC, 2000, JAMA-J AM MED ASSOC, V284, P1105, DOI 10.1001/jama.284.9.1105
   Wilkerson L, 2009, MED TEACH, V31, P812, DOI 10.1080/01421590903049806
NR 35
TC 6
Z9 7
U1 0
U2 10
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1382-4996
EI 1573-1677
J9 ADV HEALTH SCI EDUC
JI Adv. Health Sci. Educ.
PD AUG
PY 2016
VL 21
IS 3
BP 643
EP 657
DI 10.1007/s10459-015-9657-2
PG 15
WC Education & Educational Research; Education, Scientific Disciplines;
   Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research; Health Care Sciences & Services
GA DQ4IZ
UT WOS:000379168800010
PM 26692262
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Tao, SH
   Qiu, RH
   Xu, B
   Ping, Y
AF Tao, Shaohua
   Qiu, Runhe
   Xu, Bo
   Ping, Yuan
TI Micro-behaviour with Reinforcement Knowledge-aware Reasoning for
   Explainable Recommendation
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Micro -behaviour; Knowledge graph; Deep reinforcement learning;
   Recommendation; Explanation
AB Existing practical recommendation scenarios involve multiple micro-behaviour user-item interactions, such as clicks, page views, add-to-favourites, and purchases, which provide fine-grained and a better in-depth understanding of the user's preference. Furthermore, some recommendation methods have incorporated item knowledge into the micro-behaviour of user-item interaction. Although some have proved effective, two insights are often neglected. First, they fail to combine micro-behaviour with the relation of the knowledge graph (KG), and the semantic relationship between micro-behaviour and relation is not captured. Second, they do not provide explicit reasoning for micro-behaviour from user-item interaction data. These insights motivated us to propose a novel model of Micro-behaviour with Reinforcement Knowledge-aware Reasoning for Explainable Recommendation (MBKR), which incorporates micro-behaviour and the KG into reinforcement learning for explainable recommendation. Specifically, the model learns the behaviour by user-item propagation and the relation from the KG and combines the two to calculate the behavioural strength to mine user's interests. In addition, we designed a Shawo-relational path that combines recommendation and interpretability by providing rational paths; these paths capture the semantics of behaviours and relations. Finally, we extensively evaluated our method on several large-scale benchmark datasets, and the results indicate that the proposed method is more effective in providing recommendations than state-of-the-art methods.(c) 2022 Elsevier B.V. All rights reserved.
C1 [Tao, Shaohua; Qiu, Runhe] Donghua Univ, Coll Informat Sci & Technol, Shanghai, Peoples R China.
   [Tao, Shaohua; Ping, Yuan] XuChang Univ, Sch Informat Engn, Xuchang, Henan, Peoples R China.
   [Tao, Shaohua] Donghua Univ, Engn Res Ctr Digitized Text & Fash Technol, Minist Educ, Shanghai, Peoples R China.
   [Xu, Bo] Donghua Univ, Sch Comp Sci & Technol, Shanghai, Peoples R China.
RP Tao, SH; Qiu, RH (通讯作者)，Donghua Univ, Coll Informat Sci & Technol, Shanghai, Peoples R China.
EM 1179211@mail.dhu.eud.cn; qiurh@dhu.edu.cn; xubo@dhu.edu.cn;
   pyuan.lhn@xcu.edu.cn
OI Xu, Bo/0000-0002-2083-4307
FU Plan For Scientific Innovation Talent of He?nan Province, China
   [184100510012]; Key Technologies R&D Program of He?nan Province, China
   [212102210084]; Innovation Scientists and Technicians Troop Construction
   Projects of He?nan Province Key Technologies R & D Program of Henan
   Province, China [192102210295]
FX Acknowledgements This work is supported by the Plan For Scientific
   Innovation Talent of He?nan Province, China under Grant 184100510012,
   Key Technologies R&D Program of He?nan Province, China under Grant
   212102210084, and Innovation Scientists and Technicians Troop
   Construction Projects of He?nan Province Key Technologies R & D Program
   of Henan Province, China under Grant NO. 192102210295.
CR Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Chen C, 2021, AAAI CONF ARTIF INTE, V35, P3958
   Deng Z.-H, 2019, ARXIV19010470
   Gao C, 2019, PROC INT CONF DATA, P1554, DOI 10.1109/ICDE.2019.00140
   Gu YL, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P223, DOI 10.1145/3336191.3371827
   He XN, 2018, IEEE T KNOWL DATA EN, V30, P2354, DOI 10.1109/TKDE.2018.2831682
   Huang J, 2018, ACM/SIGIR PROCEEDINGS 2018, P505, DOI 10.1145/3209978.3210017
   Jin BW, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P659, DOI 10.1145/3397271.3401072
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Meng WJ, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P1091, DOI 10.1145/3397271.3401098
   Milletari F., 2016, COMMUN ACM
   Mnih V, 2015, NATURE, V518, P529, DOI 10.1038/nature14236
   Peter V, 2017, ARXIV17101090
   Sarwar B, 2001, P 10 INT C WORLD WID, P285
   Sutton R. S., 2017, REINFORCEMENT LEARNI
   Tao SH, 2021, KNOWL-BASED SYST, V227, DOI 10.1016/j.knosys.2021.107217
   Wang CY, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P109, DOI 10.1145/3397271.3401131
   Wang HW, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P968, DOI 10.1145/3292500.3330836
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang PF, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P209, DOI 10.1145/3397271.3401134
   Wang X, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P99, DOI 10.1145/3366423.3380098
   Wang X, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P165, DOI 10.1145/3331184.3331267
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang Z., 2016, IJCAI, P1293
   Wu C.F.F.-L, 2019, ARXIV19020715
   Xia L.-H, 2020, P 34 ASS ADVANCEMENT, P4486
   Xian YK, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P285, DOI 10.1145/3331184.3331203
   Zhang FZ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P353, DOI 10.1145/2939672.2939673
   Zhao KZ, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P239, DOI 10.1145/3397271.3401171
   Zhou MZ, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P727, DOI 10.1145/3159652.3159671
   Zhou SJ, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P179, DOI 10.1145/3397271.3401174
NR 34
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD SEP 5
PY 2022
VL 251
AR 109300
DI 10.1016/j.knosys.2022.109300
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3B0JT
UT WOS:000827637200003
DA 2022-08-23
ER

PT J
AU Shao, Z
   Cao, XY
   Yuan, S
   Wang, YL
AF Shao, Zhou
   Cao, Xiangying
   Yuan, Sha
   Wang, Yongli
TI ELAD: An Entity Linking Based Affiliation Disambiguation Framework
SO IEEE ACCESS
LA English
DT Article
DE Knowledge based systems; Joining processes; Encyclopedias; Electronic
   publishing; Internet; Computer science; Institution name disambiguation
   (IND); knowledge graph; entity linking
ID AUTHOR AFFILIATIONS; NAMES
AB The number of papers has exploded as more and more people and more types of institutions participating in scientific research. At the same time, institution name disambiguation (IND) is getting more sophisticated, which is critical for research assessment, scholar alignment, etc. Previous knowledge-based and rule-based methods require knowledge and rules prepared in advance, which cannot cope with growing and changing data and learning rules, especially for data with a long period and abundant sources. This paper proposes an automatic learning framework to solve the problem, which is based on entity linking, entity type recognition, candidate generation, and result selection. Experiments show that precision and recall is much higher than the traditional method, ELAD learns more knowledge from the knowledge graph, and it can deal with ever-changing and ever-increasing data. What & x2019;s more, it solves many problems that cannot be solved by traditional methods: the connection between institution entities, mistakes correction, and the reduction of manual and pre-prepared knowledge. At last, for the case study, we develop two applications based on ELAD which proves its reliability.
C1 [Shao, Zhou; Wang, Yongli] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China.
   [Cao, Xiangying] Renmin Univ China, Informat Sch, Beijing 100872, Peoples R China.
   [Yuan, Sha] Beijing Acad Artificial Intelligence, Beijing 100086, Peoples R China.
   [Shao, Zhou; Yuan, Sha] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China.
   [Shao, Zhou] Southwest Univ Sci & Technol, Sch Comp Sci & Technol, Mianyang 621010, Sichuan, Peoples R China.
   [Shao, Zhou] China Acad Engn Phys, Grad Sch, Mianyang, Sichuan, Peoples R China.
RP Wang, YL (通讯作者)，Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China.
EM yongliwang@mail.njust.edu.cn
OI Shao, Zhou/0000-0002-6265-7310
FU National Natural Science Foundation of China [61941113, 61806111];
   Fundamental Research Fund for the Central Universities [30918015103,
   30918012204]; Nanjing Science and Technology Development Plan Project
   [201805036]; China Academy of Engineering Consulting Research Project
   [2019-ZD-1-02-02]; National Social Science Foundation [18BTQ073]; State
   Grid Technology Project [5211XT190033]; "13th Five-Year'' equipment
   field fund [61403120501]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61941113 and Grant 61806111, in part by
   the Fundamental Research Fund for the Central Universities under Grant
   30918015103 and Grant 30918012204, in part by the Nanjing Science and
   Technology Development Plan Project under Grant 201805036, in part by
   the ``13th Five-Year'' equipment ~eld fund under Grant 61403120501, in
   part by the China Academy of Engineering Consulting Research Project
   under Grant 2019-ZD-1-02-02, in part by the National Social Science
   Foundation under Grant 18BTQ073, and in part by the State Grid
   Technology Project under Grant 5211XT190033.
CR Abramo G, 2011, J INFORMETR, V5, P618, DOI 10.1016/j.joi.2011.06.001
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], 2009, ICIQ
   Chan KC, 2016, MANAG FINANC, V42, P292, DOI 10.1108/MF-09-2015-0247
   CHVATAL V, 1975, J APPL PROBAB, V12, P306, DOI 10.2307/3212444
   Cuxac P, 2013, SCIENTOMETRICS, V97, P47, DOI 10.1007/s11192-013-1025-5
   Huang SQ, 2014, SCIENTOMETRICS, V99, P823, DOI 10.1007/s11192-013-1214-2
   Jacob Ferosh, 2014, 2014 International Conference on Collaboration Technologies and Systems (CTS), P86, DOI 10.1109/CTS.2014.6867547
   Jiang Y, 2011, J AM SOC INF SCI TEC, V62, P1029, DOI 10.1002/asi.21538
   Jonnalagadda S, 2010, J BIOMED DISCOV COLL, V5, P50
   Majumdar SN, 2005, PHYS REV E, V72, DOI 10.1103/PhysRevE.72.020901
   Moed H, 2014, SCIENTOMETRICS, V101, P1987, DOI 10.1007/s11192-014-1307-6
   Morillo F, 2013, SCIENTOMETRICS, V94, P207, DOI 10.1007/s11192-012-0733-6
   Navarro G, 2001, ACM COMPUT SURV, V33, P31, DOI 10.1145/375360.375365
   Orduna-Malea E, 2017, ONLINE INFORM REV, V41, P762, DOI 10.1108/OIR-10-2016-0302
   Sabah F, 2019, LIBR HI TECH, V37, P19, DOI 10.1108/LHT-03-2018-0036
   Silberztein M., 2005, P HLT EMNLP INT DEM, P10
   [孙海霞 Sun Haixia], 2018, [数据分析与知识发现, Data Analysis and Knowledge Discovery], V2, P88
   Sun HX, 2017, STUD HEALTH TECHNOL, V245, P1338, DOI 10.3233/978-1-61499-830-3-1338
   Taskin Z, 2014, SCIENTOMETRICS, V98, P347, DOI 10.1007/s11192-013-1004-x
   Taskin Z, 2013, PROCD SOC BEHV, V73, P544, DOI 10.1016/j.sbspro.2013.02.089
   Wang Z., 2013, P 12 INT SEM WEB C, V1035, P121
   Zhang J, 2017, LECT NOTES ARTIF INT, V10565, P172, DOI 10.1007/978-3-319-69005-6_15
   Zhang YT, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1002, DOI 10.1145/3219819.3219859
   Zhu HY, 2017, J NANOPART RES, V19, DOI 10.1007/s11051-017-4056-7
NR 28
TC 3
Z9 4
U1 1
U2 19
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 70519
EP 70526
DI 10.1109/ACCESS.2020.2986826
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA LK4BX
UT WOS:000530806800001
OA gold
DA 2022-08-23
ER

PT J
AU Han, XR
   Chen, J
   Lu, JH
   Chen, YG
   Du, XY
AF Han, Xueran
   Chen, Jun
   Lu, Jiaheng
   Chen, Yueguo
   Du, Xiaoyong
TI PivotE: Revealing and Visualizing the Underlying Entity Structures for
   Exploration
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB A Web-scale knowledge graph (KG) typically contains millions of entities and thousands of entity types. Due to the lack of a pre-defined data schema such as the ER model, entities in KGs are loosely coupled based on their relationships, which brings challenges for effective accesses of the KGs in a structured manner like SPARQL. This demonstration presents an entity-oriented exploratory search prototype system that is able to support search and explore KGs in a exploratory search manner, where local structures of KGs can be dynamically discovered and utilized for guiding users. The system applies a path-based ranking method for recommending similar entities and their relevant information as exploration pointers. The interface is designed to assist users to investigate a domain (particular type) of entities, as well as to explore the knowledge graphs in various relevant domains. The queries are dynamically formulated by tracing the users' dynamic clicking (exploration) behaviors.
   In this demonstration, we will show how our system visualize the underlying entity structures, as well as explain the semantic correlations among them in a unified interface, which not only assist users to learn about the properties of entities in many aspects but also guide them to further explore the information space.
C1 [Han, Xueran; Chen, Jun; Chen, Yueguo; Du, Xiaoyong] Renmin Univ China, Beijing, Peoples R China.
   [Lu, Jiaheng] Univ Helsinki, Helsinki, Finland.
RP Han, XR (通讯作者)，Renmin Univ China, Beijing, Peoples R China.
EM hanxueran@ruc.edu.cn; chenjun2013@ruc.edu.cn; jiaheng.lu@helsinki.fi;
   chenyueguo@ruc.edu.cn; duyong@ruc.edu.cn
OI Lu, Jiaheng/0000-0003-2067-454X
FU National Key Research and Development Program [2018YFB1004401]; National
   Science Foundation of China [U1711261, 61472426]
FX Yueguo Chen is the corresponding author. This work is supported by
   National Key Research and Development Program (No. 2018YFB1004401) and
   the National Science Foundation of China under grants U1711261, No.
   61472426.
CR Chen J, 2018, J WEB SEMANT, V52-53, P33, DOI 10.1016/j.websem.2018.09.001
   Cheng T., 2007, THESIS
   Huang FR, 2015, PROC INT CONF DATA, P1408, DOI 10.1109/ICDE.2015.7113388
   Ponte Jay Michael, 1998, THESIS
   White RW., 2009, SYNTHESIS LECT INFOR, V1, P1, DOI DOI 10.2200/S00174ED1V01Y200901ICR003
   Zhang XL, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1101, DOI 10.1145/3077136.3080732
NR 6
TC 1
Z9 1
U1 1
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD AUG
PY 2019
VL 12
IS 12
BP 1966
EP 1969
DI 10.14778/3352063.3352111
PG 4
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JO5VL
UT WOS:000497646000048
OA Green Published
DA 2022-08-23
ER

PT J
AU Guan, XD
   Huang, C
   Yang, J
   Li, AN
AF Guan, Xudong
   Huang, Chong
   Yang, Juan
   Li, Ainong
TI Remote Sensing Image Classification with a Graph-Based Pre-Trained
   Neighborhood Spatial Relationship
SO SENSORS
LA English
DT Article
DE remote sensing; image classification; SVM (Support Vector Machine);
   knowledge graph; object-based image analysis; fuzzy classification;
   graph theory
ID OBJECT-BASED ANALYSIS; LAND-COVER; EXPERT-SYSTEM; SCENE CLASSIFICATION;
   NEURAL-NETWORK; KNOWLEDGE; INFORMATION; GIS; SEGMENTATION; ONTOLOGY
AB Previous knowledge of the possible spatial relationships between land cover types is one factor that makes remote sensing image classification "smarter". In recent years, knowledge graphs, which are based on a graph data structure, have been studied in the community of remote sensing for their ability to build extensible relationships between geographic entities. This paper implements a classification scheme considering the neighborhood relationship of land cover by extracting information from a graph. First, a graph representing the spatial relationships of land cover types was built based on an existing land cover map. Empirical probability distributions of the spatial relationships were then extracted using this graph. Second, an image was classified based on an object-based fuzzy classifier. Finally, the membership of objects and the attributes of their neighborhood objects were joined to decide the final classes. Two experiments were implemented. Overall accuracy of the two experiments increased by 5.2% and 0.6%, showing that this method has the ability to correct misclassified patches using the spatial relationship between geo-entities. However, two issues must be considered when applying spatial relationships to image classification. The first is the "siphonic effect" produced by neighborhood patches. Second, the use of global spatial relationships derived from a pre-trained graph loses local spatial relationship in-formation to some degree.
C1 [Guan, Xudong; Li, Ainong] Chinese Acad Sci, Res Ctr Digital Mt & Remote Sensing Applicat, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China.
   [Huang, Chong] Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China.
   [Yang, Juan] Shaanxi Energy Inst, Xianyang 712000, Peoples R China.
RP Li, AN (通讯作者)，Chinese Acad Sci, Res Ctr Digital Mt & Remote Sensing Applicat, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China.
EM guanxd@imde.ac.cn; huangch@lreis.ac.cn; sxnyyj2014@163.com;
   ainongli@imde.ac.cn
OI Guan, XuDong/0000-0003-0030-2335
FU National Science Foundation of China [41901309, 41701433, 42090015];
   Youth Talent Team Program of the Institute of Mountain Hazards and
   Environment, CAS [SDSQB2020000032, Y8R2230230]; Sichuan Science and
   Technology Program [2020JDJQ0003]; Second Tibetan Plateau Scientific
   Expedition and Research Program [2019QZKK0308]; CAS "Light of West
   China" Program
FX This work was supported in part by the National Science Foundation of
   China under grant 41901309, 41701433 and 42090015, the CAS "Light ofWest
   China" Program, the Youth Talent Team Program of the Institute of
   Mountain Hazards and Environment, CAS under grant SDSQB2020000032,
   Y8R2230230, Sichuan Science and Technology Program under grant
   2020JDJQ0003 and the Second Tibetan Plateau Scientific Expedition and
   Research Program under grant 2019QZKK0308.
CR Batuwita R, 2010, IEEE T FUZZY SYST, V18, P558, DOI 10.1109/TFUZZ.2010.2042721
   Belgiu M, 2014, REMOTE SENS LETT, V5, P530, DOI 10.1080/2150704X.2014.930563
   Belgiu M, 2014, REMOTE SENS-BASEL, V6, P1347, DOI 10.3390/rs6021347
   Benz U, 2001, INT GEOSCI REMOTE SE, P1427, DOI 10.1109/IGARSS.2001.976867
   Benz UC, 2004, ISPRS J PHOTOGRAMM, V58, P239, DOI 10.1016/j.isprsjprs.2003.10.002
   Bouziani M, 2010, IEEE T GEOSCI REMOTE, V48, P3198, DOI 10.1109/TGRS.2010.2044508
   CHANG CL, 1974, IEEE T COMPUT, VC 23, P1179, DOI 10.1109/T-C.1974.223827
   Chen Z, 2012, INT J REMOTE SENS, V33, P3048, DOI 10.1080/01431161.2011.625055
   Cheng G, 2020, IEEE J-STARS, V13, P3735, DOI 10.1109/JSTARS.2020.3005403
   Cheung AKL, 2015, INT J GEOGR INF SCI, V29, P580, DOI 10.1080/13658816.2014.989856
   Cui W, 2019, REMOTE SENS-BASEL, V11, DOI 10.3390/rs11091044
   Dale MRT, 2010, ANNU REV ECOL EVOL S, V41, P21, DOI 10.1146/annurev-ecolsys-102209-144718
   Dianat R, 2010, IEEE GEOSCI REMOTE S, V7, P215, DOI 10.1109/LGRS.2009.2031686
   Dobson MC, 1996, IEEE T GEOSCI REMOTE, V34, P83, DOI 10.1109/36.481896
   Fan RE, 2005, J MACH LEARN RES, V6, P1889
   Forestier G, 2013, COMPUT GEOSCI-UK, V54, P88, DOI 10.1016/j.cageo.2012.11.023
   Forestier G, 2012, COMPUT ENVIRON URBAN, V36, P470, DOI 10.1016/j.compenvurbsys.2012.01.003
   Ghimire B, 2010, REMOTE SENS LETT, V1, P45, DOI 10.1080/01431160903252327
   GOODENOUGH DG, 1987, IEEE T GEOSCI REMOTE, V25, P349, DOI 10.1109/TGRS.1987.289805
   Gu YT, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9102110
   Guarino N, 1998, FR ART INT, V46, P3
   Guo QH, 2007, GISCI REMOTE SENS, V44, P24, DOI 10.2747/1548-1603.44.1.24
   Hardin PJ, 1997, PHOTOGRAMM ENG REM S, V63, P735
   Hong DH, 2003, FUZZY SET SYST, V138, P271, DOI 10.1016/S0165-0114(02)00514-6
   Jabari S, P ASPRS 2014 ANN C L
   Jabari S, 2013, ALGORITHMS, V6, P762, DOI 10.3390/a6040762
   KARTIKEYAN B, 1995, IEEE T GEOSCI REMOTE, V33, P58, DOI 10.1109/36.368222
   Kussul N, 2017, IEEE GEOSCI REMOTE S, V14, P778, DOI 10.1109/LGRS.2017.2681128
   Li YS, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12234003
   Li YF, 2020, MATH BIOSCI ENG, V17, P4443, DOI 10.3934/mbe.2020245
   Luo B, 2014, IEEE T GEOSCI REMOTE, V52, P1451, DOI 10.1109/TGRS.2013.2251468
   Ma L, 2016, PATTERN RECOGN LETT, V83, P133, DOI 10.1016/j.patrec.2016.01.022
   Masjedi A, 2016, IEEE T GEOSCI REMOTE, V54, P932, DOI 10.1109/TGRS.2015.2469691
   Metternicht G, 2001, ECOL MODEL, V144, P163, DOI 10.1016/S0304-3800(01)00371-4
   Murai H, 1997, INT J REMOTE SENS, V18, P811, DOI 10.1080/014311697218773
   Ouyang S, 2021, REMOTE SENS-BASEL, V13, DOI 10.3390/rs13010119
   Pacifici F, 2009, REMOTE SENS ENVIRON, V113, P1276, DOI 10.1016/j.rse.2009.02.014
   Pu SL, 2021, REMOTE SENS-BASEL, V13, DOI 10.3390/rs13030526
   Pu S, 2009, ISPRS J PHOTOGRAMM, V64, P575, DOI 10.1016/j.isprsjprs.2009.04.001
   Qiao C, 2015, INT J DIGIT EARTH, V8, P710, DOI 10.1080/17538947.2014.925517
   Rejichi S, 2015, IEEE J-STARS, V8, P2138, DOI 10.1109/JSTARS.2015.2433257
   Rezaee M, 2018, IEEE J-STARS, V11, P3030, DOI 10.1109/JSTARS.2018.2846178
   SARMA LCS, 1994, SADHANA-ACAD P ENG S, V19, P93
   Scott GJ, 2017, IEEE GEOSCI REMOTE S, V14, P549, DOI 10.1109/LGRS.2017.2657778
   Sebari I, 2013, ISPRS J PHOTOGRAMM, V79, P171, DOI 10.1016/j.isprsjprs.2013.02.006
   Tobler W, 2004, ANN ASSOC AM GEOGR, V94, P304, DOI 10.1111/j.1467-8306.2004.09402009.x
   Visser F, 2018, HYDROBIOLOGIA, V812, P157, DOI 10.1007/s10750-016-2928-y
   Weinberger KQ, 2009, J MACH LEARN RES, V10, P207
   Witharana C, 2021, REMOTE SENS-BASEL, V13, DOI 10.3390/rs13040558
   Xu C, 2021, ISPRS INT J GEO-INF, V10, DOI 10.3390/ijgi10020061
   Xu YH, 2021, IEEE T GEOSCI REMOTE, V59, P1604, DOI 10.1109/TGRS.2020.2999962
   Zhang LP, 2016, IEEE GEOSC REM SEN M, V4, P22, DOI 10.1109/MGRS.2016.2540798
   Zhao LJ, 2019, MULTIMED TOOLS APPL, V78, P9667, DOI 10.1007/s11042-018-6548-6
   Zhao M, 2018, IEEE T IMAGE PROCESS, V27, P2731, DOI 10.1109/TIP.2018.2810516
NR 54
TC 1
Z9 1
U1 8
U2 17
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD AUG
PY 2021
VL 21
IS 16
AR 5602
DI 10.3390/s21165602
PG 28
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA UH2YM
UT WOS:000689803200001
PM 34451044
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Knez, T
   Gasperlin, D
   Bajec, M
   Zitnik, S
AF Knez, Timotej
   Gasperlin, Domen
   Bajec, Marko
   Zitnik, Slavko
TI Blockchain-Based Transaction Manager for Ontology Databases
SO INFORMATICA
LA English
DT Article
DE Semantic Web; blockchain; ontologies; databases; IPFS; Ethereum
ID SEMANTIC WEB
AB Knowledge graphs are commonly represented by ontology-based databases. Tracking the provenance of ontological changes and ensuring ontology consistency is important. In this work, we propose a transaction manager for ontology-based database manipulation that combines blockchain and Semantic Web technologies. The latter is used for the efficient querying and modification of data, whereas the blockchain is used for the secure storage and tracking of changes. The blockchain enables a decentralized setup and data restoration. We evaluate our solution by measuring cost and time. Our solution introduces some overhead for updates whereas querying works at the same speed as the underlying ontology database.
C1 [Knez, Timotej; Gasperlin, Domen; Bajec, Marko; Zitnik, Slavko] Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana, Slovenia.
RP Knez, T (通讯作者)，Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana, Slovenia.
EM timotej.knez@fri.uni-lj.si; domen.gasperlin@gmail.com;
   marko.bajec@fri.uni-lj.si; slavko.zitnik@fri.uni-lj.si
OI Knez, Timotej/0000-0001-7506-5739
CR Abu Naim B, 2019, 2019 SIXTH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS: SYSTEMS, MANAGEMENT AND SECURITY (IOTSMS), P420
   Apache Jena, 2021, REASONERS RULE ENGIN
   Benet J., 2014, IPFS CONTENT ADDRESS
   Binance team, 2022, INTR BNB CHAIN EV BI
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Buterin V., 2014, CISC VIS NETW IND GL
   Cano-Benito J, 2019, LECT NOTES BUS INF P, V373, P220, DOI 10.1007/978-3-030-36691-9_19
   Choi S., 2018, ARXIV PREPRINT ARXIV
   Decker S, 2000, IEEE INTERNET COMPUT, V4, P63, DOI 10.1109/4236.877487
   Donnelly K, 2006, STUD HEALTH TECHNOL, V121, P279
   Edelman G., 2021, FATHER WEB3 WANTS YO
   Efanov D, 2018, PROCEDIA COMPUT SCI, V123, P116, DOI 10.1016/j.procs.2018.01.019
   English M., 2016, P COMP SCI C U BONN, P47
   Gasperlin D, 2021, BLOCKCHAIN BASED ONT
   Haber S., 1991, Journal of Cryptology, V3, P99
   Hector U.-R., 2020, ABS200809518 CORR, DOI 10.48550/arXiv.2008.09518
   Horrocks I., 2003, J WEB SEMANT, V1, P7, DOI DOI 10.1016/J.WEBSEM.2003.07.001
   Khattak AM, 2009, PROC INT C TOOLS ART, P449, DOI 10.1109/ICTAI.2009.42
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Liang Y., 2005, P 1 AKT DOCTORAL S
   Markovic M, 2019, 2019 SIXTH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS: SYSTEMS, MANAGEMENT AND SECURITY (IOTSMS), P116
   McBride B., 2004, HDB ONTOLOGIES, P51, DOI [10.1007/978-3-540-24750-0_3, DOI 10.1007/978-3-540-24750-0_3]
   McGuinness DL, 2004, W3C RECOMMENDATION, V10, P2004
   Nakamoto S., 2008, BITCOIN PEER TO PEER
   Nizamuddin N, 2019, COMPUT ELECTR ENG, V76, P183, DOI 10.1016/j.compeleceng.2019.03.014
   Noy N.F., 2001, KNOWLEDGE SYSTEMS LA, P32
   O'Shea S., 2019, MIND GAP EXPLORING P
   Perez J, 2006, LECT NOTES COMPUT SC, V4273, P30
   Preston I., 2020, JAVA IPFS HTTP CLIEN
   Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62
   Shortliffe E. H., 1976, MYCIN COMPUTER BASED
   Speer R, 2012, LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P3679
   Steichen M, 2018, IEEE 2018 INTERNATIONAL CONGRESS ON CYBERMATICS / 2018 IEEE CONFERENCES ON INTERNET OF THINGS, GREEN COMPUTING AND COMMUNICATIONS, CYBER, PHYSICAL AND SOCIAL COMPUTING, SMART DATA, BLOCKCHAIN, COMPUTER AND INFORMATION TECHNOLOGY, P1499, DOI 10.1109/Cybermatics_2018.2018.00253
   Stevens R, 2000, Brief Bioinform, V1, P398
   Sun JJ, 2020, INT SYMP DISTR COMPU, P279, DOI 10.1109/DCABES50732.2020.00079
   Taye Mohammad Mustafa, 2010, J COMPUTING, V2
   Ugarte H. E., 2017, MORE PRAGMATIC WEB 3
NR 37
TC 0
Z9 0
U1 1
U2 1
PU INST MATHEMATICS & INFORMATICS
PI VILNIUS
PA 9 SAULETEKIS AV, VILNIUS LT-10222, LITHUANIA
SN 0868-4952
EI 1822-8844
J9 INFORMATICA-LITHUAN
JI Informatica
PY 2022
VL 33
IS 2
BP 343
EP 364
DI 10.15388/22-INFOR490
PG 22
WC Computer Science, Information Systems; Mathematics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Mathematics
GA 2V3GA
UT WOS:000823737700006
DA 2022-08-23
ER

PT J
AU Yan, XQ
   Ye, YD
   Mao, YQ
   Yu, H
AF Yan, Xiaoqiang
   Ye, Yangdong
   Mao, Yiqiao
   Yu, Hui
TI Shared-Private Information Bottleneck method for cross-model clustering
SO IEEE ACCESS
LA English
DT Article
DE Cross-modal clustering; information bottleneck; mutual information;
   knowledge graph; social images
AB Recently, the cross-modal analysis has drawn much attention due to the rapid growth and widespread emergence of multimodal data. It integrates multiple modalities to improve learning and generalization performance. However, most previous methods just focus on learning a common shared feature space for all modalities and ignore the private information hidden in each individual modality. To address this problem, we propose a novel shared-private information bottleneck (SPIB) method for cross-modal clustering. First, we devise a hybrid words model and a consensus clustering model to construct the shared information of multiple modalities, which capture the statistical correlation of low-level features and the semantic relations of the high-level clustering partitions, respectively. Second, the shared information of multiple modalities and the private information of individual modalities are maximally preserved through a unified information maximization function. Finally, the optimization of SPIB function is performed by a sequential "draw-and-merge" procedure, which guarantees the function converges to a local maximum. Besides, to solve the lack of tags in cross-modal social images, we also investigate the use of structured prior knowledge in the form of knowledge graph to enrich the information in semantic modality and design a novel semantic similarity measurement for social images. The experimental results on four types of cross-modal datasets demonstrate that our method outperforms the state-of-the-art approaches.
C1 [Yan, Xiaoqiang; Ye, Yangdong; Mao, Yiqiao] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450052, Henan, Peoples R China.
   [Yu, Hui] Univ Portsmouth, Sch Creat Technol, Portsmouth PO1 2UP, Hants, England.
RP Ye, YD (通讯作者)，Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450052, Henan, Peoples R China.
EM ieydye@zzu.edu.cn
RI ARSLAN, Okan/AAA-3232-2020
OI Yu, Hui/0000-0002-7655-9228
FU National Natural Science Foundation of China [61772475]; National Key
   Research and Development Program of China [2018YFB1201403]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61772475, and in part by the National
   Key Research and Development Program of China under Grant
   2018YFB1201403.
CR [Anonymous], 2016, P INT JOINT C ART IN
   [Anonymous], 2007, MACHINE LEARNING MUL, DOI DOI 10.1007/978-3-540-78155-4
   Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214
   Barnard K, 2001, EIGHTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOL II, PROCEEDINGS, P408, DOI 10.1109/ICCV.2001.937654
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13
   Chaudhuri K., 2009, P 26 ANN INT C MACH, P129, DOI DOI 10.1145/1553374.1553391
   Chen XL, 2015, PROC CVPR IEEE, P5298, DOI 10.1109/CVPR.2015.7299167
   Chua T-S, 2009, P ACM INT C IM VID R, P48
   Chum O, 2007, IEEE I CONF COMP VIS, P496, DOI 10.1109/cvpr.2007.383172
   Cover TM., 2006, ELEMENTS INFORM THEO
   Daume H., 2011, P 28 INT C MACH LEAR, P393, DOI DOI 10.5555/3104482.3104532
   Everingham M, 2010, INT J COMPUT VISION, V88, P303, DOI 10.1007/s11263-009-0275-4
   Grubinger M., 2006, P INT C LANG RES EV, V2, P1
   Han, 2013, P 2013 SIAM INT C DA, P252, DOI DOI 10.1137/1.9781611972832.28
   Hong S, 2016, IEEE T PATTERN ANAL, V38, DOI 10.1109/TPAMI.2015.2487982
   Huang D, 2018, IEEE T CYBERNETICS, V48, P1460, DOI 10.1109/TCYB.2017.2702343
   Huang D, 2016, IEEE T KNOWL DATA EN, V28, P1312, DOI 10.1109/TKDE.2015.2503753
   Huang H., 2013, P 23 INT JOINT C ART, P2598, DOI DOI 10.5555/2540128.2540503
   Hwang SY, 2011, PURE APPL CHEM, V83, P233, DOI 10.1351/PAC-CON-10-09-35
   Jin C, 2015, AAAI CONF ARTIF INTE, P151
   Joachims T., 1998, P 10 EUR C MACH LEAR, P137, DOI DOI 10.1007/BFB0026683
   Khan I, 2018, IEEE ACCESS, V6, P73158, DOI 10.1109/ACCESS.2018.2874038
   Kuehne H, 2011, IEEE I CONF COMP VIS, P2556, DOI 10.1109/ICCV.2011.6126543
   Liu H, 2019, INT J AUTOM COMPUT, V16, DOI 10.1007/s11633-018-1163-6
   Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94
   Memisevic R, 2012, IEEE T PATTERN ANAL, V34, P778, DOI 10.1109/TPAMI.2011.154
   Motiian S, 2016, PROC CVPR IEEE, P1496, DOI 10.1109/CVPR.2016.166
   Nejatian S, 2018, NEUROCOMPUTING, V276, P55, DOI 10.1016/j.neucom.2017.06.082
   Patwardhan S., 2006, P EACL 2006 WORKSH M, P1
   Peng YX, 2016, IEEE T CIRC SYST VID, V26, P583, DOI 10.1109/TCSVT.2015.2400779
   Rashtchian Cyrus, 2010, P NAACL HLT 2010 WOR, P139
   Rasiwasia N., 2010, P 18 ACM INT C MULT, DOI DOI 10.1145/1873951.1873987
   Rodriguez A, 2014, SCIENCE, V344, P1492, DOI 10.1126/science.1242072
   Slonim N., 2000, SIGIR Forum, V34, P208
   Strehl A., 2003, Journal of Machine Learning Research, V3, P583, DOI 10.1162/153244303321897735
   Tishby N., 1999, P ANN ALL C COMM CON, P368
   Wang JY, 2016, AAAI CONF ARTIF INTE, P3618
   Wang Y, 2017, IEEE T IMAGE PROCESS, V26, P1393, DOI 10.1109/TIP.2017.2655449
   Wang Y, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P999, DOI 10.1145/2766462.2767825
   Wu AM, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1029
   Wu L, 2019, IEEE T IMAGE PROCESS, V28, P1602, DOI 10.1109/TIP.2018.2878970
   Wu YP, 2018, IEEE T MULTIMEDIA, V20, P1418, DOI 10.1109/TMM.2017.2771477
   Xiao Cai, 2011, 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), P1977, DOI 10.1109/CVPR.2011.5995740
   Xu C, 2014, IEEE T PATTERN ANAL, V36, P1559, DOI 10.1109/TPAMI.2013.2296528
   Xu X, 2017, IEEE ACCESS, V5, P6688, DOI 10.1109/ACCESS.2016.2624267
   Yan XQ, 2017, PROC CVPR IEEE, P4049, DOI 10.1109/CVPR.2017.431
   Yan XQ, 2015, KNOWL-BASED SYST, V84, P34, DOI 10.1016/j.knosys.2015.03.028
   Zhang J, 2018, IEEE T MULTIMEDIA, V20, P2400, DOI 10.1109/TMM.2018.2804763
   Zhang XG, 2018, IEEE ACCESS, V6, P66816, DOI 10.1109/ACCESS.2018.2878733
NR 50
TC 6
Z9 6
U1 0
U2 3
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 36045
EP 36056
DI 10.1109/ACCESS.2019.2904554
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HT4VG
UT WOS:000464560800001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Unni, DR
   Moxon, SAT
   Bada, M
   Brush, M
   Bruskiewich, R
   Caufield, JH
   Clemons, PA
   Dancik, V
   Dumontier, M
   Fecho, K
   Glusman, G
   Hadlock, JJ
   Harris, NL
   Joshi, A
   Putman, T
   Qin, GR
   Ramsey, SA
   Shefchek, KA
   Solbrig, H
   Soman, K
   Thessen, AE
   Haendel, MA
   Bizon, C
   Mungall, CJ
AF Unni, Deepak R.
   Moxon, Sierra A. T.
   Bada, Michael
   Brush, Matthew
   Bruskiewich, Richard
   Caufield, J. Harry
   Clemons, Paul A.
   Dancik, Vlado
   Dumontier, Michel
   Fecho, Karamarie
   Glusman, Gustavo
   Hadlock, Jennifer J.
   Harris, Nomi L.
   Joshi, Arpita
   Putman, Tim
   Qin, Guangrong
   Ramsey, Stephen A.
   Shefchek, Kent A.
   Solbrig, Harold
   Soman, Karthik
   Thessen, Anne E.
   Haendel, Melissa A.
   Bizon, Chris
   Mungall, Christopher J.
CA Biomed Data Translator Consortium
TI Biolink Model: A universal schema for knowledge graphs in clinical,
   biomedical, and translational science
SO CTS-CLINICAL AND TRANSLATIONAL SCIENCE
LA English
DT Review
AB Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.
C1 [Unni, Deepak R.] European Mol Biol Lab, Genome Biol Unit, Heidelberg, Germany.
   [Unni, Deepak R.; Moxon, Sierra A. T.; Caufield, J. Harry; Harris, Nomi L.; Mungall, Christopher J.] Lawrence Berkeley Natl Lab, Div Environm Genom & Syst Biol, Berkeley, CA 94720 USA.
   [Bada, Michael; Brush, Matthew; Putman, Tim; Shefchek, Kent A.; Thessen, Anne E.; Haendel, Melissa A.] Univ Colorado, Ctr Hlth AI, Anschutz Med Campus, Aurora, CO USA.
   [Bruskiewich, Richard] Star Informat, Sooke, BC, Canada.
   [Clemons, Paul A.; Dancik, Vlado] Broad Inst, Chem Biol & Therapeut Sci Program, Cambridge, MA USA.
   [Dumontier, Michel] Maastricht Univ, Inst Data Sci, Maastricht, Netherlands.
   [Fecho, Karamarie; Bizon, Chris] Univ N Carolina, Renaissance Comp Inst, Chapel Hill, NC 27515 USA.
   [Glusman, Gustavo; Hadlock, Jennifer J.; Joshi, Arpita; Qin, Guangrong] Inst Syst Biol, Seattle, WA USA.
   [Ramsey, Stephen A.] Oregon State Univ, Dept Biomed Sci, Corvallis, OR 97331 USA.
   [Solbrig, Harold] Johns Hopkins Univ, Baltimore, MD USA.
   [Soman, Karthik] Univ Calif San Francisco, Dept Neurol, San Francisco, CA USA.
RP Mungall, CJ (通讯作者)，Lawrence Berkeley Natl Lab, One Cyclotron Rd,MS 977, Berkeley, CA 94720 USA.
EM cjmungall@lbl.gov
OI Dancik, Vlado/0000-0002-5970-6660; Duvvuri, Venkata/0000-0001-7356-338X;
   Fecho, Karamarie/0000-0002-6704-9306; Hadlock,
   Jennifer/0000-0001-6103-7606
FU NCATS Biomedical Data Translator program [OT2TR003434, OT2TR003436,
   OT2TR003428, OT2TR003448, OT2TR003427, OT2TR003430, OT2TR003433,
   OT2TR003450, OT2TR003437, OT2TR003443, OT2TR003441]; Office of the
   Director, National Institutes of Health [R24-OD011883]; National Human
   Genome Research Institute [7RM1HG010860-02]; Office of Science, Office
   of Basic Energy Sciences, of the U.S. Department of Energy
   [DE-AC0205CH11231]; NCATS [OT2TR003449, OT2TR003445, OT2TR003422,
   OT2TR003435, OT3TR002026, OT3TR002020, OT3TR002025, OT3TR002019,
   OT3TR002027, OT2TR002517, OT2TR002514, OT2TR002515, OT2TR002584,
   OT2TR002520, 75N95021P00636]
FX This work was supported in part by the NCATS Biomedical Data Translator
   program (Other Transaction Awards OT2TR003434, OT2TR003436, OT2TR003428,
   OT2TR003448, OT2TR003427, OT2TR003430, OT2TR003433, OT2TR003450,
   OT2TR003437, OT2TR003443, OT2TR003441, OT2TR003449, OT2TR003445,
   OT2TR003422, OT2TR003435, OT3TR002026, OT3TR002020, OT3TR002025,
   OT3TR002019, OT3TR002027, OT2TR002517, OT2TR002514, OT2TR002515,
   OT2TR002584, and OT2TR002520; Contract number 75N95021P00636).
   Additional funding was provided by the Office of the Director, National
   Institutes of Health (grant award R24-OD011883), the National Human
   Genome Research Institute (grant award 7RM1HG010860-02), and the
   Director, Office of Science, Office of Basic Energy Sciences, of the
   U.S. Department of Energy under Contract No. DE-AC0205CH11231.
CR Alshahrani Mohammed, 2017, 2017 International Conference on the Frontiers and Advances in Data Science (FADS). Proceedings, P18, DOI 10.1109/FADS.2017.8253186
   [Anonymous], OWL 2 WEB ONTOLOGY L
   Ashburner M, 2001, GENOME RES, V11, P1425
   Austin CP, 2019, CTS-CLIN TRANSL SCI, V12, P85, DOI 10.1111/cts.12595
   Dumontier M, 2014, J BIOMED SEMANT, V5, DOI 10.1186/2041-1480-5-14
   Fecho K, 2022, CTS-CLIN TRANSL SCI, V15, P1838, DOI 10.1111/cts.13301
   Fecho K, 2019, CTS-CLIN TRANSL SCI, V12, P86, DOI 10.1111/cts.12591
   Hettne KM, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0149621
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Hogan A., 2020, ARXIV CSAI
   Jackson R, 2021, DATABASE-OXFORD, DOI 10.1093/database/baab069
   Joachimiak MP., 2021, KG MICROBE REFERENCE
   McMurry JA, 2017, PLOS BIOL, V15, DOI 10.1371/journal.pbio.2001414
   Mungall CJ., 2014, CEUR WORKSHOP P, P25, DOI DOI 10.1101/010090
   National Physical Laboratory, 1961, S INT C MACH TRANSL
   Nelson CA, 2021, LIFE-BASEL, V11, DOI 10.3390/life11010042
   Putman TE, 2017, DATABASE-OXFORD, DOI 10.1093/database/bax025
   Reese JT, 2021, PATTERNS, V2, DOI 10.1016/j.patter.2020.100155
   Samuel J., 2017, METADATA SEMANTIC RE, P167, DOI [10.1007/978-3-319-70863-8_16, DOI 10.1007/978-3-319-70863-8_16]
   Shapiro Stuart C., 1987, ENCY ARTIFICIAL INTE
   Shefchek KA, 2020, NUCLEIC ACIDS RES, V48, pD704, DOI 10.1093/nar/gkz997
   Sternberg PW, 2022, GENETICS, V220, DOI 10.1093/genetics/iyac022
   Vasilakes JA., 2018, ANNOTATED SEMANTIC P
   Vasilevsky N., 2020, CEUR WORKSHOP P CEUR
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wilkinson Mark D, 2016, Sci Data, V3, P160018, DOI 10.1038/sdata.2016.18
NR 26
TC 1
Z9 1
U1 3
U2 3
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1752-8054
EI 1752-8062
J9 CTS-CLIN TRANSL SCI
JI CTS-Clin. Transl. Sci.
PD AUG
PY 2022
VL 15
IS 8
BP 1848
EP 1855
DI 10.1111/cts.13302
EA JUN 2022
PG 8
WC Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Research & Experimental Medicine
GA 3S3DN
UT WOS:000806301500001
OA Green Published, Green Accepted, Green Submitted
DA 2022-08-23
ER

PT J
AU Guo, XT
   Gao, H
   Zou, ZN
AF Guo, Xintong
   Gao, Hong
   Zou, Zhaonian
TI Distributed processing of regular path queries in RDF graphs
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph; RDF/SPARQL; Regular path queries; Graph summarization;
   Graph partitioning
AB SPARQL 1.1 offers a type of navigational query for RDF systems, called regular path query (RPQ). A regular path query allows for retrieving node pairs with the paths between them satisfying regular expressions. Regular path queries are always difficult to be evaluated efficiently because of the possible large search space. Thus there has been no scalable and practical solution so far. In this paper, we present Leon+, an in-memory distributed framework, to address the RPQ problem in the context of the knowledge graph. To reduce search space and mitigate mounting communication costs, Leon+ takes advantage of join-ahead pruning via a novel RDF summarization technique together with a path partitioning strategy. We also develop a subtle cost model to devise query plans to achieve high efficiency for complex RPQs. As there has been no available RPQ benchmark, we create micro-benchmarks on both synthetic and real-world datasets. A thorough experimental evaluation is presented between our approach and the state-of-the-art RDF stores. The results show that our approach outperforms 5x faster than the competitors on single RPQ. For query workload, it saves up to 1/2 time and 2/3 communication overheads over the baseline method.
C1 [Guo, Xintong; Gao, Hong] Harbin Inst Technol, Harbin, Heilongjiang, Peoples R China.
   [Zou, Zhaonian] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China.
RP Gao, H (通讯作者)，Harbin Inst Technol, Harbin, Heilongjiang, Peoples R China.
EM xintong.guo@hit.edu.cn; honggao@hit.edu.cn; znzou@hit.edu.cn
FU National Natural Science Foundation of China [61532015, 61672189,
   U19A2059]; National Key Research and Development Program of China
   [2019YFB2101902]
FX This work is supported by the Joint Funds of the National Natural
   Science Foundation of China No.U19A2059, the National Key Research and
   Development Program of China No.2019YFB2101902, and National Natural
   Science Foundation of China Nos. 61532015 and 61672189.
CR Abul-Basher Z, 2017, **DATA OBJECT**, DOI [10.5441/002/edbt.2017.47, DOI 10.5441/002/EDBT.2017.47]
   Andreev K, 2006, THEOR COMPUT SYST, V39, P929, DOI 10.1007/s00224-006-1350-7
   Arias M, 2011, ARXIVABS11035043
   Baier J, 2017, PROCEEDINGS OF THE 28TH ACM CONFERENCE ON HYPERTEXT AND SOCIAL MEDIA (HT'17), P165, DOI 10.1145/3078714.3078731
   Bonifati A., 2019, ANAL STUDY LARGE SPA
   Dey S, 2013, P JOINT EDBT ICDT 20, DOI [10.1145/2457317.2457353, DOI 10.1145/2457317.2457353]
   Erling O, 2010, SEMANTIC WEB INFORMATION MANAGEMENT, P501, DOI 10.1007/978-3-642-04329-1_21
   Even G, 1999, SIAM J COMPUT, V28, P2187, DOI 10.1137/S0097539796308217
   Fan WF, 2011, PROC INT CONF DATA, P39, DOI 10.1109/ICDE.2011.5767858
   Garey MR, 1990, COMPUTERS INTRACTABI, DOI [10.5555/574848, DOI 10.5555/574848]
   Guo XT, 2019, LECT NOTES COMPUT SC, V11446, P742, DOI 10.1007/978-3-030-18576-3_44
   Gurajada S, 2014, SIGMOD C, DOI [10.1145/2588555:2610511, DOI 10.1145/2588555:2610511]
   Harbi R, 2016, VLDB J, V25, P355, DOI 10.1007/s00778-016-0420-y
   Hellmann S, 2009, OTM C, DOI [10.1007/978-3-642-05151-7_33, DOI 10.1007/978-3-642-05151-7_33]
   Karypis G, 1998, SIAM J SCI COMPUT, V20, P359, DOI 10.1137/S1064827595287997
   Konstas I, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P195, DOI 10.1145/1571941.1571977
   Koschmieder A, 2012, LECT NOTES COMPUT SC, V7338, P177, DOI 10.1007/978-3-642-31235-9_12
   Losemann Katja, 2012, P 31 ACM SIGMOD SIGA, P101, DOI DOI 10.1145/2213556
   Meimaris M, 2017, PROC INT CONF DATA, P497, DOI 10.1109/ICDE.2017.106
   MENDELZON AO, 1995, SIAM J COMPUT, V24, P1235, DOI 10.1137/S009753979122370X
   Neumann T, 2011, PROC INT CONF DATA, P984, DOI 10.1109/ICDE.2011.5767868
   Neumann T, 2010, VLDB J, V19, P91, DOI 10.1007/s00778-009-0165-y
   Poulovassilis, 2016, EXTENDING DATABASE T, P636
   Scott J, 2006, J COMPUT BIOL, V13, P133, DOI 10.1089/cmb.2006.13.133
   Selmer P., 2015, CEUR WORKSHOP PROC, V1330, P149
   Seufert S, 2013, 1 INT WORKSH GRAPH D, P14, DOI DOI 10.1145/2484425.2484443
   Seufert S, 2013, PROC INT CONF DATA, P1009, DOI 10.1109/ICDE.2013.6544893
   Tetzel F, 2017, P 5 INT WORKSH GRAPH, DOI [10.1145/3078447.3078458, DOI 10.1145/3078447.3078458]
   THOMPSON K, 1968, COMMUN ACM, V11, P419, DOI 10.1145/363347.363387
   Valstar LDJ, 2017, SIGMOD'17: PROCEEDINGS OF THE 2017 ACM INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P345, DOI 10.1145/3035918.3035955
   Wadhwa S, 2019, INT CONF MANAGE DATA, P1463, DOI 10.1145/3299869.3319882
   Yakovets N., 2013, AMW, V1087
   Yakovets N, 2016, SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1875, DOI 10.1145/2882903.2882944
   Zou L, 2014, INFORM SYST, V40, P47, DOI 10.1016/j.is.2013.10.003
NR 34
TC 1
Z9 1
U1 1
U2 6
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0219-1377
EI 0219-3116
J9 KNOWL INF SYST
JI Knowl. Inf. Syst.
PD APR
PY 2021
VL 63
IS 4
BP 993
EP 1027
DI 10.1007/s10115-020-01536-2
EA JAN 2021
PG 35
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RE4XH
UT WOS:000607483600003
DA 2022-08-23
ER

PT J
AU Waagmeester, A
   Stupp, G
   Burgstaller-Muehlbacher, S
   Good, BM
   Griffith, M
   Griffith, OL
   Hanspers, K
   Hermjakob, HN
   Hudson, TS
   Hybiske, K
   Keating, SM
   Manske, M
   Mayers, M
   Mietchen, D
   Mitraka, E
   Pico, AR
   Putman, T
   Riutta, A
   Queralt-Rosinach, N
   Schriml, LM
   Shafee, T
   Slenter, D
   Stephan, R
   Thornton, K
   Tsueng, G
   Tu, R
   Ul-Hasan, S
   Willighagen, E
   Wu, CL
   Su, AI
AF Waagmeester, Andra
   Stupp, Gregory
   Burgstaller-Muehlbacher, Sebastian
   Good, Benjamin M.
   Griffith, Malachi
   Griffith, Obi L.
   Hanspers, Kristina
   Hermjakob, HiErni Ng
   Hudson, Toby S.
   Hybiske, Kevin
   Keating, Sarah M.
   Manske, Magal
   Mayers, Michael
   Mietchen, Daniel
   Mitraka, Elvira
   Pico, Alexander R.
   Putman, Timothy
   Riutta, Anders
   Queralt-Rosinach, Nuria
   Schriml, Lynn M.
   Shafee, Thomas
   Slenter, Denise
   Stephan, Ralf
   Thornton, Katherine
   Tsueng, Ginger
   Tu, Roger
   Ul-Hasan, Sabah
   Willighagen, Egon
   Wu, Chunlei
   Su, Andrew, I
TI SCIENCE FORUM Wikidata as a knowledge graph for the life sciences
SO ELIFE
LA English
DT Article
ID DISORDER
AB Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.
C1 [Waagmeester, Andra] Micelio, Antwerp, Belgium.
   [Stupp, Gregory; Good, Benjamin M.; Mayers, Michael; Putman, Timothy; Queralt-Rosinach, Nuria; Tsueng, Ginger; Tu, Roger; Ul-Hasan, Sabah; Wu, Chunlei; Su, Andrew, I] Scripps Res Inst, Dept Integrat Struct & Computat Biol, La Jolla, CA 92037 USA.
   [Burgstaller-Muehlbacher, Sebastian] Univ Vienna, Ctr Integrat Bioinformat Vienna, Max Perutz Labs, Vienna, Austria.
   [Burgstaller-Muehlbacher, Sebastian] Med Univ Vienna, Vienna, Austria.
   [Griffith, Malachi; Griffith, Obi L.] Washington Univ, Sch Med, McDonnell Genome Inst, St Louis, MO 63110 USA.
   [Hanspers, Kristina; Pico, Alexander R.; Riutta, Anders] Gladstone Inst, Inst Data Sci & Biotechnol, San Francisco, CA USA.
   [Hermjakob, HiErni Ng; Keating, Sarah M.] European Bioinformat Inst EMBL EBI, Hinxton, England.
   [Hudson, Toby S.] Univ Sydney, Sch Chem, Sydney, NSW, Australia.
   [Hybiske, Kevin] Univ Washington, Div Allergy & Infect Dis, Dept Med, Seattle, WA 98195 USA.
   [Manske, Magal] Wellcome Trust Sanger Inst, Hinxton, England.
   [Mietchen, Daniel] Univ Virginia, Sch Data Sci, Charlottesville, VA USA.
   [Mitraka, Elvira; Schriml, Lynn M.] Univ Maryland, Sch Med, Baltimore, MD 21201 USA.
   [Shafee, Thomas] La Trobe Univ, Dept Anim Plant & Soil Sci, Melbourne, Vic, Australia.
   [Slenter, Denise; Willighagen, Egon] Maastricht Univ, Dept Bioinformat BiGCaT, NUTRIM, Maastricht, Netherlands.
   [Thornton, Katherine] Yale Univ, Yale Univ Lib, New Haven, CT USA.
RP Su, AI (通讯作者)，Scripps Res Inst, Dept Integrat Struct & Computat Biol, La Jolla, CA 92037 USA.
EM asu@scripps.edu
RI Griffith, Malachi/P-1285-2014; Wu, Chunlei/ABF-4720-2020; Hudson,
   Toby/B-3856-2012; Schriml, Lynn/AFO-9472-2022; Griffith, Obi/P-1286-2014
OI Griffith, Malachi/0000-0002-6388-446X; Wu, Chunlei/0000-0002-2629-6124;
   Burgstaller-Muehlbacher, Sebastian/0000-0003-4640-3510; Hanspers,
   Kristina/0000-0001-5410-599X; Hybiske, Kevin/0000-0002-2967-3079;
   Hermjakob, Henning/0000-0001-8479-0262; Putman,
   Timothy/0000-0002-4291-0737; Stephan, Ralf/0000-0002-4650-631X; Riutta,
   Anders/0000-0002-4693-0591; Stupp, Gregory/0000-0002-0644-7212; Keating,
   Sarah/0000-0002-3356-3542; Griffith, Obi/0000-0002-0843-4271; Tsueng,
   Ginger/0000-0001-9536-9115; Manske, Magnus/0000-0001-5916-0947
FU National Institute of General Medical Sciences [R01 GM089820, U54
   GM114833, R01 GM100039]; National Human Genome Research Institute
   [R00HG007940]; National Cancer Institute [U24CA237719]; V Foundation for
   Cancer Research [V2018-007]; National Institute of Allergy and
   Infectious Diseases [R01 Al126785]; National Center for Advancing
   Translational Sciences [UL1 TR002550]
FX National Institute of General Medical Sciences R01 GM089820 Andrew I Su;
   National Institute of General Medical Sciences U54 GM114833 Henning
   Hermjakob Andrew I Su; National Institute of General Medical Sciences
   R01 GM100039 Alexander R Pico; National Human Genome Research Institute
   R00HG007940 Malachi Griffith; National Cancer Institute U24CA237719
   Malachi Griffith; V Foundation for Cancer Research V2018-007 Malachi
   Griffith; National Institute of Allergy and Infectious Diseases R01
   Al126785 Kevin Hybiske; National Center for Advancing Translational
   Sciences UL1 TR002550 Andrew I Su; The funders had no role in study
   design, data collection and interpretation, or the decision to submit
   the work for publication.
CR Agarwala R, 2018, NUCLEIC ACIDS RES, V46, pD8, DOI [10.1093/nar/gkr1184, 10.1093/nar/gks1189, 10.1093/nar/gkx1095, 10.1093/nar/gky1069, 10.1093/nar/gkv1290]
   Amberger Joanna S, 2017, Curr Protoc Bioinformatics, V58, DOI 10.1002/cpbi.27
   Ayers P, 2019, WIKICITE 2018 2019 C
   Bastian F, 2008, LECT N BIOINFORMAT, V5109, P124, DOI 10.1007/978-3-540-69828-9_12
   Bateman A, 2019, NUCLEIC ACIDS RES, V47, pD506, DOI 10.1093/nar/gky1049
   Bauer S, 2012, BIOINFORMATICS, V28, P2502, DOI 10.1093/bioinformatics/bts471
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bunt SM, 2012, DATABASE-OXFORD, DOI 10.1093/database/bas024
   Burgstaller-Muehlbacher S, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw015
   Burley SK, 2019, NUCLEIC ACIDS RES, V47, pD520, DOI 10.1093/nar/gky949
   Caglayan AO, 2015, EUR J MED GENET, V58, P39, DOI 10.1016/j.ejmg.2014.08.008
   Chandras C, 2009, DATABASE-OXFORD, DOI 10.1093/database/bap017
   Chibucos MC, 2014, DATABASE-OXFORD, DOI 10.1093/database/bau075
   D Vrandeak, 2012, P 21 INT C WORLD WID, DOI [10.1145/2187980.2183242, DOI 10.1145/2187.2188242]
   Das R., 2017, GO WALK ARRIVE ANSWE
   de Coronado S, 2009, J BIOMED INFORM, V42, P530, DOI 10.1016/j.jbi.2009.01.003
   El-Gebali S, 2019, NUCLEIC ACIDS RES, V47, pD427, DOI 10.1093/nar/gky995
   Enns GM, 2014, GENET MED, V16, P751, DOI 10.1038/gim.2014.22
   Fabregat A, 2018, NUCLEIC ACIDS RES, V46, pD649, DOI [10.1093/nar/gkx1132, 10.1093/nar/gkz1031]
   Gabella Chiara, 2017, F1000Res, V6, DOI 10.12688/f1000research.12989.2
   Gil Y, 2017, LECT NOTES COMPUT SC, V10588, P231, DOI 10.1007/978-3-319-68204-4_24
   Griffith M, 2017, NAT GENET, V49, P170, DOI 10.1038/ng.3774
   Harding SD, 2018, NUCLEIC ACIDS RES, V46, pD1091, DOI 10.1093/nar/gkx1121
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Horai H, 2010, J MASS SPECTROM, V45, P703, DOI 10.1002/jms.1777
   Jacobsen A, 2018, P 11 INT C SEM WEB A, V2275
   Kohler S, 2017, NUCLEIC ACIDS RES, V45, pD865, DOI 10.1093/nar/gkw1039
   Kohler S, 2009, AM J HUM GENET, V85, P457, DOI 10.1016/j.ajhg.2009.09.003
   Kohler S, 2020, ZENODO, DOI [10.5281/zenodo.3620979, DOI 10.5281/ZENODO.3620979]
   Lam C, 2017, GENET MED, V19, P160, DOI 10.1038/gim.2016.75
   Lewis Janine, 2017, Transl Sci Rare Dis, V2, P77, DOI 10.3233/TRD-170011
   Lin XV, 2018, MULTIHOP KNOWLEDGE G
   Maiella S, 2018, EUR J MED GENET, V61, P706, DOI 10.1016/j.ejmg.2018.01.013
   Manske M., 2020, MIX N MATCH
   Mayers MD, 2020, ZENODO, DOI [10.5281/zenodo.3650811, DOI 10.5281/ZENODO.3620811]
   Mayers MD, 2020, ZENODO, DOI [10.5281/zendo.3620812, DOI 10.5281/ZENODO3620812]
   McKusick VA, 2007, AM J HUM GENET, V80, P588, DOI 10.1086/514346
   Mitchell AL, 2019, NUCLEIC ACIDS RES, V47, pD351, DOI 10.1093/nar/gky1100
   Mora-Cantallops M, 2019, DATA TECHNOL APPL, V53, P250, DOI 10.1108/DTA-12-2018-0110
   Mungall CJ, 2017, NUCLEIC ACIDS RES, V45, pD712, DOI 10.1093/nar/gkw1128
   Nelson SJ, 2011, J AM MED INFORM ASSN, V18, P441, DOI 10.1136/amiajnl-2011-000116
   Nielsen F., 2018, WIKIDATA SCHOLIA HUB
   Nielsen FA, 2017, IN SY AP IN WE HC, V10577, P237, DOI 10.1007/978-3-319-70407-4_36
   Pawson AJ, 2014, NUCLEIC ACIDS RES, V42, pD1098, DOI 10.1093/nar/gkt1143
   Putman T, 2019, DATABASE-OXFORD, DOI 10.1093/database/baz041
   Putman TE, 2017, DATABASE-OXFORD, DOI 10.1093/database/bax025
   Reiser L, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw018
   S Burgstallem-Muehlbacher, 2020, ZENODO, DOI [10.5281/zenodo.3621065, DOI 10.5281/ZENODO.362135]
   Sansone SA, 2019, NAT BIOTECHNOL, V37, P358, DOI 10.1038/s41587-019-0080-8
   Schriml LM, 2019, NUCLEIC ACIDS RES, V47, pD955, DOI 10.1093/nar/gky1032
   Shin JM, 2005, NUCLEIC ACIDS RES, V33, pD238
   Slenter DN, 2018, NUCLEIC ACIDS RES, V46, pD661, DOI 10.1093/nar/gkx1064
   Smedley D, 2015, NUCLEIC ACIDS RES, V43, pW589, DOI 10.1093/nar/gkv350
   Southan C, 2016, NUCLEIC ACIDS RES, V44, pD1054, DOI 10.1093/nar/gkv1037
   Sprague ER, 2017, J MED LIBR ASSOC, V105, P207, DOI [10.5195/iml.2017.89, DOI 10.10595/JMLA.2017.89]
   Sud M, 2007, NUCLEIC ACIDS RES, V35, pD527, DOI 10.1093/nar/gkl838
   Tu R, 2020, ZENODO, DOI [10.5281/zenodo.3609142, DOI 10.5281/ZEN0D0.3609142]
   Ul-Hasan S, 2020, ZENODO, DOI [10.5281/zendo.1436000, DOI 10.5281/ZEND0.1436000]
   Ursu O, 2017, NUCLEIC ACIDS RES, V45, pD932, DOI 10.1093/nar/gkw993
   van Iersel MP, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-5
   Wang MX, 2016, NAT BIOTECHNOL, V34, P828, DOI 10.1038/nbt.3597
   Wang YL, 2009, NUCLEIC ACIDS RES, V37, pW623, DOI 10.1093/nar/gkp456
   Wilkinson MD, 2019, SCI DATA, V6, DOI 10.1038/s41597-019-0184-5
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Williams AJ, 2017, J CHEMINFORMATICS, V9, DOI 10.1186/s13321-017-0247-6
   Wohlgemuth G, 2016, NAT BIOTECHNOL, V34, P1099, DOI 10.1038/nbt.3689
   Xiang W, 2017, DEEPPATH REINFORCEME
   Xin JW, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2041-5
   Zerbino DR, 2018, NUCLEIC ACIDS RES, V46, pD754, DOI 10.1093/nar/gkx1098
NR 69
TC 19
Z9 19
U1 5
U2 18
PU eLIFE SCIENCES PUBL LTD
PI CAMBRIDGE
PA SHERATON HOUSE, CASTLE PARK, CAMBRIDGE, CB3 0AX, ENGLAND
SN 2050-084X
J9 ELIFE
JI eLife
PD MAR 17
PY 2020
VL 9
AR e52614
DI 10.7554/eLife.52614
PG 15
WC Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Life Sciences & Biomedicine - Other Topics
GA KW4MU
UT WOS:000521140900001
PM 32180547
OA Green Submitted, Green Published, gold
DA 2022-08-23
ER

PT J
AU Li, J
   Maharjan, B
   Xie, B
   Tao, C
AF Li, Juan
   Maharjan, Bikesh
   Xie, Bo
   Tao, Cui
TI A Personalized Voice-Based Diet Assistant for Caregivers of Alzheimer
   Disease and Related Dementias: System Development and Validation
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE Alzheimer disease; dementia; diet; knowledge; ontology; voice assistant
AB Background: The world's aging population is increasing, with an expected increase in the prevalence of Alzheimer disease and related dementias (ADRD). Proper nutrition and good eating behavior show promise for preventing and slowing the progression of ADRD and consequently improving patients with ADRD's health status and quality of life. Most ADRD care is provided by informal caregivers, so assisting caregivers to manage patients with ADRD's diet is important.
   Objective: This study aims to design, develop, and test an artificial intelligence-powered voice assistant to help informal caregivers manage the daily diet of patients with ADRD and learn food and nutrition-related knowledge.
   Methods: The voice assistant is being implemented in several steps: construction of a comprehensive knowledge base with ontologies that define ADRD diet care and user profiles, and is extended with external knowledge graphs; management of conversation between users and the voice assistant; personalized ADRD diet services provided through a semantics-based knowledge graph search and reasoning engine; and system evaluation in use cases with additional qualitative evaluations.
   Results: A prototype voice assistant was evaluated in the lab using various use cases. Preliminary qualitative test results demonstrate reasonable rates of dialogue success and recommendation correctness.
   Conclusions: The voice assistant provides a natural, interactive interface for users, and it does not require the user to have a technical background, which may facilitate senior caregivers' use in their daily care tasks. This study suggests the feasibility of using the intelligent voice assistant to help caregivers manage patients with ADRD's diet.
C1 [Li, Juan; Maharjan, Bikesh] North Dakota State Univ, Fargo, ND USA.
   [Xie, Bo] Univ Texas Austin, Austin, TX 78712 USA.
   [Tao, Cui] Univ Texas Hlth Sci Ctr Houston, 7000 Fannin St Suite 600, Houston, TX 77030 USA.
RP Tao, C (通讯作者)，Univ Texas Hlth Sci Ctr Houston, 7000 Fannin St Suite 600, Houston, TX 77030 USA.
EM cui.tao@uth.tmc.edu
OI Tao, Cui/0000-0002-4267-1924; Xie, Bo/0000-0002-6016-6008; Maharjan,
   Bikesh/0000-0002-7284-1808
FU National Science Foundation Division of Information and Intelligent
   Systems Award [1722913]
FX This study was supported, in part, by the National Science Foundation
   Division of Information and Intelligent Systems Award 1722913.
CR Al-Ajlan Ajlan, 2015, International Journal of Machine Learning and Computing, V5, P106, DOI 10.7763/IJMLC.2015.V5.492
   Alzheimer's Association, 2016, Alzheimers Dement, V12, P459
   [Anonymous], 2015, DIABETES CARE, V38, pS8, DOI 10.2337/dc15-S005
   [Anonymous], 2014, CURRENT POPULATION R
   Benson T., 2016, PRINCIPLES HLTH INTE
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Calvanese D, 2005, P 20 NAT C ART INT A, P602
   Centers for Disease Control and Prevention, CAR PERS ALZH DIS RE
   Gabb GM, 2016, MED J AUSTRALIA, V205, P85, DOI 10.5694/mja16.00526
   Gabrielli S, 2017, JMIR MHEALTH UHEALTH, V5, DOI 10.2196/mhealth.7080
   Gaugler J, 2019, ALZHEIMERS DEMENT, V15, P321, DOI 10.1016/j.jalz.2019.01.010
   Holtzman DM, 2011, SCI TRANSL MED, V3, DOI 10.1126/scitranslmed.3002369
   Horrocks I., 2004, W3C MEMB SUBMISS
   Hosking LJ, 2018, PROCEEDINGS OF GEOSHANGHAI 2018 INTERNATIONAL CONFERENCE: ROCK MECHANICS AND ROCK ENGINEERING, P30, DOI 10.1007/978-981-13-0113-1_4
   Jones RW, 2014, GLOBAL CLINICAL TRIALS FOR ALZHEIMER'S DISEASE: DESIGN, IMPLEMENTATION, AND STANDARDIZATION, P33, DOI 10.1016/B978-0-12-411464-7.00003-1
   Kilduff L, 2019, FACT SHEET AGING US
   Krotzsch M, 2012, PERSPECTIVES ONTOLOG, P3
   Lange KW, 2017, FOOD SCI HUM WELL, V6, P1, DOI 10.1016/j.fshw.2016.10.003
   Li J., 2018, P IEEE 20 INT C E HL, P1, DOI [10.1109/HealthCom.2018.8531107., DOI 10.1109/HEALTHCOM.2018.8531107]
   Mauch CE, 2018, JMIR MHEALTH UHEALTH, V6, DOI 10.2196/11867
   Morris MC, 2015, ALZHEIMERS DEMENT, V11, P1007, DOI 10.1016/j.jalz.2014.11.009
   Otaegui-Arrazola A, 2014, EUR J NUTR, V53, P1, DOI 10.1007/s00394-013-0561-3
   Petrock V, 2019, EMARKETER
   Sterling G., 2018, ANAL AMAZON ECHO OWN
   Swaminathan A, 2014, FRONT AGING NEUROSCI, V6, DOI 10.3389/fnagi.2014.00282
   US Dept of Agriculture Center for Nutrition Policy and Promotion, DIET GUID AM 2015 20
   WHO, 2018, AG HLTH
   WintersMiner LA, 2015, PRACTICAL PREDICTIVE ANALYTICS AND DECISIONING SYSTEMS FOR MEDICINE: INFORMATICS ACCURACY AND COST-EFFECTIVENESS FOR HEALTHCARE ADMINISTRATION AND DELIVERY INCLUDING MEDICAL RESEARCH, P1
   Wishart DS, 2012, PRINCIPLES PHARMACOG, P55
   Wlodarek D, 2019, NUTRIENTS, V11, DOI 10.3390/nu11010169
   Yiannopoulou KG, 2013, THER ADV NEUROL DISO, V6, P19, DOI 10.1177/1756285612461679
NR 31
TC 4
Z9 4
U1 7
U2 23
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD SEP 21
PY 2020
VL 22
IS 9
AR e19897
DI 10.2196/19897
PG 11
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA PF6EY
UT WOS:000599146300005
PM 32955452
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Cifuentes-Silva, F
   Fernandez-Alvarez, D
   Labra-Gayo, JE
AF Cifuentes-Silva, Francisco
   Fernandez-Alvarez, Daniel
   Emilio Labra-Gayo, Jose
TI National Budget as Linked Open Data: New Tools for Supporting the
   Sustainability of Public Finances
SO SUSTAINABILITY
LA English
DT Article
DE national budget law; fiscal sustainability; sustainability; public
   funds; linked open data; visualization; knowledge graph; Semantic Web;
   SPARQL
AB This paper presents the visualization of national budget, a tool based on Semantic Web technologies that shows by graphic representations the Chilean budget law published annually, and their execution by each state agency. We describe the processes for consuming open data from the Budget National Agency, and how this data is transformed and published to linked open data, based on a National Budget Ontology. Although similar initiatives have been developed on transparency and public budget around the world, we consider that there is no previous experience showing optimized access mechanisms both for human and machine readable, providing in each case the highest level of aggregation, granularity and interoperability, making it understandable and easy to process complex data and legislation. As part of our analysis, we describe a recent scenario of usage in the context of the socio-political crisis in Chile, where we discuss the possible impact of the linked open dataset and data visualizations for distribution and control of funds, on the premise that this type of tools can support the decision making and sustainability of public finances. Finally, we present the results of our budget knowledge graph and the lessons learned during the development, allowing to replicate the process and enabling potential uses of the published data in other contexts.
C1 [Cifuentes-Silva, Francisco] Univ Oviedo, Dept Comp Sci, Oviedo 33007, Spain.
   [Cifuentes-Silva, Francisco; Fernandez-Alvarez, Daniel; Emilio Labra-Gayo, Jose] Biblioteca Congreso Nacl Chile, Victoria S-N,Edificio Congreso Nacl Chile, Valparaiso 2340000, Chile.
RP Cifuentes-Silva, F (通讯作者)，Univ Oviedo, Dept Comp Sci, Oviedo 33007, Spain.; Cifuentes-Silva, F (通讯作者)，Biblioteca Congreso Nacl Chile, Victoria S-N,Edificio Congreso Nacl Chile, Valparaiso 2340000, Chile.
EM francisco.cifuentes@weso.es; danifdezalvarez@gmail.com; labra@uniovi.es
RI Gayo, José Emilio Labra/L-9181-2014
OI Gayo, José Emilio Labra/0000-0001-8907-5348; Fernandez-Alvarez,
   Daniel/0000-0002-8666-7660; Cifuentes Silva,
   Francisco/0000-0001-6570-5421
FU Spanish Ministry of Economy and Competitiveness [TIN2017-88877-R]
FX We wish to thank to the Library of Congress of Chile who has funded this
   project. The described research activities were partially funded by the
   Spanish Ministry of Economy and Competitiveness (Society
   challenges:TIN2017-88877-R).
CR Abdullah N.A.S., 2017, P INT C SOFT COMP DA, P209
   [Anonymous], 2012, OWL 2 WEB ONT LANG D
   Attard J, 2015, GOV INFORM Q, V32, P399, DOI 10.1016/j.giq.2015.07.006
   Baker T., 2000, D LIB MAGAZINE, V6, P3, DOI DOI 10.1045/october2000-baker
   Bastida F, 2007, PUBLIC ADMIN, V85, P667, DOI 10.1111/j.1467-9299.2007.00664.x
   Bechhofer S., 2009, SKOS SIMPLE KNOWLEDG
   Berners-Lee T., LINKED DATA DESIGN I
   Bulai VC, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11205632
   Cifuentes-Silva F, 2019, LECT NOTES COMPUT SC, V11503, P558, DOI 10.1007/978-3-030-21348-0_36
   Cure O., 2015, RDF DATABASE SYST, P41
   Di Iorio A, 2019, COMPUTERS, V8, DOI 10.3390/computers8020049
   Direccion de Presupuestos, 2017, INSTR EJ PRES SECT P
   Escobar P, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10030779
   Fensel A, 2020, INFORMATION, V11, DOI 10.3390/info11050253
   Fernandez-alvarez D., 2018, P INT SEM WEB C MONT
   Guha R., 2014, RDF SCHEMA 1 1 W3C R
   Hoffner K, 2016, SEMANT WEB, V7, P95, DOI 10.3233/SW-150172
   Inkinen T., 2019, J OPEN INNOV-TECHNOL, V5, P1
   Janssen M, 2012, INFORM SYST MANAGE, V29, P258, DOI 10.1080/10580530.2012.716740
   Knublauch H, 2017, SHAPES CONSTRAINT LA
   Knublauch H., 2017, W3C CANDIDATE RECOMM, V11, P8
   Labra Gayo J.E., 2014, P 10 INT C SEM SYST
   Musyaffa FA, 2018, LECT NOTES COMPUT SC, V10845, P433, DOI 10.1007/978-3-319-91662-0_35
   Mz AN, 2019, 2019 IEEE 9TH SYMPOSIUM ON COMPUTER APPLICATIONS & INDUSTRIAL ELECTRONICS (ISCAIE), P81, DOI 10.1109/ISCAIE.2019.8743993
   OECD, 2011, GOV GLANC 2011, DOI [10.1787/gov_glance-2011-en, DOI 10.1787/GOV_GLANCE-2011-EN]
   Prud'hommeaux E, 2014, P 10 INT C SEM SYST, P32, DOI DOI 10.1145/2660517.2660523
   Republica de Chile, 2017, LEY PRES SECT PUBL A
   Sedmihradska L., 2013, ACTA VSFS, V7, P109
   Segel E, 2010, IEEE T VIS COMPUT GR, V16, P1139, DOI 10.1109/TVCG.2010.179
   Shneiderman B, 1996, IEEE SYMPOSIUM ON VISUAL LANGUAGES, PROCEEDINGS, P336, DOI 10.1109/VL.1996.545307
   Solbrig HR, 2017, J BIOMED INFORM, V67, P90, DOI 10.1016/j.jbi.2017.02.009
   Thuluva A.S., 2018, P ESWC 2018 CRET GRE
   Ubaldi B., 2013, OECD WORKING PAPERS, DOI [10.1787/5k46bj4f03s7-en, DOI 10.1787/5K46BJ4F03S7-EN]
   Vlasov V, 2014, PROC CONF OPEN INNOV, P129, DOI 10.1109/FRUCT.2014.7000922
   World Wide Web Consortium (W3C), 2014, RDF 11 CONC ABSTR SY
NR 35
TC 1
Z9 1
U1 0
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUN
PY 2020
VL 12
IS 11
AR 4551
DI 10.3390/su12114551
PG 12
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA MC6JX
UT WOS:000543391800214
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Humayun, F
   Domingo-Fernandez, D
   George, AAP
   Hopp, MT
   Syllwasschy, BF
   Detzel, MS
   Hoyt, CT
   Hofmann-Apitius, M
   Imhof, D
AF Humayun, Farah
   Domingo-Fernandez, Daniel
   George, Ajay Abisheck Paul
   Hopp, Marie-Therese
   Syllwasschy, Benjamin F.
   Detzel, Milena S.
   Hoyt, Charles Tapley
   Hofmann-Apitius, Martin
   Imhof, Diana
TI A Computational Approach for Mapping Heme Biology in the Context of
   Hemolytic Disorders
SO FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
LA English
DT Article
DE heme; hemolytic disorders; signaling pathways; knowledge graphs;
   biological expression language
ID LABILE HEME; INTRAVASCULAR HEMOLYSIS; ONTOLOGY; RECEPTOR; PERSPECTIVES;
   INFLAMMATION; ACTIVATION; INDUCTION; PATHWAYS; OVERLOAD
AB Heme is an iron ion-containing molecule found within hemoproteins such as hemoglobin and cytochromes that participates in diverse biological processes. Although excessive heme has been implicated in several diseases including malaria, sepsis, ischemia-reperfusion, and disseminated intravascular coagulation, little is known about its regulatory and signaling functions. Furthermore, the limited understanding of heme's role in regulatory and signaling functions is in part due to the lack of curated pathway resources for heme cell biology. Here, we present two resources aimed to exploit this unexplored information to model heme biology. The first resource is a terminology covering heme-specific terms not yet included in standard controlled vocabularies. Using this terminology, we curated and modeled the second resource, a mechanistic knowledge graph representing the heme's interactome based on a corpus of 46 scientific articles. Finally, we demonstrated the utility of these resources by investigating the role of heme in the Toll-like receptor signaling pathway. Our analysis proposed a series of crosstalk events that could explain the role of heme in activating the TLR4 signaling pathway. In summary, the presented work opens the door to the scientific community for exploring the published knowledge on heme biology.
C1 [Humayun, Farah; George, Ajay Abisheck Paul; Hopp, Marie-Therese; Syllwasschy, Benjamin F.; Detzel, Milena S.; Imhof, Diana] Univ Bonn, Pharmaceut Inst, Pharmaceut Biochem & Bioanalyt, Bonn, Germany.
   [Humayun, Farah; Domingo-Fernandez, Daniel; Hoyt, Charles Tapley; Hofmann-Apitius, Martin] Fraunhofer Inst Algorithms & Sci Comp SCAI, Dept Bioinformat, St Augustin, Germany.
RP Imhof, D (通讯作者)，Univ Bonn, Pharmaceut Inst, Pharmaceut Biochem & Bioanalyt, Bonn, Germany.; Domingo-Fernandez, D; Hofmann-Apitius, M (通讯作者)，Fraunhofer Inst Algorithms & Sci Comp SCAI, Dept Bioinformat, St Augustin, Germany.
EM daniel.domingo.fernandez@scai.fraunhofer.de;
   martin.hofmann-apitius@scai.fraunhofer.de; dimhof@uni-bonn.de
RI Domingo-Fernández, Daniel/AGH-3497-2022; Hoyt, Charles
   Tapley/N-6315-2019
OI Domingo-Fernández, Daniel/0000-0002-2046-6145; Hoyt, Charles
   Tapley/0000-0003-4423-4370; Schmalohr, Benjamin
   Franz/0000-0001-6710-5251; Hopp, Marie-T./0000-0002-0883-7564
FU University of Bonn; Fraunhofer-Gesellschaft
FX This work was financially supported by the University of Bonn to DI and
   the Fraunhofer-Gesellschaft to MH-A is gratefully acknowledged.
CR Ali M, 2019, BIOINFORMATICS, V35, P3538, DOI 10.1093/bioinformatics/btz117
   Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Benjamini Y, 2001, ANN STAT, V29, P1165
   Catlett NL, 2013, BMC BIOINFORMATICS, V14, DOI 10.1186/1471-2105-14-340
   Conran N, 2014, ACTA HAEMATOL-BASEL, V132, P97, DOI 10.1159/000356836
   Cote R, 2010, NUCLEIC ACIDS RES, V38, pW155, DOI 10.1093/nar/gkq331
   CUBUK C, 2019, NPJ SYST BIOL APPL, V5, DOI DOI 10.1101/367334
   Degtyarenko K, 2008, NUCLEIC ACIDS RES, V36, pD344, DOI 10.1093/nar/gkm791
   Dey S, 2012, J BIOL CHEM, V287, P26630, DOI 10.1074/jbc.M112.341255
   Dickinson-Copeland CM, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0142328
   DOMINGOFERNANDE.D, 2019, BMC BIOINFORMATICS, V20, DOI DOI 10.1101/451625
   Donato M, 2013, GENOME RES, V23, P1885, DOI 10.1101/gr.153551.112
   Dutra FF, 2014, FRONT PHARMACOL, V5, DOI 10.3389/fphar.2014.00115
   Effenberger-Neidnicht K, 2018, INFLAMMATION, V41, P1569, DOI 10.1007/s10753-018-0810-y
   Esteban-Medina M, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-2969-0
   Fabregat A, 2018, NUCLEIC ACIDS RES, V46, pD649, DOI [10.1093/nar/gkx1132, 10.1093/nar/gkz1031]
   Ferreira A, 2008, J MOL MED, V86, P1097, DOI 10.1007/s00109-008-0368-5
   Figueiredo RT, 2007, J BIOL CHEM, V282, P20221, DOI 10.1074/jbc.M610737200
   Fisher R.A., 1992, BREAKTHROUGHS STAT, P66, DOI DOI 10.1007/978-1-4612-4380-9_6
   Fortes GB, 2012, BLOOD, V119, P2368, DOI 10.1182/blood-2011-08-375303
   Fujita KA, 2014, MOL NEUROBIOL, V49, P88, DOI 10.1007/s12035-013-8489-4
   Garcia-Santos D, 2018, BLOOD, V131, P236, DOI 10.1182/blood-2017-07-798728
   Gouveia Z, 2017, FEBS J, V284, P3278, DOI 10.1111/febs.14192
   Hama M, 2012, ARTHRITIS RHEUM-US, V64, P1518, DOI 10.1002/art.33497
   Horng T, 2002, NATURE, V420, P329, DOI 10.1038/nature01180
   Hoyt CT, 2018, DATABASE-OXFORD, DOI 10.1093/database/bay126
   Huang HF, 2015, WORLD J GASTROENTERO, V21, P2937, DOI 10.3748/wjg.v21.i10.2937
   IJssennagger N, 2012, PLOS ONE, V7, DOI [10.1371/journal.pone.0049868, 10.1371/journal.pone.0043260]
   Jeney V, 2002, BLOOD, V100, P879, DOI 10.1182/blood.V100.3.879
   Kanehisa M, 2017, NUCLEIC ACIDS RES, V45, pD353, DOI 10.1093/nar/gkw1092
   Khanna S, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-29433-3
   Kuhl T, 2014, CHEMBIOCHEM, V15, P2024, DOI 10.1002/cbic.201402218
   Kuperstein I, 2015, ONCOGENESIS, V4, DOI 10.1038/oncsis.2015.19
   L'Acqua C, 2015, BRIT J HAEMATOL, V168, P175, DOI 10.1111/bjh.13183
   Larsen R, 2010, SCI TRANSL MED, V2, DOI 10.1126/scitranslmed.3001118
   Lin S, 2012, J NEUROINFLAMM, V9, DOI 10.1186/1742-2094-9-46
   Lin TA, 2010, J INFECT DIS, V202, P624, DOI 10.1086/654929
   Litvinov Rustem I, 2017, ISBT Sci Ser, V12, P176, DOI 10.1111/voxs.12331
   Marai GE, 2019, PLOS COMPUT BIOL, V15, DOI 10.1371/journal.pcbi.1007244
   Martins R, 2018, CURR OPIN IMMUNOL, V50, P14, DOI 10.1016/j.coi.2017.10.005
   Meng ZY, 2017, J IMMUNOL, V198, P820, DOI 10.4049/jimmunol.1600334
   Merle NS, 2019, P NATL ACAD SCI USA, V116, P6280, DOI 10.1073/pnas.1814797116
   Min H, 2017, MOL BRAIN, V10, DOI 10.1186/s13041-017-0305-z
   Mizuno S, 2012, BMC SYST BIOL, V6, DOI 10.1186/1752-0509-6-52
   Nagy E, 2010, ARTERIOSCL THROM VAS, V30, P1347, DOI 10.1161/ATVBAHA.110.206433
   Ostaszewski M, 2019, BRIEF BIOINFORM, V20, P659, DOI 10.1093/bib/bby024
   Palsson-McDermott EM, 2004, IMMUNOLOGY, V113, P153, DOI 10.1111/j.1365-2567.2004.01976.x
   Park Y, 2014, INT IMMUNOPHARMACOL, V22, P400, DOI 10.1016/j.intimp.2014.07.023
   Piazza M, 2011, INNATE IMMUN-LONDON, V17, P293, DOI 10.1177/1753425910369020
   Poulos TL, 2014, CHEM REV, V114, P3919, DOI 10.1021/cr400415k
   Pratt D, 2015, CELL SYST, V1, P302, DOI 10.1016/j.cels.2015.10.001
   Rodriguez-Esteban R, 2015, DATABASE-OXFORD, DOI 10.1093/database/bav116
   ROGERS FB, 1963, B MED LIBR ASSOC, V51, P114
   Roumenina LT, 2016, TRENDS MOL MED, V22, P200, DOI 10.1016/j.molmed.2016.01.004
   Seya T, 2005, INT J BIOCHEM CELL B, V37, P524, DOI 10.1016/j.biocel.2004.07.018
   Shannon P, 2003, GENOME RES, V13, P2498, DOI 10.1101/gr.1239303
   Slenter DN, 2018, NUCLEIC ACIDS RES, V46, pD661, DOI 10.1093/nar/gkx1064
   Smith A, 2015, FRONT PHYSIOL, V6, DOI 10.3389/fphys.2015.00187
   Smith Ann, 2009, P184, DOI 10.1007/978-0-387-78518-9_11
   Soares MP, 2016, CURR OPIN IMMUNOL, V38, P94, DOI 10.1016/j.coi.2015.11.006
   Sudan K, 2019, FREE RADICAL BIO MED, V137, P131, DOI 10.1016/j.freeradbiomed.2019.04.024
   Tolosano E, 2010, ANTIOXID REDOX SIGN, V12, P305, DOI 10.1089/ars.2009.2787
   Vinchi F, 2013, CIRCULATION, V127, P1317, DOI 10.1161/CIRCULATIONAHA.112.130179
   Vogel S, 2018, BRIT J HAEMATOL, V180, P761, DOI 10.1111/bjh.15117
   Wagener FADTG, 2003, BLOOD, V102, P521, DOI 10.1182/blood-2002-07-2248
   Zhang L., 2011, HEME BIOL SECRET LIF, V1st, DOI [10.1142/7484, DOI 10.1142/7484]
NR 66
TC 9
Z9 9
U1 0
U2 0
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-4185
J9 FRONT BIOENG BIOTECH
JI Front. Bioeng. Biotechnol.
PD MAR 6
PY 2020
VL 8
AR 74
DI 10.3389/fbioe.2020.00074
PG 10
WC Biotechnology & Applied Microbiology; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biotechnology & Applied Microbiology; Science & Technology - Other
   Topics
GA LC7JH
UT WOS:000525508400001
PM 32211383
OA gold, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Zheng, W
   Cheng, JY
   Wu, XX
   Sun, RY
   Wang, XL
   Sun, XB
AF Zheng, Wei
   Cheng, JingYuan
   Wu, Xiaoxue
   Sun, Ruiyang
   Wang, Xiaolong
   Sun, Xiaobing
TI Domain knowledge-based security bug reports prediction
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Software security; Security bug report prediction; Domain knowledge;
   Knowledge graph; Entity recognition
ID PERFORMANCE
AB To eliminate security attack risks of software products, the security bug report (SBR) prediction has been increasingly investigated. However, there is still much room for improving the performance of automatic SBR prediction. This work is inspired by the work of two recent studies proposed by Peters et al. and Wu et al., which focused on SBR prediction and have been published on the top tier journal TSE (IEEE Transactions on Software Engineering). The goal of this work is to improve the effectiveness of supervised machine learning-based SBR prediction with the help of software security domain knowledge. First, we split the words in summary and description fields of the SBRs. Then, we use customized relationships to label entities and build a rule-based entity recognition corpus. After that, we establish relationships between entities and construct knowledge graphs. The information of CWE (Common Weakness Enumeration) is used to expand our corpus and the security-related words and phrases are integrated. Finally, we predict SBRs from target project by calculating the cosine similarity between our integrated corpus and the target bug reports. Our experimental evaluation on 5 open-source SBR datasets shows that our domain knowledge-guided approach could improve the effectiveness of SBRs prediction by 52% in terms of F1-score on average. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Zheng, Wei; Cheng, JingYuan; Sun, Ruiyang; Wang, Xiaolong] Northwestern Polytech Univ, Sch Software Engn, Xian, Peoples R China.
   [Wu, Xiaoxue; Sun, Xiaobing] Yangzhou Univ, Coll Informat Engn, Yangzhou, Jiangsu, Peoples R China.
RP Wu, XX (通讯作者)，Yangzhou Univ, Coll Informat Engn, Yangzhou, Jiangsu, Peoples R China.
EM xiaoxuewu@yzu.edu.cn
OI wu, xiaoxue/0000-0002-7567-3643
FU Key R&D Program in Shaanxi Province, China [2021GY-041]; National
   Nat-ural Science Foundation of China special project capability-based
   construction method and execution mechanisms for ubiquitous operating
   systems [62141208]
FX This research was supported by the 2021 Key R&D Program in Shaanxi
   Province, China (2021GY-041) and the National Nat-ural Science
   Foundation of China special project capability-based construction method
   and execution mechanisms for ubiquitous operating systems (62141208) .
CR Alfred Rayner, 2014, International Journal of Machine Learning and Computing, V4, P103, DOI 10.7763/IJMLC.2014.V4.428
   [Anonymous], 2022, J SOFTW, DOI [10.13328/j. cnki.jos.006369, DOI 10.13328/J.CNKI.JOS.006369]
   Behl D, 2014, PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON RELIABILTY, OPTIMIZATION, & INFORMATION TECHNOLOGY (ICROIT 2014), P294, DOI 10.1109/ICROIT.2014.6798341
   Channabasamma, 2021, Inventive Computation and Information Technologies. Proceedings of ICICIT 2020. Lecture Notes in Networks and Systems (LNNS 173), P395, DOI 10.1007/978-981-33-4305-4_30
   Chaparro O, 2019, ESEC/FSE'2019: PROCEEDINGS OF THE 2019 27TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, P86, DOI 10.1145/3338906.3338947
   Chaparro O, 2017, ESEC/FSE 2017: PROCEEDINGS OF THE 2017 11TH JOINT MEETING ON FOUNDATIONS OF SOFTWARE ENGINEERING, P396, DOI 10.1145/3106237.3106285
   Cheng X, 2021, ACM T SOFTW ENG METH, V30, DOI 10.1145/3436877
   CWE, 2020, ABOUT CWE
   cwe, 2019, TOP 25 CWES
   de Andrade GC, 2019, ABAKOS, V7, P64
   Fan Y, 2019, INT J NEUROSCI, V129, P30, DOI 10.1080/00207454.2018.1492575
   Fernandes D., 2018, 7 INT C DAT SCI TEC
   Gegick Michael, 2010, Proceedings of the 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), P11, DOI 10.1109/MSR.2010.5463340
   Georgios D., 2018, EVOL SYST, P1
   Gokul P.P., 2018, 2017 2 IEEE INT C RE
   Gomaa W. H., 2014, IJCA, V68, P13
   Gorinski P.J., 2019, NAMED ENTITY RECOGNI
   Goseva-Popstojanova K, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY (QRS 2018), P344, DOI 10.1109/QRS.2018.00047
   Grootendorst M., 2020, **DATA OBJECT**, DOI 10.5281/zenodo.4461265
   Guia J., 2017, 19 INT C ENT INF SYS
   Gunawan D, 2018, J PHYS CONF SER, V978, DOI 10.1088/1742-6596/978/1/012120
   Han ZB, 2018, 2018 25TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION AND REENGINEERING (SANER 2018), P456, DOI 10.1109/SANER.2018.8330232
   Jiang Y, 2020, INFORM SOFTWARE TECH, V124, DOI 10.1016/j.infsof.2020.106314
   Lin JJ, 2021, NEURAL COMPUT APPL, V33, P681, DOI 10.1007/s00521-020-05057-5
   Lin ZQ, 2017, J COMPUT SCI TECH-CH, V32, P242, DOI 10.1007/s11390-017-1718-y
   Liu WJ, 2020, AAAI CONF ARTIF INTE, V34, P2901
   Lu JT, 2021, KNOWL-BASED SYST, V225, DOI 10.1016/j.knosys.2021.107098
   Lv W., 2020, 2020 IEEE WORLD C SE
   Martin B., 2011, COMMON WEAKNESS ENUM, V7515
   Ni P., 2021, LOCATION NAMED ENTIT
   Oudah M, 2017, NAT LANG ENG, V23, P441, DOI 10.1017/S1351324916000097
   Schmitt X, 2019, 2019 SIXTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORKS ANALYSIS, MANAGEMENT AND SECURITY (SNAMS), P338, DOI 10.1109/SNAMS.2019.8931850
   Shu R., 2019, INT C AUTOMATED SOFT
   Singh Akhilesh Kumar, 2021, 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), P130, DOI 10.1109/CSNT51715.2021.9509650
   Takano Y., 2020, IMPROVING DOCUMENT S
   Wang CB, 2018, COMPUT GEOSCI-UK, V112, P112, DOI 10.1016/j.cageo.2017.12.007
   Wang J., 2020, 2020 IEEE WORLD C SE
   WHO, 2012, WORLD MALARIA REPORT 2012, P1
   Wijayasekara D, 2012, C HUM SYST INTERACT, P89, DOI 10.1109/HSI.2012.22
   Wu XX, 2021, IEEE T SOFTWARE ENG, V48, P2541, DOI 10.1109/TSE.2021.3063727
   Wu XX, 2021, INFORM SOFTWARE TECH, V133, DOI 10.1016/j.infsof.2021.106530
   Wu XX, 2020, J SYST SOFTWARE, V160, DOI 10.1016/j.jss.2019.110456
   Xie X, 2014, P INT COMP SOFTW APP, P107, DOI 10.1109/COMPSAC.2014.17
   Yang B, 2021, PROC INT CONF SOFTW, P761, DOI 10.1109/ICSE43902.2021.00075
   Yang XL, 2017, J COMPUT SCI TECH-CH, V32, P181, DOI 10.1007/s11390-017-1713-3
   Yu HZ, 2021, J INF SCI, V47, P783, DOI 10.1177/0165551520932510
   Yu TT, 2019, IEEE T SOFTWARE ENG, V45, P558, DOI 10.1109/TSE.2018.2791521
   Zaman S, 2011, P 8 WORKING C MINING, P93
   Zheng W, 2021, IEEE T RELIAB, V70, P1658, DOI 10.1109/TR.2021.3118026
   Zheng Z, 2019, CHINESE J AERONAUT, V32, P176, DOI 10.1016/j.cja.2018.04.011
   Zheng Z, 2017, IEEE T SERV COMPUT, V10, P448, DOI 10.1109/TSC.2015.2475957
   Zheng Z, 2016, KNOWL-BASED SYST, V114, P36, DOI 10.1016/j.knosys.2016.09.021
   Zheng ZB, 2018, INT J WEB GRID SERV, V14, P352, DOI 10.1504/IJWGS.2018.095647
   Zhou C, 2021, EMPIR SOFTW ENG, V26, DOI 10.1007/s10664-021-10010-8
   Zhou C, 2020, J SYST SOFTWARE, V165, DOI 10.1016/j.jss.2020.110572
   Zimmermann Thomas, 2010, Proceedings of the Third IEEE International Conference on Software Testing, Verification and Validation (ICST 2010), P421, DOI 10.1109/ICST.2010.32
NR 56
TC 9
Z9 9
U1 8
U2 8
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD APR 6
PY 2022
VL 241
AR 108293
DI 10.1016/j.knosys.2022.108293
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V2EK
UT WOS:000788156700011
DA 2022-08-23
ER

PT J
AU Weichselbraun, A
   Steixner, J
   Brasoveanu, AMP
   Scharl, A
   Gobel, M
   Nixon, LJB
AF Weichselbraun, Albert
   Steixner, Jakob
   Brasoveanu, Adrian M. P.
   Scharl, Arno
   Goebel, Max
   Nixon, Lyndon J. B.
TI Automatic Expansion of Domain-Specific Affective Models for Web
   Intelligence Applications
SO COGNITIVE COMPUTATION
LA English
DT Article
DE Affective models; Hourglass of emotions; Language models; Embeddings;
   Knowledge graphs
ID SENTIMENT ANALYSIS; KNOWLEDGE; ADAPTATION; EMOTION; NEWS
AB Sentic computing relies on well-defined affective models of different complexity-polarity to distinguish positive and negative sentiment, for example, or more nuanced models to capture expressions of human emotions. When used to measure communication success, even the most granular affective model combined with sophisticated machine learning approaches may not fully capture an organisation's strategic positioning goals. Such goals often deviate from the assumptions of standardised affective models. While certain emotions such as Joy and Trust typically represent desirable brand associations, specific communication goals formulated by marketing professionals often go beyond such standard dimensions. For instance, the brand manager of a television show may consider fear or sadness to be desired emotions for its audience. This article introduces expansion techniques for affective models, combining common and commonsense knowledge available in knowledge graphs with language models and affective reasoning, improving coverage and consistency as well as supporting domain-specific interpretations of emotions. An extensive evaluation compares the performance of different expansion techniques: (i) a quantitative evaluation based on the revisited Hourglass of Emotions model to assess performance on complex models that cover multiple affective categories, using manually compiled gold standard data, and (ii) a qualitative evaluation of a domain-specific affective model for television programme brands. The results of these evaluations demonstrate that the introduced techniques support a variety of embeddings and pre-trained models. The paper concludes with a discussion on applying this approach to other scenarios where affective model resources are scarce.
C1 [Weichselbraun, Albert] Univ Appl Sci Grisons, Chur, Switzerland.
   [Steixner, Jakob; Brasoveanu, Adrian M. P.; Nixon, Lyndon J. B.] MODUL Technol, Vienna, Austria.
   [Scharl, Arno; Nixon, Lyndon J. B.] MODUL Univ Vienna, Vienna, Austria.
   [Weichselbraun, Albert; Scharl, Arno; Goebel, Max] WebLyzard Technol, Vienna, Austria.
RP Weichselbraun, A (通讯作者)，Univ Appl Sci Grisons, Chur, Switzerland.; Weichselbraun, A (通讯作者)，WebLyzard Technol, Vienna, Austria.
EM albert.weichselbraun@fhgr.ch; steixner@modultech.eu;
   adrian.brasoveanu@modul.ac.at; arno.scharl@modul.ac.at;
   goebel@weblyzard.com; nixon@modultech.eu
RI ; Scharl, Arno/N-3411-2013; Weichselbraun, Albert/C-3832-2014
OI Nixon, Lyndon/0000-0001-7091-4543; Scharl, Arno/0000-0001-5346-9521;
   Weichselbraun, Albert/0000-0001-6399-045X; Steixner,
   Jakob/0000-0001-9874-6641; Gobel, Max/0000-0001-6756-5231
FU University of Applied Sciences of the Grisons; Innosuisse [25587.2
   PFES-ES]; European Union's Horizon 2020 Research and Innovation
   Programme [780656]; Austrian Federal Ministry for Climate Action,
   Environment, Energy, Mobility and Technology (BMK) via the ICT of the
   Future Program [867551]
FX Open Access funding provided by the University of Applied Sciences of
   the Grisons (www.fhgr.ch).This research has been partially funded
   through the following projects: the MedMon project (www.fhgr.ch/medmon)
   funded by Innosuisse (No. 25587.2 PFES-ES); the ReTV project
   (www.retv-project.eu) funded by the European Union's Horizon 2020
   Research and Innovation Programme (No. 780656) and the EPOCH project
   (www.epoch-project.eu) funded by the Austrian Federal Ministry for
   Climate Action, Environment, Energy, Mobility and Technology (BMK) via
   the ICT of the Future Program (GA No. 867551).
CR Aaker JL, 1997, J MARKETING RES, V34, P347, DOI 10.2307/3151897
   Ahmed M, 2020, NEURAL COMPUT APPL, V32, P14719, DOI 10.1007/s00521-020-04824-8
   Akhtar MS, 2020, IEEE COMPUT INTELL M, V15, P64, DOI 10.1109/MCI.2019.2954667
   Balahur A., 2018, ARXIV PREPRINT ARXIV, P31
   Bijari K, 2020, EXPERT SYST APPL, V144, DOI 10.1016/j.eswa.2019.113090
   Camacho D, 2021, FUTURE GENER COMP SY, V114, P318, DOI 10.1016/j.future.2020.08.007
   Cambria Erik, 2012, Cognitive Behavioural Systems (COST 2012). International Training School. Revised Selected Papers, P144, DOI 10.1007/978-3-642-34584-5_11
   Cambria E, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P105, DOI 10.1145/3340531.3412003
   Cambria E, 2017, IEEE INTELL SYST, V32, P74, DOI 10.1109/MIS.2017.4531228
   Cambria E, 2015, COGN COMPUT, V7, P183, DOI 10.1007/s12559-015-9325-0
   Cavallari S, 2019, IEEE COMPUT INTELL M, V14, P39, DOI 10.1109/MCI.2019.2919396
   Chatterjee A., 2019, P 13 INT WORKSH SEM, P39
   Chaturvedi I, 2018, INFORM FUSION, V44, P65, DOI 10.1016/j.inffus.2017.12.006
   Chauhan DS, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P4351
   Chi E. A., 2020, P 58 ANN M ASS COMP, P5564, DOI DOI 10.18653/V1/2020.ACL-MAIN.493
   Choi JD, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P387
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dragoni M, 2018, IEEE INTELL SYST, V33, P77, DOI 10.1109/MIS.2018.033001419
   Du CN, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P4019
   EKMAN P, 1992, COGNITION EMOTION, V6, P169, DOI 10.1080/02699939208411068
   Ettinger A, 2020, T ASSOC COMPUT LING, V8, P34, DOI 10.1162/tacl_a_00298
   Ghosal D, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3198
   Graff M, 2020, IEEE COMPUT INTELL M, V15, P76, DOI 10.1109/MCI.2019.2954668
   Hewitt J., 2019, LONG SHORT PAPERS, V1, P4129, DOI DOI 10.18653/V1/N19-1419
   Nguyen HL, 2019, FUTURE GENER COMP SY, V100, P10, DOI 10.1016/j.future.2019.05.016
   Huangfu LW, 2013, 2013 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS: BIG DATA, EMERGENT THREATS, AND DECISION-MAKING IN SECURITY INFORMATICS, P116, DOI 10.1109/ISI.2013.6578799
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Jou B, 2015, MM'15: PROCEEDINGS OF THE 2015 ACM MULTIMEDIA CONFERENCE, P159, DOI 10.1145/2733373.2806246
   Joulin A., 2017, P C EUR CHAPT ASS CO, V2, P427, DOI [DOI 10.18653/V1/E17-2068, 10.18653/v1/e17-2068]
   Khandelwal A, 2020, PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020), P5739
   Kowsari K, 2019, INFORMATION, V10, DOI 10.3390/info10040150
   Kucher K, 2018, COMPUT GRAPH FORUM, V37, P71, DOI 10.1111/cgf.13217
   Lajante M, 2019, J RETAIL CONSUM SERV, V50, P305, DOI 10.1016/j.jretconser.2018.07.005
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lengauer G, 2012, JOURNALISM, V13, P179, DOI 10.1177/1464884911427800
   Li HF, 2007, 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS, VOLS 1-5, P2222, DOI 10.1109/ROBIO.2007.4522515
   Liu WJ, 2020, AAAI CONF ARTIF INTE, V34, P2901
   Mehta Y, 2020, ARTIF INTELL REV, V53, P2313, DOI 10.1007/s10462-019-09770-z
   Miller GA, 2007, LANG RESOUR EVAL, V41, P209, DOI 10.1007/s10579-007-9044-6
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Plutchik R., 1980, THEORIES EMOTION, P3
   Poria S, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P527
   Posner J, 2005, DEV PSYCHOPATHOL, V17, P715, DOI 10.1017/S0954579405050340
   Reeck C, 2016, TRENDS COGN SCI, V20, P47, DOI 10.1016/j.tics.2015.09.003
   Saravia E., 2018, PROC 2018 C EMPIRICA, P3687
   Sarker A, 2018, J AM MED INFORM ASSN, V25, P1274, DOI 10.1093/jamia/ocy114
   Satapathy R, 2019, LECT NOTES COMPUT SC, V11917, P177, DOI 10.1007/978-3-030-34980-6_20
   Scharl A, 2019, VIDEO VERIFICATION F, P281, DOI [10.1007/978-3-030-26752-0_10, DOI 10.1007/978-3-030-26752-0_10]
   Scharl A, 2017, IEEE SYST J, V11, P762, DOI 10.1109/JSYST.2015.2466439
   Seifollahi S, 2019, J INTELL INF SYST, V52, P57, DOI 10.1007/s10844-018-0504-9
   Shivhare SN, 2015, 2015 INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION & AUTOMATION (ICCCA), P52, DOI 10.1109/CCAA.2015.7148343
   Soares AP, 2012, BEHAV RES METHODS, V44, P256, DOI 10.3758/s13428-011-0131-7
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Susanto Y, 2020, IEEE INTELL SYST, V35, P96, DOI 10.1109/MIS.2020.2992799
   Valvoda J, 2020, P 58 ANN M ASS COMP, P7389
   Vaswani A, 2017, ADV NEUR IN, V30
   Vig J, 2019, PROCEEDINGS OF THE 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, (ACL 2019), P37
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang ZX, 2020, MULTIMED TOOLS APPL, V79, P35553, DOI [10.1109/TCYB.2020.3038694, 10.1007/s11042-019-08328-z]
   Weichselbraun A, 2013, P 7 INT C KNOWL CAPT, P129, DOI [10.1145/2479832.2479861, DOI 10.1145/2479832.2479861]
   Weichselbraun A, 2019, 2 C LANG DAT KNOWL L, DOI 10.4230/OASIcs.LDK.2019.14
   Weichselbraun A, 2017, IEEE INTELL SYST, V32, P80, DOI 10.1109/MIS.2017.57
   Weichselbraun A, 2015, 2015 7TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT (IC3K), P511
   Wolf T., 2019, P 2020 C EMP METH NA
   Xia R, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1003
   Xing FZ, 2019, INFORM PROCESS MANAG, V56, P554, DOI 10.1016/j.ipm.2018.11.002
   Xing FZ, 2018, ARTIF INTELL REV, V50, P49, DOI 10.1007/s10462-017-9588-9
   Yim J, 2017, PROC CVPR IEEE, P7130, DOI 10.1109/CVPR.2017.754
   Zhao CJ, 2020, KNOWL-BASED SYST, V191, DOI 10.1016/j.knosys.2019.105254
   Zhong XS, 2020, COGN COMPUT, V12, P844, DOI 10.1007/s12559-020-09714-8
   Zhuang L, 2020, J WEB SEMANT, V60, DOI 10.1016/j.websem.2019.100544
NR 71
TC 2
Z9 2
U1 1
U2 1
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1866-9956
EI 1866-9964
J9 COGN COMPUT
JI Cogn. Comput.
PD JAN
PY 2022
VL 14
IS 1
SI SI
BP 228
EP 245
DI 10.1007/s12559-021-09839-4
EA JAN 2021
PG 18
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Neurosciences & Neurology
GA YG8GW
UT WOS:000613042000002
PM 33552304
OA Green Accepted, hybrid, Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Rowland, A
   Folmer, E
   Beek, W
   Wenneker, R
AF Rowland, Alexandra
   Folmer, Erwin
   Beek, Wouter
   Wenneker, Rob
TI Interoperability and Integration: An Updated Approach to Linked Data
   Publication at the Dutch Land Registry
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE linked spatial data; knowledge graph; semantic technologies;
   interoperability; semantic modelling
AB Kadaster, the Dutch National Land Registry and Mapping Agency, has been actively publishing their base registries as linked (open) spatial data for several years. To date, a number of these base registers as well as a number of external datasets have been successfully published as linked data and are publicly available. Increasing demand for linked data products and the availability of new linked data technologies have highlighted the need for a new, innovative approach to linked data publication within the organisation in the interest of reducing the time and costs associated with said publication. The new approach to linked data publication is novel in both its approach to dataset modelling, transformation, and publication architecture. In modelling whole datasets, a clear distinction is made between the Information Model and the Knowledge Model to capture both the organisation-specific requirements and to support external, community standards in the publication process. The publication architecture consists of several steps where instance data are loaded from their source as GML and transformed using an Enhancer and published in the triple store. Both the modelling and publication architecture form part of Kadaster's larger vision for the development of the Kadaster Knowledge Graph through the integration of the various linked datasets.
C1 [Rowland, Alexandra; Folmer, Erwin] Kadaster, NL-7500 AE Enschede, Netherlands.
   [Rowland, Alexandra] Univ Twente, Fac Elect Engn Math & Comp Sci EEMCS, NL-7500 AE Enschede, Netherlands.
   [Folmer, Erwin] Univ Twente, Fac Behav Management & Social Sci BMS, NL-7500 AE Enschede, Netherlands.
   [Beek, Wouter] Kadaster, NL-1043 BP Amsterdam, Netherlands.
   [Beek, Wouter] Triply, NL-1043 BP Amsterdam, Netherlands.
   [Wenneker, Rob] Kadaster, NL-7311 KZ Apeldoorn, Netherlands.
RP Rowland, A (通讯作者)，Kadaster, NL-7500 AE Enschede, Netherlands.; Rowland, A (通讯作者)，Univ Twente, Fac Elect Engn Math & Comp Sci EEMCS, NL-7500 AE Enschede, Netherlands.
EM lexi.rowland@kadaster.nl; erwin.folmer@utwente.nl; wouter@triply.cc;
   rob.wenneker@kadaster.nl
OI Rowland, Alexandra/0000-0002-2339-6357; Folmer,
   Erwin/0000-0002-7845-1763
CR Bucher B., SPATIAL LINKED DATA
   Debruyne C, 2017, LECT NOTES COMPUT SC, V10588, P66, DOI 10.1007/978-3-319-68204-4_6
   Folmer E., 2017, P FREE OP SOURC SOFT, V17
   Folmer E, 2020, IEEE ACCESS, V8, P70822, DOI 10.1109/ACCESS.2020.2984691
   Goodwin J., 2008, T GIS, V12, P19, DOI [10.1111/j.1467-9671.2008.01133.x, DOI 10.1111/J.1467-9671.2008.01133.X]
   Open Geospatial Consortium, GEOSPARQL A GEOGR QU
   Ronzhin S, 2019, INT J SPAT DATA INFR, V14, P83, DOI 10.2902/1725-0463.2019.14.art4
   Ronzhin S, 2019, INFORMATION, V10, DOI 10.3390/info10100310
   Rowland A., 2021, P 4 INT WORKSH GEOSP
   Rowland A, 2020, ISPRS INT J GEO-INF, V9, DOI 10.3390/ijgi9120753
   Tandy J., 2017, SPATIAL DATA WEB BES
   van den Brink L, 2019, SEMANT WEB, V10, P95, DOI 10.3233/SW-180305
   van den Brink L, 2014, INT J SPAT DATA INFR, V9, P59, DOI 10.2902/1725-0463.2014.09.art3
   World Wide Web Consortium, 2009, W3C WORK GROUP NOT
   World Wide Web Consortium, JSON LD 1 1 W3C REC
   World Wide Web Consortium, 2014, W3C WORK GROUP NOT
   World Wide Web Consortium Shapes Constraint Language (SHACL), 2017, W3C RECOMMENDATION
   World Wide Web Consortium SPARQL 1.1 Overview, 2013, W3C RECOMMENDATION
   World Wide Web Consortium (W3C), 2012, OWL 2 WEB ONTOLOGY L, V2nd
   World Wide Web Consortium (W3C), 2014, RDF SCHEM 1 1 W3C RE
NR 20
TC 2
Z9 2
U1 2
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2220-9964
J9 ISPRS INT J GEO-INF
JI ISPRS Int. J. Geo-Inf.
PD JAN
PY 2022
VL 11
IS 1
AR 51
DI 10.3390/ijgi11010051
PG 12
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA YN4SR
UT WOS:000747250900001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Zhou, L
   Li, JM
   Gu, ZQ
   Qiu, J
   Gupta, BB
   Tian, ZH
AF Zhou, Ling
   Li, Jianming
   Gu, Zhaoquan
   Qiu, Jing
   Gupta, Brij B.
   Tian, Zhihong
TI PANNER: POS-Aware Nested Named Entity Recognition Through Heterogeneous
   Graph Neural Network
SO IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
LA English
DT Article; Early Access
DE Attention mechanism; heterogeneous graph; knowledge graph (KG); nested
   named entity recognition (Nested NER)
AB Nested named entity recognition (Nested NER) in knowledge graph (KG) aims at obtaining all meaningful entities, including nested entities for sentences in longer text region. Those obtained entities are to facilitate downstream applications, such as relation extraction, entity resolution, and coreference resolution. This task, however, is challenging not only because of the demand to detect the boundary of the entity but also due to the complexity of those hierarchically nested entities. Since a substantial amount of work has been made to Flat NER (or Nested NER), a few of them can explicitly acquire the position of the entity and utilize the grammatical construction of text. In this work, we propose PANNER, a POS-aware Nested NER model, to solve all the above issues. Specifically, we first construct a heterogeneous graph by introducing the part-of-speech (POS) information of the word. Second, we design a dilated random walk (DRW) algorithm based on a grammatical path to sample a fixed size of neighbors for each node. Third, we aggregate the message from different types of neighbors through an attention mechanism. Finally, we use a bidirectional decoding module to recognize and categorize all the flat and nested entities based on the node embedding in a layer-wise manner. Our extensive experiments show the effectiveness of PANNER in both flat and nested NER.
C1 [Zhou, Ling; Li, Jianming; Gu, Zhaoquan; Qiu, Jing; Tian, Zhihong] Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China.
   [Zhou, Ling; Li, Jianming] Peng Cheng Lab, Cyberspace Secur Res Ctr, Shenzhen 518000, Peoples R China.
   [Gupta, Brij B.] Asia Univ, Dept Comp Sci & Informat Engn, Taichung 40704, Taiwan.
   [Gupta, Brij B.] King Abdulaziz Univ, Jeddah 21589, Saudi Arabia.
   [Gupta, Brij B.] Natl Inst Technol Kurukshetra, Kurukshetra 136119, Haryana, India.
RP Qiu, J (通讯作者)，Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China.; Gupta, BB (通讯作者)，Asia Univ, Dept Comp Sci & Informat Engn, Taichung 40704, Taiwan.
EM iamzhouling@163.com; gutengzczy@gmail.com; zqgu@gzhu.edu.cn;
   qiujing@gzhu.edu.cn; tianzhihong@gzhu.edu.cn
RI Gupta, Brij B/E-9813-2011
OI Gupta, Brij B/0000-0003-4929-4698; tian, zhihong/0000-0002-9409-5359;
   Zhou, Ling/0000-0003-0771-6805
FU Guangdong Province Key Research and Development Plan [2019B010136003];
   National Key Research and Development Plan Grant [2018YFB1800702,
   2018YFB1004003, PCL2021A02]; National Natural Science Foundation of
   China [U20B2046, U1636215, 61871140, U1803263]; Guangdong Province
   Universities and Colleges Pearl River Scholar Funded Scheme; Guangdong
   Higher Education Innovation Group [2020KCXTD007]; Guangzhou Higher
   Education Innovation Group [202032854]
FX This work was supported in part by the Guangdong Province Key Research
   and Development Plan under Grant 2019B010136003; in part by the National
   Key Research and Development Plan Grant 2018YFB1800702, Grant
   2018YFB1004003, and Grant PCL2021A02; in part by the National Natural
   Science Foundation of China under Grant U20B2046, Grant U1636215, Grant
   61871140, and Grant U1803263; in part by the Guangdong Province
   Universities and Colleges Pearl River Scholar Funded Scheme (2019); in
   part by the Guangdong Higher Education Innovation Group under Grant
   2020KCXTD007; and in part by the Guangzhou Higher Education Innovation
   Group under Grant 202032854.
CR Alex B, 2007, P WORKSH BIONLP 2007, P65, DOI DOI 10.3115/1572392.1572404
   Ballesteros M., 2016, P 2016 C N AM CHAPT, P260, DOI [10.18653/v1/N16-1030, DOI 10.18653/V1/N16-1030]
   Bikel D. M., 1997, ANLC, P194, DOI DOI 10.3115/974557.974586
   Byrne K, 2007, ICSC 2007: INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING, PROCEEDINGS, P589, DOI 10.1109/ICSC.2007.107
   Dahiya A, 2021, FUTURE GENER COMP SY, V117, P193, DOI 10.1016/j.future.2020.11.027
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Finkel J.R., 2009, P 2009 C EMPIRICAL M, P141
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Ju M., 2018, LONG PAPERS, V1, P1446, DOI 10.18653/v1/N18-1131
   Jue W., 2020, P 58 ANN M ASS COMPU, P5918
   Kalchbrenner N, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P655, DOI 10.3115/v1/p14-1062
   Katiyar A., 2018, P 2018 C N AM CHAPT, V1, P861
   Kim JD, 2003, BIOINFORMATICS, V19, pi180, DOI 10.1093/bioinformatics/btg1023
   Kingma D. P., 2017, ARXIV
   Kipf Thomas N., 2017, INT C LEARN REPR ICL
   Krizhevsky Alex, 2017, Communications of the ACM, V60, P84, DOI 10.1145/3065386
   Lafferty J., 2001, P 18 INT C MACH LEAR
   Lin H., 2019, P C EMP METH NAT LAN, P6232
   Lin HY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P5182
   Lu W., 2015, P 2015 C EMP METH NA, P857, DOI DOI 10.18653/V1/D15
   Luan Y., 2019, NAACL HLT, P3036, DOI 10.18653/v1/N19-1308
   Luo CC, 2021, IEEE T IND INFORM, V17, P5810, DOI 10.1109/TII.2020.3038761
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   Miwa M., 2018, P 2018 C EMP METH NA, P2843, DOI DOI 10.18653/V1/D18-1309
   Muis Aldrian Obaja, 2017, P 2017 C EMP METH NA, P2608, DOI DOI 10.18653/V1/D17-1276
   Qiu J, 2020, IEEE T COMPUT SOC SY, V7, P225, DOI 10.1109/TCSS.2019.2946181
   Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
   Seyler D, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P241
   Sui D., 2019, P 2019 C EMP METH NA, P3830, DOI 10.18653/v1/ D19-1396
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Tan CAQ, 2020, AAAI CONF ARTIF INTE, V34, P9016
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang B., 2018, P C EMP METH NAT LAN, P204
   Wang M., 2019, P ICLR WORKSH REPR L
   Wang X, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2022, DOI 10.1145/3308558.3313562
   Xu MB, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1237, DOI 10.18653/v1/P17-1114
   Zhang CX, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P793, DOI 10.1145/3292500.3330961
   Zhang J, 2004, J BIOMED INFORM, V37, P411, DOI 10.1016/j.jbi.2004.08.005
   Zhang KH, 2014, IEEE T PATTERN ANAL, V36, P2002, DOI 10.1109/TPAMI.2014.2315808
   Zhang Yunyi., 2020, P 58 ANN M ASS COMP, P8151
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
   Zheng C., 2019, P 2019 C EMPIRICAL M, P357, DOI DOI 10.18653/V1/D19-1034
NR 42
TC 0
Z9 0
U1 10
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2329-924X
J9 IEEE T COMPUT SOC SY
JI IEEE Trans. Comput. Soc. Syst.
DI 10.1109/TCSS.2022.3159366
EA MAR 2022
PG 9
WC Computer Science, Cybernetics; Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0F1QW
UT WOS:000777143000001
DA 2022-08-23
ER

PT J
AU Pirro, G
AF Pirro, Giuseppe
TI Building relatedness explanations from knowledge graphs
SO SEMANTIC WEB
LA English
DT Article
DE Knolwedge graphs; explanations; patterns; relatedness-based querying
ID DISCOVERY
AB Knowledge graphs (KGs) are a key ingredient to complement search results, discover entities and their relations and support several knowledge discovery tasks. We face the problem of building relatedness explanations, that is, graphs that can explain how a pair of entities is related in a KG. Explanations can be used in a variety of tasks; from exploratory search to query answering. We formalize the notion of explanation and present two algorithms. The first, E4D (Explanations from Data), assembles explanations starting from all paths interlinking the source and target entity in the data. The second algorithm E4S (Explanations from Schema) builds explanations focused on a specific relatedness perspective expressed by providing a predicate. E4S first generates candidate explanation patterns at the level of schema; then, it assembles explanations by proceeding to their verification in the data. Given a set of paths, found by E4D or E4S, we describe different criteria to build explanations based on information-theory, diversity, and their combination. As a concrete use-case of relatedness explanations, we introduce relatedness-based KG querying, which revisits the query-by-example paradigm from the perspective of relatedness explanations. We implemented all the machinery in the RECAP tool, which is based on RDF and SPARQL. We discuss an evaluation of the explanation building algorithms and a comparison of RECAP with related systems on real-world data.
C1 [Pirro, Giuseppe] CNR, ICAR, Italian Natl Res Council, Arcavacata Di Rende, CS, Italy.
   [Pirro, Giuseppe] Univ Koblenz Landau, WeST Grp, Mainz, Germany.
RP Pirro, G (通讯作者)，CNR, ICAR, Italian Natl Res Council, Arcavacata Di Rende, CS, Italy.
EM pirro@icar.cnr.it
RI Pirrò, Giuseppe/AAZ-4500-2020; Pirrò, Giuseppe/AAF-1993-2019
OI Pirrò, Giuseppe/0000-0002-7499-5798
CR Aggarwal N, 2016, LECT NOTES COMPUT SC, V9989, P35, DOI 10.1007/978-3-319-47602-5_8
   Aleman-Meza B, 2005, IEEE INTERNET COMPUT, V9, P37, DOI 10.1109/MIC.2005.63
   Aleman-Meza B., 2003, SWDB, P33
   [Anonymous], 2011, 11114503 ARXIV
   Anyanwu K., 2003, WWW, P690, DOI [DOI 10.1145/775152.775249, 10.1145/775152.775249]
   ANYANWU K, 2005, P 14 INT C WORLD WID, P117, DOI DOI 10.1145/1060745.1060766
   Bianchi F, 2017, LECT NOTES COMPUT SC, V10249, P120, DOI 10.1007/978-3-319-58068-5_8
   Cheng G, 2017, IEEE T KNOWL DATA EN, V29, P2388, DOI 10.1109/TKDE.2017.2735970
   Cheng G, 2016, LECT NOTES COMPUT SC, V9981, P119, DOI 10.1007/978-3-319-46523-4_8
   Cheng G, 2014, LECT NOTES COMPUT SC, V8797, P422, DOI 10.1007/978-3-319-11915-1_27
   Deng F., 2012, P 21 ACM INT C INF P
   Di Noia T, 2016, ACM T INTEL SYST TEC, V8, DOI 10.1145/2899005
   Faloutsos C., 2004, P 10 ACM SIGKDD INT, P118, DOI DOI 10.1145/1014052.1014068
   Fang L, 2011, PROC VLDB ENDOW, V5, P241, DOI 10.14778/2078331.2078339
   Fionda V, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3755
   Fionda V, 2017, LECT NOTES COMPUT SC, V10587, P296, DOI 10.1007/978-3-319-68288-4_18
   Fionda V, 2017, PROC VLDB ENDOW, V10, P1913, DOI 10.14778/3137765.3137807
   Fionda V, 2017, LECT NOTES COMPUT SC, V10249, P19, DOI 10.1007/978-3-319-58068-5_2
   Fionda V, 2015, AAAI CONF ARTIF INTE, P102
   Fionda V, 2016, ARTIF INTELL, V239, P143, DOI 10.1016/j.artint.2016.07.003
   Franconi E, 2013, LECT NOTES COMPUT SC, V8218, P101, DOI 10.1007/978-3-642-41335-3_7
   Haase P, 2009, LECT NOTES COMPUT SC, V5554, P445, DOI 10.1007/978-3-642-02121-3_34
   Harris S., 2013, SPARQL 1 1 QUERY LAN
   Heath T., 2011, LINKED DATA EVOLVING
   Heim P, 2010, LECT NOTES COMPUT SC, V6088, P303, DOI 10.1007/978-3-642-13486-9_21
   Heim P, 2009, LECT NOTES COMPUT SC, V5887, P182, DOI 10.1007/978-3-642-10543-2_21
   Herman I, 2000, IEEE T VIS COMPUT GR, V6, P24, DOI 10.1109/2945.841119
   Hulpus I, 2015, LECT NOTES COMPUT SC, V9366, P442, DOI 10.1007/978-3-319-25007-6_26
   JAYARAM N, 2015, IEEE T KNOWL DATA EN, V27, P2797, DOI DOI 10.1109/TKDE.2015.2426696
   Kasneci G., 2009, P 18 ACM C INF KNOWL, P1653, DOI DOI 10.1145/1645953.1646196
   Kochut KJ, 2007, LECT NOTES COMPUT SC, V4519, P145
   Magliacane Sara, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P344, DOI 10.1007/978-3-642-35176-1_22
   Mendes P.N., 2010, WEB SCI C
   Munoz S, 2009, J WEB SEMANT, V7, P220, DOI 10.1016/j.websem.2009.07.003
   Nakashole N, 2012, PROC VLDB ENDOW, V5, P1982, DOI 10.14778/2367502.2367553
   Page L., 1999, 199966 STANF INFOLAB
   Perez J, 2009, ACM T DATABASE SYST, V34, DOI 10.1145/1567274.1567278
   Pirro G, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16 COMPANION), P235, DOI 10.1145/2872518.2890533
   Pirro G, 2015, LECT NOTES COMPUT SC, V9366, P622, DOI 10.1007/978-3-319-25007-6_36
   Pirro Giuseppe, 2012, P 26 AAAI C ART INT, P129
   Ramalcrishnan Cartic, 2005, SIGKDD EXPLORATIONS, V7, P56, DOI DOI 10.1145/1117454.1117462
   Ribeiro-Neto, 1999, MODERN INFORM RETRIE, VVolume 463
   Saeedi Alieh, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P576, DOI 10.1007/978-3-319-93417-4_37
   Saif H, 2017, LECT NOTES COMPUT SC, V10249, P571, DOI 10.1007/978-3-319-58068-5_35
   Sheth A, 2005, J DATABASE MANAGE, V16, P33, DOI 10.4018/jdm.2005010103
   Shi C, 2017, IEEE T KNOWL DATA EN, V29, P17, DOI 10.1109/TKDE.2016.2598561
   Sun Y., 2012, SYNTH LECT DATA MIN, V3, P1, DOI DOI 10.2200/S00433ED1V01Y201207DMK005
   Tiddi I, 2016, LECT NOTES COMPUT SC, V9981, P581, DOI 10.1007/978-3-319-46523-4_35
   Traverso-Ribon I, 2016, LECT NOTES COMPUT SC, V10024, P666, DOI 10.1007/978-3-319-49004-5_43
   Tsuruoka Y, 2011, BIOINFORMATICS, V27, pI111, DOI 10.1093/bioinformatics/btr214
   Voskarides N, 2018, ACM/SIGIR PROCEEDINGS 2018, P765, DOI 10.1145/3209978.3210031
   Voskarides N, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P564
   Wang YW, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2351, DOI 10.1145/3132847.3133161
NR 53
TC 2
Z9 2
U1 2
U2 7
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2019
VL 10
IS 6
BP 963
EP 990
DI 10.3233/SW-190348
PG 28
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JH5WI
UT WOS:000492839300002
DA 2022-08-23
ER

PT J
AU Azzam, M
   Beckmann, R
AF Azzam, Mark
   Beckmann, Rasmus
TI How AI Helps to Increase Organizations' Capacity to Manage Complexity -
   A Research Perspective and Solution Approach Bridging Different
   Disciplines
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article; Early Access
DE Organizations; Complexity theory; Decision making; Market research;
   Uncertainty; Task analysis; Law; Artificial intelligence; knowledge
   graph; natural language processing; research management; social systems
   theory; strategic intelligence; strategic management
ID SCIENCE
AB A major challenge for organizations and their strategic management today is the increasingly complex internal and external information and knowledge environment. To tackle this problem, we want to provide a fresh perspective on how to construct a modern AI-based socio-technical support system for strategic management. Methodologically, we provide a deep problem analysis and use the resulting insights to provide a solution approach drawing on different disciplines and bridging the gap between social science and computer science. Starting from challenges for strategic analysis in the age of information-overload, we analyze organizations as social communication systems. This yields a deeper understanding of organizations processing strategic information and their complex information environment. Based on the resulting insights, we propose our framework design by building on text mining and knowledge graph technology and give some guidance for its implementation. The novel system generates strategic intelligence by tapping into qualitative data: defining constantly changing information needs, interpreting information in its context as well as in reflection to the organization and making new knowledge promptly available to the right people. Throughout, we give illustrating examples from the domain of strategic research management.
C1 [Azzam, Mark; Beckmann, Rasmus] German Aerosp Ctr DLR, D-51147 Cologne, Germany.
   [Azzam, Mark] German Fed Minist Environm Nat Conservat Nucl Saf, D-11055 Berlin, Germany.
RP Azzam, M (通讯作者)，German Aerosp Ctr DLR, D-51147 Cologne, Germany.; Azzam, M (通讯作者)，German Fed Minist Environm Nat Conservat Nucl Saf, D-11055 Berlin, Germany.
EM mark.azzam@bmuv.bund.de; rasmus.beckmann@dlr.de
CR Ackoff R.L., 1989, J APPL SYSTEMS ANAL, V16, P3, DOI DOI 10.1002/9781444303179.CH3
   Adams GS, 2021, NATURE, V592, P258, DOI 10.1038/s41586-021-03380-y
   Akroyd J., 2021, DATA CENT ENG, V2, pe14, DOI [10.1017/dce.2021.10, DOI 10.1017/DCE.2021.10]
   Albert M., 2020, OXFORD RES ENCY POLI
   Gomez JA, 2018, ENVIRON SCI POLLUT R, V25, P977, DOI 10.1007/s11356-016-8339-9
   Allen P., 2011, SAGE HDB COMPLEXITY, DOI [10.4135/9781446201084., DOI 10.4135/9781446201084]
   [Anonymous], 2006, KNOWLEDGE MANAGEMENT
   ANSOFF HI, 1975, CALIF MANAGE REV, V18, P21, DOI 10.2307/41164635
   Auer S, 2018, P INT C WEB INT MIN, DOI DOI 10.1186/1475-2859-8-19
   Bai JR, 2022, JACS AU, V2, P292, DOI 10.1021/jacsau.1c00438
   Barber H.F., 1992, J MANAG DEV, V11, P4, DOI 10.1108/02621719210018208
   Beaufre A., 1965, INTRO STRATEGY
   Beckmann R., 2011, CLAUSEWITZ TRIFFT LU, V1st
   Ben Sassi D, 2015, INT CONF RES CHAL, P486, DOI 10.1109/RCIS.2015.7128910
   Bennett N, 2014, BUS HORIZONS, V57, P311, DOI 10.1016/j.bushor.2014.01.001
   Bodendorf F., 2006, DATEN WISSENSMANAGEM, V2nd
   Boettke PJ, 2008, AM J ECON SOCIOL, V67, P331, DOI 10.1111/j.1536-7150.2008.00573.x
   Brem A, 2021, IEEE T ENG MANAGE, DOI 10.1109/TEM.2021.3109983
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Colson E., WHAT AI DRIVEN DECIS
   Davenport T.H., 2018, J BUS ANAL, V1, P73, DOI [10.1080/2573234X.2018.1543535, DOI 10.1080/2573234X.2018.1543535]
   Davenport T. H., RISE STRATEGY MACHIN
   de Waal A, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11236860
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dorner D., 2000, LOGIK MIBETALINGENS, V13th
   Drucker PF, 1954, PRACTICE MANAGEMENT
   Estermann T., 2020, IMPACT COVID 19 CRIS
   Fensel D., 2020, KNOWLEDGE GRAPHS, P1, DOI DOI 10.1007/978-3-030-37439-6
   Furst J., 2021, OPEN J INTERNET THIN, V7, P32
   Galkin M, 2017, ICEIS: PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS - VOL 2, P88, DOI 10.5220/0006325200880098
   Ghimire Awishkar, 2020, 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), P441, DOI 10.1109/I-SMAC49090.2020.9243318
   Graetz F., 2002, MANAGE DECIS, V40, P456, DOI DOI 10.1108/00251740210430434
   Graf R., 2017, WISSENSCHAFTSMANAGEM, P344
   Green A., ARTIF INTELL
   Grobman G. M., 2005, Public Administration Quarterly, V29, P350
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   Hoglund L, 2018, INT PUBLIC MANAG J, V21, P822, DOI 10.1080/10967494.2018.1427161
   Hutchinson P, 2021, IEEE T ENG MANAGE, V68, P628, DOI 10.1109/TEM.2020.2977222
   Ignatow Gabe, 2017, TEXT MINING GUIDEBOO
   Janis I.L., 1972, VICTIMS GROUPTHINK P
   Jarrahi MH, 2018, BUS HORIZONS, V61, P577, DOI 10.1016/j.bushor.2018.03.007
   Jeong C.H., 2019, P 2019 INT APPL COMP, P1
   Keding C., 2021, MANAGEMENT REV Q, V71, P91, DOI [10.1007/s11301-020-00181-x, DOI 10.1007/S11301-020-00181-X]
   Keen A., ARANGODB
   Kuehl S, 2020, SYST RES BEHAV SCI, V37, P496, DOI 10.1002/sres.2685
   Kuhlen R., 1985, INFORMATIK FACHBERIC, P1
   Kunc M., 2018, STRATEGIC ANALYTICS, DOI [10.1002/9781119519638, DOI 10.1002/9781119519638]
   Ku┬nig W., 2017, GRUNDZUGE WIRTSCHAFT, DOI 10.1007/978-3-662-53362-8
   Levy D. L., 2000, PUBLIC ADM PUBLIC PO, V79
   Long QX, 2020, NAT MED, V26, P845, DOI 10.1038/s41591-020-0897-1
   Luhmann N., 1964, FUNKTIONEN FOLGEN FO
   Luhmann N., 1995, SOCIAL SYSTEMS
   Luhmann N., 2011, INTRO SYSTEMS THEORY, V1st
   Luhmann N., 1975, ZUKUNFT PHILOSOPHIE, P85
   Luhmann Niklas, 1986, OKOLOGISCHE KOMMUNIK
   Marchand D., 2007, MANAGING STRATEGIC I, P1
   Miller R, 2018, TRANSFORMING THE FUTURE: ANTICIPATION IN THE 21ST CENTURY, P1
   MINTZBERG H, 1994, HARVARD BUS REV, V72, P107
   Muhlroth C, 2022, IEEE T ENG MANAGE, V69, P493, DOI 10.1109/TEM.2020.2989214
   Mulligan C., STRATEGY ANALYTICS R
   Osoba O.A., 2017, INTELLIGENCE OUR IMA
   Porter M., 1980, COMPETITIVE STRATEGY
   Porter ME., 1996, HARVARD BUS REV, V74, P61
   Radanliev P, 2021, AI SOC, V36, P783, DOI 10.1007/s00146-020-01049-0
   Rana NP, 2022, EUR J INFORM SYST, V31, P364, DOI 10.1080/0960085X.2021.1955628
   Rasche A, 2020, CRIT PERSPECT ACCOUN, V73, DOI 10.1016/j.cpa.2017.03.004
   Rowley J, 2007, J INF SCI, V33, P163, DOI 10.1177/0165551506070706
   Safavi T, 2019, IEEE DATA MINING, P528, DOI 10.1109/ICDM.2019.00063
   Sarpong D, 2018, TECHNOL ANAL STRATEG, V30, P625, DOI 10.1080/09537325.2018.1463934
   Schwartz R., 2022, NAT I STANDARDS TECH, V1270, DOI [10.6028/NIST.SP.1270, DOI 10.6028/NIST.SP.1270]
   Sharma R, 2014, EUR J INFORM SYST, V23, P433, DOI 10.1057/ejis.2014.17
   Shih MJ, 2010, EXPERT SYST APPL, V37, P2882, DOI 10.1016/j.eswa.2009.09.001
   Silberg J., NOTES FRONTIER TACKL
   Song DZ, 2019, IEEE T SERV COMPUT, V12, P356, DOI 10.1109/TSC.2017.2711600
   Szalay A, 2006, NATURE, V440, P413, DOI 10.1038/440413a
   Tat M., 2017, J INT SOCIAL RES, V10, P994, DOI [10.17719/jisr.2017.1832, DOI 10.17719/JISR.2017.1832]
   THUCYDIDES, 1998, PELOPONNESIAN WAR
   Von Clausewitz C, 2008, WAR
   Vos J.-P., 2006, ADV ORG STUDIES NIKL, P363
   WASON PC, 1968, Q J EXP PSYCHOL, V20, P273, DOI 10.1080/14640746808400161
   Weingart P, 2000, PUBLIC UNDERST SCI, V9, P261, DOI 10.1088/0963-6625/9/3/304
   Xu KQ, 2011, DECIS SUPPORT SYST, V50, P743, DOI 10.1016/j.dss.2010.08.021
   Xu Mark, 2010, International Journal of Strategic Information Technology and Applications, V1, P1, DOI 10.4018/jsita.2010100101
   Zins C, 2007, J AM SOC INF SCI TEC, V58, P479, DOI 10.1002/asi.20508
NR 84
TC 0
Z9 0
U1 5
U2 5
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
DI 10.1109/TEM.2022.3179107
EA JUN 2022
PG 14
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA 2S0SJ
UT WOS:000821511300001
DA 2022-08-23
ER

PT J
AU Roman, D
   Alexiev, V
   Paniagua, J
   Elvesaeter, B
   von Zernichow, BM
   Soylu, A
   Simeonov, B
   Taggart, C
AF Roman, Dumitru
   Alexiev, Vladimir
   Paniagua, Javier
   Elvesaeter, Brian
   von Zernichow, Bjorn Marius
   Soylu, Ahmet
   Simeonov, Boyan
   Taggart, Chris
TI The euBusinessGraph ontology: A lightweight ontology for harmonizing
   basic company information
SO SEMANTIC WEB
LA English
DT Article
DE Company data; open data; linked data; ontology; business knowledge graph
ID BIG; DESIGN
AB Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon.
C1 [Roman, Dumitru; Elvesaeter, Brian; von Zernichow, Bjorn Marius] SINTEF AS, Trondheim, Norway.
   [Alexiev, Vladimir; Simeonov, Boyan] Ontotext, Sofia, Bulgaria.
   [Paniagua, Javier] SpazioDati, Trento, Italy.
   [Soylu, Ahmet] OsloMet Oslo Metropolitan Univ, Oslo, Norway.
   [Taggart, Chris] OpenCorporates, London, England.
RP Roman, D (通讯作者)，SINTEF AS, Trondheim, Norway.
EM dumitru.roman@sintef.no; vladimir.alexiev@ontotext.com;
   paniagua@spaziodati.eu; brian.elvesater@sintef.no;
   BjornMarius.vonZernichow@sintef.no; ahmet.soylu@oslomet.no;
   boyan.simeonov@ontotext.com; chris.taggart@opencorporates.com
RI ; Alexiev, Vladimir/J-3403-2017
OI Elvesaeter, Brian/0000-0001-7304-4950; Alexiev,
   Vladimir/0000-0001-7508-7428
FU EC [732003, 732590, 780247, BG16RFOP002-1.005-0168-C01, 821518]
FX The work in this article was partly funded by the EC H2020 projects
   euBusinessGraph (grant 732003), EWShopp (grant 732590), TheyBuyForYou
   (780247), CIMA (grant BG16RFOP002-1.005-0168-C01), and further reused in
   InnoRate (821518). Special thanks to the members of the euBusinessGraph
   project consortium for stimulating discussions around various aspects of
   basic company information, especially to Tatiana Tarasova, Fredrik
   Seehusen, and David Norheim for their initial involvement in the
   development of the ontology.
CR Alexander K., 2011, DESCRIBING LINKED DA
   Alexiev V., 2017, P 13 INT C SEM SYST
   Alexiev V., 2018, EUBUSINESSGRAPH SEMA
   Baker T, 2013, J WEB SEMANT, V20, P35, DOI 10.1016/j.websem.2013.05.001
   Bansal SK, 2015, COMPUTER, V48, P42, DOI 10.1109/MC.2015.76
   Barzdins J., 2011, CEUR WORKSHOP P, V796
   Bennett M, 2013, J BANK REGUL, V14, P255, DOI 10.1057/jbr.2013.13
   Bizer C, 2011, LECT NOTES COMPUT SC, V7051, P1, DOI 10.1007/978-3-642-24577-0_1
   Corcho O., 2006, ONTOLOGIES SOFTWARE, P1, DOI DOI 10.1007/3-540-34518-3_1
   Cutrona V., 2019, CEUR WORKSHOP P, V2456, P209
   Dekkers M, 2013, ASSET DESCRIPTION ME
   Ehrhart T., 2019, P SEM STAT SEMSTATS
   EU ISA Programme Core Vocabularies Working Group, 2015, ISA PROGR LOC COR VO
   Eurostat, 2008, EUR METH WORK PAP
   Eurostat, 2019, METHODOLOGICAL MANUA, DOI [10.2785/930137, DOI 10.2785/930137]
   Munoz-Soro JF, 2016, SEMANT WEB, V7, P295, DOI 10.3233/SW-150195
   Giese M, 2015, COMPUTER, V48, P60, DOI 10.1109/MC.2015.82
   Guha RV, 2016, COMMUN ACM, V59, P44, DOI 10.1145/2844544
   ISO/TC 68/SC 8 Technical Committee, 2019, 68SC8 ISOTC
   Janssen M, 2017, INFORM SYST FRONT, V19, P189, DOI 10.1007/s10796-017-9746-2
   Kader S.A.E., CEUR WORKSHOP P, V2549
   Knublauch H, 2017, SHAPES CONSTRAINT LA
   Maurino A, 2019, PROCEEDINGS OF THE FIFTH INTERNATIONAL WORKSHOP ON DATA SCIENCE FOR MACRO-MODELING (DSMM 2019), DOI 10.1145/3336499.3338012
   Mcdaniel M, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3329124
   Noy N. F., 2001, ONTOLOGY DEV
   Principe RAA, 2018, LECT NOTES COMPUT SC, V11155, P170, DOI 10.1007/978-3-319-98192-5_32
   Prud'hommeaux E, 2014, P 10 INT C SEM SYST, P32, DOI DOI 10.1145/2660517.2660523
   Reynolds D., 2014, ORG ONTOLOGY
   Roman D, 2018, SEMANT WEB, V9, P393, DOI 10.3233/SW-170263
   Roman D, 2016, LECT NOTES COMPUT SC, V9989, P101, DOI 10.1007/978-3-319-47602-5_21
   Semantic Interoperability Community, 2019, E GOV COR VOC EUR CO
   Simperl E., 2018, P MTSR 2018, P317, DOI [10.1007/978-3-030-14401-2_29, DOI 10.1007/978-3-030-14401-2_29]
   Soylu Ahmet, 2020, The Semantic Web - ISWC 2020. 19th International Semantic Web Conference. Lecture Notes in Computer Science (LNCS 12507), P430, DOI 10.1007/978-3-030-62466-8_27
   Soylu A., 2020, CEUR WORKSHOP P, V2721
   Soylu A., 2018, CEUR WORKSHOP P, V2262
   Soylu A., 2019, CEUR WORKSHOP P, V2456
   Soylu A, 2019, LECT NOTES COMPUT SC, V11701, P230, DOI 10.1007/978-3-030-29374-1_19
   Suarez-Figueroa M.C., 2012, ONTOLOGY ENG NETWORK
   Sukhobok D, 2016, LECT NOTES COMPUT SC, V9989, P134, DOI 10.1007/978-3-319-47602-5_27
   United Nations, 2007, INT STAND IND CLASS
   van Hage WR, 2011, J WEB SEMANT, V9, P128, DOI 10.1016/j.websem.2011.03.003
   Working Group for Describing Public Services, 2016, COR PUBL SERV VOC AP
NR 42
TC 4
Z9 4
U1 1
U2 1
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2022
VL 13
IS 1
BP 41
EP 68
DI 10.3233/SW-210424
PG 28
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XE9KR
UT WOS:000723701000003
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Yu, Y
   Huang, KX
   Zhang, C
   Glass, LM
   Sun, JM
   Xiao, C
AF Yu, Yue
   Huang, Kexin
   Zhang, Chao
   Glass, Lucas M.
   Sun, Jimeng
   Xiao, Cao
TI SumGNN: multi-typed drug interaction prediction via efficient knowledge
   graph summarization
SO BIOINFORMATICS
LA English
DT Article
AB Motivation: Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g. experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task.
   Results: To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multichannel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction.
C1 [Yu, Yue; Zhang, Chao] Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA.
   [Huang, Kexin] Harvard TH Chan Sch Publ Hlth, Hlth Data Sci, Boston, MA 02115 USA.
   [Glass, Lucas M.; Xiao, Cao] IQVIA, Analyt Ctr Excellence, Cambridge, MA 02139 USA.
   [Glass, Lucas M.] Temple Univ, Dept Stat, Philadelphia, PA 19122 USA.
   [Sun, Jimeng] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA.
RP Xiao, C (通讯作者)，IQVIA, Analyt Ctr Excellence, Cambridge, MA 02139 USA.
EM cao.xiao@iqvia.com
FU National Science Foundation [SCH-2014438, IIS-1418511, CCF-1533768,
   IIS-1838042]; National Institute of Health award [NIH R01
   1R01NS107291-01, R56HL138415]
FX This work was in part supported by the National Science Foundation award
   SCH-2014438, IIS-1418511, CCF-1533768, IIS-1838042, the National
   Institute of Health award NIH R01 1R01NS107291-01 and R56HL138415.
CR Alsentzer E, 2020, P NEURIPS
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Burkhardt H.A., 2019, 752022 BIORXIV
   Cai D, 2020, AAAI CONF ARTIF INTE, V34, P7464
   Celebi R, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-3284-5
   Chiang WL, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P257, DOI 10.1145/3292500.3330925
   COHEN J, 1960, EDUC PSYCHOL MEAS, V20, P37, DOI 10.1177/001316446002000104
   Dai Y., 2020, 07341 ARXIV
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Gysi DM, 2021, P NATL ACAD SCI USA, V118, DOI 10.1073/pnas.2025581118
   Himmelstein DS, 2015, PLOS COMPUT BIOL, V11, DOI 10.1371/journal.pcbi.1004259
   Huang K., 2020, NEURIPS
   Huang KX, 2020, AAAI CONF ARTIF INTE, V34, P702
   Huang KX, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-77766-9
   Jones PK, 2015, EXPERT REV CARDIOVAS, V13, P1263, DOI 10.1586/14779072.2015.1095090
   Karim MR, 2019, ACM-BCB'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND HEALTH INFORMATICS, P113, DOI 10.1145/3307339.3342161
   Karypis, 2020, DRKG DRUG REPURPOSIN
   King DB, 2015, ACS SYM SER, V1214, P1
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Li P, 2015, BIOINFORMATICS, V31, P2007, DOI 10.1093/bioinformatics/btv080
   Liang C, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1054, DOI 10.1145/3394486.3403149
   Lin X, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2739
   Mikolov T., 2013, NEURAL INFORM PROCES
   Percha B, 2013, TRENDS PHARMACOL SCI, V34, P178, DOI 10.1016/j.tips.2013.01.006
   Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
   Rogers D, 2010, J CHEM INF MODEL, V50, P742, DOI 10.1021/ci100050t
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Ryu JY, 2018, P NATL ACAD SCI USA, V115, pE4304, DOI 10.1073/pnas.1803294115
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Shang J, 2019, ARXIV190600346
   Shi HZ, 2020, PROC INT CONF DATA, P1818, DOI 10.1109/ICDE48307.2020.00178
   Srinivasa R.S., 2020, ARXIV200608796
   Su C, 2020, BRIEF BIOINFORM, V21, P182, DOI 10.1093/bib/bby117
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Takeda T, 2017, J CHEMINFORMATICS, V9, DOI 10.1186/s13321-017-0200-8
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Tatonetti NP, 2012, SCI TRANSL MED, V4, DOI 10.1126/scitranslmed.3003377
   Teru K., 2020, P 37 INT C MACH LEAR, P9448
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Tzeng RC, 2019, PR MACH LEARN RES, V97
   Uszkoreit Jakob, 2018, P C N AM CHAPT ASS C
   Velickovic P, 2019, 7 INT C LEARN REPR
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang G., 2018, P 2018 C EMPIRICAL M, P2246, DOI DOI 10.18653/V1/D18-1248
   Wang HW, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P968, DOI 10.1145/3292500.3330836
   Wang M., 2017, ARXIV PREPRINT ARXIV, V1712, P8875
   Wang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P950, DOI 10.1145/3292500.3330989
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Xu KYL, 2018, PR MACH LEARN RES, V80
   Yu D., 2020, 07331 ARXIV, P07331
   Yu Y., 2020, IMWUT
   Zeng Hanqing, 2020, P ICLR
   Zhang MH, 2018, ADV NEUR IN, V31
   Zitnik M., 2018, BIOSNAP DATASETS STA
   Zitnik M, 2018, BIOINFORMATICS, V34, P457, DOI 10.1093/bioinformatics/bty294
NR 55
TC 8
Z9 8
U1 9
U2 18
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD SEP 15
PY 2021
VL 37
IS 18
BP 2988
EP 2995
DI 10.1093/bioinformatics/btab207
EA MAR 2021
PG 8
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA XS1XH
UT WOS:000732709000024
PM 33769494
OA Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Duan, YC
   Lu, ZH
   Zhou, ZB
   Sun, XB
   Wu, J
AF Duan, Yucong
   Lu, Zhihui
   Zhou, Zhangbing
   Sun, Xiaobing
   Wu, Jie
TI Data Privacy Protection for Edge Computing of Smart City in a DIKW
   Architecture
SO ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Knowledge graph; Privacy protection; Typed resources; Edge computing;
   Smart city
ID KNOWLEDGE; SECURITY
AB Current trend of shifting computing from centralized Cloud to Edge has not only empowered huge amount of IoT devices with the capability of the accumulation of individualized computing and storage as a flexible whole, but also brought along new privacy challenges originating in emerging new usage requests on the accumulated content or resources from multiple sources of various integrated devices at the Edge. In this work, we focus on modeling the privacy content of multiple sources through mapping them as resources of types of Data, Information and Knowledge in the well-known DIKW architecture. We propose to categorize content objects and relationships uniformly as typed resources of data, information, and knowledge, according to our formalized DIKW architecture composing a meta model of DIKW and extended Data Graph, Information Graph and Knowledge Graph. We further propose to categorize target privacy resources of data and information according to their presence in the modeled searching space in our DIKW architecture as explicit and implicit divisions. Thereafter we propose protection solutions according to explicit and implicit divisions for privacy target concerning typed data. The efficiency and performance potential of our processing solution originates in a multiple dimensional modeling strategy of typed data, which is modeled solely with various frequencies of various meta level dimensions.
C1 [Duan, Yucong] Hainan Univ, Coll Informat Sci & Technol, Haikou, Hainan, Peoples R China.
   [Lu, Zhihui] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China.
   [Zhou, Zhangbing] China Univ Geosci Beijing, Beijing, Peoples R China.
   [Sun, Xiaobing] Yangzhou Univ, Dept Elect Engn, Yangzhou, Jiangsu, Peoples R China.
   [Wu, Jie] Minist Educ, Engn Res Ctr Cyber Secur Auditing & Monitoring, Shanghai, Peoples R China.
RP Lu, ZH (通讯作者)，Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China.
EM duanyucong@hotmail.com; lzh@fudan.edu.cn; zhangbing.zhou@gmail.com;
   sundomore@163.com; jwu@fudan.edu.cn
FU NSFC [61662021, 61873309, 61572137, 61728202]; Shanghai 2018 Innovation
   Action Plan project, China [18510760200]; CERNET, China [NGII20180607];
   2017 Research Projects of Shanghai Science and Technology Commission
   [17DZ1101000]; Inner Mongolia Provincial Education Department
   [NJSY17428];  [ZDYF2017128]
FX We acknowledge Hainan, China Project No. ZDYF2017128, NSFC under Grant
   (No. 61662021, No. 61873309, No. 61572137 and No. 61728202), Shanghai
   2018 Innovation Action Plan project, China under Grant No. 18510760200 -
   Research on smart city big data processing technology based on cloud-fog
   mixed mode and CERNET, China project No. NGII20180607, 2017 Research
   Projects of Shanghai Science and Technology Commission under Grant No.
   17DZ1101000-Research of data circulation market system construction, and
   Inner Mongolia Provincial Education Department 2017 Project under Grant
   No. NJSY17428-Research on sharing architecture and model of inner
   mongolia vocational education distance virtual simulation training
   platform.
CR Ackoff R.L., 1989, J APPL SYSTEMS ANAL, V16, P3, DOI DOI 10.1002/9781444303179.CH3
   [Anonymous], 2007, 7 IEEE INT C DAT MIN
   APPELT DE, 1993, IJCAI-93, VOLS 1 AND 2, P1172
   Benson K, 2011, PROCEEDINGS OF THE 3RD ACM WORKSHOP CLOUD COMPUTING SECURITY WORKSHOP (CCSW'11), P73
   Chawla S, 2005, LECT NOTES COMPUT SC, V3378, P363
   Chen M, 2009, IEEE COMPUT GRAPH, V29, P12, DOI 10.1109/MCG.2009.6
   Coffman T, 2004, COMMUN ACM, V47, P45, DOI 10.1145/971617.971643
   Duan YC, 2017, INT J DISTRIB SENS N, V13, DOI 10.1177/1550147717743700
   Duan YC, 2017, 2017 IEEE/ACIS 15TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING RESEARCH, MANAGEMENT AND APPLICATIONS (SERA), P327, DOI 10.1109/SERA.2017.7965747
   Duan YC, 2016, INT J SERV TECHNOL M, V22, P120, DOI 10.1504/IJSTM.2016.077670
   Fayyad U, 1996, COMMUN ACM, V39, P27, DOI 10.1145/240455.240464
   Gai KK, 2018, IEEE T IND INFORM, V14, P3590, DOI 10.1109/TII.2017.2780885
   Lopez PG, 2015, ACM SIGCOMM COMP COM, V45, P37, DOI 10.1145/2831347.2831354
   Gaudeul A, 2017, J ECON BEHAV ORGAN, V141, P233, DOI 10.1016/j.jebo.2017.07.007
   Ghahramani Z, 2015, NATURE, V521, P452, DOI 10.1038/nature14541
   Grishman R., 1997, Information Extraction. A Multidisciplinary Approach to an Emerging Information Technology International Summer School, SCIE-97, P10
   Guu K., 2015, CORR
   Holsapple CW, 2001, DECIS SUPPORT SYST, V31, P39, DOI 10.1016/S0167-9236(00)00118-4
   Hundepool A., 2012, STAT DISCLOSURE CONT
   Krumm J, 2009, PERS UBIQUIT COMPUT, V13, P391, DOI 10.1007/s00779-008-0212-5
   Li YB, 2016, IEEE T COMPUT, V65, P1339, DOI 10.1109/TC.2015.2470247
   McSherry F, 2009, ACM SIGMOD/PODS 2009 CONFERENCE, P19
   Meiklejohn S., 2015, CORR
   Minervini P, 2015, 2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), P162, DOI 10.1109/ICMLA.2015.132
   Ossher H, 2001, COMMUN ACM, V44, P43, DOI 10.1145/383845.383856
   Pujara J, 2013, LECT NOTES COMPUT SC, V8218, P542, DOI 10.1007/978-3-642-41335-3_34
   Qiu MK, 2018, FUTURE GENER COMP SY, V80, P421, DOI 10.1016/j.future.2016.01.006
   Roberts R., 1998, CRITIQUE PURE REASON
   Shi WS, 2016, IEEE INTERNET THINGS, V3, P637, DOI 10.1109/JIOT.2016.2579198
   Smith C, 1998, KENYON REV, V20, P44
   Song ZY, 2018, WIREL COMMUN MOB COM, DOI 10.1155/2018/3794175
   Soria-Comas J, 2016, DATA SCI ENG, V1, P21, DOI 10.1007/s41019-015-0001-x
   Sowa J., 2000, KNOWLEDGE REPRESENTA
   Staab S, 2001, IEEE INTELL SYST APP, V16, P26, DOI 10.1109/5254.912382
   Subashini S, 2011, J NETW COMPUT APPL, V34, P1, DOI 10.1016/j.jnca.2010.07.006
   Sun YC, 2014, INT J DISTRIB SENS N, DOI 10.1155/2014/190903
   Tarr P., 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002), P107, DOI 10.1109/ICSE.1999.841000
   Xiao H., 2016, CORR
   Xu J., 2016, CORR
   Yucong Duan, 2009, International Journal of Software Science and Computational Intelligence, V1, P90, DOI 10.4018/jssci.2009062505
   Zhang JL, 2018, IEEE ACCESS, V6, P18209, DOI 10.1109/ACCESS.2018.2820162
   Zheleva E., 2007, PINKDD, P153, DOI DOI 10.1007/978-3-540-78478-4-9
NR 42
TC 21
Z9 22
U1 7
U2 32
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0952-1976
EI 1873-6769
J9 ENG APPL ARTIF INTEL
JI Eng. Appl. Artif. Intell.
PD MAY
PY 2019
VL 81
BP 323
EP 335
DI 10.1016/j.engappai.2019.03.002
PG 13
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Engineering, Multidisciplinary; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science; Engineering
GA HZ3BE
UT WOS:000468721700027
DA 2022-08-23
ER

PT J
AU Shi, CC
   Lu, WB
   Song, R
AF Shi, Chengchun
   Lu, Wenbin
   Song, Rui
TI Determining the Number of Latent Factors in Statistical Multi-Relational
   Learning
SO JOURNAL OF MACHINE LEARNING RESEARCH
LA English
DT Article
DE Information criteria; Knowledge graph; Model selection consistency;
   RESCAL model; Statistical relational learning; Tensor factorization
AB Statistical relational learning is primarily concerned with learning and inferring relationships between entities in large-scale knowledge graphs. Nickel et al. (2011) proposed a RESCAL tensor factorization model for statistical relational learning, which achieves better or at least comparable results on common benchmark data sets when compared to other state-of-the-art methods. Given a positive integer s, RESCAL computes an s-dimensional latent vector for each entity. The latent factors can be further used for solving relational learning tasks, such as collective classification, collective entity resolution and link-based clustering.
   The focus of this paper is to determine the number of latent factors in the RESCAL model. Due to the structure of the RESCAL model, its log-likelihood function is not concave. As a result, the corresponding maximum likelihood estimators (MLEs) may not be consistent. Nonetheless, we design a specific pseudometric, prove the consistency of the MLEs under this pseudometric and establish its rate of convergence. Based on these results, we propose a general class of information criteria and prove their model selection consistencies when the number of relations is either bounded or diverges at a proper rate of the number of entities. Simulations and real data examples show that our proposed information criteria have good finite sample properties.
C1 [Shi, Chengchun; Lu, Wenbin; Song, Rui] North Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA.
RP Shi, CC (通讯作者)，North Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA.
EM CSHI4@NCSU.EDU; LU@STAT.NCSU.EDU; RSONG@NCSU.EDU
FU NCI NIH HHS [P01 CA142538] Funding Source: Medline
CR Allen G., 2012, P ART INT STAT, P27
   Audibert JY, 2007, ANN STAT, V35, P608, DOI 10.1214/009053606000001217
   Bai JS, 2002, ECONOMETRICA, V70, P191, DOI 10.1111/1468-0262.00273
   Banerjee S., 2014, LINEAR ALGEBRA MATRI, DOI DOI 10.1201/B17040
   Boyd S, 2011, TRENDS MACH LEARN, V3, P1, DOI DOI 10.1561/2200000016
   Chi EC, 2012, SIAM J MATRIX ANAL A, V33, P1272, DOI 10.1137/110859063
   Fan YY, 2013, J R STAT SOC B, V75, P531, DOI 10.1111/rssb.12001
   Galassi M., 2015, GNU SCI LIB REFERENC
   Getoor L., 2011, P ACM SIGMOD INT C M, P1195, DOI DOI 10.1145/1989323.1989451
   HARSHMAN RA, 1994, COMPUT STAT DATA AN, V18, P39, DOI 10.1016/0167-9473(94)90132-5
   Kemp C., 2006, AAAI, V3, P5
   Kok S., 2007, P 24 INT C MACH LEAR, P433, DOI DOI 10.1145/1273496.1273551
   Kolda TG, 2009, SIAM REV, V51, P455, DOI 10.1137/07070111X
   Madan A, 2012, IEEE PERVAS COMPUT, V11, P36, DOI 10.1109/MPRV.2011.79
   Mendelson S, 2008, CONSTR APPROX, V28, P277, DOI 10.1007/s00365-007-9005-8
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M., 2013, ARXIV13062084
   Nickel M, 2013, THESIS
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Nowicki K, 2001, J AM STAT ASSOC, V96, P1077, DOI 10.1198/016214501753208735
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136
   Sun WW, 2017, J R STAT SOC B, V79, P899, DOI 10.1111/rssb.12190
   Tsybakov AB, 2004, ANN STAT, V32, P135
   TUCKER LR, 1966, PSYCHOMETRIKA, V31, P279, DOI 10.1007/BF02289464
   van der Vaart A., 1996, WEAK CONVERGENCE EMP, DOI [10.1007/978-1-4757-2545-2, DOI 10.1007/978-1-4757-2545-2]
   Wang Y., 2017, ARXIV151106324
   Yang Y, 2016, J AM STAT ASSOC, V111, P656, DOI 10.1080/01621459.2015.1029129
   Zhang X, 2016, J MACH LEARN RES, V17
NR 29
TC 2
Z9 2
U1 0
U2 0
PU MICROTOME PUBL
PI BROOKLINE
PA 31 GIBBS ST, BROOKLINE, MA 02446 USA
SN 1532-4435
J9 J MACH LEARN RES
JI J. Mach. Learn. Res.
PY 2019
VL 20
AR 23
PG 38
WC Automation & Control Systems; Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA HL4EJ
UT WOS:000458668200001
PM 31983896
DA 2022-08-23
ER

PT J
AU Wei, X
   Liang, C
   Chen, WH
AF Wei, Xue
   Liang, Chen
   Chen, Wenhui
TI Exploring Current Status and Evolutionary Trends on the Paid Use of
   State-Owned Forest Resources in China: A Bibliometric Perspective
SO SUSTAINABILITY
LA English
DT Review
DE state-owned forest resources; paid use; bibliometric analysis;
   co-occurrence network
ID ECOSYSTEM SERVICES; MANAGEMENT; BENEFITS
AB State-owned forest resources occupy an important position in China, and the development of their paid use will help to improve the economic benefits of these resources. For this study, 451 journal documents involving the paid use of state-owned forest resources in the CNKI database of China from 2008 to 2021 were selected as samples. Combining qualitative reviews with quantitative analysis, statistical analysis software was used as an analytical tool. The knowledge maps can be visualized by cluster analysis, multidimensional scaling (MDS), and co-occurrence network analysis. The change laws of this research in the time dimension were obtained using developing trend analysis. The results are as follows: 1. The number of research documents on the paid use of state-owned forest resources is increasing. 2. The core authors account for 29.27%; the research impact is relatively scattered. 3. Research institutions are primarily colleges and universities. 4. The support of provincially funded projects accounts for the highest proportion. 5. There is a relatively stable number of journals in this research field. Forestry Economy, Green Science and Technology and China Forestry Economy are the top three journals in terms of citation impact. 6. The existing research topics mainly focus on the development status of paid use, forest tourism and forest health, and the under-forestry economy (under-forestry planting, breeding, and product processing). 7. The intermediary centralities of state-owned forest farms and under-forestry economy are the highest, followed by forest tourism and forest experience, etc. With time and the promulgation of policies, the research focus in this field has gradually shifted from forest assets and forestry economics to ecotourism and forest health, and research on forest carbon sequestration is a technical branch worthy of attention in the future.
C1 [Wei, Xue; Liang, Chen; Chen, Wenhui] Beijing Forestry Univ, Sch Econ & Management, Beijing 100083, Peoples R China.
RP Chen, WH (通讯作者)，Beijing Forestry Univ, Sch Econ & Management, Beijing 100083, Peoples R China.
EM weixue336699@163.com; liangchen139@163.com; chenwenhui77@163.com
OI Liang, Chen/0000-0002-9923-0580; Wei, Xue/0000-0002-3240-9973
FU China-UK LTS Beijing representative offices. Research on the Impact of
   China's National Reserve Forest Project from the Perspective of the
   Global Timber Supply Chain and Forest Governance [L19-15 03/2022]
FX This research was funded by the China-UK LTS Beijing representative
   offices. Research on the Impact of China's National Reserve Forest
   Project from the Perspective of the Global Timber Supply Chain and
   Forest Governance (grant number L19-15 03/2022).
CR Bai JD, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13063119
   Bouzembrak Y, 2019, TRENDS FOOD SCI TECH, V94, P54, DOI 10.1016/j.tifs.2019.11.002
   Bradford S. C., 1976, COLLECTION MANAGEMEN, V1, P95, DOI [10.1300/J105v01n03_06., DOI 10.1300/J105V01N03_06]
   Bu C.L, 2021, SCI TECHNOL INF, V53, P84
   Cao W., 2014, CHINA POPUL RESOUR E, V24, P100, DOI [10.3969/j.issn.1002-2104.2014.08.013, DOI 10.3969/J.ISSN.1002-2104.2014.08.013]
   Ceccherini G, 2020, NATURE, V583, P72, DOI 10.1038/s41586-020-2438-y
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen Q.H, 2018, FOR EC, V40, P66, DOI [10.13843/j.cnki.lyjj.2018.10.012, DOI 10.13843/J.CNKI.LYJJ.2018.10.012]
   Cheng Y., 2021, RURAL EC, V11, P62
   Costanza R, 1997, NATURE, V387, P253, DOI 10.1038/387253a0
   De Solla Price D.J., 1963, LITTLE SCI BIG SCI, P48
   Demyanyk Y, 2010, OMEGA-INT J MANAGE S, V38, P315, DOI 10.1016/j.omega.2009.09.007
   Diaz-Balteiro L, 2017, ECOL INDIC, V72, P322, DOI 10.1016/j.ecolind.2016.06.025
   Dong J.Y., 2019, THESIS BEIJING FORES, DOI [10.26949/d.cnki.gblyu.2019.000332, DOI 10.26949/D.CNKI.GBLYU.2019.000332]
   Donohue JC., 1973, UNDERSTANDING SCI LI, P49
   Du Y., 2016, FOR RESOUR MANAG, V6, P111, DOI [10.13466/j.cnki.lyzygl.2016.06.021, DOI 10.13466/J.CNKI.LYZYGL.2016.06.021]
   FAO, 2020, GLOBAL FOREST RESOUR, DOI 10.4060/ca8753-n?
   Ferguson MWJ, 2016, NATURE, V538, P453, DOI 10.1038/538453a
   FREEMAN LC, 1979, SOC NETWORKS, V1, P215, DOI 10.1016/0378-8733(78)90021-7
   [国靖 Guo Jing], 2017, [中南林业科技大学学报, Journal of Central South University of Forestry & Technology], V37, P118
   HARDIN G, 1968, SCIENCE, V162, P1243, DOI 10.1126/science.162.3859.1243
   Hu XiaoJun, 2018, Journal of Zhejiang A&F University, V35, P537
   Karlsson M, 2008, J CLEAN PROD, V16, P1536, DOI 10.1016/j.jclepro.2007.08.017
   Koponen K, 2018, RENEW SUST ENERG REV, V81, P2271, DOI 10.1016/j.rser.2017.05.292
   Kuang Y., 2018, THESIS ANHUI U HEFEI
   Laakkonen A, 2019, FOREST POLICY ECON, V99, P157, DOI 10.1016/j.forpol.2018.07.003
   Lambini CK, 2014, FOREST POLICY ECON, V38, P178, DOI 10.1016/j.forpol.2013.09.006
   Lei C., 2008, DATA ANAL KNOWLEDGE, V8, P70, DOI 10.3969/j.issn.1003-3513.2008.08.013
   Li J., 2017, CITESPACE SCI TECHNO, P112
   Li J.R., 2018, FOR EC, V40, P28, DOI [10.13843/j.cnki.lyjj.2018.03.007, DOI 10.13843/J.CNKI.LYJJ.2018.03.007]
   Li J.X, 2021, RURAL SCI TECHNOL, V12, P95, DOI [10.19345/j.cnki.1674-7909.2021.01.049, DOI 10.19345/J.CNKI.1674-7909.2021.01.049]
   Li Z.W., 2017, FOR PROD IND, V44, P56, DOI [10.19531/j.issn1001-5299.201711013, DOI 10.19531/J.ISSN1001-5299.201711013]
   Liu J., 2014, WHOLE NETWORK ANAL P, V2nd, P144
   Liu J.C., 2010, RES MANAGEMENT STATE, P214
   Liu L.J., 2021, FOR EC, V43, P83, DOI [10.13843/j.cnki.lyjj.20210428.002, DOI 10.13843/J.CNKI.LYJJ.20210428.002]
   Liu SL, 2019, FOREST POLICY ECON, V98, P44, DOI 10.1016/j.forpol.2018.06.001
   Liu T., 2017, FOR EC, V39, P39, DOI [10.13843/j.cnki.lyjj.2017.02.008, DOI 10.13843/J.CNKI.LYJJ.2017.02.008]
   Lu S.C., 2018, JIANGXI AGR, V22, P97, DOI [10.19394/j.cnki.issn1674-4179.2018.22.084, DOI 10.19394/J.CNKI.ISSN1674-4179.2018.22.084]
   Luo M., 2014, CHIN J MANAG, V11, P1680, DOI 10.3969/j.issn.1672-884x.2014.11.014
   Lv X., 2007, FOR EC, V8, P74
   Ma C.J, 2019, MOD AGR SCI TECHNOL, V23, P137
   Mishra Shubhanshu, 2016, Dlib Mag, V22, DOI 10.1045/september2016-mishra
   National Forestry and Grassland Administration, 2019, CHIN FOR RES REP 201, P5
   National Forestry and Grassland Administration of China, SUGG ACC PAID US FOR
   Nielsen AB, 2007, LANDSCAPE URBAN PLAN, V80, P63, DOI 10.1016/j.landurbplan.2006.06.003
   Pankowska M, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11051460
   Pelyukh O, 2021, FOREST POLICY ECON, V131, DOI 10.1016/j.forpol.2021.102561
   Peng B., 2014, J GUANGXI U NATL, V36, P168
   Qi Y., 2018, FOR EC, V40, P14, DOI [10.13843/j.cnki.lyjj.2018.02.003, DOI 10.13843/J.CNKI.LYJJ.2018.02.003]
   Shang H.P., 2012, SCI RES, V30, P1476, DOI [10.16192/j.cnki.1003-2053.2012.10.006, DOI 10.16192/J.CNKI.1003-2053.2012.10.006]
   Shu Yi, 2019, World Forestry Research, V32, P51, DOI 10.13348/j.cnki.sjlyyj.2019.0024.y
   Song ML, 2019, J ENVIRON MANAGE, V248, DOI 10.1016/j.jenvman.2019.07.003
   State Council of China, GUID OP REF SYST PAI
   Sun J.F, 2018, RURAL SCI TECHNOL, V18, P26, DOI [10.19345/j.cnki.1674-7909.2018.18.019, DOI 10.19345/J.CNKI.1674-7909.2018.18.019]
   United Nations, 2015, TRANSF OUR WORLD 203
   United Nations Department of Economic and Social Affairs, 2016, UN FOR INSTR
   United Nations Department of Economic and Social Affairs, 2017, UN STRAT PLAN FOR 20
   Wang T.J, 2010, FOR PRACT TECHNOL, V5, P42, DOI [10.13456/j.cnki.lykt.2010.05.032, DOI 10.13456/J.CNKI.LYKT.2010.05.032]
   Wang XX, 2021, INFORM SCIENCES, V547, P328, DOI 10.1016/j.ins.2020.08.036
   Wei J, 2021, ENVIRON SCI POLLUT R, V28, P18850, DOI 10.1007/s11356-021-12908-8
   Wu GuoChun, 2020, Journal of Northeast Forestry University, V48, P129
   Wu H., 2019, FOR EC, V41, P78, DOI [10.13843/j.cnki.lyjj.2019.07.012, DOI 10.13843/J.CNKI.LYJJ.2019.07.012]
   Wu P.O., 2016, FOR EC ISSUES, V36, P406, DOI [10.16832/j.cnki.1005-9709.2016.05.004, DOI 10.16832/J.CNKI.1005-9709.2016.05.004]
   Xu P., 2016, FOR RESOUR MANAG, V1, P19, DOI [10.13466/j.cnki.lyzygl.2016.01.005, DOI 10.13466/J.CNKI.LYZYGL.2016.01.005]
   Yan R.H., 2018, FOR EC ISSUES, V38, P28, DOI [10.16832/j.cnki.1005-9709.2018.01.005, DOI 10.16832/J.CNKI.1005-9709.2018.01.005]
   Yang H., 2013, THESIS JILIN U CHANG
   Yao X.Q., 2007, FOR SURV PLAN, V32, P75, DOI [10.3969/j.issn.1671-3168.2007.06.020, DOI 10.3969/J.ISSN.1671-3168.2007.06.020]
   Yin Z.J., 2020, MOD URBAN RES, V10, P97, DOI [10.3969/j.issn.1009-6000.2020.10.013, DOI 10.3969/J.ISSN.1009-6000.2020.10.013]
   Farias FZ, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su131810154
   Zhang CJ, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11041150
   Zhang W.M., 2020, MACROECON MANAG, V3, P73, DOI [10.19709/j.cnki.11-3199/f.20200326.006, DOI 10.19709/J.CNKI.11-3199/F.20200326.006]
   Zhang Y., 2011, INFORM SCIENCES, V10, P1589
   Zheng M.S., 2020, FOR EC, V42, P30, DOI [10.13843/j.cnki.lyjj.20200811.005, DOI 10.13843/J.CNKI.LYJJ.20200811.005]
   Zhou Z., 2020, CHINA EC, V2, P137, DOI [10.13691/j.cnki.cn23-1539/f.2020.02.037, DOI 10.13691/J.CNKI.CN23-1539/F.2020.02.037]
NR 74
TC 0
Z9 0
U1 6
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAY
PY 2022
VL 14
IS 9
AR 5516
DI 10.3390/su14095516
PG 23
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA 1E5NT
UT WOS:000794535700001
OA gold
DA 2022-08-23
ER

PT J
AU Mohammed, S
   Fiaidhi, J
AF Mohammed, Sabah
   Fiaidhi, Jinan
TI Establishment of a mindmap for medical e-Diagnosis as a service for
   graph-based learning and analytics
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article; Early Access
DE Microservices; GraphQL; FHIR; Service federation; Service schema;
   e-Diagnosis as a service
AB As medical services increasingly trying to harness advances in connected medical devices and the use of artificial intelligence, a new modeling strategy is essentially required to enable developers of the emerging medical applications to organize, integrate, and retain information in this new era of service-oriented healthcare. Such strategy needs to adhere to the principle of knowledge coupling that was advocated by Lawrence Weed, the father of modern problem-oriented medical records, in early 1970s where he defined the way medical information should be described for higher decision making. There has never been a more compelling time to use knowledge coupling related to both medical knowledge and the services build around them. The new digital technologies such as microservices, graph-based databases, Internet of Healthcare Things, and Cloud Computing as well as the non-digital disruptive events such as the pandemic have accelerated the adoption of new notions of service integration and knowledge coupling to provide the long waited solution for interoperability in healthcare as well as higher level of knowledge integration and analytics. The cornerstone of every new change is pointing toward the use of microservices and knowledge graph APIs to be able to thrive and lead in the uncertainty and healthcare change. This research paper uses the notion of mindmap to push conversation and guide scholars in developing effective microservice-based care systems that utilize knowledge graphs and the new care standards including the HL7 FHIR. Central to our mindmap is the GraphQL graph-based technology and the medical diagnosis as a service. This mindmap is the starting research point of our MITACS 2021 and NSERC DDG 2021 projects.
C1 [Mohammed, Sabah; Fiaidhi, Jinan] Lakehead Univ, Dept Comp Sci, Thunder Bay, ON, Canada.
RP Mohammed, S (通讯作者)，Lakehead Univ, Dept Comp Sci, Thunder Bay, ON, Canada.
EM sabah.mohammed@lakeheadu.ca; jfiaidhi@lakeheaduu.ca
FU NSERC Discovery Grant 2021 (Discovery Development Grant)
   [DDG-2021-00014]; MITACS [MITACS_IT22305]
FX This research is funded by the first author NSERC Discovery Grant 2021
   (Discovery Development Grant DDG-2021-00014) and the MITACS research
   project of both authors (MITACS_IT22305).
CR Aceto G, 2020, J IND INF INTEGR, V18, DOI 10.1016/j.jii.2020.100129
   Ahir Yogesh, 2019, 2019 International Conference on Communication and Signal Processing (ICCSP), P0153, DOI 10.1109/ICCSP.2019.8698073
   Albert SM, 2011, TELEMED E-HEALTH, V17, P304, DOI 10.1089/tmj.2010.0156
   Berner ES, 2008, AM J MED, V121, P2, DOI 10.1016/j.amjmed.2008.01.001
   Bettoni GN, 2021, APPL HL7 FHIR MICROS
   Biehl M., 2018, GRAPHQL API DESIGN, V5
   Brito G, 2020, IEEE 17TH INTERNATIONAL CONFERENCE ON SOFTWARE ARCHITECTURE (ICSA 2020), P81, DOI 10.1109/ICSA47634.2020.00016
   Cassettari L, 2019, IND 4 0 ITS APPL HEA
   Cillessen FHJM, 2012, METHOD INFORM MED, V51, P507, DOI 10.3414/ME11-01-0064
   Dane A, 2021, NETFLIX TECHNOL 0222
   Doi K, 2007, COMPUT MED IMAG GRAP, V31, P198, DOI 10.1016/j.compmedimag.2007.02.002
   Farouzi A, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P3377, DOI 10.1145/3340531.3417429
   Goyal P, 2018, KNOWL-BASED SYST, V151, P78, DOI 10.1016/j.knosys.2018.03.022
   Heredia JS, 2019, INT C APPL TECHN, P286
   Kozhevnikov VA., 2021, THEOR APPL SCI, V1, P180, DOI [10.15863/TAS.2021.01.93.31, DOI 10.15863/TAS.2021.01.93.31]
   Mukhiya SK, 2019, PROCEDIA COMPUT SCI, V160, P338, DOI 10.1016/j.procs.2019.11.082
   Mun SK, 2021, FRONT MOL BIOSCI, V7, DOI 10.3389/fmolb.2020.614258
   Stunkel P, 2020, J OBJECT TECHNOL, V19, DOI 10.5381/jot.2020.19.2.a18
NR 18
TC 0
Z9 0
U1 1
U2 6
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0941-0643
EI 1433-3058
J9 NEURAL COMPUT APPL
JI Neural Comput. Appl.
DI 10.1007/s00521-021-06200-6
EA SEP 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UT4QE
UT WOS:000698101100001
DA 2022-08-23
ER

PT J
AU Wang, C
   Xu, SS
AF Wang, Chang
   Xu, Shuangshuang
TI Construction of the Evaluation Index System of Physical Education
   Teaching in Colleges and Universities Based on Scientific Knowledge
   Graph
SO MOBILE INFORMATION SYSTEMS
LA English
DT Article
AB The purpose of physical education (PE) teaching in general institutions of higher education is to enhance students' fitness and mental quality, which in turn improves students' physiological ability and sports skills and finally enables students to develop healthy lifestyle. The conventional assessment of students' PE instruction only focuses on the evaluation of their body condition and sports skills, ignoring the cultivation of their mental and physical development and healthy living habits and evaluating students with a relatively single evaluation standard, lacking the cultivation of "teaching to suit the individual." At present, the evaluation method of instructional quality of PE teachers in China's institutions of higher education is relatively single and dominated by qualitative methods, which cannot be applied to instructional innovation. The evaluation of physical education teaching quality plays an important role in building a complete and effective teaching process, which plays an important role in judging whether the formulation of physical education teaching objectives and principles is reasonable, and the evaluation of students' learning behavior and learning effect. This article takes the relevant theory of scientific knowledge graph as the guiding idea, based on the current situation of PE teaching assessment in institutions of higher education, and employs experimental research methods to conduct empirical studies on 10 universities in Jilin Province. The empirical findings suggest that students' satisfaction with the new evaluation method is 83.68 points on average, and the accuracy rate is 84.54 points on average; teachers' satisfaction with the new evaluation method is 86.12 points on average, and the accuracy rate is 88.4 points on average.
C1 [Wang, Chang] Northeast Normal Univ, Sch Phys Educ, Changchun 130024, Jilin, Peoples R China.
   [Xu, Shuangshuang] Jilin Inst Phys Educ, Sch Sports Hlth & Technol, Changchun 130022, Jilin, Peoples R China.
RP Xu, SS (通讯作者)，Jilin Inst Phys Educ, Sch Sports Hlth & Technol, Changchun 130022, Jilin, Peoples R China.
EM wangc358@nenu.edu.cn; 0766@jlsu.edu.cn
CR Cao XZ, 2022, MOBILE NETW APPL, DOI 10.1007/s11036-021-01884-5
   Chernova L, 2020, INNOV TECHNOL SCI SO, P86, DOI [10.30837/itssi.2020.14.086, DOI 10.30837/ITSSI.2020.14.086]
   Ekanayake EMAC, 2019, ENG CONSTR ARCHIT MA, V26, P499, DOI 10.1108/ECAM-06-2018-0252
   Fan SX, 2020, SCI PROGRAMMING-NETH, V2020, DOI 10.1155/2020/8842463
   Fan Tiantian, 2021, Zhonghua Wei Zhong Bing Ji Jiu Yi Xue, V33, P433, DOI 10.3760/cma.j.cn121430-20201009-00658
   Fuji M, 2019, FUJITSU SCI TECH J, V55, P58
   Guo B, 2019, ADV CIV ENG, V2019, DOI 10.1155/2019/5161579
   Guo JQ, 2018, COGN SYST RES, V52, P521, DOI 10.1016/j.cogsys.2018.07.028
   Klemish D, 2018, J SPORT SCI, V36, P171, DOI 10.1080/02640414.2017.1288296
   Koide Yoshinori, 2019, J Phys Ther Sci, V31, P53, DOI 10.1589/jpts.31.53
   Liu YW, 2020, J ADV TRANSPORT, V2020, DOI 10.1155/2020/7985719
   Ming N, 2021, REGION ED RES REV, V3, P1, DOI [10.32629/rerr.v3i3.386, DOI 10.32629/RERR.V3I3.386]
   Shi ZH, 2016, INT J DISTRIB SENS N, V12, DOI 10.1177/1550147716657266
   Stein Matthew K, 2019, JCO Precis Oncol, V3, P1, DOI 10.1200/PO.19.00211
   Sun Z, 2022, MATH PROBL ENG, V2022, DOI 10.1155/2022/2245666
   Tan Z, 2018, SCI PROGRAMMING-NETH, V2018, DOI 10.1155/2018/6325635
   Xiao GH, 2019, KNOWL ORGAN, V46, P71, DOI 10.5771/0943-7444-2019-1-71
NR 17
TC 0
Z9 0
U1 2
U2 2
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1574-017X
EI 1875-905X
J9 MOB INF SYST
JI Mob. Inf. Syst.
PD JUN 20
PY 2022
VL 2022
AR 4225081
DI 10.1155/2022/4225081
PG 11
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 2S5LY
UT WOS:000821834700002
OA gold
DA 2022-08-23
ER

PT J
AU Zhu, RC
   Hu, XF
   Li, X
   Ye, H
AF Zhu, Rongchen
   Hu, Xiaofeng
   Li, Xin
   Ye, Han
TI A Personalized and Practical Method for Analyzing the Risk of Chemical
   Terrorist Attacks
SO IEEE ACCESS
LA English
DT Article
DE Chemical terrorist attack; Bayesian network; knowledge graph; risk
   analysis
ID PROBABILISTIC RISK; BAYESIAN NETWORKS; FRAMEWORK; PATTERNS
AB The chemical terrorist attack is a type of unconventional terrorism that threatens the safety of cities. This kind of terrorist attack is highly concealed and difficult to be detected. Once the attack is successful, the consequences will be severe and the scope of impact will be enormous. Therefore, public security and emergency departments need to perform risk analysis and dynamic knowledge update to reduce risk or mitigate the effects of accidents. In order to quickly and effectively analyze the risk of chemical terrorist attacks, this article proposed a hybrid approach (B-R model) to analyze the risk of chemical terrorist attacks. First, a modular and customizable Bayesian network (BN) model library was built, which can satisfy users to select multi-dimensional risk factors. Based on the personalized BN, a risk knowledge graph (RKG) is constructed with multi-source data to realize the combination of risk analysis and knowledge acquisition. Then the threat degree of terrorist organizations, the strength of defensive forces, and the risk value of targets is calculated and displayed. The BN-RKG method provides data and theoretical support for defenders & x2019; resource allocation and emergency decision-making. Finally, a case study was conducted for a hypothetical scenario analysis. The result shows that the hybrid method can help with risk control and have the potential to support practical policymaking.
C1 [Zhu, Rongchen; Hu, Xiaofeng; Li, Xin; Ye, Han] Peoples Publ Secur Univ China, Sch Informat Technol & Network Secur, Beijing 102628, Peoples R China.
RP Hu, XF (通讯作者)，Peoples Publ Secur Univ China, Sch Informat Technol & Network Secur, Beijing 102628, Peoples R China.
EM huxiaofeng@ppsuc.edu.cn
RI Zhu, Rongchen/AAZ-2501-2020
OI Zhu, Rongchen/0000-0002-8379-5579
FU National Key Research and Development Program of China [2018YFC0809702];
   National Natural Science Foundation of China [71704183]
FX This work was funded by the National Key Research and Development
   Program of China under Grant 2018YFC0809702 and the National Natural
   Science Foundation of China under Grant 71704183.
CR Akgun I, 2010, EXPERT SYST APPL, V37, P3561, DOI 10.1016/j.eswa.2009.10.035
   Olsen S J, 2000, MMWR CDC Surveill Summ, V49, P1
   Argenti F, 2017, SAFETY SCI, V94, P181, DOI 10.1016/j.ssci.2016.11.022
   Bajpai S, 2010, J HAZARD MATER, V173, P258, DOI 10.1016/j.jhazmat.2009.08.078
   Balk SJ, 2000, PEDIATRICS, V105, P662
   BONATTI PA, 2019, DAGSTUHL REPORTS, V8, P29, DOI DOI 10.4230/DAGREP.8.9.29
   Boyd KA, 2016, STUD CONFL TERROR, V39, P712, DOI 10.1080/1057610X.2016.1141003
   Brown GG, 2011, RISK ANAL, V31, P196, DOI 10.1111/j.1539-6924.2010.01492.x
   Cox LA, 2008, RISK ANAL, V28, P1749, DOI 10.1111/j.1539-6924.2008.01142.x
   Eder J. S., 2012, US patent, Patent No. [13/404,109, 13404109]
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Emmett SR, 2020, GEN ASPECTS POISONIN, V48, P182
   Ezell BC, 2010, RISK ANAL, V30, P575, DOI 10.1111/j.1539-6924.2010.01401.x
   [傅子洋 Fu Ziyang], 2016, [灾害学, Journal of Catastrophology], V31, P184
   Garcia RJB, 2016, RISK ANAL, V36, P2258, DOI 10.1111/risa.12574
   Henretig FM, 2002, J PEDIATR-US, V141, P311, DOI 10.1067/mpd.2002.127408
   HU H, 1989, JAMA-J AM MED ASSOC, V262, P660
   Husmeier D, 2005, ADV INFO KNOW PROC, P17
   Hyams KC, 2002, J HEALTH POLIT POLIC, V27, P273, DOI 10.1215/03616878-27-2-273
   Jha K, 2016, PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY, AND HEALTH INFORMATICS, P317, DOI 10.1145/2975167.2975200
   Kaur M, 2016, INT CONF CONNECT VEH, P19, DOI 10.1109/ICCVE.2016.4
   Keeney GL, 2010, RISK ANAL, V30, P1803, DOI 10.1111/j.1539-6924.2010.01472.x
   Keim Mark E, 2003, Prehosp Disaster Med, V18, P193
   Kendall R. J., 2016, NEW DEV BIOL CHEM TE
   Khakzad N, 2018, PROCESS SAF PROG, V37, P211, DOI 10.1002/prs.11914
   Khan AS, 2001, PUBLIC HEALTH REP, V116, P3, DOI 10.1093/phr/116.1.3
   Lafree G, 2007, TERROR POLIT VIOLENC, V19, P181, DOI 10.1080/09546550701246817
   Luko S. N., QUALITY ENG, V25, P451
   Major J, 2002, J RISK FINANCE, V4, P15, DOI DOI 10.1108/eb022950
   Nie SJ, 2016, PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA), P18
   Okumura T, 1996, ANN EMERG MED, V28, P129, DOI 10.1016/S0196-0644(96)70052-5
   Olama MM, 2010, PROC SPIE, V7666, DOI 10.1117/12.849464
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Perry S, 2017, J QUANT CRIMINOL, V33, P727, DOI 10.1007/s10940-016-9309-6
   Regens JL, 2015, J COGN ENG DECIS MAK, V9, P295, DOI 10.1177/1555343415592730
   Rezazadeh A, 2019, RELIAB ENG SYST SAFE, V191, DOI 10.1016/j.ress.2018.04.021
   Sandler T., 2003, Simulation & Gaming, V34, P319, DOI 10.1177/1046878103255492
   Santos C, 2019, PREHOSP DISASTER MED, V34, P385, DOI 10.1017/S1049023X19004539
   Strack Columb, 2017, CTC SENTINEL, V10, P19
   TANG Z, 2019, SUSTAINABILITY BASEL, V0011, DOI DOI 10.3390/su11020306
   Toure I., 2016, P IEEE S TECHN HOM S, P1, DOI [10.1109/THS.2016.7568906., DOI 10.1109/THS.2016.7568906, 10.1109/ths.2016.7568906]
   Tutun S, 2017, EXPERT SYST APPL, V78, P358, DOI 10.1016/j.eswa.2017.02.029
   Volkanovski A, 2009, RELIAB ENG SYST SAFE, V94, P1116, DOI 10.1016/j.ress.2009.01.004
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Webber J., 2018, PROGRAMMATIC INTRO N
   [魏静 Wei Jing], 2015, [中国科学院大学学报, Journal of University of Chinese Academy of Sciences], V32, P264
   White G, 2013, J QUANT CRIMINOL, V29, P295, DOI 10.1007/s10940-012-9181-y
   Willis HH, 2008, RISK ANAL, V28, P325, DOI 10.1111/j.1539-6924.2008.01022.x
   Xia T, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI), P194, DOI 10.1109/ISI.2019.8823450
   Yan JH, 2018, FRONT COMPUT SCI-CHI, V12, P55, DOI 10.1007/s11704-016-5228-9
   Yang ZL, 2009, RISK ANAL, V29, P95, DOI 10.1111/j.1539-6924.2008.01158.x
   Zhang J., 2010, TSINGHUA SCI TECHNOL, V15, P604, DOI [10.1016/S1007-0214(10)70107-3, DOI 10.1016/S1007-0214(10)70107-3]
   Zhou L, 2003, J AM SOC INF SCI TEC, V54, P115, DOI 10.1002/asi.10193
   Zhu RC, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17062051
NR 54
TC 0
Z9 0
U1 3
U2 14
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 81711
EP 81723
DI 10.1109/ACCESS.2020.2991305
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA ML5JL
UT WOS:000549502200004
OA gold
DA 2022-08-23
ER

PT J
AU Wu, FP
   Gao, JF
   Kang, J
   Wang, XX
   Niu, Q
   Liu, JX
   Zhang, LY
AF Wu, Fengping
   Gao, Jinfang
   Kang, Jie
   Wang, Xuexue
   Niu, Qing
   Liu, Jiaxi
   Zhang, Liyun
TI Knowledge Mapping of Exosomes in Autoimmune Diseases: A Bibliometric
   Analysis (2002-2021)
SO FRONTIERS IN IMMUNOLOGY
LA English
DT Article
DE bibliometrics; exosomes; autoimmune diseases; CiteSpace; VOSviewers
ID MESENCHYMAL STEM-CELLS; DENDRITIC CELLS; EXTRACELLULAR VESICLES;
   T-CELLS; DOWN-REGULATION; RENAL FIBROSIS; STROMAL CELLS; B-LYMPHOCYTES;
   MICRORNAS; THERAPY
AB BackgroundAutoimmune diseases (AIDs) are a class of chronic disabling diseases characterized by inflammation and damage to muscles, joints, bones, and internal organs. Recent studies have shown that much progress has been made in the research of exosomes in AIDs. However, there is no bibliometric analysis in this research field. This study aims to provide a comprehensive overview of the knowledge structure and research hotspots of exosomes in AIDs through bibliometrics. MethodPublications related to exosomes in AIDs from 2002 to 2021 were searched on the web of science core collection (WoSCC) database. VOSviewers, CiteSpace and R package "bibliometrix" were used to conduct this bibliometric analysis. Results312 articles from 48 countries led by China and the United States were included. The number of publications related to exosomes in AIDs is increasing year by year. Central South University, Sun Yat Sen University, Tianjin Medical University and University of Pennsylvania are the main research institutions. Frontiers in immunology is the most popular journal in this field, and Journal of Immunology is the most co-cited journal. These publications come from 473 authors among which Ilias Alevizos, Qianjin Lu, Wei Wei, Jim Xiang and Ming Zhao had published the most papers and Clotilde Thery was co-cited most often. Studying the mechanism of endogenous exosomes in the occurrence and development of AIDs and the therapeutic strategy of exogenous exosomes in AIDs are the main topics in this research field. "Mesenchymal stem cells", "microRNA", "biomarkers", "immunomodulation", and "therapy" are the primary keywords of emerging research hotspots. ConclusionThis is the first bibliometric study that comprehensively summarizes the research trends and developments of exosomes in AIDs. This information identifies recent research frontiers and hot directions, which will provide a reference for scholars studying exosomes.
C1 [Wu, Fengping; Niu, Qing] Shanxi Med Univ, Sch Basic Med Sci, Taiyuan, Peoples R China.
   [Gao, Jinfang; Zhang, Liyun] Shanxi Med Univ, Shanxi Bethune Hosp, Tongji Shanxi Hosp, Shanxi Acad Med Sci,Hosp 3, Taiyuan, Peoples R China.
   [Kang, Jie; Wang, Xuexue; Liu, Jiaxi] Shanxi Med Univ, Shanxi Bethune Hosp, Tongji Shanxi Hosp, Shanxi Acad Med Sci,Hosp 3, Taiyuan, Peoples R China.
RP Zhang, LY (通讯作者)，Shanxi Med Univ, Shanxi Bethune Hosp, Tongji Shanxi Hosp, Shanxi Acad Med Sci,Hosp 3, Taiyuan, Peoples R China.
EM 1315710223@qq.com
CR Alevizos I, 2010, NAT REV RHEUMATOL, V6, P391, DOI 10.1038/nrrheum.2010.81
   Alvarez-Erviti L, 2011, NAT BIOTECHNOL, V29, P341, DOI 10.1038/nbt.1807
   Alvaro-Gracia JM, 2017, ANN RHEUM DIS, V76, P196, DOI 10.1136/annrheumdis-2015-208918
   Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007
   Baharlooi H, 2021, SCAND J IMMUNOL, V93, DOI 10.1111/sji.13013
   Bai LL, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-04559-y
   Blazquez R, 2014, FRONT IMMUNOL, V5, DOI 10.3389/fimmu.2014.00556
   Budoni M, 2013, CELL TRANSPLANT, V22, P369, DOI 10.3727/096368911X582769
   Burrello J, 2016, FRONT CELL DEV BIOL, V4, DOI 10.3389/fcell.2016.00083
   Buzas EI, 2014, NAT REV RHEUMATOL, V10, P356, DOI 10.1038/nrrheum.2014.19
   Cai ZJ, 2012, CELL RES, V22, P607, DOI 10.1038/cr.2011.196
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chuang HC, 2022, ARTHRITIS RHEUMATOL, V74, P92, DOI 10.1002/art.41920
   Colletti M, 2019, INT J MOL SCI, V20, DOI 10.3390/ijms20184337
   Colombo M, 2014, ANNU REV CELL DEV BI, V30, P255, DOI 10.1146/annurev-cellbio-101512-122326
   Cooper GS, 2009, J AUTOIMMUN, V33, P197, DOI 10.1016/j.jaut.2009.09.008
   Cortes-Troncoso J, 2020, JCI INSIGHT, V5, DOI 10.1172/jci.insight.133497
   Cosenza S, 2018, THERANOSTICS, V8, P1399, DOI 10.7150/thno.21072
   Cui XJ, 2019, J CLIN ENDOCR METAB, V104, P4607, DOI 10.1210/jc.2019-00273
   Dehghanbanadaki H, 2022, DIABETES METAB SYND, V16, DOI 10.1016/j.dsx.2022.102450
   Ding YJ, 2020, EXP BIOL MED, V245, P1177, DOI 10.1177/1535370220934736
   Djouad F, 2003, BLOOD, V102, P3837, DOI 10.1182/blood-2003-04-1193
   Djouad F, 2006, TRANSPLANTATION, V82, P1060, DOI 10.1097/01.tp.0000236098.13804.0b
   Edhayan G, 2016, ARTHRITIS RES THER, V18, DOI 10.1186/s13075-016-0984-3
   Fan Y, 2019, STEM CELLS DEV, V28, P44, DOI 10.1089/scd.2018.0015
   Favaro E, 2016, DIABETOLOGIA, V59, P325, DOI 10.1007/s00125-015-3808-0
   Galazka G, 2018, MULT SCLER J, V24, P449, DOI 10.1177/1352458517696597
   Gallo A, 2016, EBIOMEDICINE, V10, P216, DOI 10.1016/j.ebiom.2016.06.041
   Gallo A, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0030679
   Garcia-Vives E, 2020, INT J MOL SCI, V21, DOI 10.3390/ijms21041372
   Ghafouri-Fard S, 2021, FRONT CELL DEV BIOL, V9, DOI 10.3389/fcell.2021.653296
   Gilkeson GS, 2022, ARTHRITIS RHEUMATOL, V74, P752, DOI 10.1002/art.42081
   Gomzikova MO, 2019, FRONT IMMUNOL, V10, DOI 10.3389/fimmu.2019.02663
   Guay C, 2019, CELL METAB, V29, P348, DOI 10.1016/j.cmet.2018.09.011
   Hu W, 2022, FRONT ENDOCRINOL, V12, DOI 10.3389/fendo.2021.682145
   Huang XQ, 2019, J TRANSL MED, V17, DOI 10.1186/s12967-019-1810-x
   Huldani H, 2022, INT IMMUNOPHARMACOL, V106, DOI 10.1016/j.intimp.2022.108634
   Ichii O, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0110383
   Jasim SA, 2022, STEM CELL RES THER, V13, DOI 10.1186/s13287-022-02782-7
   Ke LX, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.00842
   Khare D, 2018, FRONT IMMUNOL, V9, DOI 10.3389/fimmu.2018.03053
   Kimura K, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-017-02406-2
   Kordelas L, 2014, LEUKEMIA, V28, P970, DOI 10.1038/leu.2014.41
   Lai PL, 2019, BIOMARK RES, V7, DOI 10.1186/s40364-019-0156-0
   Lakhter AJ, 2018, DIABETOLOGIA, V61, P1124, DOI 10.1007/s00125-018-4559-5
   Lee S, 2020, IMMUNOL LETT, V225, P16, DOI 10.1016/j.imlet.2020.06.006
   Li C, 2020, INT J INFECT DIS, V96, P696, DOI 10.1016/j.ijid.2020.05.014
   Li WG, 2020, CLINICS, V75, DOI 10.6061/clinics/2020/e1528
   Li ZJ, 2019, INT IMMUNOPHARMACOL, V67, P268, DOI 10.1016/j.intimp.2018.12.001
   Liu D, 2020, J MOL MED, V98, P409, DOI 10.1007/s00109-020-01882-2
   Liu H, 2020, FRONT IMMUNOL, V11, DOI 10.3389/fimmu.2020.01912
   Lu CC, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.00294
   Lu MR, 2021, FRONT IMMUNOL, V12, DOI 10.3389/fimmu.2021.566299
   Ma D, 2021, FRONT CARDIOVASC MED, V8, DOI 10.3389/fcvm.2021.734514
   Meng HY, 2020, BMC MUSCULOSKEL DIS, V21, DOI 10.1186/s12891-020-3159-y
   Meng Q, 2020, FRONT PHYSIOL, V11, DOI 10.3389/fphys.2020.00441
   Miao Y, 2018, PEERJ, V6, DOI 10.7717/peerj.5477
   Momen-Heravi F, 2014, NANOMED-NANOTECHNOL, V10, P1517, DOI 10.1016/j.nano.2014.03.014
   Montecalvo A, 2012, BLOOD, V119, P756, DOI 10.1182/blood-2011-02-338004
   Mu CY, 2017, J LEUKOCYTE BIOL, V101, P1221, DOI 10.1189/jlb.3A0716-295RR
   Nojehdehi S, 2018, J CELL BIOCHEM, V119, P9433, DOI 10.1002/jcb.27260
   Okoye IS, 2014, IMMUNITY, V41, P89, DOI [10.1016/j.immuni.2014.08.008, 10.1016/j.immuni.2014.05.019]
   Oo S, 2020, J CARDIAC SURG, V35, P2943, DOI 10.1111/jocs.14941
   Pan XL, 2018, J INFORMETR, V12, P481, DOI 10.1016/j.joi.2018.03.005
   Pasquali L, 2020, J EUR ACAD DERMATOL, V34, P1248, DOI 10.1111/jdv.16203
   Rao YJ, 2020, FRONT CELL DEV BIOL, V8, DOI 10.3389/fcell.2020.551681
   Raposo G, 1996, J EXP MED, V183, P1161, DOI 10.1084/jem.183.3.1161
   Raposo G, 2013, J CELL BIOL, V200, P373, DOI 10.1083/jcb.201211138
   Riazifar M, 2019, ACS NANO, V13, P6670, DOI 10.1021/acsnano.9b01004
   Rodriguez-Muguruza S, 2021, FRONT IMMUNOL, V12, DOI 10.3389/fimmu.2021.790880
   Rui K, 2021, CELL MOL IMMUNOL, V18, P440, DOI 10.1038/s41423-020-00587-3
   Salvi V, 2018, JCI INSIGHT, V3, DOI 10.1172/jci.insight.98204
   Seo Y, 2019, STEM CELLS INT, V2019, DOI 10.1155/2019/5126156
   Shen ZW, 2021, FRONT IMMUNOL, V12, DOI 10.3389/fimmu.2021.749192
   Shi SZ, 2021, CLIN EXP MED, V21, P181, DOI 10.1007/s10238-020-00624-5
   Smolen JS, 2016, LANCET, V388, P2023, DOI 10.1016/S0140-6736(16)30173-8
   Sole C, 2015, NEPHROL DIAL TRANSPL, V30, P1488, DOI 10.1093/ndt/gfv128
   Song JY, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-09827-5
   Sun Y, 2020, BIOL RES, V53, DOI 10.1186/s40659-020-00299-y
   Synnestvedt Marie B, 2005, AMIA Annu Symp Proc, P724
   Tan LN, 2021, FRONT IMMUNOL, V12, DOI 10.3389/fimmu.2021.630112
   Tan LN, 2016, AUTOIMMUNITY, V49, P357, DOI 10.1080/08916934.2016.1191477
   Tangtanatakul P, 2019, ASIAN PAC J ALLERGY, V37, P189, DOI 10.12932/AP-130318-0280
   Teles RHG, 2021, CURR ONCOL, V28, P4504, DOI 10.3390/curroncol28060382
   Thery C, 2001, J IMMUNOL, V166, P7309, DOI 10.4049/jimmunol.166.12.7309
   Thery C, 2002, NAT IMMUNOL, V3, P1156, DOI 10.1038/ni854
   Thery C, 2002, NAT REV IMMUNOL, V2, P569, DOI 10.1038/nri855
   Thery C, 2018, J EXTRACELL VESICLES, V7, DOI 10.1080/20013078.2018.1535750
   Thery C, 2009, NAT REV IMMUNOL, V9, P581, DOI 10.1038/nri2567
   Thery Clotilde, 2006, Curr Protoc Cell Biol, VChapter 3, DOI 10.1002/0471143030.cb0322s30
   Tomiyama T, 2017, CELL MOL IMMUNOL, V14, P276, DOI 10.1038/cmi.2015.86
   Valadi H, 2007, NAT CELL BIOL, V9, P654, DOI 10.1038/ncb1596
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Wang B, 2019, BIOMED RES INT, V2019, DOI 10.1155/2019/1495130
   Wang GY, 2020, CLIN TRANSL MED, V10, DOI 10.1002/ctm2.113
   Wang LP, 2018, CELL PHYSIOL BIOCHEM, V50, P1754, DOI 10.1159/000494793
   Wang S, 2022, INT J NANOMED, V17, P1757, DOI 10.2147/IJN.S355366
   Wang Y, 2020, FASEB J, V34, P6215, DOI 10.1096/fj.201902964R
   Wang YL, 2019, J CELL BIOCHEM, V120, P1133, DOI 10.1002/jcb.26659
   Wermuth PJ, 2017, CLIN EXP RHEUMATOL, V35, pS21
   Witwer KW, 2013, J EXTRACELL VESICLES, V2, DOI 10.3402/jev.v2i0.20360
   Wu HY, 2022, J ORTHOP SURG RES, V17, DOI 10.1186/s13018-022-03068-7
   Wu HY, 2021, FRONT MED-LAUSANNE, V8, DOI 10.3389/fmed.2021.787228
   Wu XH, 2019, AUTOIMMUN REV, V18, P593, DOI 10.1016/j.autrev.2018.12.007
   Xie YF, 2013, BIOCHEM BIOPH RES CO, V438, P152, DOI 10.1016/j.bbrc.2013.07.044
   Xie YF, 2010, J IMMUNOL, V185, P5268, DOI 10.4049/jimmunol.1000386
   Xu DH, 2019, J CELL PHYSIOL, V234, P1502, DOI 10.1002/jcp.27014
   Xu HY, 2020, MUSCLE NERVE, V61, P401, DOI 10.1002/mus.26785
   Xu KY, 2020, J TRANSL MED, V18, DOI 10.1186/s12967-020-02609-0
   Xu S, 2012, STEM CELLS, V30, P266, DOI 10.1002/stem.787
   Yanez-Mo M, 2015, J EXTRACELL VESICLES, V4, DOI 10.3402/jev.v4.27066
   Yang CJ, 2021, FRONT IMMUNOL, V12, DOI 10.3389/fimmu.2021.714832
   Yang N, 2021, FREE RADICAL BIO MED, V171, P203, DOI 10.1016/j.freeradbiomed.2021.04.033
   Yeung AWK, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17072449
   Yeung AWK, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.00037
   Yin WF, 2017, INFLAMMATION, V40, P1460, DOI 10.1007/s10753-017-0589-2
   Yoo J, 2017, ARTHRITIS RES THER, V19, DOI 10.1186/s13075-017-1334-9
   Yu L, 2013, EUR J IMMUNOL, V43, P2461, DOI 10.1002/eji.201243295
   Yu Y, 2022, TISSUE ENG REGEN MED, V19, P141, DOI 10.1007/s13770-021-00405-5
   Zhang B, 2014, STEM CELLS DEV, V23, P1233, DOI 10.1089/scd.2013.0479
   Zhang B, 2021, FRONT IMMUNOL, V11, DOI 10.3389/fimmu.2020.575712
   Zhang HF, 2011, CELL MOL IMMUNOL, V8, P23, DOI 10.1038/cmi.2010.59
   Zhang KY, 2018, ACS APPL MATER INTER, V10, P30081, DOI 10.1021/acsami.8b08449
   Zhang XL, 2020, FOODS, V9, DOI 10.3390/foods9020166
   Zhao Y, 2020, J AUTOIMMUN, V115, DOI 10.1016/j.jaut.2020.102540
   Zheng J, 2020, INT IMMUNOPHARMACOL, V78, DOI 10.1016/j.intimp.2019.105985
   Zhou H, 2013, J AM SOC NEPHROL, V24, P1073, DOI 10.1681/ASN.2012080849
NR 128
TC 0
Z9 0
U1 1
U2 1
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-3224
J9 FRONT IMMUNOL
JI Front. Immunol.
PD JUL 22
PY 2022
VL 13
AR 939433
DI 10.3389/fimmu.2022.939433
PG 19
WC Immunology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Immunology
GA 3O6WK
UT WOS:000836975100001
PM 35935932
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Timakum, T
   Lee, S
   Song, M
AF Timakum, Tatsawan
   Lee, Soobin
   Song, Min
TI Exploring the research landscape of data warehousing and mining based on
   DaWaK Conference full-text articles
SO DATA & KNOWLEDGE ENGINEERING
LA English
DT Article
DE Content analysis; Data warehousing knowledge mapping; Data warehousing
   research trends; Co-authorship network analysis
ID CO-AUTHORSHIP NETWORKS; INTELLECTUAL STRUCTURE; KNOWLEDGE;
   BIOINFORMATICS; CONSUMPTION; EXTRACTION; EVOLUTION; BENEFITS; TRENDS;
   IMPACT
AB The international conference on Data Warehousing and Knowledge Discovery (DaWaK) has become a pivotal place to exchange experiences and knowledge among researchers and practitioners in big data analytics. The conference has been essential to data warehousing and data analytics for the last 21 years (1999-2019). This study explored the knowledge structure embedded in the DaWaK Conference papers and examined the research trends over time. It also analyzed the performance of published papers, authors, and their affiliations and countries and visualized a collaboration network in DaWaK. We applied several text mining techniques, including co-word analysis, topic modeling, co-author network analysis, and network visualization. The study's findings indicate that the core topics are data mining techniques, algorithm performance, and information systems. The popular topic trends are associated with database encryption, whereas the topics related to online analytical processing (OLAP) technology are in decline. The research metrics results demonstrate that the DaWaK papers were cited 6,262 times, with an h-index of 34 for the 722 DaWaK papers. The article titled "Outlier Detection Using Replicator Neural Networks"reached the most citations (177), and the most productive author was Bellatreche, Ladjel (15 papers). Nanyang Technological University is the most frequently mentioned as the author's affiliation, the United States is the country with the largest number of authors, and the National Science Foundation was the largest funding agency that supported the DaWaK researchers. Moreover, the authorship network of Bellatreche, Ladjel is the largest collaboration network in the DaWaK scholar community. The outcomes of this study would be beneficial for comprehending the knowledge in data warehousing and the relevant cross-disciplinary areas of research and collaboration networks in this field.
C1 [Timakum, Tatsawan] Chiang Mai Rajabhat Univ, Dept Informat Sci, Chiang Mai, Thailand.
   [Lee, Soobin; Song, Min] Yonsei Univ, Dept Lib & Informat Sci, Seoul, South Korea.
RP Song, M (通讯作者)，Yonsei Univ, Dept Lib & Informat Sci, Seoul, South Korea.
EM tatsawan_tim@cmru.ac.th; bini122@naver.com; min.song@yonsei.ac.kr
RI timakum, tatsawan/L-7999-2018
OI timakum, tatsawan/0000-0002-9877-0323
FU National Research Foundation of Korea (NRF) - Korea government (MSIT)
   [NRF-2019R1A2C2002577]
FX This work was supported by the National Research Foundation of Korea
   (NRF) grant funded by the Korea government (MSIT) [No.
   NRF-2019R1A2C2002577] .
CR A.O.D. Community, 2011, AP OPENNLP DEV DOC P
   Al-Shaebi A, 2017, PROCEDIA MANUF, V11, P778, DOI 10.1016/j.promfg.2017.07.179
   Alghamdi R, 2015, INT J ADV COMPUT SC, V6, P147
   An J., 2017, DATA INF MANAG, V30, P460, DOI [10.1515/dim-2017--0002, DOI 10.1515/DIM-2017--0002]
   [Anonymous], 2004, DATA SCI J, DOI DOI 10.2481/DSJ.3.80
   Bastian M., 2009, P 3 INT AAAI C WEB S, V3, P361
   Bimonte Sandro, 2016, International Journal of Business Information Systems, V21, P117
   Blei D.M., 2006, ACM INT C P SERIES, P113
   Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008
   Bornmann L, 2014, J INFORMETR, V8, P895, DOI 10.1016/j.joi.2014.09.005
   Brandes U, 2001, J MATH SOCIOL, V25, P163, DOI 10.1080/0022250X.2001.9990249
   Budiawan W, 2018, E3S WEB CONF, V73, DOI 10.1051/e3sconf/20187312007
   Butts CT, 2008, ASIAN J SOC PSYCHOL, V11, P13, DOI 10.1111/j.1467-839X.2007.00241.x
   CALLON M, 1991, SCIENTOMETRICS, V22, P155, DOI 10.1007/BF02019280
   Charidimou A, 2016, J NEUROL NEUROSUR PS, V87, P252, DOI 10.1136/jnnp-2015-310690
   Chen C., 2017, REPRESENTING SCI KNO, DOI [10.1007/978--3-319--62543--0, DOI 10.1007/978--3-319--62543--0]
   Chen CM, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0223994
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen CM, 1999, INFORM PROCESS MANAG, V35, P401, DOI 10.1016/S0306-4573(98)00068-5
   Cheng F.F., 2018, LIBR HI TECH, DOI [10.1108/LHT-01--2018--0004, DOI 10.1108/LHT-01--2018--0004]
   Combinatoricslib 2.0 Features, 2013, COMB VER SIMPL JAV L
   Cui Y, 2018, ELECTRON COMMER RES, V18, P837, DOI 10.1007/s10660-018-9288-9
   CULNAN MJ, 1987, MIS QUART, V11, P341, DOI 10.2307/248680
   Darko A, 2020, AUTOMAT CONSTR, V112, DOI 10.1016/j.autcon.2020.103081
   DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
   Evans R Scott, 2012, AMIA Annu Symp Proc, V2012, P189
   Ferreira JC, 2015, IEEE T INTELL TRANSP, V16, P2653, DOI 10.1109/TITS.2015.2414663
   Gurcan F, 2019, IEEE ACCESS, V7, P82541, DOI 10.1109/ACCESS.2019.2924075
   Hazeleton V., 2000, CORPORATE COMMUNICAT, V5, P81, DOI DOI 10.1108/13563280010372513
   He Q, 1999, LIBR TRENDS, V48, P133
   He YL, 2002, INFORM PROCESS MANAG, V38, P491, DOI 10.1016/S0306-4573(01)00046-2
   He Y, 2018, IEEE T KNOWL DATA EN, V30, P460, DOI 10.1109/TKDE.2017.2767599
   Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50, DOI 10.1145/312624.312649
   Hu XH, 2004, PROCEEDINGS OF THE 2004 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, P244
   Hu XH, 2004, IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2004), PROCEEDINGS, P77, DOI 10.1109/WI.2004.10165
   Jeong YK, 2016, J INFORMETR, V10, P273, DOI 10.1016/j.joi.2016.01.003
   Jeong YK, 2014, J INFORMETR, V8, P197, DOI 10.1016/j.joi.2013.12.001
   Khasseh AA, 2017, INFORM PROCESS MANAG, V53, P705, DOI 10.1016/j.ipm.2017.02.001
   Kim HJ, 2016, J INFORMETR, V10, P954, DOI 10.1016/j.joi.2016.07.007
   Kim SuYeon, 2015, [Journal of the Korean Society for Information Management, 정보관리학회지], V32, P135, DOI 10.3743/KOSIM.2015.32.1.135
   Kim Y, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0196104
   Konkiel S., 2013, B AM SOC INFORM SCI, V39, P22, DOI [10.1002/bult.2013.1720390408, DOI 10.1002/BULT.2013.1720390408]
   Landauer TK, 1998, DISCOURSE PROCESS, V25, P259, DOI 10.1080/01638539809545028
   Li EY, 2013, RES POLICY, V42, P1515, DOI 10.1016/j.respol.2013.06.012
   Lian TH, 2016, KNOWL ORGAN, V43, P440
   Liu XM, 2005, INFORM PROCESS MANAG, V41, P1462, DOI 10.1016/j.ipm.2005.03.012
   Lu L., 2013, ENCY SYSTEMBIOL, P647, DOI 10.1007/978-1-4419-9863-7_874
   Lyman JA, 2008, CLIN LAB MED, V28, P55, DOI 10.1016/j.cll.2007.10.003
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   McCallum, 2008, P 24 C UNC ART INT, P411
   Mingers J, 2015, EUR J OPER RES, V246, P1, DOI 10.1016/j.ejor.2015.04.002
   Moro S, 2015, EXPERT SYST APPL, V42, P1314, DOI 10.1016/j.eswa.2014.09.024
   Nasar Z, 2018, SCIENTOMETRICS, V117, P1931, DOI 10.1007/s11192-018-2921-5
   Newman MEJ, 2004, PHYS REV E, V69, DOI [10.1103/PhysRevE.70.056131, 10.1103/PhysRevE.69.026113]
   Newman MEJ, 2001, PHYS REV E, V64, DOI [10.1103/PhysRevE.64.016132, 10.1103/PhysRevE.64.016131]
   Opsahl T, 2010, SOC NETWORKS, V32, P245, DOI 10.1016/j.socnet.2010.03.006
   Perrotta F, 2019, TRANSPORT RES D-TR E, V67, P109, DOI 10.1016/j.trd.2018.11.001
   Pilkington A, 2009, J OPER MANAG, V27, P185, DOI 10.1016/j.jom.2008.08.001
   Qian YX, 2019, STUD HEALTH TECHNOL, V264, P308, DOI 10.3233/SHTI190233
   Roelofs E, 2013, RADIOTHER ONCOL, V108, P174, DOI 10.1016/j.radonc.2012.09.019
   Rowley-Jolivet E, 2002, ENGL SPECIF PURP, V21, P19, DOI 10.1016/S0889-4906(00)00024-7
   Singh N, 2019, ENTERP INF SYST-UK, V13, P801, DOI 10.1080/17517575.2019.1612098
   Song M, 2018, J ASSOC INF SCI TECH, V69, P1355, DOI 10.1002/asi.24060
   Song M, 2017, J ASSOC INF SCI TECH, V68, P1564, DOI 10.1002/asi.23840
   Song M, 2015, J BIOMED INFORM, V57, P320, DOI 10.1016/j.jbi.2015.08.008
   Song M, 2015, SCIENTOMETRICS, V102, P905, DOI 10.1007/s11192-014-1372-x
   Song M, 2013, SPRINGERPLUS, V2, DOI 10.1186/2193-1801-2-186
   Song M, 2014, SCIENTOMETRICS, V101, P397, DOI 10.1007/s11192-014-1246-2
   Song M, 2014, IEEE INTELL SYST, V29, P18, DOI 10.1109/MIS.2014.20
   Song M, 2013, SCIENTOMETRICS, V96, P183, DOI 10.1007/s11192-012-0900-9
   Spil T. A. M., 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences, DOI 10.1109/HICSS.2002.994108
   Su HN, 2010, SCIENTOMETRICS, V85, P65, DOI 10.1007/s11192-010-0259-8
   Thompson Riverse University Library, 2019, MEAS RES IMP IMP BIB
   Thusoo A., 2010, P 2010 ACM SIGMOD IN, P1013, DOI [DOI 10.1145/1807167.1807278, 10.1145/1807167.1807278]
   Timakum T, 2020, J LIBR INF SCI, V52, P345, DOI 10.1177/0961000618793977
   Van Biljon J., 2018, P 6 INT C M4D MOB CO, P7
   Van Eck N., 2014, MEASURING SCHOLARLY, P285, DOI [10.1007/978-3-319-10377-8_13, 10.1007/978-3-319-10377-8_13(InEng.)]
   van Eck NJ, 2014, J INFORMETR, V8, P802, DOI 10.1016/j.joi.2014.07.006
   Wang WH, 2020, SOFT COMPUT, V24, P8173, DOI 10.1007/s00500-019-04384-7
   Wang XD, 2014, CYBERN INF TECHNOL, V14, P145, DOI 10.2478/cait-2014-0051
   Wei W, 2019, DISASTER MED PUBLIC, V13, P405, DOI 10.1017/dmp.2018.63
   Wolfe AW, 1997, AM ETHNOL, V24, P219, DOI 10.1525/ae.1997.24.1.219
   Yang Y.N., 2017, P 22 INT C ADV CONST
   Zhao ZY, 2015, 2015 12TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), P1642, DOI 10.1109/FSKD.2015.7382191
NR 86
TC 0
Z9 0
U1 1
U2 3
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0169-023X
EI 1872-6933
J9 DATA KNOWL ENG
JI Data Knowl. Eng.
PD SEP
PY 2021
VL 135
AR 101926
DI 10.1016/j.datak.2021.101926
EA SEP 2021
PG 19
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA WA3ZW
UT WOS:000702828000007
DA 2022-08-23
ER

PT J
AU Zhang, Y
   Cao, BL
   Wang, YF
   Peng, TQ
   Wang, XH
AF Zhang, Yan
   Cao, Bolin
   Wang, Yifan
   Peng, Tai-Quan
   Wang, Xiaohua
TI When Public Health Research Meets Social Media: Knowledge Mapping From
   2000 to 2018
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Review
DE social media; public health; infodemiology; infoveillance; topic
   modeling; research theme; research method
ID RISK-FACTORS; NETWORKING SITES; SELF-MANAGEMENT; CHRONIC DISEASE;
   INTERVENTIONS; COMMUNICATION; CHALLENGES; OPPORTUNITIES; METAANALYSIS;
   INFORMATION
AB Background: Social media has substantially changed how people confront health issues. However, a comprehensive understanding of how social media has altered the foci and methods in public health research remains lacking.
   Objective: This study aims to examine research themes, the role of social media, and research methods in social media-based public health research published from 2000 to 2018.
   Methods: A dataset of 3419 valid studies was developed by searching a list of relevant keywords in the Web of Science and PubMed databases. In addition, this study employs an unsupervised text-mining technique and topic modeling to extract research themes of the published studies. Moreover, the role of social media and research methods adopted in those studies were analyzed.
   Results: This study identifies 25 research themes, covering different diseases, various population groups, physical and mental health, and other significant issues. Social media assumes two major roles in public health research: produce substantial research interest for public health research and furnish a research context for public health research. Social media provides substantial research interest for public health research when used for health intervention, human-computer interaction, as a platform of social influence, and for disease surveillance, risk assessment, or prevention. Social media acts as a research context for public health research when it is mere reference, used as a platform to recruit participants, and as a platform for data collection. While both qualitative and quantitative methods are frequently used in this emerging area, cutting edge computational methods play a marginal role.
   Conclusions: Social media enables scholars to study new phenomena and propose new research questions in public health research. Meanwhile, the methodological potential of social media in public health research needs to be further explored.
C1 [Zhang, Yan; Cao, Bolin; Wang, Yifan; Wang, Xiaohua] Shenzhen Univ, Sch Media & Commun, Rm809,Bldg L7,South Campus,Nanhai Ave, Shenzhen, Peoples R China.
   [Peng, Tai-Quan] Michigan State Univ, Dept Commun, E Lansing, MI 48824 USA.
RP Cao, BL (通讯作者)，Shenzhen Univ, Sch Media & Commun, Rm809,Bldg L7,South Campus,Nanhai Ave, Shenzhen, Peoples R China.
EM caobolin@szu.edu.cn
RI Peng, Tai-Quan/B-3176-2011
OI Peng, Tai-Quan/0000-0002-2588-7491; Wang, Yifan/0000-0003-0831-2319;
   Cao, Bolin/0000-0001-8743-1361
FU National Social Science Fund of China [19ZDA324, 18CXW017]; Junior
   Faculty Supporting Fund at Shenzhen University [QNFC1903, RC109127]
FX This study was supported by National Social Science Fund of China
   (19ZDA324 and 18CXW017), Junior Faculty Supporting Fund at Shenzhen
   University (QNFC1903), and a research grant awarded to TQP at Michigan
   State University (RC109127).
CR Agarwal R, 2010, INFORM SYST RES, V21, P796, DOI 10.1287/isre.1100.0327
   Almutairi N, 2018, J HEALTH COMMUN, V23, P233, DOI 10.1080/10810730.2018.1423648
   Amankwah-Amoah J, 2016, TECHNOL FORECAST SOC, V110, P167, DOI 10.1016/j.techfore.2015.10.022
   ANASTASI A, 1986, ANNU REV PSYCHOL, V37, P1, DOI 10.1146/annurev.ps.37.020186.000245
   Tran BX, 2019, J MED INTERNET RES, V21, DOI 10.2196/15511
   Bernard S, 2018, JAAPA-J AM ACAD PHYS, V31, P41, DOI 10.1097/01.JAA.0000546481.02560.4e
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bogaerts A, 2017, BMC PREGNANCY CHILDB, V17, DOI 10.1186/s12884-017-1336-2
   Cao BL, 2017, J MED INTERNET RES, V19, DOI 10.2196/jmir.7251
   Carroll JK, 2019, J GEN INTERN MED, V34, P1782, DOI 10.1007/s11606-019-05102-7
   Cartledge P, 2013, MED TEACH, V35, P847, DOI 10.3109/0142159X.2013.804909
   Chang T, 2013, J MED INTERNET RES, V15, DOI 10.2196/jmir.2852
   Charron L, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0125154
   Chen X, 2019, SOCIAL WEB HLTH RES, P31, DOI DOI 10.1007/978-3-030-14714-3_3
   Crawford R, 2016, PUBLIC HEALTH, V141, P186, DOI 10.1016/j.puhe.2016.09.018
   Dehlin JM, 2019, JMIR PUBLIC HLTH SUR, V5, P162, DOI 10.2196/12822
   Dowshen N, 2015, AIDS BEHAV, V19, pS106, DOI 10.1007/s10461-014-0991-9
   Dreisbach C, 2019, INT J MED INFORM, V125, P37, DOI 10.1016/j.ijmedinf.2019.02.008
   Driouchi AA, 2013, ICTS HLTH ED SOCIOEC
   Fitzpatrick T, 2018, BMC INFECT DIS, V18, DOI 10.1186/s12879-018-3403-3
   Ginsberg J, 2009, NATURE, V457, P1012, DOI 10.1038/nature07634
   Giustini Dean, 2018, Online J Public Health Inform, V10, pe215, DOI 10.5210/ojphi.v10i2.8270
   Golder SA, 2014, ANNU REV SOCIOL, V40, P129, DOI 10.1146/annurev-soc-071913-043145
   Guntuku SC, 2017, CURR OPIN BEHAV SCI, V18, P43, DOI 10.1016/j.cobeha.2017.07.005
   Huang DC, 2018, SCI TOTAL ENVIRON, V612, P1293, DOI 10.1016/j.scitotenv.2017.09.017
   Jashinsky J, 2014, CRISIS, V35, P51, DOI 10.1027/0227-5910/a000234
   Kampmeijer R, 2016, BMC HEALTH SERV RES, V16, DOI 10.1186/s12913-016-1522-3
   Kass-Hout TA, 2013, BRIT MED BULL, V108, P5, DOI 10.1093/bmb/ldt028
   Kazemi DM, 2017, J HEALTH COMMUN, V22, P413, DOI 10.1080/10810730.2017.1303556
   Kim JN, 2010, HEALTH COMMUN, V25, P487, DOI 10.1080/10410236.2010.507160
   Lazer D, 2014, SSRN ELECT J, DOI [10.2139/ssrn.2408560, DOI 10.2139/SSRN.2408560]
   Lee KT, 2013, CYBERPSYCH BEH SOC N, V16, P413, DOI 10.1089/cyber.2012.0553
   Lennox J, 2018, INT J DRUG POLICY, V58, P13, DOI 10.1016/j.drugpo.2018.04.009
   Ling Rebecca, 2016, JMIR Public Health Surveill, V2, pe156, DOI 10.2196/publichealth.6504
   Lorig K, 2009, DIABETES EDUCATOR, V35, P641, DOI 10.1177/0145721709335006
   Menefee HK, 2016, J MED INTERNET RES, V18, DOI 10.2196/jmir.5949
   Mita G, 2016, NUTR REV, V74, P237, DOI 10.1093/nutrit/nuv106
   Moorhead SA, 2013, J MED INTERNET RES, V15, DOI 10.2196/jmir.1933
   Narang B, 2019, MED CARE, V57, pS184, DOI 10.1097/MLR.0000000000001035
   Odone A, 2015, HUM VACC IMMUNOTHER, V11, P72, DOI 10.4161/hv.34313
   Patel R, 2015, AM J MED, V128, P1335, DOI 10.1016/j.amjmed.2015.06.015
   Paton C, 2011, Yearb Med Inform, V6, P21
   Peng TQ, 2019, ASIAN J COMMUN, V29, P205, DOI 10.1080/01292986.2019.1602911
   Peng TQ, 2013, NEW MEDIA SOC, V15, P644, DOI 10.1177/1461444812462846
   Ramo DE, 2012, J MED INTERNET RES, V14, DOI 10.2196/jmir.1878
   Roberts CA, 2016, J ASTHMA, V53, P948, DOI 10.3109/02770903.2016.1171339
   Sadah SA, 2016, J MED INTERNET RES, V18, DOI 10.2196/jmir.5327
   Swanton R, 2015, SEX TRANSM INFECT, V91, P14, DOI 10.1136/sextrans-2014-051743
   Taggart T, 2015, J MED INTERNET RES, V17, DOI 10.2196/jmir.4387
   Tang WM, 2018, PLOS MED, V15, DOI 10.1371/journal.pmed.1002645
   Thackeray R, 2012, BMC PUBLIC HEALTH, V12, DOI 10.1186/1471-2458-12-242
   Tran Bach Xuan, 2017, Addict Behav Rep, V6, P45, DOI 10.1016/j.abrep.2017.07.001
   Tucker JD, 2016, CLIN INFECT DIS, V63, DOI 10.1093/cid/ciw297
   van Atteveldt W, 2018, COMMUN METHODS MEAS, V12, P81, DOI 10.1080/19312458.2018.1458084
   Vijaykumar S, 2018, AM J INFECT CONTROL, V46, P549, DOI 10.1016/j.ajic.2017.10.015
   Vorderstrasse A, 2016, CURR DIABETES REP, V16, DOI 10.1007/s11892-016-0756-0
   Walters K, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0198835
   Wenxiu P., 2015, J ED SOC RES, V5, P245, DOI [10.36941/jesr, DOI 10.5901/JESR.2015.V5N3P245]
   Whitehead L, 2016, J MED INTERNET RES, V18, DOI 10.2196/jmir.4883
   Williams G, 2014, BMJ OPEN, V4, DOI 10.1136/bmjopen-2013-003926
   Willis EA, 2017, J TELEMED TELECARE, V23, P263, DOI 10.1177/1357633X16630846
   World Health Organization, 2017, WHO PUBL LIST TOP EM
   Young SD, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0199527
   Zhang MWB, 2016, OBES SURG, V26, P163, DOI 10.1007/s11695-015-1890-5
   Zhang MWB, 2014, JMIR MHEALTH UHEALTH, V2, DOI 10.2196/mhealth.2827
NR 65
TC 7
Z9 7
U1 18
U2 42
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD AUG 13
PY 2020
VL 22
IS 8
AR e17582
DI 10.2196/17582
PG 15
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Health Care Sciences & Services; Medical Informatics
GA NW5MZ
UT WOS:000575057300005
PM 32788156
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Xu, CM
   Zhou, YM
   Wang, Q
   Ma, ZY
   Zhu, Y
AF Xu, Chenming
   Zhou, Yangming
   Wang, Qi
   Ma, Zhiyuan
   Zhu, Yan
TI Detecting Hypernymy Relations Between Medical Compound Entities Using a
   Hybrid-Attention Based Bi-GRU-CapsNet Model
SO IEEE ACCESS
LA English
DT Article
DE Capsule network; medical compound entities; electronic health records;
   hybrid attention mechanism; hypernymy detection
ID SIMILARITY; SYSTEM
AB Named entities composed of multiple continuous words frequently occur in domain-specific knowledge graphs. In general, these named entities are composable and extensible, such as names of symptoms and diseases in the medical domain. Unlike the general entities, we address them as compound entities, and try to identify hypernymy relations between them. Hypernymy detection between compound entities plays a critical role in domain-specific knowledge graph construction. Traditional hypernymy detection approaches do not perform well on compound entities for two reasons. One is the lack of contextual information, and the other is the absence of compound entities, i.e. out-of-vocabulary (OOV) problem. In this paper, we propose a hybrid-attention-based method called Bi-GRU-CapsNet for the detection of hypernymy relations. The hybrid attention mechanism consists of heuristic attention and self-adaptive attention, which are used for the lack of contextual information. The attentions focus on the differences of two compound entities on the lexical and semantic level, respectively. For OOV problem, the English words or Chinese characters in compound entities are fed into bidirectional gated recurrent units (Bi-GRUs). Additionally, we use capsule network (CapsNet) to determine the existence of hypernymy relations under different cases. Experimental results show that our proposed method outperforms other baseline methods on both English and Chinese corpora of symptom and disease pairs.
C1 [Xu, Chenming; Zhou, Yangming] East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China.
   [Zhou, Yangming; Wang, Qi; Ma, Zhiyuan] East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China.
   [Xu, Chenming; Zhu, Yan] East China Univ Sci & Technol, Sch Sci, Shanghai 200237, Peoples R China.
RP Zhou, YM (通讯作者)，East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China.; Zhou, YM (通讯作者)，East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China.; Zhu, Y (通讯作者)，East China Univ Sci & Technol, Sch Sci, Shanghai 200237, Peoples R China.
EM ymzhou@ecust.edu.cn; zhuygraph@ecust.edu.cn
RI Chenming, XU/AAI-2607-2020
OI Chenming, XU/0000-0001-6723-1012; Wang, Qi/0000-0002-6792-887X; Zhu,
   Yan/0000-0001-9734-9644; Ma, Zhiyuan/0000-0003-2153-5824; Zhou,
   Yangming/0000-0002-4254-6517
FU National Key Research and Development Program of China for Precision
   Medical Research [2018YFC0910500]; Shanghai Sailing Program
   [19YF1412400]; Fundamental Research Funds for the Central Universities
   [222201817006]; National Major Scientific and Technological Special
   Project for Significant New Drugs Development [2018ZX09201008]
FX This work was supported in part by the National Key Research and
   Development Program of China for Precision Medical Research under Grant
   2018YFC0910500, in part by the Shanghai Sailing Program under Grant
   19YF1412400, in part by the Fundamental Research Funds for the Central
   Universities under Grant 222201817006, and in part by the National Major
   Scientific and Technological Special Project for Significant New Drugs
   Development under Grant 2018ZX09201008.
CR Bahdanau D., 2014, 3 INT C LEARN REPR M
   Baroni M., 2011, P GEMS 2011 WORKSH G, P1
   Baroni M., 2012, P 13 C EUR CHAPT ASS, P23, DOI DOI 10.1111/J.1551-6709.2009.01068.X
   Biran Or, 2013, P 6 INT JOINT C NAT, P788
   Chang Haw-Shiuan, 2017, ARXIV171000880
   Cho K., 2014, ARXIV14061078, P1724
   Donnelly K, 2006, STUD HEALTH TECHNOL, V121, P279
   Etzioni O., 2006, 21 NATL C ART INT BO, V2, P1517
   Fu R., 2013, EMNLP, P1224
   Fu RJ, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1199
   Hearst MA, 1992, P 14 C COMP LING, P539, DOI DOI 10.3115/992133.992154
   Kotlerman L, 2010, NAT LANG ENG, V16, P359, DOI 10.1017/S1351324910000124
   Lee JB, 2006, BIOINFORMATICS, V22, P665, DOI 10.1093/bioinformatics/btl010
   Lenci Alessandro, 2012, P 1 JOINT C LEX COMP, P75
   Levy O., 2015, P 2015 C N AM CHAPT, P970
   Li JY, 2015, LECT NOTES COMPUT SC, V9313, P623, DOI 10.1007/978-3-319-25255-1_51
   Li Y., 2016, ARXIV160707956
   Liu YG, 2014, INT J MOB COMPUT MUL, V6, P20, DOI 10.4018/IJMCMC.2014100102
   McNamee P., 2008, P 3 INT JOINT C NAT, P799
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Ng, 2016, P C EMP METH NAT LAN, P403, DOI DOI 10.18653/V1/D16-1039
   Nguyen K. A., 2017, ARXIV PREPRINT ARXIV
   Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI 10.3115/v1/D14-1162
   Ritter A., 2009, AAAI SPRING S LEARN, P88
   Roller S., 2016, P 2016 C EMP METH NA
   Roller Stephen, 2014, P COLING 2014 25 INT, P1025
   Sabour S, 2017, ADV NEURAL INFORM PR, P3856
   Santus E., 2014, P 14 C EUR CHAPT ASS, P38
   Santus E., 2016, ARXIV160308702
   Santus Enrico, 2015, P 4 WORKSH LINK DAT
   Shwartz V., 2016, ARXIV160306076
   Shwartz V., 2016, ARXIV161204460
   Snow R., 2005, ADV NEURAL INFORM PR, P1297
   Snow R, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P801
   Sogancioglu G, 2017, BIOINFORMATICS, V33, pI49, DOI 10.1093/bioinformatics/btx238
   Tan L., 2015, P 9 INT WORKSH SEM E, P932
   Tianwen Jiang, 2017, Information Retrieval. 23rd China Conference, CCIR 2017. Proceedings: LNCS 10390, P136, DOI 10.1007/978-3-319-68699-8_11
   Turney PD, 2015, NAT LANG ENG, V21, P437, DOI 10.1017/S1351324913000387
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Wang C., 2017, P C EMP METH NAT LAN, P1190
   Wang C., 2016, COLING, P1350
   Wang CY, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1965, DOI 10.1145/3308558.3313439
   Wang Q, 2019, J BIOMED INFORM, V92, DOI 10.1016/j.jbi.2019.103133
   Wang Q, 2018, IEEE INT C BIOINFORM, P1031, DOI 10.1109/BIBM.2018.8621408
   Yamane J., 2016, P COLING 2016 26 INT, P1871
   Yu Z., 2015, P INT JOINT C ART IN, p1290~1397
   Zeiler M.D., 2012, ARXIV PREPRINT ARXIV
   Zhou YM, 2014, WD SCI P COMP ENG, V9, P487
NR 48
TC 1
Z9 1
U1 1
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 175693
EP 175702
DI 10.1109/ACCESS.2019.2957827
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA KF7DI
UT WOS:000509399500044
OA gold
DA 2022-08-23
ER

PT J
AU Zhu, GG
   Iglesias, CA
AF Zhu, Ganggao
   Iglesias, Carlos A.
TI Exploiting semantic similarity for named entity disambiguation in
   knowledge graphs
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Entity linking; Named entity disambiguation; Context similarity;
   Semantic similarity; Word embedding; Knowledge graph
ID INFORMATION-CONTENT; LINKING; RECOGNITION; WORDNET; QUERIES; BASE
AB With the increasing popularity of large scale Knowledge Graph (KG)s, many applications such as semantic analysis, search and question answering need to link entity mentions in texts to entities in KGs. Because of the polysemy problem in natural language, entity disambiguation is thus a key problem in current research. Existing disambiguation methods have considered entity prominence, context similarity and entity-entity relatedness to discriminate ambiguous entities, which are mainly working on document or paragraph level texts containing rich contextual information, and based on lexical matching for computing context similarity. When meeting short texts containing limited contextual information, such as web queries, questions and tweets, those conventional disambiguation methods are not good at handling single entity mention and measuring context similarity. In order to enhance the performance of disambiguation methods based on context similarity with such short texts, we propose SCSNED method for disambiguation based on semantic similarity between contextual words and informative words of entities in KGs. Specially, we exploit the effectiveness of both knowledge-based and corpus-based semantic similarity methods for entity disambiguation with SCSNED. Moreover, we propose a Category2Vec embedding model based on joint learning of word and category embedding, in order to compute word-category similarity for entity disambiguation. We show the effectiveness of these proposed methods with illustrative examples, and evaluate their effectiveness in a comparative experiment for entity disambiguation in real world web queries, questions and tweets. The experimental results have identified the effectiveness of different semantic similarity methods, and demonstrated the improvement of semantic similarity methods in SCSNED and Category2Vec over the conventional context similarity baseline. We further compare the proposed approaches with the state of the art entity disambiguation systems and show the performances of the proposed approaches are among the best performing systems. In addition, one important feature of the proposed approaches using semantic similarity, is the potential application on any existing KGs since they mainly use common features of entity descriptions and categories. Another contribution of the paper is an updated survey on background of entity disambiguation in KGs and semantic similarity methods. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.
C1 [Zhu, Ganggao; Iglesias, Carlos A.] Univ Politecn Madrid, Escuela Tecn Super Ingenieros Telecomunicac, Dept Ingn Sistemas Telemat, Ave Complutense 30, Madrid, Spain.
RP Zhu, GG (通讯作者)，Univ Politecn Madrid, Escuela Tecn Super Ingenieros Telecomunicac, Dept Ingn Sistemas Telemat, Ave Complutense 30, Madrid, Spain.
EM gzhu@dit.upm.es; cif@dit.upm.es
RI FERNANDEZ, CARLOS ANGEL A. IGLESIAS/I-2181-2015
OI FERNANDEZ, CARLOS ANGEL A. IGLESIAS/0000-0002-1755-2712
FU Spanish Ministry of Economy and Competitiveness under the R&D project
   SEMOLA [TEC2015-68284-R]; Spanish Ministry of Economy and
   Competitiveness under the R&D project EmoSpaces [RTC-2016-5053-7];
   Regional Government of Madrid through the project MOSI-AGIL-CM
   [P2013/ICE-3019]; EU Structural Funds FSE; FEDER; European Union through
   the project MixedEmotions [141111]
FX This work is supported by the Spanish Ministry of Economy and
   Competitiveness under the R&D projects SEMOLA (TEC2015-68284-R) and
   EmoSpaces (RTC-2016-5053-7), by the Regional Government of Madrid
   through the project MOSI-AGIL-CM (grant P2013/ICE-3019, co-funded by EU
   Structural Funds FSE and FEDER), and by the European Union through the
   project MixedEmotions (Grant Agreement no: 141111).
CR Aggarwal N, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P151, DOI 10.1145/2740908.2742851
   Agirre Eneko, 2009, P 12 C EUR CHAPT ASS, DOI DOI 10.3115/1609067.1609070
   Alhelbawy Ayman, 2014, P 25 INT C COMP LING, P1544
   [Anonymous], 2012, INT WORLD WID WEB C
   [Anonymous], 2007, P 2007 JOINT C EMP M, DOI DOI 10.1145/2187836.2187900
   [Anonymous], 2006, PROCEESINGS 11 C EUR
   Baeza-Yates R., 1999, MODERN INFORM RETRIE, V463
   Baroni M, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P238
   Bekkerman R., 2011, P 17 ACM SIGKDD INT, P231
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bizer C, 2011, SEMANTIC SERVICES, INTEROPERABILITY AND WEB APPLICATIONS: EMERGING CONCEPTS, P205, DOI 10.4018/978-1-60960-593-3.ch008
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Blanco R, 2015, WSDM'15: PROCEEDINGS OF THE EIGHTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P179, DOI 10.1145/2684822.2685317
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Budanitsky A., 2001, WORKSH WORDN OTH LEX, V2, P2
   Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13
   Cano AE, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16 COMPANION), P1041, DOI 10.1145/2872518.2893528
   Carmel D., 2014, ACM SIGIR FORUM, V48, P63, DOI DOI 10.1145/2701583.2701591
   Ceccarelli D, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P139, DOI 10.1145/2505515.2505711
   Church K. W., 1990, Computational Linguistics, V16, P22
   Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48
   Cornolti M, 2013, P 22 INT C WORLD WID, P249, DOI DOI 10.1145/2488388.2488411
   DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
   Derczynski L, 2015, INFORM PROCESS MANAG, V51, P32, DOI 10.1016/j.ipm.2014.10.006
   Dou Shen, 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P131, DOI 10.1145/1148170.1148196
   Dredze M., 2010, P 23 INT C COMP LING, P277
   Fang W., 2016, P 20 SIGNLL C COMP N, P260
   Garcia NF, 2014, J ARTIF INTELL RES, V49, P733, DOI 10.1613/jair.4129
   Ferragina P., 2010, P 19 ACM INT C INF K, P1625, DOI DOI 10.1145/1871437.1871689
   Ferragina P, 2012, IEEE SOFTWARE, V29, P70, DOI 10.1109/MS.2011.122
   Finkel J. R., 2005, P 43 ANN M ASS COMP, P363, DOI DOI 10.3115/1219840.1219885
   Francis WN., 1964, BROWN CORPUS MANUAL
   Francis-Landau M., 2016, P 2016 C N AM CHAPT, P1256
   Ganea OE, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P927, DOI 10.1145/2872427.2882988
   Gattani A, 2013, PROC VLDB ENDOW, V6, P1126, DOI 10.14778/2536222.2536237
   Gligorov R., 2007, P 16 INT C WORLD WID, P767, DOI [10.1145/1242572.1242676., DOI 10.1145/1242572.1242676]
   Guo JF, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/1571941.1571989
   Guo S., 2013, HLT NAACL, V2013, P1020
   Guo Y., 2011, IJCNLP, P1018
   Gutierrez Yoan, 2012, Computational Linguistics and Intelligent Text Processing. 13th International Conference (CICLing 2012). Proceedings, Part I, P225, DOI 10.1007/978-3-642-28604-9_19
   Gutierrez Y., 2017, KNOWLEDGE BASED SYST
   Habib MB, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (ACL-IJCNLP 2015): SYSTEM DEMONSTRATIONS, P31
   Hachey B, 2013, ARTIF INTELL, V194, P130, DOI 10.1016/j.artint.2012.04.005
   Hachey B, 2011, LECT NOTES COMPUT SC, V6997, P213, DOI 10.1007/978-3-642-24434-6_16
   Hagen M., 2011, P 20 INT C WORLD WID, P97, DOI [10.1145/1963405.1963423, DOI 10.1145/1963405.1963423]
   Han X., 2011, P 49 ANN M ASS COMP, P945
   Han XP, 2011, PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), P765
   Hasibi F., 2016, P 2016 ACM INT C THE, P209, DOI DOI 10.1145/2970398.2970406
   Hasibi F., 2015, P 2015 INT C THEOR I, P171, DOI DOI 10.1145/2808194.2809473
   He Z., 2013, P ACL
   Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527
   Hoffart J., 2011, P EMNLP, DOI DOI 10.3115/V1/D11-1072
   Hoffart J., 2012, P 21 ACM INT C INF K, P545, DOI DOI 10.1145/2396761.2396832
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Houlsby N., 2014, 36 EUR C IR RES ECIR, P335, DOI [10.1007/978-3-319-06028-6_28, DOI 10.1007/978-3-319-06028-6_28]
   Hulpus I, 2015, LECT NOTES COMPUT SC, V9366, P442, DOI 10.1007/978-3-319-25007-6_26
   Ji H., 2011, P 49 ANN M ASS COMP, V1, P1148
   Jiang J. J., 1997, COMPUT LINGUIST, P15
   Kataria S.S., 2011, KDD, P1037, DOI DOI 10.1145/2020408.2020574
   Kulkarni S, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P457
   Le Q., 2014, P 31 INT C MACH LEAR, P1188, DOI DOI 10.1007/978-1-4614-3223-4
   LESK Michael, 1986, P 5 ANN INT C SYST D, V5, P24, DOI [10.1145/318723.318728, DOI 10.1145/318723.318728]
   Levy O, 2015, T ASSOC COMPUT LING, P3, DOI DOI 10.1162/TACL_A_00134
   Lin D., 1998, Machine Learning. Proceedings of the Fifteenth International Conference (ICML'98), P296
   Liu X., 2013, ACL, P1304
   Medelyan O, 2008, P AAAI WIKIAI WORK, P19
   Meij E., 2012, P 5 ACM INT C WEB SE, P563, DOI DOI 10.1145/2124295.2124364
   Meij E, 2011, J WEB SEMANT, V9, P418, DOI 10.1016/j.websem.2011.04.001
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   Meymandpour R, 2016, KNOWL-BASED SYST, V109, P276, DOI 10.1016/j.knosys.2016.07.012
   Mihalcea R., 2007, P 16 ACM C C INF KNO, P233, DOI DOI 10.1145/1321440.1321475
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Milne D., 2008, P 17 ACM C INF KNOWL, P509, DOI DOI 10.1145/1458082.1458150
   Milne D, 2013, ARTIF INTELL, V194, P222, DOI 10.1016/j.artint.2012.06.007
   Moro A., 2014, TACL, V2, P231, DOI DOI 10.1162/TACL_A_00179
   Nadeau D, 2007, LINGUIST INVESTIG, V30, P3
   Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355
   Passant A., 2010, AAAI SPRING S LINK D, V77, P123
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Pereira Nunes Bernardo, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P548
   Piccinno F., 2014, P 1 INT WORKSH ENT R, P55, DOI [10 . 1145 / 2633211.2634350, DOI 10.1145/2633211.2634350]
   Pilz A, 2011, P 20 ACM INT C INF K, P857, DOI DOI 10.1145/2063576.2063700
   Pirro G, 2012, AAAI
   Plu J., 2016, CCIS, P17, DOI DOI 10.1007/978-3-319-46565-4
   Pound Jeffrey, 2012, P 21 ACM INT C INF K, P305, DOI DOI 10.1145/2396761.2396803
   Pu KQ, 2008, PROC VLDB ENDOW, V1, P909, DOI 10.14778/1453856.1453955
   RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528
   Ratinov L., 2011, COMPUT LINGUIST, V1, P1375
   Resnik P, 1995, INT JOINT CONF ARTIF, P448
   Ritter A., 2011, P 2011 C EMP METH NA, P1524
   Roder M, 2014, LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P3529
   RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
   Sanchez D, 2012, EXPERT SYST APPL, V39, P7718, DOI 10.1016/j.eswa.2012.01.082
   Sawant Uma, 2013, P 22 INT C WORLD WID, P1099, DOI DOI 10.1145/2488388.2488484
   Shekarpour S, 2015, J WEB SEMANT, V30, P39, DOI 10.1016/j.websem.2014.06.002
   Shen W., 2012, P 18 ACM SIGKDD INT, P1424, DOI [DOI 10.1145/2339530.2339753, 10.1145/2339530.2339753.]
   Shen W, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P68
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Shi BX, 2017, KNOWL-BASED SYST, V119, P20, DOI 10.1016/j.knosys.2016.11.025
   Sil A, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P2369, DOI 10.1145/2505515.2505601
   Sun Y, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1333
   Turney PD, 2010, J ARTIF INTELL RES, V37, P141, DOI 10.1613/jair.2934
   TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/h0026750
   Usbeck R., 2014, 13 INT SEM WEB C
   Usbeck R, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1133, DOI 10.1145/2736277.2741626
   Waitelonis J., 2016, P 6 WORKSH NEEL CHAL
   Xianpei H., 2009, P 18 ACM C INF KNOWL, P215, DOI DOI 10.1145/1645953.1645983
   Zhang L, 2016, 2016 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2016), P49, DOI [10.1109/WI.2016.18, 10.1109/WI.2016.0018]
   Zhibiao Wu, 1994, 32nd Annual Meeting of the Association for Computational Linguistics. Proceedings of the Conference, P133
   Zhu G, 2017, KNOWLEDGE BASED SYST
   Zhu GG, 2017, IEEE T KNOWL DATA EN, V29, P72, DOI 10.1109/TKDE.2016.2610428
   Zwicklbauer S, 2016, LECT NOTES COMPUT SC, V9678, P182, DOI 10.1007/978-3-319-34129-3_12
NR 113
TC 47
Z9 51
U1 8
U2 161
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD JUL 1
PY 2018
VL 101
BP 8
EP 24
DI 10.1016/j.eswa.2018.02.011
PG 17
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Operations Research & Management Science
GA GA7ER
UT WOS:000428498300002
OA hybrid
DA 2022-08-23
ER

PT J
AU Brunkert, T
   Simon, M
   Ruppen, W
   Zuniga, F
AF Brunkert, Thekla
   Simon, Michael
   Ruppen, Wilhelm
   Zuniga, Franziska
TI A Contextual Analysis to Explore Barriers and Facilitators of Pain
   Management in Swiss Nursing Homes
SO JOURNAL OF NURSING SCHOLARSHIP
LA English
DT Article
DE COM-B; mixed-methods; nursing home; pain management
ID PREVALENCE; RESIDENTS; KNOWLEDGE
AB Purpose As part of a contextual analysis, this study aimed to generate a comprehensive understanding of barriers and facilitators to pain management in nursing homes to identify potential leverage points for future implementation studies. Design An explanatory sequential mixed-methods study embedded in a cross-sectional study in 20 Swiss nursing homes (data collection: July-December 2016). Methods Quantitative data were collected via care worker questionnaire surveys comprising 20 items assessing perceptions of barriers to pain management. Descriptive statistics were computed. In the subsequent qualitative strand we conducted four focus group discussions with care workers (registered nurses, licensed practical nurses, and nursing aides) using a knowledge-mapping approach. Findings of both strands were merged and mapped onto domains of the Capability, Opportunity, and Motivation determine Behavior (COM-B) system, a model for behavior, to identify determinants for behavior change. Findings Data from 343 completed care worker surveys (response rate 67.3%) and four focus groups with care workers were analyzed. Items rated most problematic were as follows: lack of availability of nonpharmacological treatment (60.9%), lack of application of nonpharmacological treatment (53.6%), reluctance of residents to report pain (51.1%), and lack of time for a comprehensive pain assessment (50.5%). Focus groups partly corroborated quantitative findings and complemented them with facilitators, such as close collaboration with physicians and further barriers (e.g., organizational factors such as high turnover and a lack of established routines in pain management). Conclusions Our approach using a behavioral model highlighted a need for implementation strategies that improve pain management knowledge and focus on motivational aspects to establish new routines and habits related to pain management among care workers. Clinical Relevance Our findings suggest that future approaches to improve pain management in nursing homes should go beyond provision of education and training. To establish new practices or adapt existing ones, a more complex approach (e.g., introduction of external or internal facilitators) is necessary to influence motivation and ultimately change behavior.
C1 [Brunkert, Thekla; Simon, Michael; Zuniga, Franziska] Univ Basel, Fac Med, DPH, Nursing Sci INS, Bernoullistr 28, CH-4056 Basel, Switzerland.
   [Simon, Michael] Bern Univ Hosp, Inselspital, Nursing Res Unit, Bern, Switzerland.
   [Ruppen, Wilhelm] Univ Hosp Basel, Dept Anaesthesia, Surg Intens Care, Prehosp Emergency Med & Pain Therapy, Basel, Switzerland.
RP Zuniga, F (通讯作者)，Univ Basel, Fac Med, DPH, Nursing Sci INS, Bernoullistr 28, CH-4056 Basel, Switzerland.
EM franziska.zuniga@unibas.ch
RI Simon, Michael/AAF-9781-2022; Simon, Michael/C-5601-2008; Simon,
   Michael/AFK-2509-2022; Brunkert, Thekla/AFT-1078-2022
OI Simon, Michael/0000-0003-2349-7219; Simon, Michael/0000-0003-2349-7219;
   Brunkert, Thekla/0000-0002-0369-1826
CR Abdulla A, 2013, AGE AGEING, V42, pI1, DOI 10.1093/ageing/afs200
   AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
   [Anonymous], 2018, R LANG ENV STAT COMP
   [Anonymous], MIXED METHODS HLTH S
   [Anonymous], BEHAV CHANGE WHEEL G
   Burgess-Allen J, 2010, HEALTH EXPECT, V13, P406, DOI 10.1111/j.1369-7625.2010.00594.x
   Creswell J., 2018, DESIGNING CONDUCTING
   Fleischmann N, 2016, BMC FAM PRACT, V17, DOI 10.1186/s12875-016-0522-z
   Flodgren G, 2011, COCHRANE DB SYST REV, DOI [10.1002/14651858.CD000125.pub4, 10.1002/14651858.CD000125]
   Gran SV, 2010, INT J OLDER PEOPLE N, V5, P25, DOI 10.1111/j.1748-3743.2009.00195.x
   Grimshaw JM, 2012, IMPLEMENT SCI, V7, DOI 10.1186/1748-5908-7-50
   Hadjistavropoulos T, 2011, PSYCHOL BULL, V137, P910, DOI 10.1037/a0023876
   Herman AD, 2009, J AM GERIATR SOC, V57, P1258, DOI 10.1111/j.1532-5415.2009.02315.x
   Ickowicz E, 2009, PAIN MED, V10, P1062, DOI 10.1111/j.1526-4637.2009.00699.x
   Jones Katherine R, 2005, J Am Med Dir Assoc, V6, P10, DOI 10.1016/j.jamda.2004.12.010
   Jones KR, 2004, GERONTOLOGIST, V44, P469, DOI 10.1093/geront/44.4.469
   Jones TL, 2015, INT J NURS STUD, V52, P1121, DOI 10.1016/j.ijnurstu.2015.02.012
   Kaasalainen S, 2010, CAN J AGING, V29, P503, DOI 10.1017/S0714980810000528
   Knopp-Sihota JA, 2016, J AM MED DIR ASSOC, V17, DOI 10.1016/j.jamda.2016.09.016
   Liu JYW, 2014, J ADV NURS, V70, P1065, DOI 10.1111/jan.12259
   Maneesriwongul W, 2004, J ADV NURS, V48, P175, DOI 10.1111/j.1365-2648.2004.03185.x
   Martin R, 2005, CAN J NURS RES, V37, P142
   Mayring P., 2010, QUALITATIVE INHALTSA, DOI DOI 10.1007/978-3-531-92052-8_42
   Michie S, 2008, APPL PSYCHOL-INT REV, V57, P660, DOI 10.1111/j.1464-0597.2008.00341.x
   Michie S, 2011, IMPLEMENT SCI, V6, DOI 10.1186/1748-5908-6-10
   Powell BJ, 2017, J BEHAV HEALTH SER R, V44, P177, DOI 10.1007/s11414-015-9475-6
   Smith TO, 2016, RHEUMATOL INT, V36, P55, DOI 10.1007/s00296-015-3322-1
   Swafford KL, 2014, GERIATR NURS, V35, P423, DOI 10.1016/j.gerinurse.2014.06.001
   Takai Y, 2010, PAIN MANAG NURS, V11, P209, DOI 10.1016/j.pmn.2010.08.006
   Tarzian Anita J, 2005, J Am Med Dir Assoc, V6, pS13, DOI 10.1016/j.jamda.2005.03.016
   Veal F, 2018, PAIN MANAG NURS, V19, P177, DOI 10.1016/j.pmn.2017.10.002
NR 31
TC 7
Z9 7
U1 3
U2 8
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1527-6546
EI 1547-5069
J9 J NURS SCHOLARSHIP
JI J. Nurs. Scholarsh.
PD JAN
PY 2020
VL 52
IS 1
BP 14
EP 22
DI 10.1111/jnu.12508
PG 9
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA JZ7SF
UT WOS:000505307200003
PM 31898860
OA Bronze, Green Accepted
DA 2022-08-23
ER

PT J
AU Luo, DK
   Liang, WP
   Ma, BA
   Xue, DB
AF Luo, Dankun
   Liang, Wenping
   Ma, Biao
   Xue, Dongbo
TI Global trends of indocyanine green fluorescence navigation in
   laparoscopic cholecystectomy: bibliometrics and knowledge atlas analysis
SO SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES
LA English
DT Article; Early Access
DE Bibliometric; Knowledge map; VOSviewer
ID BILE-DUCT INJURIES; CHOLANGIOGRAPHY; IDENTIFICATION
AB Background Indocyanine green fluorescence cholangiography is a new type of cholangiography that has emerged in recent years. It has many advantages, such as simplicity, safety, and efficiency, and is expected to become a routine procedure for intraoperative cholangiography in the future. However, there is no bibliometric analysis of horizontal generalization in this field. Therefore, this study aims to assess the current research status and developmental trends in this field from a global perspective and to help guide the focus of future research. Methods This study was conducted by retrieving the literature related to ICG fluorescence cholangiography published in English from 2000 to 2021 from Web of Science. The Histcite. Pro tool was used to analyze the bibliometric indicators, and VOSviewer was used to visualize the research trends and hotspots in this field. Results 192 related studies were finally included. For the past 20 years, the number of related publications in this field have increased yearly, with 2010 being a breakthrough point for research in this field. Research has developed rapidly since 2013 and may continue to expand in the next several years. The USA and Japan play leading roles in this field. "SURG ENDOSC", "SURG INNOV", and "J LAPAROENDOSC ADV S" are core journals that publish high-quality literature and the latest developments in this field. Some authors who have a large number of high-quality publications include Ishizawa T and Kokudo N. The current research hotspots in this field are focused on exploring the factors that affect ICG cholangiography and optimization measures. Conclusions In general, the number of documents related to ICG fluorescence cholangiography is growing rapidly, with the USA and Japan being influential countries and China being one of the major producers of these documents. The optimization of this new technology will affect key developments in future research.
C1 [Luo, Dankun; Liang, Wenping; Ma, Biao; Xue, Dongbo] Harbin Med Univ, Dept Gen Surg, Affiliated Hosp 1, 23 You Zheng St, Harbin 150001, Heilongjiang, Peoples R China.
   [Luo, Dankun; Liang, Wenping; Ma, Biao; Xue, Dongbo] Harbin Med Univ, Key Lab Hepatosplen Surg, Affiliated Hosp 1, Minist Educ, Harbin 150001, Heilongjiang, Peoples R China.
RP Ma, BA; Xue, DB (通讯作者)，Harbin Med Univ, Dept Gen Surg, Affiliated Hosp 1, 23 You Zheng St, Harbin 150001, Heilongjiang, Peoples R China.; Ma, BA; Xue, DB (通讯作者)，Harbin Med Univ, Key Lab Hepatosplen Surg, Affiliated Hosp 1, Minist Educ, Harbin 150001, Heilongjiang, Peoples R China.
EM luodankun@hrbmu.edu.cn; lwp_0205@163.com; mabiao@hrbmu.edu.cn;
   xuedongbo@hrbmu.edu.cn
FU National Natural Science Foundation of China [81602337, 81570579];
   Education Department of Heilongjiang Province [UNPYSCT-2017062]; Natural
   Science Foundation of Heilongjiang Province [LH2021H050]
FX This research was funded by the National Natural Science Foundation of
   China (Grant No. 81602337. Grant No. 81570579.) and Education Department
   of Heilongjiang Province (Grant No. UNPYSCT-2017062) and the Natural
   Science Foundation of Heilongjiang Province (Grant No. LH2021H050).
CR Aoki T, 2010, J HEPATO-BIL-PAN SCI, V17, P590, DOI 10.1007/s00534-009-0197-0
   Boogerd LSF, 2017, SURG INNOV, V24, P386, DOI 10.1177/1553350617702311
   Booij KAC, 2015, ENDOSCOPY, V47, P40, DOI 10.1055/s-0034-1390908
   Daskalaki D, 2014, SURG INNOV, V21, P615, DOI 10.1177/1553350614524839
   Gao H, 2018, CHINESE MED J-PEKING, V131, P2968, DOI 10.4103/0366-6999.247215
   Gao Q, 2020, ARCH OSTEOPOROS, V15, DOI 10.1007/s11657-020-0705-z
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Huang FY, 2021, MEDICINE, V100, DOI 10.1097/MD.0000000000026100
   Huang TJ, 2020, SPINE, V45, pE721, DOI 10.1097/BRS.0000000000003381
   Huang XQ, 2019, J TRANSL MED, V17, DOI 10.1186/s12967-019-1810-x
   Ishizawa T, 2010, BRIT J SURG, V97, P1369, DOI 10.1002/bjs.7125
   Ke LX, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.00842
   Landman MP, 2013, HPB, V15, P252, DOI 10.1111/j.1477-2574.2012.00586.x
   Ma CQ, 2021, FRONT ONCOL, V10, DOI 10.3389/fonc.2020.627891
   Osayi SN, 2015, SURG ENDOSC, V29, P368, DOI 10.1007/s00464-014-3677-5
   Schols RM, 2013, SURG ENDOSC, V27, P1530, DOI 10.1007/s00464-012-2635-3
   Sheffield KM, 2012, J AM COLL SURGEONS, V214, P668, DOI 10.1016/j.jamcollsurg.2011.12.033
   Verbeek FPR, 2014, SURG ENDOSC, V28, P1076, DOI 10.1007/s00464-013-3305-9
   Vlek SL, 2017, SURG ENDOSC, V31, P2731, DOI 10.1007/s00464-016-5318-7
   Wang S, 2021, FRONT IMMUNOL, V12, DOI 10.3389/fimmu.2021.669539
   Way LW, 2003, ANN SURG, V237, P460, DOI 10.1097/00000658-200304000-00004
   Wu HY, 2021, FRONT CELL DEV BIOL, V9, DOI 10.3389/fcell.2021.646311
   Yang CX, 2020, WORLD NEUROSURG, V134, pE405, DOI 10.1016/j.wneu.2019.10.087
   Yao RQ, 2020, INT J BIOL SCI, V16, P27, DOI 10.7150/ijbs.37496
   Zarrinpar A, 2016, SURG INNOV, V23, P360, DOI 10.1177/1553350616637671
   Zhang J, 2021, FRONT ONCOL, V11, DOI 10.3389/fonc.2021.686726
   Zhang TC, 2020, J CANCER, V11, P4823, DOI 10.7150/jca.44126
   Zhou HQ, 2018, PEERJ, V6, DOI 10.7717/peerj.4354
   Zhu YL, 2021, ORTHOP TRAUMATOL-SUR, V107, DOI 10.1016/j.otsr.2021.102988
   Zou LX, 2019, MEDICINE, V98, DOI 10.1097/MD.0000000000014394
NR 30
TC 0
Z9 0
U1 11
U2 13
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0930-2794
EI 1432-2218
J9 SURG ENDOSC
JI Surg. Endosc.
DI 10.1007/s00464-021-08988-9
EA JAN 2022
PG 13
WC Surgery
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Surgery
GA YG6LK
UT WOS:000742597300001
PM 35029767
DA 2022-08-23
ER

PT J
AU Zhang, JH
   Wang, CH
   Sheng, Y
   Palta, M
   Czito, B
   Willett, C
   Zhang, J
   Jensen, PJ
   Yin, FF
   Wu, QW
   Ge, YR
   Wu, QJ
AF Zhang, Jiahan
   Wang, Chunhao
   Sheng, Yang
   Palta, Manisha
   Czito, Brian
   Willett, Christopher
   Zhang, Jiang
   Jensen, P. James
   Yin, Fang-Fang
   Wu, Qiuwen
   Ge, Yaorong
   Wu, Q. Jackie
TI An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation
   Therapy
SO INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
LA English
DT Article
ID AT-RISK; RADIOTHERAPY; ADENOCARCINOMA; GEMCITABINE; CANCER
AB Purpose: Pancreas stereotactic body radiation therapy (SBRT) treatment planning requires planners to make sequential, timeconsuming interactions with the treatment planning system to reach the optimal dose distribution. We sought to develop a reinforcement learning (RL)-based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently.
   Methods and Materials: The focus of pancreas SBRT planning is finding a balance between organ-at-risk sparing and planning target volume (PTV) coverage. Planners evaluate dose distributions and make planning adjustments to optimize PTV coverage while adhering to organ-at-risk dose constraints. We formulated such interactions between the planner and treatment planning system into a finite-horizon RL model. First, planning status features were evaluated based on human planners' experience and defined as planning states. Second, planning actions were defined to represent steps that planners would commonly implement to address different planning needs. Finally, we derived a reward system based on an objective function guided by physician-assigned constraints. The planning bot trained itself with 48 plans augmented from 16 previously treated patients, and generated plans for 24 cases in a separate validation set.
   Results: All 24 bot-generated plans achieved similar PTV coverages compared with clinical plans while satisfying all clinical planning constraints. Moreover, the knowledge learned by the bot could be visualized and interpreted as consistent with human planning knowledge, and the knowledge maps learned in separate training sessions were consistent, indicating reproducibility of the learning process.
   Conclusions: We developed a planning bot that generates high-quality treatment plans for pancreas SBRT. We demonstrated that the training phase of the bot is tractable and reproducible, and the knowledge acquired is interpretable. As a result, the RL planning bot can potentially be incorporated into the clinical workflow and reduce planning inefficiencies. (C) 2020 Elsevier Inc. All rights reserved.
C1 [Zhang, Jiahan; Wang, Chunhao; Sheng, Yang; Palta, Manisha; Czito, Brian; Willett, Christopher; Zhang, Jiang; Jensen, P. James; Yin, Fang-Fang; Wu, Qiuwen; Wu, Q. Jackie] Duke Univ, Med Ctr, Dept Radiat Oncol, Durham, NC 27708 USA.
   [Ge, Yaorong] Univ North Carolina Charlotte, Charlotte, NC USA.
RP Zhang, JH (通讯作者)，Duke Univ, Med Ctr, Dept Radiat Oncol, Durham, NC 27708 USA.
EM jiahan.zhang@duke.edu
RI Zhang, Jiang/GPF-5042-2022; Zhang, Jiahan/V-1294-2019
OI Zhang, Jiahan/0000-0002-4288-6503; Yin, Fang-Fang/0000-0002-2025-4740;
   Wu, Qiuwen/0000-0003-0748-7280
FU National Institutes of Health [R01CA201212]; Varian Medical Systems
FX The authors have no conflicts of interests to report. This work is
   partially supported by an National Institutes of Health grant
   (#R01CA201212) and a master research grant by Varian Medical Systems.
CR [Anonymous], 2017, ECLIPSE PHOTON ELECT, V5
   Appenzoller LM, 2012, MED PHYS, V39, P7446, DOI 10.1118/1.4761864
   de Geus SWL, 2017, CANCER-AM CANCER SOC, V123, P4158, DOI 10.1002/cncr.30856
   Dongbin Z., 2016, 2016 IEEE S SER COMP, P1, DOI DOI 10.1109/SSCI.2016.7849837
   Gurka MK, 2013, RADIAT ONCOL, V8, DOI 10.1186/1748-717X-8-44
   Herman JM, 2015, CANCER-AM CANCER SOC, V121, P1128, DOI 10.1002/cncr.29161
   Mnih V, 2015, NATURE, V518, P529, DOI 10.1038/nature14236
   Riedmiller M, 2009, AUTON ROBOT, V27, P55, DOI 10.1007/s10514-009-9120-4
   Rummery G A, 1994, ONLINE Q LEARNING US
   Rwigema JCM, 2011, AM J CLIN ONCOL-CANC, V34, P63, DOI 10.1097/COC.0b013e3181d270b4
   Shen CY, 2020, MED PHYS, V47, P2329, DOI 10.1002/mp.14114
   Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
   TESAURO G, 1995, COMMUN ACM, V38, P58, DOI 10.1145/203330.203343
   Tousi MR, 2008, 2008 IEEE 2ND INTERNATIONAL POWER AND ENERGY CONFERENCE: PECON, VOLS 1-3, P1198, DOI 10.1109/PECON.2008.4762658
   Wu BB, 2011, INT J RADIAT ONCOL, V79, P1241, DOI 10.1016/j.ijrobp.2010.05.026
   Yuan LL, 2012, MED PHYS, V39, P6868, DOI 10.1118/1.4757927
   Zhong J, 2017, CANCER-AM CANCER SOC, V123, P3486, DOI 10.1002/cncr.30706
   Zhu XF, 2011, MED PHYS, V38, P719, DOI 10.1118/1.3539749
NR 18
TC 11
Z9 11
U1 1
U2 4
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0360-3016
EI 1879-355X
J9 INT J RADIAT ONCOL
JI Int. J. Radiat. Oncol. Biol. Phys.
PD MAR 15
PY 2021
VL 109
IS 4
BP 1076
EP 1085
DI 10.1016/j.ijrobp.2020.10.019
EA FEB 2021
PG 10
WC Oncology; Radiology, Nuclear Medicine & Medical Imaging
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Oncology; Radiology, Nuclear Medicine & Medical Imaging
GA RB3IK
UT WOS:000632008000027
PM 33115686
OA Green Accepted, Green Submitted
DA 2022-08-23
ER

PT J
AU Pan, HT
   Xi, ZQ
   Yu, XT
   Sun, XQ
   Wei, XQ
   Wang, K
AF Pan, Haotian
   Xi, Ziqi
   Yu, Xintong
   Sun, Xuqiu
   Wei, Xuqiang
   Wang, Ke
TI Knowledge Mapping Analysis of International Research on Acupuncture for
   Low Back Pain Using Bibliometrics
SO JOURNAL OF PAIN RESEARCH
LA English
DT Article
DE low back pain; acupuncture; bibliometric analysis; VOSviewer; CiteSpace
ID TRIAL; METAANALYSIS; GUIDELINE; DIAGNOSIS; CARE
AB Objective: Acupuncture has made recommendations to relieve low back pain (LBP) in a few guidelines. However, few articles focused on the papers published before, which may reflect the trend of study, main researchers, reciprocal collaboration, and other information in this field. In this study, we utilize the bibliometric analysis methods to objectively analyze the current trend and research hotspots about this field, aiming to provide a foundation and reference for future study. Methods: The Web of Science database was searched for publications related to acupuncture therapy for treating low back pain between 1985 and 2021. VOSviewer and CiteSpace were used to analyze the annual publication, authors and co-cited authors and their countries (regions) and institutions, journals and co-cited journals, co-cited references, co-occurrence keywords, burst keywords, and the relevant centrality. Results: A total of 1279 papers were retrieved from the database, and the trend of annual publications maintained growth. Article was the most document type (867). The USA (451) and the Kyung Hee University (49) were the most productive country (region) and institution, respectively, while the USA (0.37) and University of Maryland (0.13) had the highest centrality. MacPherson, Hugh and Sherman, Karen J. published the most papers in this field (32), and Ernst, Edzard became the most influential author (474 co-citations). Journal of Alternative and Complementary Medicine was the most productive journal (79), whereas SPINE was the most influential one (3111 co-citations). Haake's (2007) article had the most co-citation count (186), while Furlan's (2005) article had the highest centrality (0.23). The three frontier topics were pain management, disease, and protocol. Conclusion: The present study investigated publications on acupuncture for treating low back pain using bibliometric analysis methods, which may help researchers explore the directions for the future.
C1 [Pan, Haotian; Xi, Ziqi; Yu, Xintong; Wei, Xuqiang; Wang, Ke] Shanghai Univ Tradit Chinese Med, Yueyang Hosp Integrated Tradit Chinese & Western, Acupuncture Anesthesia Clin Res Inst, Shanghai 200437, Peoples R China.
   [Sun, Xuqiu; Wang, Ke] Shanghai Univ Tradit Chinese Med, Yueyang Hosp Integrated Tradit Chinese & Western, Off Natl Clin Res Base TCM, Shanghai, Peoples R China.
RP Wang, K (通讯作者)，Shanghai Univ Tradit Chinese Med, Yueyang Hosp Integrated Tradit Chinese & Western, Acupuncture Anesthesia Clin Res Inst, Shanghai 200437, Peoples R China.
EM wangke8430@163.com
OI Wang, Ke/0000-0002-5845-7228; Xi, Ziqi/0000-0001-6163-331X; Pan,
   Hao-Tian/0000-0002-4944-349X
FU National Natural Science Fund of China [81973940]; Accelerating the
   Development of Chinese Medicine Three-Year Action Plan of Shanghai [ZY
   (2018-2020) -CCCX-2004-04]; Clinical Key Specialty Construction
   Foundation of Shanghai [shslczdzk04701]; Shanghai Clinical Research
   Center for Acupuncture and Moxibustion [20MC1920500]
FX The project was funded by the National Natural Science Fund of China
   (81973940) , Accelerating the Development of Chinese Medicine Three-Year
   Action Plan of Shanghai (No. ZY (2018-2020) -CCCX-2004-04) , Clinical
   Key Specialty Construction Foundation of Shanghai (No. shslczdzk04701) ,
   and Shanghai Clinical Research Center for Acupuncture and Moxibustion
   (20MC1920500) .
CR Bogduk N, 2009, PAIN, V147, P17, DOI 10.1016/j.pain.2009.08.020
   Brinkhaus B, 2006, ARCH INTERN MED, V166, P450
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Chen YP, 2019, MEDICINE, V98, DOI 10.1097/MD.0000000000016271
   Cherkin DC, 2009, ARCH INTERN MED, V169, P858, DOI 10.1001/archinternmed.2009.65
   Cherkin DC, 2001, ARCH INTERN MED, V161, P1081, DOI 10.1001/archinte.161.8.1081
   Chou R, 2007, ANN INTERN MED, V147, P478, DOI 10.7326/0003-4819-147-7-200710020-00006
   da Silva T, 2019, J PHYSIOTHER, V65, P159, DOI 10.1016/j.jphys.2019.04.010
   Deng R, 2020, MEDICINE, V99, DOI 10.1097/MD.0000000000018930
   Dionne CE, 2008, SPINE, V33, P95, DOI 10.1097/BRS.0b013e31815e7f94
   Dowell D, 2016, JAMA-J AM MED ASSOC, V315, P1624, DOI 10.1001/jama.2016.1464
   Foster NE, 2018, LANCET, V391, P2368, DOI 10.1016/S0140-6736(18)30489-6
   Fuentes RC, 2020, CAN FAM PHYSICIAN, V66, P186
   Furlan AD, 2005, SPINE, V30, P944, DOI 10.1097/01.brs.0000158941.21571.01
   Gao Z, 2021, J PAIN RES, V14, DOI 10.2147/JPR.S300911
   Haake M, 2007, ARCH INTERN MED, V167, P1892, DOI 10.1001/archinte.167.17.1892
   Hartvigsen J, 2018, LANCET, V391, P2356, DOI 10.1016/S0140-6736(18)30480-X
   Kim H, 2020, NEUROIMAGE, V217, DOI 10.1016/j.neuroimage.2020.116899
   Kim JH, 2021, TRIALS, V22, DOI 10.1186/s13063-021-05038-6
   Knezevic NN, 2021, LANCET, V398, P78, DOI 10.1016/S0140-6736(21)00733-9
   Lee IS, 2020, J PAIN RES, V13, P367, DOI 10.2147/JPR.S235047
   Lee IS, 2019, ACUPUNCT MED, V37, P375, DOI 10.1177/0964528419884327
   Lewis K, 2010, CLIN J PAIN, V26, P60, DOI 10.1097/AJP.0b013e3181bad71e
   Li T, 2019, TRIALS, V20, DOI 10.1186/s13063-019-3566-2
   Liang YD, 2017, J PAIN RES, V10, P951, DOI 10.2147/JPR.S132808
   Liao JL, 2019, MEDICINE, V98, DOI 10.1097/MD.0000000000016478
   Liu X, 2021, J PAIN RES, V14, P1141, DOI 10.2147/JPR.S308006
   Ma Y, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0168123
   MacPherson H, 2004, COMPLEMENT THER MED, V12, P38, DOI 10.1016/S0965-2299(03)00125-0
   Maher C, 2017, LANCET, V389, P736, DOI 10.1016/S0140-6736(16)30970-9
   Manheimer E, 2005, ANN INTERN MED, V142, P651, DOI 10.7326/0003-4819-142-8-200504190-00014
   Qaseem Amir, 2017, Ann Intern Med, V166, P514, DOI 10.7326/M16-2367
   Torres SF, 2019, TRIALS, V20, DOI 10.1186/s13063-019-3813-6
   Treede RD, 2019, PAIN, V160, P19, DOI 10.1097/j.pain.0000000000001384
   van Eck NJ, 2017, SCIENTOMETRICS, V111, P1053, DOI 10.1007/s11192-017-2300-7
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vickers AJ, 2012, ARCH INTERN MED, V172, P1444, DOI 10.1001/archinternmed.2012.3654
   Vlaeyen JWS, 2018, NAT REV DIS PRIMERS, V4, DOI 10.1038/s41572-018-0052-1
   Wang LJ, 2021, J PAIN RES, V14, P1097, DOI 10.2147/JPR.S310385
   Wei XQ, 2019, BMC MUSCULOSKEL DIS, V20, DOI 10.1186/s12891-019-2894-4
   Wen Q, 2021, FRONT NEUROSCI-SWITZ, V15, DOI 10.3389/fnins.2021.730322
   Witt C, 2005, LANCET, V366, P136, DOI 10.1016/S0140-6736(05)66871-7
   Witt CM, 2006, AM J EPIDEMIOL, V164, P487, DOI 10.1093/aje/kwj224
   Yeganeh M, 2017, COMPLEMENT THER CLIN, V27, P11, DOI 10.1016/j.ctcp.2016.11.012
   Yuan S, 2020, MEDICINE, V99, DOI 10.1097/MD.0000000000019117
   Zhang JH, 2021, FRONT NEUROSCI-SWITZ, V14, DOI 10.3389/fnins.2020.620555
   Zhang Y, 2020, ENVIRON SCI POLLUT R, V27, P15515, DOI 10.1007/s11356-020-07646-2
   Zhong XY, 2021, MEDICINE, V100, DOI 10.1097/MD.0000000000025199
NR 48
TC 1
Z9 1
U1 12
U2 15
PU DOVE MEDICAL PRESS LTD
PI ALBANY
PA PO BOX 300-008, ALBANY, AUCKLAND 0752, NEW ZEALAND
SN 1178-7090
J9 J PAIN RES
JI J. Pain Res.
PY 2021
VL 14
BP 3733
EP 3746
DI 10.2147/JPR.S340992
PG 14
WC Clinical Neurology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA XP4UK
UT WOS:000730861700002
PM 34916841
OA Green Published, gold
DA 2022-08-23
ER

PT S
AU Noll, M
   Frohlich, D
   Schiebel, E
AF Noll, M
   Frohlich, D
   Schiebel, E
BE Karagiannis, D
   Reimer, U
TI Knowledge maps of knowledge management tools - Information visualization
   with BibTechMon
SO PRACTICAL ASPECTS OF KNOWLEDGE MANAGEMENT
SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
LA English
DT Article; Proceedings Paper
CT 4th International Conference on Practical Aspects of Knowledge
   Management
CY DEC 02-03, 2002
CL VIENNA, AUSTRIA
SP Univ Vienna, Inst Informat & Business Informat, Dept Knowledge Management
AB Companies, R&D-departments or managers are confronted with an increasing amount of information which has to be analysed corresponding to their importance and applicability for the company and the current question. For this purpose information from internal or external sources like internal documents, patent information, literature quotations or internet information has to be identified, surveyed, read, distributed and used. Especially the search for relevant information and the decision about their applicability gets more and more time-consuming. In order to reduce the efforts for structuring and sorting information the Department of Technology Management of the ARC Seibersdorf research GmbH has developed the software tool BibTechMon for structuring, visualising and analysing large amounts of information, Several thousands of documents available from external or in-house databases or from internet can be worked up with the software BibTechMon, The advantage of this method is based on the possibility of content-based structuring of the information, the identification of subtopics, the visualisation of the contents, of structure and connections of the information and the subtopics, Application fields are widespread - from patent management, technology monitoring or analysis of internal documents up to competitive analysis of companies and products and identification of co-operation behaviour of persons or institutions. Therefore BibTechMon allows the mapping of information to support internal communication, information retrieval and strategic decision support. Due to its different fields of application in the context of knowledge management, BibTechMon is part of a fast growing number of software tools designed to support knowledge management. The exploding amount of software products on that sector make it rather impossible to get a sufficient overview or to select the appropriate tool for a given question. In order to support the analysis and classification of software tools for knowledge management a survey was performed and analysed with BibTechMon. Using this example the possibilities of our method will be demonstrated as well as an over-view over the results of our survey will be given.
C1 ARC Seibersdorf Res GMBH, Dept Technol Management, A-2444 Seibersdorf, Austria.
   ARC Arsenal Res, Energy & Dr Technol, Business Area Monitoring, A-1030 Vienna, Austria.
RP Noll, M (通讯作者)，ARC Seibersdorf Res GMBH, Dept Technol Management, A-2444 Seibersdorf, Austria.
RI Schiebel, Edgar/C-6529-2009
OI Schiebel, Edgar/0000-0002-6701-2968
CR BOHMANN T, 1999, WERKZEUGE WISSENSMAN
   CALLON M, 1983, SOC SCI INFORM, V22, P191, DOI 10.1177/053901883022002003
   FOCKER E, 2001, WERKZEUGE WISSENSMAN
   GLANZEL W, 1993, RES EVALUAT, V3, P1
   GLANZEL W, 1992, RES EVALUAT, V3, P122
   GRUPP H, 1990, KNOWLEDGE INTERFACE
   Kim D, 1993, SLOAN MANAGEMENT FAL, P37
   Kopcsa A, 1998, J AM SOC INFORM SCI, V49, P7
   KOSTOFF R, 1993, CO WORD ANAL, P63
   LEYERSDORF K, 1989, RES POLICY, V18, P209
   NOLL M, 2000, P R D MAN C MANCH JU
   NOLL M, 2000, P 10 INT FOR TECHN M
   NOLL M, 2000, P EP C VIENN OCT
   NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14
   PROBST G, 1999, WISSEN MANAGEN UNTER
   RIP A, 1984, SCIENTOMETRICS, V6
   Senge P.M., 1994, 5 DISCIPLINE
   Turner W. A., 1988, HDB QUANTITATIVE SCI, P291
   *U WASH, 2000, KNOWL MAN TOOLS
   Van Raan AFJ, 1993, RES EVALUAT, V3, P151
   VANRAAN AFJ, 1998, HDB QUANTITATIVE STU
   Von Krogh G., 2000, ENABLING KNOWLEDGE C
   WIDHALM C, 1999, OEFZSS0051 ARC
NR 23
TC 4
Z9 4
U1 0
U2 13
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-00314-2
J9 LECT NOTES ARTIF INT
PY 2002
VL 2569
BP 14
EP 27
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BW15A
UT WOS:000180993200003
DA 2022-08-23
ER

PT J
AU Forouzandeh, S
   Berahmand, K
   Rostami, M
AF Forouzandeh, Saman
   Berahmand, Kamal
   Rostami, Mehrdad
TI Presentation of a recommender system with ensemble learning and graph
   embedding: a case on MovieLens
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Recommender systems; Ensemble learning; Fuzzy rules; Decision tree;
   Graph embedding; Heterogeneous knowledge graph
ID MACHINE; INFORMATION; NETWORKS; INTERNET; USERS
AB Information technology has spread widely, and extraordinarily large amounts of data have been made accessible to users, which has made it challenging to select data that are in accordance with user needs. For the resolution of the above issue, recommender systems have emerged, which much help users go through the process of decision-making and selecting relevant data. A recommender system predicts users' behavior to be capable of detecting their interests and needs, and it often uses the classification technique for this purpose. It may not be sufficiently accurate to employ single classification, where not all cases can be examined, which makes the method inappropriate to specific problems. In this research, group classification and the ensemble learning technique were used for increasing prediction accuracy in recommender systems. Another issue that is raised here concerns user analysis. Given the large size of the data and a large number of users, the process of user needs analysis and prediction (using a graph in most cases, representing the relations between users and their selected items) is complicated and cumbersome in recommender systems. Graph embedding was also proposed for resolution of this issue, where all or part of user behavior can be simulated through the generation of several vectors, resolving the problem of user behavior analysis to a large extent while maintaining high efficiency. In this research, individuals most similar to the target user were classified using ensemble learning, fuzzy rules, and the decision tree, and relevant recommendations were then made to each user with a heterogeneous knowledge graph and embedding vectors. This study was performed on the MovieLens datasets, and the obtained results indicated the high efficiency of the presented method.
C1 [Forouzandeh, Saman] Univ Appl Sci & Technol, Ctr Tehran Municipal ICT Org, Dept Comp Engn, Tehran, Iran.
   [Berahmand, Kamal] Queensland Univ Technol, Dept Sci & Engn, Brisbane, Qld, Australia.
   [Rostami, Mehrdad] Univ Kurdistan, Dept Comp Engn, Sanandaj, Iran.
RP Forouzandeh, S (通讯作者)，Univ Appl Sci & Technol, Ctr Tehran Municipal ICT Org, Dept Comp Engn, Tehran, Iran.
EM saman.forouzandeh@gmail.com; kamal.berahmand@hdr.qut.edu.au;
   m.rostami@eng.uok.ac.ir
RI Rostami, Mehrdad/AFU-5740-2022; Rostami, Mehrdad/W-5970-2019; berahmand,
   kamal/ABH-1804-2020
OI Rostami, Mehrdad/0000-0001-5710-217X; berahmand,
   kamal/0000-0003-4459-0703
CR Aghdam AR., 2019, WEBOLOGY, V16
   Bai J, 2019, INFORM SYST, V81, P82, DOI 10.1016/j.is.2018.11.008
   Barkan O., 2016, 2016 IEEE 26 INT WOR, P1, DOI DOI 10.1109/MLSP.2016.7738886
   Basile P, 2019, INFORM SYST, V86, P1, DOI 10.1016/j.is.2019.07.001
   Ben-Lhachemi N, 2018, PROCEDIA COMPUT SCI, V127, P7, DOI 10.1016/j.procs.2018.01.092
   Berahmand K, 2019, COMPUTING, V101, P1711, DOI 10.1007/s00607-018-0684-8
   Berahmand K, 2018, CHAOS SOLITON FRACT, V110, P41, DOI 10.1016/j.chaos.2018.03.014
   Boongoen T, 2018, COMPUT SCI REV, V28, P1, DOI 10.1016/j.cosrev.2018.01.003
   Borras J, 2014, EXPERT SYST APPL, V41, P7370, DOI 10.1016/j.eswa.2014.06.007
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Chang SY, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P119, DOI 10.1145/2783258.2783296
   da Costa Fortes A., 2014, P 20 BRAZ S MULT WEB, P47, DOI DOI 10.1145/2664551.2664556
   Dietterich TG, 2000, LECT NOTES COMPUT SC, V1857, P1, DOI 10.1007/3-540-45014-9_1
   Forouzandeh S, 2015, INT J COMPUT APPL, V124, P1
   Forouzandeh S, 2020, COMPUT SCI ENG, V22, P62, DOI 10.1109/MCSE.2018.2875321
   Forouzandeh S, 2018, INT J WEB INF SYST, V14, P158, DOI 10.1108/IJWIS-07-2017-0053
   Forouzandeh S, 2017, INT J COMPUT SCI NET, V17, P46
   Forouzeh S., 2014, WEBOLOGY, V11, P1
   Golzardi E, 2019, PHYSICA A, V527, DOI 10.1016/j.physa.2019.121269
   Grbovic M, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1809, DOI 10.1145/2783258.2788627
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Guardia-Sebaoun E., 2015, P 9 ACM C REC SYST, P281, DOI DOI 10.1145/2792838.2799676
   Hamilton W.L., 2017, ARXIV170905584
   HO TK, 1994, IEEE T PATTERN ANAL, V16, P66, DOI 10.1109/34.273716
   Islam MZ, 2019, LECT NOTES ARTIF INT, V11439, P29, DOI 10.1007/978-3-030-16148-4_3
   Jahrer M., 2010, P 16 ACM SIGKDD INT, P693, DOI DOI 10.1145/1835804.1835893
   Jendoubi S, 2017, KNOWL-BASED SYST, V121, P58, DOI 10.1016/j.knosys.2017.01.014
   Khan Z, 2020, NEUROCOMPUTING, V380, P246, DOI 10.1016/j.neucom.2019.09.080
   Koren Y, 2011, RECOMMENDER SYSTEMS HANDBOOK, P145, DOI 10.1007/978-0-387-85820-3_5
   Krawczyk B, 2017, INFORM FUSION, V37, P132, DOI 10.1016/j.inffus.2017.02.004
   Krogh A., 1995, Advances in Neural Information Processing Systems 7, P231
   Lerato M, 2015, 2015 International Conference on Computing, Communication and Security (ICCCS)
   Liang B, 2015, INT CONF SOFTW ENG, P894, DOI 10.1109/ICSESS.2015.7339198
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Mohammadpour T, 2019, GENOMICS, V111, P1902, DOI 10.1016/j.ygeno.2019.01.001
   Nie FP, 2017, AAAI CONF ARTIF INTE, P2422
   Nilashi M, 2018, EXPERT SYST APPL, V92, P507, DOI 10.1016/j.eswa.2017.09.058
   Nilashi M, 2017, COMPUT IND ENG, V109, P357, DOI 10.1016/j.cie.2017.05.016
   Nilashi M, 2014, EXPERT SYST APPL, V41, P3879, DOI 10.1016/j.eswa.2013.12.023
   Palumbo E, 2020, EXPERT SYST APPL, V151, DOI 10.1016/j.eswa.2020.113235
   Barbin JP, 2020, J AMB INTEL HUM COMP, V11, P1339, DOI 10.1007/s12652-019-01451-7
   Pennington J., 2014, EMNLP, P1532
   Perozzi Bryan, 2014, P 20 ACM SIGKDD INT
   Porta A, 2001, IEEE T BIO-MED ENG, V48, P1282, DOI 10.1109/10.959324
   Pujahari A, 2019, INFORM SCIENCES, V490, P126, DOI 10.1016/j.ins.2019.03.064
   Qiu L, 2018, NEUROCOMPUTING, V278, P144, DOI 10.1016/j.neucom.2017.05.100
   Le Q, 2014, PR MACH LEARN RES, V32, P1188
   Ren JT, 2019, DECIS SUPPORT SYST, V125, DOI 10.1016/j.dss.2019.113115
   Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1
   Ristoski P, 2014, COMM COM INF SC, V475, P150, DOI 10.1007/978-3-319-12024-9_19
   Rostami M, 2020, GENOMICS, V112, P4370, DOI 10.1016/j.ygeno.2020.07.027
   Sadeghian A, 2019, ARXIV191003943
   Seo YD, 2017, EXPERT SYST APPL, V69, P135, DOI 10.1016/j.eswa.2016.10.024
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Valcarce D, 2019, ENG APPL ARTIF INTEL, V85, P347, DOI 10.1016/j.engappai.2019.06.020
   Vasile F, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P225, DOI 10.1145/2959100.2959160
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang X, 2017, AAAI CONF ARTIF INTE, P203
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wei XK, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1611, DOI 10.1145/3038912.3052575
   Wolpert DH, 2002, SOFT COMPUTING AND INDUSTRY, P25
   Wozniak M, 2014, INFORM FUSION, V16, P3, DOI 10.1016/j.inffus.2013.04.006
   Xie Y, 2019, INFORM SCIENCES, V495, P37, DOI 10.1016/j.ins.2019.05.001
   Yu LY, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1295, DOI 10.1145/3097983.3098189
   Yue X, 2020, BIOINFORMATICS, V36, P1241, DOI 10.1093/bioinformatics/btz718
   Zareie A, 2019, INFORM SCIENCES, V493, P217, DOI 10.1016/j.ins.2019.04.033
   Zenobi Gabriele, 2001, P 12 EUR C MACH LEAR, P576, DOI DOI 10.1007/3-540-44795-4_49
   Zhang F, 2016, KNOWL-BASED SYST, V96, P96, DOI 10.1016/j.knosys.2015.12.025
   Zhang MX, 2018, INFORM SCIENCES, V453, P389, DOI 10.1016/j.ins.2018.04.022
   Zhang WN, 2019, NEUROCOMPUTING, V334, P206, DOI 10.1016/j.neucom.2019.01.028
   Zhang W, 2016, NEUROCOMPUTING, V173, P979, DOI 10.1016/j.neucom.2015.08.054
   Zhou C, 2017, AAAI CONF ARTIF INTE, P2942
   Zhou H, 2019, EXPERT SYST APPL, V136, P276, DOI 10.1016/j.eswa.2019.06.045
NR 74
TC 22
Z9 22
U1 1
U2 10
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
EI 1573-7721
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD FEB
PY 2021
VL 80
IS 5
BP 7805
EP 7832
DI 10.1007/s11042-020-09949-5
EA OCT 2020
PG 28
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA QM1VQ
UT WOS:000587058000011
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Wang, Q
   Hao, YS
AF Wang, Qi
   Hao, Yongsheng
TI ALSTM: An attention-based long short-term memory framework for knowledge
   base reasoning
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge base; LSTM; Attention; Memory; Logical rule; Deep learning
ID DEEP NEURAL-NETWORKS; GAME; GO
AB Knowledge Graphs (KGs) have been applied to various application scenarios including Web searching, Q&A, recommendation system, natural language processing and so on. However, the vast majority of Knowledge Bases (KBs) are incomplete, necessitating a demand for KB completion (KBC). Methods of KBC used in the mainstream current knowledge base include the latent factor model, the random walk model and recent popular methods based on reinforcement learning, which performs well in their respective areas of expertise. Recurrent neural network (RNN) and its variants model temporal data by remembering information for long periods, however, whether they also have the ability to use the information they have already remembered to achieve complex reasoning in the knowledge graph. In this paper, we produce a novel framework (ALSTM) based on the Attention mechanism and Long Short-Term Memory (LSTM), which associates structure learning with parameter learning of first-order logical rules in an end-to-end differentiable neural networks model. In this framework, we designed a memory system and employed a multi-head dot product attention (MHDPA) to interact and update the memories embedded in the memory system for reasoning purposes. This is also consistent with the process of human cognition and reasoning, looking for enlightenment for the future in historical memory. In addition, we explored the use of inductive bias in deep learning to facilitate learning of entities, relations, and rules. Experiments establish the efficiency and effectiveness of our model and show that our method achieves better performance in tasks which include fact prediction and link prediction than baseline models on several benchmark datasets such as WN18RR, FB15K-237 and NELL-995. (c) 2020 Elsevier B.V. All rights reserved.
C1 [Wang, Qi] Fudan Univ, Sch Comp Sci, Software Engn, Shanghai, Peoples R China.
   [Hao, Yongsheng] Nanjing Univ Informat Sci & Technol, Network Ctr, Nanjing, Peoples R China.
RP Wang, Q (通讯作者)，Fudan Univ, Sch Comp Sci, Software Engn, Shanghai, Peoples R China.
EM 17110240039@fudan.edu.cn
RI Wang, Qi/CAH-9366-2022
OI Wang, Qi/0000-0003-3249-8459; Hao, Yongsheng/0000-0002-9928-006X
FU Chinese National Funding of Social Sciences [16ZDA054]; National Natural
   Science Foundation of China (NSF) [41475089]
FX The work was partly supported by the Chinese National Funding of Social
   Sciences (No. 16ZDA054), the National Natural Science Foundation of
   China (NSF) under grant (NO. 41475089).
CR [Anonymous], 2017, ARXIV170407130
   [Anonymous], 2017, P EMNLP
   [Anonymous], 2017, ADV NEUR IN
   [Anonymous], P ADV NEUR INF PROC
   [Anonymous], P ADV NEUR INF PROC
   [Anonymous], P ART INT STAT
   [Anonymous], P INT C MACH LEARN
   [Anonymous], P ADV NEUR INF PROC
   [Anonymous], 2015, ARXIV150600379, DOI DOI 10.18653/V1/D15-1082
   [Anonymous], 3 WAY MODEL COLLECTI
   [Anonymous], 2008, P ACM SIGMOD INT C M
   [Anonymous], 1987, KNOWLEDGE GRAPHS REP
   [Anonymous], P 26 INT C COMP LING
   [Anonymous], P EMP METH NAT LANG
   [Anonymous], P C EMP METH NAT LAN
   [Anonymous], 2016, ARXIV160701426
   [Anonymous], P ICML
   [Anonymous], P C EMP METH NAT LAN
   [Anonymous], 2013, P ADV NEURAL INFORM
   [Anonymous], P IEEE T PATT AN MAC
   [Anonymous], P IEEE T PATT AN MAC
   [Anonymous], P 20 INT C MACH LEAR
   Battaglia P.W., 2018, ARXIV 180601261
   Bordes A., 2011, P 25 AAAI C ART INT
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Courville A., 2016, DEEP LEARNING
   Das R., 2017, ARXIV171105851
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dunteman G.H., 1989, PRINCIPAL COMPONENTS, P69
   Dzeroski S, 2001, MACH LEARN, V43, P7, DOI 10.1023/A:1007694015589
   Glorot X., 2010, P 13 INT C ART INT S, P249
   Han JW, 2015, 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (ACL-IJCNLP 2015), P1
   Hinton G, 2012, IEEE SIGNAL PROC MAG, V29, P82, DOI 10.1109/MSP.2012.2205597
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   King DB, 2015, ACS SYM SER, V1214, P1
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Lao N., 2011, P C EMP METH NAT LAN
   Lao N, 2010, MACH LEARN, V81, P53, DOI 10.1007/s10994-010-5205-8
   LeCun Y., 2015, NATURE, V521, P436, DOI [10.1038/nature14539, DOI 10.1038/NATURE14539]
   Li Y., 2015, P 4 INT C LEARN REPR
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Mnih V, 2015, NATURE, V518, P529, DOI 10.1038/nature14236
   MUGGLETON S, 1994, J LOGIC PROGRAM, V20, P629, DOI 10.1016/0743-1066(94)90035-3
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Niepert M, 2016, PR MACH LEARN RES, V48
   Schmidhuber J, 2015, NEURAL NETWORKS, V61, P85, DOI 10.1016/j.neunet.2014.09.003
   Shu X., 2019, ARXIV190913245
   Silver D, 2017, NATURE, V550, P354, DOI 10.1038/nature24270
   Silver D, 2016, NATURE, V529, P484, DOI 10.1038/nature16961
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Sukhbaatar S., 2015, ADV NEUR IN
   Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
   Velickovic P., 2018, P INT C LEARN REPR A
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao H., 2015, ARXIV150905490
   Yang B., 2015, P INT C LEARN REPR
   Zhang Chiyuan, 2018, ARXIV PREPRINT ARXIV
NR 59
TC 9
Z9 9
U1 8
U2 53
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JUL 25
PY 2020
VL 399
BP 342
EP 351
DI 10.1016/j.neucom.2020.02.065
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LS6PS
UT WOS:000536504100003
DA 2022-08-23
ER

PT J
AU Lei, ZF
   Sun, Y
   Nanehkaran, YA
   Yang, SY
   Islam, MS
   Lei, HQ
   Zhang, DF
AF Lei, Zhenfeng
   Sun, Yuan
   Nanehkaran, Y. A.
   Yang, Shuangyuan
   Islam, Md Saiful
   Lei, Huiqing
   Zhang, Defu
TI A novel data-driven robust framework based on machine learning and
   knowledge graph for disease classification
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Disease classification; NCDs; Data fusion; Machine learning; Knowledge
   graph
ID RISK
AB As Noncommunicable Diseases (NCDs) are affected or controlled by diverse factors such as age, regionalism, timeliness or seasonality, they are always challenging to be treated accurately, which has impacted on daily life and work of patients. Unfortunately, although a number of researchers have already made some achievements (including clinical or even computer-based) on certain diseases, current situation is eager to be improved via computer technologies such as data mining and Deep Learning. In addition, the progress of NCD research has been hampered by privacy of health and medical data. In this paper, a hierarchical idea has been proposed to study the effects of various factors on diseases, and a data-driven framework named d-DC with good extensibility is presented. d-DC is able to classify the disease according to the occupation on the premise where the disease is occurring in a certain region. During collecting data, we used a combination of personal or family medical records and traditional methods to build a data acquisition model. Not only can it realize automatic collection and replenishment of data, but it can also effectively tackle the cold start problem of the model with relatively few data effectively. The diversity of information gathering includes structured data and unstructured data (such as plain texts, images or videos), which contributes to improve the classification accuracy and new knowledge acquisition. Apart from adopting machine learning methods, d-DC has employed knowledge graph (KG) to classify diseases for the first time. The vectorization of medical texts by using knowledge embedding is a novel consideration in the classification of diseases. When results are singular, the medical expert system was proposed to address inconsistencies through knowledge bases or online experts. The results of d-DC are displayed by using a combination of KG and traditional methods, which intuitively provides a reasonable interpretation to the results (highly descriptive). Experiments show that d-DC achieved the improved accuracy than the other previous methods. Especially, a fusion method called RKRE based on both ResNet and the expert system attained an average correct proportion of 86.95%, which is a good feasibility study in the field of disease classification. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Lei, Zhenfeng; Nanehkaran, Y. A.; Yang, Shuangyuan; Zhang, Defu] Xiamen Univ, Sch Informat, Xiamen 361005, Fujian, Peoples R China.
   [Sun, Yuan; Islam, Md Saiful] Univ Adelaide, Sch Elect & Elect Engn, Adelaide, SA 5005, Australia.
   [Lei, Huiqing] Zhengzhou Univ, Affiliated Hosp 1, Dept Breast Surg, Zhengzhou 450000, Henan, Peoples R China.
RP Yang, SY; Zhang, DF (通讯作者)，Xiamen Univ, Sch Informat, Xiamen 361005, Fujian, Peoples R China.
EM yangshuangyuan@xmu.edu.cn; dfzhang@xmu.edu.cn
RI Islam, Saiful/I-6737-2019; Nanehkaran, Y. A./AAN-6150-2021
OI Islam, Saiful/0000-0003-0578-4732; Nanehkaran, Y. A./0000-0002-8055-3195
FU National Natural Science Foundation of China [61672439]
FX This work is partially supported by the National Natural Science
   Foundation of China under Grant 61672439.
CR Aleksovska K, 2019, SPORTS MED-OPEN, V5, DOI 10.1186/s40798-018-0173-9
   [Anonymous], 2004, THE NEWS LETT
   Au WH, 2001, 2001 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P35, DOI 10.1109/ICDM.2001.989498
   Balakumar P, 2016, PHARMACOL RES, V113, P600, DOI 10.1016/j.phrs.2016.09.040
   Banu MAN, 2014, 2014 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING APPLICATIONS (ICICA 2014), P130, DOI 10.1109/ICICA.2014.36
   Baxter LM, 2018, CONFL HEALTH, V12, DOI 10.1186/s13031-018-0183-8
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2011, AAAI
   Bultitude JH, 2017, BRAIN, V140, P2306, DOI 10.1093/brain/awx152
   Calitz C, 2015, AM J PREV MED, V48, P462, DOI 10.1016/j.amepre.2014.10.015
   Cheng CH, 2018, J CLIN MED, V7, DOI 10.3390/jcm7060124
   Cheng H.T., 2016, P 1 WORKSHOP DEEP LE, P7, DOI DOI 10.1145/2988450.2988454
   Chiu WY, 2016, OSTEOPOROSIS INT, V27, pS759
   Cui W., 2017, RES KEY TECHNOLOGIES
   Du BY, 2018, ENVIRON GEOCHEM HLTH, V40, P903, DOI 10.1007/s10653-017-0033-4
   Forouzanfar MH, 2016, LANCET, V388, P1659, DOI 10.1016/S0140-6736(16)31679-8
   Fraser G., 1986, PREVENTIVE CARICOLOG
   Giri D, 2013, KNOWL-BASED SYST, V37, P274, DOI 10.1016/j.knosys.2012.08.011
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Greff K, 2017, IEEE T NEUR NET LEAR, V28, P2222, DOI 10.1109/TNNLS.2016.2582924
   HINKLE LE, 1968, SCIENCE, V161, P238, DOI 10.1126/science.161.3838.238
   Hulsegge G, 2016, J AM HEART ASSOC, V5, DOI 10.1161/JAHA.115.002432
   Ji GL, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P687
   Kingma D. P., 2017, ARXIV
   Kong L, 2001, CHIN GEN PRACT, V4, P927
   Lee WWM, 2016, INT J NURS STUD, V53, P331, DOI 10.1016/j.ijnurstu.2015.09.010
   Li S., 2017, ADV INF KNOWL PROCES
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lu MS, 2012, BMC PUBLIC HEALTH, V12, DOI 10.1186/1471-2458-12-413
   Meyer HE, 2015, BMJ-BRIT MED J, V350, DOI 10.1136/bmj.h321
   Minihane AM, 2015, BRIT J NUTR, V114, P999, DOI 10.1017/S0007114515002093
   Morelli T., 2010, IBM SPSS PREDICTIVE
   Peng ZY, 2016, 2016 INTERNATIONAL CONFERENCE ON MANUFACTURING SCIENCE AND INFORMATION ENGINEERING (ICMSIE 2016), P1
   Rahman NSFA, 2015, INT J E-NAVIG MARIT, V3, P1, DOI 10.1016/j.enavi.2015.12.001
   Rattehalli R., 2018, TEXTB ORAL MED
   Ruel G, 2014, CLIN NUTR, V33, P513, DOI 10.1016/j.clnu.2013.07.009
   Saidi O, 2015, BMC PUBLIC HEALTH, V15, DOI 10.1186/s12889-015-1416-z
   Semwal VB, 2017, MULTIMED TOOLS APPL, V76, P24457, DOI 10.1007/s11042-016-4110-y
   Shengyue Luo, 2018, Neural Information Processing. 25th International Conference, ICONIP 2018. Proceedings: Lecture Notes in Computer Science (LNCS 11303), P48, DOI 10.1007/978-3-030-04182-3_5
   Sun Z, 2018, NEUROIMAGE, V178, P445, DOI 10.1016/j.neuroimage.2018.05.051
   Tomizawa M, 2015, WORLD J GASTROENTERO, V21, P7500, DOI 10.3748/wjg.v21.i24.7500
   Trouwborst I, 2018, NUTRIENTS, V10, DOI 10.3390/nu10050605
   Virtanen JK, 2017, SCI REP-UK, V7, DOI 10.1038/srep39697
   Wang C., 2011, MAJOR ILLNESS INSURA
   Wang H., 2005, FOREIGN MED SCI SOC, P10
   Wang X., 2000, GREAT DICTONARY HYGI
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   World Health Organization, 2016, WHO ICD 10 INT STAT
   World Health Organization, 2018, ICD 11 CLASS DIS MAP
   WTO, 2017, WORLD HLTH STAT 2017
   Yan HM, 2004, COMPUT METH PROG BIO, V74, P1, DOI 10.1016/S0169-2607(03)00076-2
   Zhang C, 2019, FUTURE GENER COMP SY, V96, P111, DOI 10.1016/j.future.2019.01.059
   Zhao R, 2018, IEEE T IND ELECTRON, V65, P1539, DOI 10.1109/TIE.2017.2733438
   Zhong Y., 2018, GEN HOSP PEOPLES LIB
   陈伟伟, 2017, [中国循环杂志, Chinese Circulation Journal], V32, P521
NR 55
TC 15
Z9 17
U1 4
U2 53
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD JAN
PY 2020
VL 102
BP 534
EP 548
DI 10.1016/j.future.2019.08.030
PG 15
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JU8RD
UT WOS:000501936300045
DA 2022-08-23
ER

PT J
AU Yan, LM
AF Yan, Limin
TI Application of Knowledge Map Information Technology in Regional Tourism
   Innovation and Development
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
ID DESTINATION; HOSPITALITY; ENVIRONMENT; DIVERSITY; IMPACTS
AB There is a huge gap between China's tourism development model and the rapidly growing demand for social tourism services, the supply capacity of tourism resources and the demand for tourism development, the development of tourism industry and the requirements of regional socioeconomic development and structural adjustment, tourism development system and mechanism, and the existing tourism development and management model. Using the method of knowledge-based atlas analysis, this paper combs and analyzes the relevant literature on the high-quality development of tourism in China. From 2016 to 2017, the research on tourism economic benefits was relatively stable without large fluctuations, and the average number of documents issued reached 48%. From 2018 to 2019, the research heat remained stable, and the research results increased to a certain extent. At this time, the number of external documents reached the same level, with an average of 52%. After 2020, the upsurge of research on tourism economic benefits has emerged, and the number of documents issued has increased year by year, with an average of 62%. Compared with the curve in the figure, the growth and peak of the number of documents issued have a certain lag. The early research on the economic benefits of tourism at home and abroad mainly focuses on the economic benefits of regions and enterprises. With the coordinated development of tourism, society, ecology, and other related systems, it has become an inevitable requirement. Tourism economic benefit is an important concept to measure the comparative relationship between tourism economic input and output. It is also an important index to reflect the effect and level of tourism economic activities. The early research on the economic benefits of tourism at home and abroad mainly focuses on the economic benefits of regions and enterprises. With the coordinated development of tourism, society, ecology, and other related systems, it has become an inevitable requirement.
C1 [Yan, Limin] Zhengzhou Univ Sci & Technol, Dept Business Adm, Zhengzhou 450000, Henan, Peoples R China.
RP Yan, LM (通讯作者)，Zhengzhou Univ Sci & Technol, Dept Business Adm, Zhengzhou 450000, Henan, Peoples R China.
EM yan18039677593@163.com
FU Soft Science Department of Science and Technology of Henan Province
   [222400410452]
FX This research was supported by the Soft Science Department of Science
   and Technology of Henan Province (No. 222400410452).
CR Booyens I, 2018, EUR J DEV RES, V30, P749, DOI 10.1057/s41287-017-0123-2
   Divisekera S, 2018, TOURISM MANAGE, V67, P157, DOI 10.1016/j.tourman.2018.01.010
   Dong C, 2018, AUTOMAT CONSTR, V93, P112, DOI 10.1016/j.autcon.2018.05.014
   Fortes MD, 2018, BOT MAR, V61, P269, DOI 10.1515/bot-2018-0008
   Gardiner S, 2018, J DESTIN MARK MANAGE, V10, P122, DOI 10.1016/j.jdmm.2018.07.002
   Herman F, 2018, RES POLICY, V47, P305, DOI 10.1016/j.respol.2017.10.014
   Hoang G, 2019, LEADERSHIP ORG DEV J, V40, P647, DOI 10.1108/LODJ-08-2018-0287
   Makkonen T, 2018, TOURISM MANAGE, V68, P140, DOI 10.1016/j.tourman.2018.03.008
   Marasco A, 2018, INT J CONTEMP HOSP M, V30, P2364, DOI [10.1108/ijchm-01-2018-0043, 10.1108/IJCHM-01-2018-0043]
   Martin-Rios C, 2019, TOURISM MANAGE, V70, P218, DOI 10.1016/j.tourman.2018.08.018
   Martinez-Perez A, 2019, J DESTIN MARK MANAGE, V11, P80, DOI 10.1016/j.jdmm.2018.12.002
   Mohanta JC, 2019, APPL SOFT COMPUT, V79, P391, DOI 10.1016/j.asoc.2019.03.055
   Narduzzo A, 2018, CURR ISSUES TOUR, V21, P735, DOI 10.1080/13683500.2016.1214111
   Nieves J, 2018, INT J CONTEMP HOSP M, V30, P2537, DOI 10.1108/IJCHM-07-2016-0341
   Pons-Morera C., 2017, SERV BUS, V12, P1
   Presenz A, 2019, TOURISM MANAGE, V72, P192, DOI 10.1016/j.tourman.2018.11.020
   Razzaq A, 2021, SUSTAIN DEV, V29, P176, DOI 10.1002/sd.2139
   Romao J, 2019, CURR ISSUES TOUR, V22, P1150, DOI 10.1080/13683500.2017.1366434
   Sorensen EB, 2020, TOURIST STUD, V20, P222, DOI 10.1177/1468797619894463
   Sundriyal S, 2018, J MT SCI-ENGL, V15, P2276, DOI 10.1007/s11629-017-4786-0
   Verreynne ML, 2019, TOURISM MANAGE, V72, P257, DOI 10.1016/j.tourman.2018.11.019
   Wu AH, 2020, J HOSP TOUR RES, V44, P908, DOI 10.1177/1096348020927453
   Zach FJ, 2017, TOURISM MANAGE, V62, P196, DOI 10.1016/j.tourman.2017.04.001
   Zeng Y, 2021, IEEE WIREL COMMUN, V28, P84, DOI 10.1109/MWC.001.2000327
   Zhu HP, 2018, KNOWL-BASED SYST, V143, P102, DOI 10.1016/j.knosys.2017.12.011
NR 25
TC 0
Z9 0
U1 1
U2 1
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1530-8669
EI 1530-8677
J9 WIREL COMMUN MOB COM
JI Wirel. Commun. Mob. Comput.
PD JUN 29
PY 2022
VL 2022
AR 7190415
DI 10.1155/2022/7190415
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 2Y7DM
UT WOS:000826052800007
OA gold
DA 2022-08-23
ER

PT J
AU Mu, F
   Tang, M
   Guan, Y
   Lin, R
   Zhao, MA
   Zhao, JX
   Huang, SJ
   Zhang, HY
   Wang, JW
   Tang, HF
AF Mu, Fei
   Tang, Meng
   Guan, Yue
   Lin, Rui
   Zhao, Meina
   Zhao, Jiaxin
   Huang, Shaojie
   Zhang, Haiyue
   Wang, Jingwen
   Tang, Haifeng
TI Knowledge Mapping of the Links Between the Gut Microbiota and Heart
   Failure: A Scientometric Investigation (2006-2021)
SO FRONTIERS IN CARDIOVASCULAR MEDICINE
LA English
DT Article
DE heart failure; gut microbiota; CiteSpace; VOS viewer; scientometric;
   frontier research hotspots
ID TRIMETHYLAMINE-N-OXIDE; DIET-INDUCED OBESITY; METABOLISM;
   PHOSPHATIDYLCHOLINE; HYPERTENSION; FLORA
AB BackgroundThere is considerable research value and extensive application perspectives to explore the link between gut microbiota and heart failure. The purpose of this study is to provide an overview of overall characteristics, evolutionary pathways, frontier research hotspots, and future trends in this field. MethodsResearch datasets were acquired from the Web of Science Core Collection (WoSCC) between January 1, 2006 and December 31, 2021. Three different analysis tools including one online platform, VOS viewer V1.6.17.0, and CiteSpace V5.8.R2 software were used in order to conduct collaboration network analysis, co-cited analysis, co-occurring analysis, and citation burst detection. ResultsA total of 873 publications in the WoSCC database met the requirement. The overall characteristics analysis showed that a steady growth trend in the number of publications and citations, with the predominant literature type being articles and the most frequent subject category being cardiac cardiovascular systems. The United States was the most prolific country and the center of national collaboration. Cleveland Clinic and Nathalie M. Delzenne provided the leading influence with publications, the cooperation between the institutes and authors were relatively weak. Moreover, gut microbiota, heart failure, risk factor, obesity, and inflammation were the keywords that appeared more frequently in the clustering analysis of reference co-citation and keyword co-occurrence. Burst detection analysis of top keywords showed that trimethylamine N-oxide (TMAO), bile acid, blood pressure, hypertension, and fermentation were the new research foci on the association between gut microbiota and heart failure. Strategies to improve gut microbiota hold promise as a new approach to treat heart failure. ConclusionThe comprehensive bibliometric study indicates that the structured information may be helpful in understanding research trends in the link between gut microbiota and heart failure, and locating research hotspots and gaps in this domain, especially further advances in this field will lead to significant breakthroughs in the development of novel therapeutic tools for metabolic modulation of heart failure.
C1 [Mu, Fei; Tang, Meng; Guan, Yue; Lin, Rui; Zhao, Meina; Huang, Shaojie; Wang, Jingwen] Fourth Mil Med Univ, Xijing Hosp, Dept Pharm, Xian, Peoples R China.
   [Mu, Fei; Tang, Haifeng] Fourth Mil Med Univ, Sch Pharm, Dept Chinese Mat Med & Nat Med, Xian, Peoples R China.
   [Zhao, Jiaxin] Shaanxi Univ Chinese Med, Dept Pharm, Xianyang, Peoples R China.
   [Zhang, Haiyue] Fourth Mil Med Univ, Sch Prevent Med, Dept Hlth Stat, Xian, Peoples R China.
RP Wang, JW (通讯作者)，Fourth Mil Med Univ, Xijing Hosp, Dept Pharm, Xian, Peoples R China.; Tang, HF (通讯作者)，Fourth Mil Med Univ, Sch Pharm, Dept Chinese Mat Med & Nat Med, Xian, Peoples R China.
EM wangjingwen8021@163.com; tanghaifeng0701@163.com
FU National Natural Science Foundation of China [81903837, 72074218];
   Xijing Hospital [XJZT18MJ16]
FX This research was financially supported by the National Natural Science
   Foundation of China (Nos. 81903837 and 72074218), the disciplinary
   promotion program of Xijing Hospital in basic research project
   (XJZT18MJ16).
CR Adnan S, 2017, PHYSIOL GENOMICS, V49, P96, DOI 10.1152/physiolgenomics.00081.2016
   Backhed F, 2007, P NATL ACAD SCI USA, V104, P979, DOI 10.1073/pnas.0605374104
   Badejogbin C, 2019, N-S ARCH PHARMACOL, V392, P1411, DOI 10.1007/s00210-019-01679-2
   Cai XY, 2020, BMJ-BRIT MED J, V370, DOI [10.1136/bmi.m2297, 10.1136/bmj.m2297]
   Cani PD, 2007, DIABETOLOGIA, V50, P2374, DOI 10.1007/s00125-007-0791-0
   Cani PD, 2008, DIABETES, V57, P1470, DOI 10.2337/db07-1403
   Cani PD, 2006, DIABETES, V55, P1484, DOI 10.2337/db05-1360
   Caporaso JG, 2010, NAT METHODS, V7, P335, DOI 10.1038/nmeth.f.303
   Chen CM, 2014, EXPERT OPIN BIOL TH, V14, P1295, DOI 10.1517/14712598.2014.920813
   Chen ML, 2016, MBIO, V7, DOI 10.1128/mBio.02210-15
   Chen X, 2019, CHINESE MED J-PEKING, V132, P1843, DOI 10.1097/CM9.0000000000000330
   Costanza AC, 2015, INT J CARDIOL, V179, P348, DOI 10.1016/j.ijcard.2014.11.034
   den Besten G, 2013, J LIPID RES, V54, P2325, DOI 10.1194/jlr.R036012
   Dewulf EM, 2013, GUT, V62, P1112, DOI 10.1136/gutjnl-2012-303304
   Eblimit Z, 2018, CARDIOVASC THER, V36, DOI 10.1111/1755-5922.12462
   Edgar RC, 2013, NAT METHODS, V10, P996, DOI [10.1038/NMETH.2604, 10.1038/nmeth.2604]
   Everard A, 2013, P NATL ACAD SCI USA, V110, P9066, DOI 10.1073/pnas.1219451110
   Gao JS, 2017, MOL MED REP, V16, P673, DOI 10.3892/mmr.2017.6643
   Habib G, 2009, EUR HEART J, V30, P2369, DOI 10.1093/eurheartj/ehp285
   Hayashi T, 2019, CIRC J, V83, P182, DOI 10.1253/circj.CJ-18-0468
   Heianza Y, 2017, J AM HEART ASSOC, V6, DOI 10.1161/JAHA.116.004947
   Jama HA, 2019, CARDIOVASC RES, V115, P1435, DOI 10.1093/cvr/cvz091
   Jaworska K, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0189310
   Kabir RA, 2017, J AM COLL CARDIOL, V70, P3201, DOI 10.1016/j.jacc.2017.10.065
   Kang DH, 2012, NEW ENGL J MED, V366, P2466, DOI 10.1056/NEJMoa1112843
   Karlsson FH, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms2266
   Koeth RA, 2013, NAT MED, V19, P576, DOI 10.1038/nm.3145
   Kummen M, 2018, J AM COLL CARDIOL, V71, P1184, DOI 10.1016/j.jacc.2017.12.057
   Lam V, 2012, FASEB J, V26, P1727, DOI 10.1096/fj.11-197921
   Li J, 2017, MICROBIOME, V5, DOI 10.1186/s40168-016-0222-x
   Li WS, 2020, MED J AUSTRALIA, V213, P374, DOI 10.5694/mja2.50781
   Liu HG, 2020, CURR ATHEROSCLER REP, V22, DOI 10.1007/s11883-020-00892-2
   Luedde M, 2017, ESC HEART FAIL, V4, P282, DOI 10.1002/ehf2.12155
   Marques FZ, 2017, CIRCULATION, V135, P964, DOI [10.1161/CIRCULATIONAHA.116.024545, 10.1161/circulationaha.116.024545]
   Murdoch DR, 2009, ARCH INTERN MED, V169, P463, DOI 10.1001/archinternmed.2008.603
   Murphy SP, 2020, JAMA-J AM MED ASSOC, V324, P488, DOI 10.1001/jama.2020.10262
   Nagatomo Y, 2015, J CARD FAIL, V21, P973, DOI 10.1016/j.cardfail.2015.09.017
   Organ CL, 2016, CIRC-HEART FAIL, V9, DOI 10.1161/CIRCHEARTFAILURE.115.002314
   Parnell JA, 2012, BRIT J NUTR, V107, P601, DOI 10.1017/S0007114511003163
   Pasini E, 2016, JACC-HEART FAIL, V4, P220, DOI 10.1016/j.jchf.2015.10.009
   Pluznick JL, 2013, P NATL ACAD SCI USA, V110, P4410, DOI 10.1073/pnas.1215927110
   Qin JJ, 2012, NATURE, V490, P55, DOI 10.1038/nature11450
   Sandek A, 2007, J AM COLL CARDIOL, V50, P1561, DOI 10.1016/j.jacc.2007.07.016
   Sandek A, 2014, J AM COLL CARDIOL, V64, P1092, DOI 10.1016/j.jacc.2014.06.1179
   Sandek A, 2012, INT J CARDIOL, V157, P80, DOI 10.1016/j.ijcard.2010.12.016
   Sata Y, 2020, CURR HYPERTENS REP, V22, DOI 10.1007/s11906-020-01046-0
   Schuetz P, 2013, JAMA-J AM MED ASSOC, V309, P717, DOI 10.1001/jama.2013.697
   Seldin MM, 2016, J AM HEART ASSOC, V5, DOI 10.1161/JAHA.115.002767
   Slivnick J, 2019, HEART FAIL CLIN, V15, P531, DOI 10.1016/j.hfc.2019.06.007
   Sohail MR, 2007, J AM COLL CARDIOL, V49, P1851, DOI 10.1016/j.jacc.2007.01.072
   Suzuki T, 2016, HEART, V102, P841, DOI 10.1136/heartjnl-2015-308826
   Tang WHW, 2019, NAT REV CARDIOL, V16, P137, DOI 10.1038/s41569-018-0108-7
   Tang WHW, 2017, CIRC RES, V120, P1183, DOI 10.1161/CIRCRESAHA.117.309715
   Tang WHW, 2014, J AM COLL CARDIOL, V64, P1908, DOI 10.1016/j.jacc.2014.02.617
   Tang WHW, 2013, NEW ENGL J MED, V368, P1575, DOI 10.1056/NEJMoa1109400
   Turnbaugh PJ, 2006, NATURE, V444, P1027, DOI 10.1038/nature05414
   Turnbaugh PJ, 2009, SCI TRANSL MED, V1, DOI 10.1126/scitranslmed.3000322
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Wang ZN, 2015, CELL, V163, P1585, DOI 10.1016/j.cell.2015.11.055
   Wang ZN, 2011, NATURE, V472, P57, DOI 10.1038/nature09922
   Wu GD, 2011, SCIENCE, V334, P105, DOI 10.1126/science.1208344
   Wu HY, 2021, FRONT NEUROSCI-SWITZ, V15, DOI 10.3389/fnins.2021.706105
   Wu HY, 2021, FRONT PHARMACOL, V12, DOI 10.3389/fphar.2021.717192
   Wu HY, 2021, FRONT CELL DEV BIOL, V9, DOI 10.3389/fcell.2021.646311
   Wu JD, 2020, CIRC-HEART FAIL, V13, DOI 10.1161/CIRCHEARTFAILURE.120.007054
   Yang SM, 2020, OXID MED CELL LONGEV, V2020, DOI 10.1155/2020/2563508
   Yang T, 2015, HYPERTENSION, V65, P1331, DOI 10.1161/HYPERTENSIONAHA.115.05315
   Yao HQ, 2018, PEERJ, V6, DOI 10.7717/peerj.5091
   Zheng SL, 2022, THER ADV CHRONIC DIS, V13, DOI 10.1177/20406223221081616
   Zhu WF, 2017, CIRCULATION, V135, P1671, DOI 10.1161/CIRCULATIONAHA.116.025338
   Zhu WF, 2016, CELL, V165, P111, DOI 10.1016/j.cell.2016.02.011
   Zhu XQ, 2021, FRONT PSYCHIATRY, V11, DOI 10.3389/fpsyt.2020.587670
   Zhuang RL, 2019, OBES REV, V20, P883, DOI 10.1111/obr.12843
   Zyoud SH, 2019, BMC GASTROENTEROL, V19, DOI 10.1186/s12876-019-1076-z
NR 74
TC 1
Z9 1
U1 14
U2 14
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2297-055X
J9 FRONT CARDIOVASC MED
JI Front. Cardiovasc. Med.
PD APR 28
PY 2022
VL 9
AR 882660
DI 10.3389/fcvm.2022.882660
PG 17
WC Cardiac & Cardiovascular Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Cardiovascular System & Cardiology
GA 1G5FH
UT WOS:000795872700001
PM 35571213
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Bucher, CO
   Delmas, P
   Bachmann, AO
   Gilles, I
AF Bucher, Claudia Ortoleva
   Delmas, Philippe
   Bachmann, Annie Oulevey
   Gilles, Ingrid
TI Stressors, self-reported overall health, potential protective factors
   and the workplace well-being of nurses during the COVID-19 pandemic in
   Switzerland: a longitudinal mixed-methods study protocol
SO BMJ OPEN
LA English
DT Article
DE public health; COVID-19; mental health
ID CONNOR-DAVIDSON RESILIENCE; PERCEIVED SOCIAL SUPPORT; QUALITY-OF-LIFE;
   ACUTE RESPIRATORY SYNDROME; SCALE CD-RISC; HOSPITAL WORKERS;
   PSYCHOMETRIC PROPERTIES; PSYCHOLOGICAL IMPACT; MULTIDIMENSIONAL SCALE;
   GERMAN VERSION
AB Introduction The COVID-19 pandemic was making a huge impact on Europe's healthcare systems in the spring of 2020, and most predictive models concurred that pandemic waves were in the offing. Most studies adopted a pathogenic approach to the subject; few used a salutogenic approach. These showed, however, that nurses can retain their health despite a pandemic by mobilising generalised resistance resources. Our study aims to understand how nurses working in Switzerland's hospitals protected their health and workplace well-being during the COVID-19 pandemic by investigating the moderating effects of the health resources they mobilised against the stressors inherent to the situation. The study aims to explore and describe the stressors and the resources nurses used to remain healthy during the COVID-19 pandemic. Method and analysis We will use a concurrent mixed-methods panel design with qualitative analyses ancillary to quantitative analyses. Quantitative data will be collected using electronic questionnaires at four time points over 2 years. Qualitative data will be collected using focus groups. Nurses from Switzerland's two main linguistic regions who had direct, indirect or no contact with patients with COVID-19 will be invited to participate. The a priori sample size will be at least 3631 participants at T0 and 1852 at T4. Longitudinal structural equation modelling and knowledge mapping will be used to analyse quantitative and qualitative data, respectively. The results derived from the two data types will then be compared and discussed using a side-by-side approach to determine whether they agree or disagree and how they complement each other to achieve our aims. Ethics and dissemination Nurses will receive an electronic informed consent form. The data collected will be stored on a secure server at the authors' institution. This research project was approved by the Human Research Ethics Committee of the Canton of Vaud (2020-02845).
C1 [Bucher, Claudia Ortoleva; Delmas, Philippe; Bachmann, Annie Oulevey] Univ Appl Sci & Arts Western Switzerland, HES SO, La Source Sch Nursing, Lausanne, Switzerland.
   [Gilles, Ingrid] Ctr Primary Care & Publ Hlth, Epidemiol & Hlth Syst, Lausanne, Switzerland.
RP Bucher, CO (通讯作者)，Univ Appl Sci & Arts Western Switzerland, HES SO, La Source Sch Nursing, Lausanne, Switzerland.
EM c.ortolevabucher@ecolelasource.ch
OI philippe, delmas/0000-0001-8169-470X; Ortoleva Bucher,
   Claudia/0000-0002-8411-4181; Gilles, Ingrid/0000-0003-2051-4749
FU Swiss National Science Foundation [10001C_201137/1]
FX The study is supported by the Swiss National Science Foundation
   (10001C_201137/1).
CR Aiello A, 2011, J CONTIN EDUC HEALTH, V31, P15, DOI 10.1002/chp.20096
   Aiken Linda H, 2012, BMJ, V344, pe1717, DOI 10.1136/bmj.e1717
   Angermeyer MC, 2000, WHOQOL 100 WHOQOL BR
   Antonini M., 2020, B SWISS SOCIOLOGICAL, V157, P20
   Austin CL, 2017, PSYCHOL TRAUMA-US, V9, P399, DOI 10.1037/tra0000201
   Bellinghausen Lisa, 2009, Sante Publique, V21, P365
   Burgess-Allen J, 2010, HEALTH EXPECT, V13, P406, DOI 10.1111/j.1369-7625.2010.00594.x
   Burr H, 2019, SAF HEALTH WORK-KR, V10, P482, DOI 10.1016/j.shaw.2019.10.002
   Buselli R, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17176180
   Cadell S., 2015, OJMP, V4, P53, DOI [10.4236/ojmp.2015.42006, DOI 10.4236/OJMP.2015.42006]
   Campbell-Sills L, 2007, J TRAUMA STRESS, V20, P1019, DOI 10.1002/jts.20271
   CARVER CS, 1989, J PERS SOC PSYCHOL, V56, P267, DOI 10.1037/0022-3514.56.2.267
   Chew NWS, 2020, BRAIN BEHAV IMMUN, V88, P559, DOI 10.1016/j.bbi.2020.04.049
   Chong MY, 2004, BRIT J PSYCHIAT, V185, P127, DOI 10.1192/bjp.185.2.127
   Christodoulou-Fella M, 2017, BIOMED RES INT, V2017, DOI 10.1155/2017/1908712
   COHEN S, 1983, J HEALTH SOC BEHAV, V24, P385, DOI 10.2307/2136404
   CRESWELL J., 2013, DESIGNING CONDUCTING
   Creswell J.W, 2018, RES DESIGN QUALITATI
   Deci EL, 2000, PSYCHOL INQ, V11, P227, DOI 10.1207/S15327965PLI1104_01
   Denis A, 2015, MATERN CHILD HLTH J, V19, P1245, DOI 10.1007/s10995-014-1630-9
   DIENER E, 1985, J PERS ASSESS, V49, P71, DOI 10.1207/s15327752jpa4901_13
   Diener E, 2010, SOC INDIC RES, V97, P143, DOI 10.1007/s11205-009-9493-y
   DOOSJE B, 1995, J EXP SOC PSYCHOL, V31, P410, DOI 10.1006/jesp.1995.1018
   Dupret E, 2012, SANTE PUBLIQUE, V24, P189, DOI 10.3917/spub.123.0189
   Esch T, 2013, FORSCH KOMPLEMENTMED, V20, P267, DOI 10.1159/000354414
   Evanoff BA, 2020, J MED INTERNET RES, V22, DOI 10.2196/21366
   Fawcett J., 2013, CONT NURSING KNOWLED
   Fiksenbaum L., 2007, CAN J COMMUN MENT HL, V25, P89, DOI 10.7870/cjcmh-2006-0015
   Fischer C., 2014, APPL PSYCHOL-HLTH WE, P9, DOI [10.1002/9781118539415.wbwell018, DOI 10.1002/9781118539415.WBWELL018]
   Flynn R., 2020, RAPID REV IMPACT COV
   Fuchs A, 2020, BMJ OPEN, V10, DOI 10.1136/bmjopen-2020-039832
   Gigliotti E, 2012, NURS SCI QUART, V25, P336, DOI 10.1177/0894318412457054
   Gilles I., 2020, DETERMINANTS H UNPUB
   Giuntoli L, 2017, SAGE OPEN, V7, DOI 10.1177/2158244016682293
   Goulia P, 2010, BMC INFECT DIS, V10, DOI 10.1186/1471-2334-10-322
   Guihard G, 2018, ENCEPHALE, V44, P40, DOI 10.1016/j.encep.2017.06.002
   Italian Society of Anesthesia RaICS, 2020, CLIN ETH REC ALL INT
   Klein EM, 2016, BMC PSYCHIATRY, V16, DOI 10.1186/s12888-016-0875-9
   Knoll N, 2005, EUR J PERSONALITY, V19, P229, DOI 10.1002/per.546
   Kovner CT, 2016, INT J NURS STUD, V60, P251, DOI 10.1016/j.ijnurstu.2016.05.003
   Kristensen TS, 2005, SCAND J WORK ENV HEA, V31, P438, DOI 10.5271/sjweh.948
   Lai JB, 2020, JAMA NETW OPEN, V3, DOI 10.1001/jamanetworkopen.2020.3976
   Lamiani G, 2018, STRESS HEALTH, V34, P135, DOI 10.1002/smi.2769
   Lancee WJ, 2008, PSYCHIAT SERV, V59, P91, DOI 10.1176/ps.2008.59.1.91
   Leach CW, 2008, J PERS SOC PSYCHOL, V95, P144, DOI 10.1037/0022-3514.95.1.144
   Lecocq D, 2021, NURS OPEN, V8, P104, DOI 10.1002/nop2.608
   Lelorain S, 2010, J CLIN PSYCHOL MED S, V17, P14, DOI 10.1007/s10880-009-9183-6
   Leplege A., 2007, RECH SOINS INFIRM, V88, P18
   Lindstrom B, 2012, SALUTOGENESE PETIT G
   Lindstrom B., 2017, HDB SALUTOGENESIS
   Liu Q, 2020, LANCET GLOB HEALTH, V8, pE790, DOI 10.1016/S2214-109X(20)30204-7
   Lu YC, 2006, PSYCHOTHER PSYCHOSOM, V75, P370, DOI 10.1159/000095443
   Maercker A, 2001, DIAGNOSTICA, V47, P153, DOI 10.1026//0012-1924.47.3.153
   Matsuishi K, 2012, PSYCHIAT CLIN NEUROS, V66, P353, DOI 10.1111/j.1440-1819.2012.02336.x
   Maunder RG, 2006, EMERG INFECT DIS, V12, P1924
   Maunder RG, 2008, CAN J PUBLIC HEALTH, V99, P486, DOI 10.1007/BF03403782
   Muller L, 2003, ENCEPHALE, V29, P507
   Neuman B., 2011, NEUMAN SYSTEMS MODEL
   Nickell LA, 2004, CAN MED ASSOC J, V170, P793, DOI 10.1503/cmaj.1031077
   Niu SF, 2017, BIOL RES NURS, V19, P375, DOI 10.1177/1099800417695483
   Notario-Pacheco B, 2011, HEALTH QUAL LIFE OUT, V9, DOI 10.1186/1477-7525-9-63
   Nubling Matthias, 2006, Psychosoc Med, V3, pDoc05
   Papathanassoglou EDE, 2012, AM J CRIT CARE, V21, pE41, DOI 10.4037/ajcc2012205
   Pappa S, 2020, BRAIN BEHAV IMMUN, V88, P901, DOI [10.1016/j.bbi.2020.05.026, 10.1016/j.bbi.2020.11.023]
   Pelz C, 2004, FORUM QUALITATIVE SO, V5, DOI [10.17169/fqs-5.2.601, DOI 10.17169/FQS-5.2.601]
   Pinho L., 2021, ENVIRON RES, V195, DOI [10.1016/j.envres.2021.110828, DOI 10.1016/J.ENVRES.2021.110828]
   Pocnet C, 2016, QUAL LIFE RES, V25, P2897, DOI 10.1007/s11136-016-1296-4
   Postmes T, 2013, BRIT J SOC PSYCHOL, V52, P597, DOI 10.1111/bjso.12006
   Preti E, 2020, CURR PSYCHIAT REP, V22, DOI 10.1007/s11920-020-01166-z
   Quintard B., 1994, INTRO PSYCHOLOGIE SA, P43
   Rajkumar RP, 2020, ASIAN J PSYCHIATR, V52, DOI 10.1016/j.ajp.2020.102066
   Remuzzi A, 2020, LANCET, V395, P1225, DOI 10.1016/S0140-6736(20)30627-9
   Richardson D, 2020, MORAL DISTRESS WELL
   Ryff CD, 2006, SOC SCI RES, V35, P1103, DOI 10.1016/j.ssresearch.2006.01.002
   Sarubin N, 2015, Z GESUNDHEITSPSYCHOL, V23, P112, DOI 10.1026/0943-8149/a000142
   Skevington SM, 2004, QUAL LIFE RES, V13, P299, DOI 10.1023/B:QURE.0000018486.91360.00
   Studer J, 2017, SUBST USE MISUSE, V52, P798, DOI 10.1080/10826084.2016.1264966
   Stuijfzand S, 2020, BMC PUBLIC HEALTH, V20, DOI 10.1186/s12889-020-09322-z
   Tam CWC, 2004, PSYCHOL MED, V34, P1197, DOI 10.1017/S0033291704002247
   Tedeschi RG, 1996, J TRAUMA STRESS, V9, P455, DOI 10.1007/BF02103658
   Trochim W, 2005, INT J QUAL HEALTH C, V17, P187, DOI 10.1093/intqhc/mzi038
   Vail EF, 1999, INFORM SYST MANAGE, V16, P16, DOI 10.1201/1078/43189.16.4.19990901/31199.3
   Villieux A, 2016, PERS INDIV DIFFER, V88, P1, DOI 10.1016/j.paid.2015.08.027
   Voorpostel M, 2017, SWISS HOUSEHOLD PANE
   Wang L, 2010, PSYCHIAT CLIN NEUROS, V64, P499, DOI 10.1111/j.1440-1819.2010.02130.x
   Wang M, 2017, WORK AGING RETIRE, V3, P1, DOI 10.1093/workar/waw033
   ZIMET GD, 1990, J PERS ASSESS, V55, P610, DOI 10.1207/s15327752jpa5503&4_17
NR 87
TC 1
Z9 1
U1 2
U2 3
PU BMJ PUBLISHING GROUP
PI LONDON
PA BRITISH MED ASSOC HOUSE, TAVISTOCK SQUARE, LONDON WC1H 9JR, ENGLAND
SN 2044-6055
J9 BMJ OPEN
JI BMJ Open
PD DEC
PY 2021
VL 11
IS 12
AR e057021
DI 10.1136/bmjopen-2021-057021
PG 9
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC General & Internal Medicine
GA XX1AZ
UT WOS:000736039200032
PM 34949633
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Yang, WT
   Zhang, JT
   Ma, RL
AF Yang, Wenting
   Zhang, Jiantong
   Ma, Ruolin
TI The Prediction of Infectious Diseases: A Bibliometric Analysis
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Review
DE infectious diseases; prediction; bibliometric analysis
ID EAST RESPIRATORY SYNDROME; EMERGING TRENDS; CORONAVIRUS; EPIDEMIC;
   BEHAVIOR; OUTBREAK; SCIENCE; SYSTEM; CHINA; RISK
AB Objective: The outbreak of infectious diseases has a negative influence on public health and the economy. The prediction of infectious diseases can effectively control large-scale outbreaks and reduce transmission of epidemics in rapid response to serious public health events. Therefore, experts and scholars are increasingly concerned with the prediction of infectious diseases. However, a knowledge mapping analysis of literature regarding the prediction of infectious diseases using rigorous bibliometric tools, which are supposed to offer further knowledge structure and distribution, has been conducted infrequently. Therefore, we implement a bibliometric analysis about the prediction of infectious diseases to objectively analyze the current status and research hotspots, in order to provide a reference for related researchers. Methods: We viewed "infectious disease*" and "prediction" or "forecasting" as search theme in the core collection of Web of Science from inception to 1 May 2020. We used two effective bibliometric tools, i.e., CiteSpace (Drexel University, Philadelphia, PA, USA) and VOSviewer (Leiden University, Leiden, The Netherlands) to objectively analyze the data of the prediction of infectious disease domain based on related publications, which can be downloaded from the core collection of Web of Science. Then, the leading publications of the prediction of infectious diseases were identified to detect the historical progress based on collaboration analysis, co-citation analysis, and co-occurrence analysis. Results: 1880 documents that met the inclusion criteria were extracted from Web of Science in this study. The number of documents exhibited a growing trend, which can be expressed an increasing number of experts and scholars paying attention to the field year by year. These publications were published in 427 different journals with 11 different document types, and the most frequently studied types were articles 1618 (83%). In addition, as the most productive country, the United States has provided a lot of scientific research achievements in the field of infectious diseases. Conclusion: Our study provides a systematic and objective view of the field, which can be useful for readers to evaluate the characteristics of publications involving the prediction of infectious diseases and for policymakers to take timely scientific responses.
C1 [Yang, Wenting; Zhang, Jiantong] Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China.
   [Ma, Ruolin] Michigan State Univ, Eli Broad Coll Business, E Lansing, MI 48824 USA.
RP Ma, RL (通讯作者)，Michigan State Univ, Eli Broad Coll Business, E Lansing, MI 48824 USA.
EM yangwenting_tongji@163.com; zhangjiantong@tongji.edu.cn;
   maruoli1@msu.edu
OI Zhang, Jiantong/0000-0003-1890-1117
FU National Social Science Foundation of Shanghai [2018BGL026]; Natural
   Science Foundation of China [71911156]
FX This research was funded by both the National Social Science Foundation
   of Shanghai under Grant No. 2018BGL026 and the Natural Science
   Foundation of China under Grant No. 71911156.
CR Alessa A, 2018, THEOR BIOL MED MODEL, V15, DOI 10.1186/s12976-017-0074-5
   Alraddadi B, 2016, ANN THORAC MED, V11, P128, DOI 10.4103/1817-1737.180027
   ANDERSON R M, 1991
   ANDERSON RM, 1979, NATURE, V280, P361, DOI 10.1038/280361a0
   Aylward B, 2014, NEW ENGL J MED, V371, P1481, DOI 10.1056/NEJMoa1411100
   Bai ZH, 2020, VIROL SIN, V35, P272, DOI 10.1007/s12250-020-00219-0
   Barrett C, 2011, EPIDEMICS-NETH, V3, P19, DOI 10.1016/j.epidem.2010.11.002
   Bhattacharyya S, 2010, J THEOR BIOL, V267, P276, DOI 10.1016/j.jtbi.2010.09.005
   Bundschuh M, 2013, PARASITE VECTOR, V6, DOI 10.1186/1756-3305-6-331
   Cardinal M., 1996, P 29 ANN M SOC EP RE
   Chen CM, 2014, EXPERT OPIN BIOL TH, V14, P1295, DOI 10.1517/14712598.2014.920813
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen FH, 2009, MATH BIOSCI, V217, P125, DOI 10.1016/j.mbs.2008.11.005
   Chen LM, 2015, INT J TUBERC LUNG D, V19, P717, DOI 10.5588/ijtld.14.0925
   Cheng ZKJ, 2020, INFECTION, V48, P155, DOI 10.1007/s15010-020-01401-y
   Christaki E, 2015, VIRULENCE, V6, P554, DOI 10.1080/21505594.2015.1040975
   Cruz-Calderon S, 2015, TRAVEL MED INFECT DI, V13, P202, DOI 10.1016/j.tmaid.2015.02.007
   Erfanmanesh M, 2019, COLLECT CURATION, V38, P61, DOI 10.1108/CC-12-2018-0024
   Eubank S, 2004, NATURE, V429, P180, DOI 10.1038/nature02541
   Fagbo SF, 2015, EMERG INFECT DIS, V21, P1981, DOI 10.3201/eid2111.150944
   Fang CC, 2017, SCIENTOMETRICS, V113, P1481, DOI 10.1007/s11192-017-2524-6
   Fenichel EP, 2011, P NATL ACAD SCI USA, V108, P6306, DOI 10.1073/pnas.1011250108
   Ferguson NM, 2001, NATURE, V413, P542, DOI 10.1038/35097116
   Ford TE, 2009, EMERG INFECT DIS, V15, P1341, DOI 10.3201/eid1509.081334
   Gan CM, 2015, SCIENTOMETRICS, V105, P1167, DOI 10.1007/s11192-015-1723-2
   Gavin F., 2004, P INT S ENV SOFTW SY
   Gu DX, 2019, INFORM MANAGE-AMSTER, V56, DOI 10.1016/j.im.2019.04.005
   Gu DX, 2017, ARTIF INTELL MED, V77, P31, DOI 10.1016/j.artmed.2017.02.003
   Gu DX, 2015, INT J INF TECH DECIS, V14, P597, DOI 10.1142/S0219622014500424
   Hethcote HW, 2000, SIAM REV, V42, P599, DOI 10.1137/S0036144500371907
   Huppert A, 2013, CLIN MICROBIOL INFEC, V19, P999, DOI 10.1111/1469-0691.12308
   Jewell C.P., 2007, P GISVET 2007 C COP
   Jones KE, 2008, NATURE, V451, P990, DOI 10.1038/nature06536
   Bonilla-Aldana DK, 2020, TRAVEL MED INFECT DI, V33, DOI 10.1016/j.tmaid.2020.101566
   Keeling MJ, 2001, SCIENCE, V294, P813, DOI 10.1126/science.1065973
   Kelly JC, 2010, J BONE JOINT SURG BR, V92B, P1338, DOI 10.1302/0301-620X.92B10.24867
   Kelly T.R., 2015, P 14 S INT SOC VET E
   Lai PC, 2015, INT J GEOGR INF SCI, V29, P1251, DOI 10.1080/13658816.2015.1030671
   Lee PC, 2010, INNOV-MANAG POLICY P, V12, P26, DOI 10.5172/impp.12.1.26
   Lipkin WI, 2013, NAT REV MICROBIOL, V11, P133, DOI 10.1038/nrmicro2949
   Liu W, 2020, ECNU REV ED, V3, P562
   Liu WS, 2019, SCIENTOMETRICS, V121, P1815, DOI 10.1007/s11192-019-03238-1
   Liu WS, 2016, SCIENTOMETRICS, V106, P1273, DOI 10.1007/s11192-015-1821-1
   Liu ZG, 2015, SCIENTOMETRICS, V103, P135, DOI 10.1007/s11192-014-1517-y
   Lloyd-Smith JO, 2005, NATURE, V438, P355, DOI 10.1038/nature04153
   Lou J, 2020, EUR REV MED PHARMACO, V24, P3411, DOI 10.26355/eurrev_202003_20712
   Mao L, 2012, SOC SCI MED, V74, P167, DOI 10.1016/j.socscimed.2011.10.012
   Merigo JM, 2018, INFORM SCIENCES, V432, P245, DOI 10.1016/j.ins.2017.11.054
   Morse SS, 2012, LANCET, V380, P1956, DOI 10.1016/S0140-6736(12)61684-5
   Mossong J, 2008, PLOS MED, V5, P381, DOI 10.1371/journal.pmed.0050074
   Newman MEJ, 2002, PHYS REV E, V66, DOI 10.1103/PhysRevE.66.016128
   Osareh F, 1996, LIBRI, V46, P149, DOI 10.1515/libr.1996.46.3.149
   Pastor-Satorras R, 2001, PHYS REV LETT, V86, P3200, DOI 10.1103/PhysRevLett.86.3200
   Patino-Barbosa AM, 2016, J INFECT PUBLIC HEAL, V9, P532, DOI 10.1016/j.jiph.2015.10.001
   Philip M., 2007, CLIN INFECT DIS, V44, P272
   Polgreen PM, 2010, MICROBE, V1, P459
   Racloz V, 2012, PLOS NEGLECT TROP D, V6, DOI 10.1371/journal.pntd.0001648
   Salerno J, 2017, ANN EPIDEMIOL, V27, P297, DOI 10.1016/j.annepidem.2017.05.002
   Schroeder W., 2005, P 8 INT POTSD S TICK
   Shashvat K, 2019, CURR PHARM BIOTECHNO, V20, P674, DOI 10.2174/1389201020666190612160631
   Small H, 1999, J AM SOC INFORM SCI, V50, P799, DOI 10.1002/(SICI)1097-4571(1999)50:9<799::AID-ASI9>3.0.CO;2-G
   Song JB, 2016, SCIENTOMETRICS, V107, P1111, DOI 10.1007/s11192-016-1918-1
   Su HN, 2010, SCIENTOMETRICS, V85, P65, DOI 10.1007/s11192-010-0259-8
   Sun X, 2017, IEEJ T ELECTR ELECTR, V12, P388, DOI 10.1002/tee.22389
   Tas F, 2014, TUMOR BIOL, V35, P4645, DOI 10.1007/s13277-014-1608-7
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vera-Polania Felipe, 2014, Recent Pat Antiinfect Drug Discov, V9, P195
   Wei FW, 2015, PROF GEOGR, V67, P374, DOI 10.1080/00330124.2014.983588
   Weiss RA, 2004, NAT MED, V10, pS70, DOI 10.1038/nm1150
   Weston D, 2018, BMC PUBLIC HEALTH, V18, DOI 10.1186/s12889-018-5223-1
   Wu P, 2020, EUROSURVEILLANCE, V25, P4, DOI 10.2807/1560-7917.ES.2020.25.3.2000044
   Xu S, 2020, INT J PROD RES, V58, P3508, DOI 10.1080/00207543.2020.1717011
   Xu XT, 2020, SCI CHINA LIFE SCI, V63, P457, DOI 10.1007/s11427-020-1637-5
   Yang ZF, 2020, J THORAC DIS, V12, P165, DOI 10.21037/jtd.2020.02.64
   Yu DJ, 2020, INT J MACH LEARN CYB, V11, P715, DOI 10.1007/s13042-019-01028-y
   Yu DJ, 2018, KNOWL-BASED SYST, V141, P188, DOI 10.1016/j.knosys.2017.11.018
   ZHANG L, 2020, SCIENTOMETRICS 0625, DOI DOI 10.1007/S11192-020-03572-9
   Zhang L, 2020, SCIENTOMETRICS, V124, P747, DOI 10.1007/s11192-020-03531-4
   Zhao XY, 2015, BRATISL MED J, V116, P729, DOI 10.4149/BLL_2015_143
   Zhou P, 2020, NATURE, V579, P270, DOI 10.1038/s41586-020-2012-7
   Zhu JW, 2020, SCIENTOMETRICS, V124, P1691, DOI [10.1007/s11192-020-03525-2, 10.1080/15732479.2020.1835999]
   Zhu JW, 2020, SCIENTOMETRICS, V123, P321, DOI 10.1007/s11192-020-03387-8
   Zyoud SH, 2015, HUM EXP TOXICOL, V34, P12, DOI 10.1177/0960327114531993
   Zyoud SH, 2016, BMC INFECT DIS, V16, DOI 10.1186/s12879-016-1600-5
NR 84
TC 13
Z9 13
U1 26
U2 58
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD SEP
PY 2020
VL 17
IS 17
AR 6218
DI 10.3390/ijerph17176218
PG 19
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA NO8DO
UT WOS:000569718300001
PM 32867133
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Dedie, A
   Bleimehl, T
   Tager, J
   Preusse, M
   de Angelis, MH
   Jarasch, A
AF Dedie, Angela
   Bleimehl, Tim
   Tager, Justus
   Preusse, Martin
   de Angelis, Martin Hrabe
   Jarasch, Alexander
TI DZDconnect: using connected data to fight diabetes
SO DIABETOLOGE
LA German
DT Article
DE Knowledge graph; Data management; Big data; Text mining; Diabetes
   complications
ID GRAPH
AB Data in translational healthcare research is complex and highly connected. Information on wide-spread diseases like diabetes and cancer is extensive, heterogeneous and rapidly growing. Data are available at various locations and are neither interconnectable with other data sources nor searchable. Consequently, it is difficult for researchers to access data and to cope with the amount of literature. Collecting data and knowledge is still done manually by comparing data tables. However, a flexible and efficient approach to processing biomedical data is offered by graph databases. Based on the open source Neo4j graph database, the German Center for Diabetes Research (DZD) developed DZDconnect-a knowledge graph that links data from basic research and clinical studies across sites, disciplines and species with external knowledge. DZDconnect collects, structures, interconnects and makes available various data and information on wide-spread diseases and its long-term complications. Information from well-established databases is connected on the metadata level, raw data level as well as on the insight level. In addition, in-house data from translational research can be integrated. The enabling technology is a flexible and scalable graph database. DZDconnect thus bridges the gap between healthcare research and state-of-the-art information technology and helps to make disease research faster and more efficient. With DZDconnect scientists can quickly and efficiently generate hypotheses regarding the underlying mechanisms of these diseases and how to intervene medically. DZDconnect is developed as an open-source project.
C1 [Dedie, Angela; Bleimehl, Tim; Tager, Justus; de Angelis, Martin Hrabe; Jarasch, Alexander] Deutsch Zentrum Diabetesforsch DZD, Helmholtz Zentrum Munchen, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany.
   [Preusse, Martin] Kaiser & Preusse GbR, Freiburg, Germany.
   [de Angelis, Martin Hrabe] Helmholtz Zentrum Munchen, Inst Expt Genet, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany.
   [de Angelis, Martin Hrabe] Tech Univ Munich, TUM Sch Life Sci, Chair Expt Genet, Freising Weihenstephan, Germany.
RP Jarasch, A (通讯作者)，Deutsch Zentrum Diabetesforsch DZD, Helmholtz Zentrum Munchen, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany.; de Angelis, MH (通讯作者)，Helmholtz Zentrum Munchen, Inst Expt Genet, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany.
EM hrabe@helmholtz-muenchen.de; jarasch@dzd-ev.de
RI de Angelis, Martin Hrabe/F-5531-2012
OI de Angelis, Martin Hrabe/0000-0002-7898-2353
CR Callahan TJ, 2020, ANNU REV BIOMED DA S, V3, P23, DOI 10.1146/annurev-biodatasci-010820-091627
   Cernile G, 2021, BMJ HEALTH CARE INFO, V28, DOI 10.1136/bmjhci-2020-100254
   Deutsches Zentrum fur Diabetesforschung, 2018, BIG DAT DIAB DIG ERM, P32, DOI DOI 10.1038/S41467-017-00623-3
   Frame A., 2020, 2020 IS CRUCIAL YEAR, V12, P56
   Have CT, 2013, BIOINFORMATICS, V29, P3107, DOI 10.1093/bioinformatics/btt549
   IDF Diabetes Atlas, 2019, IDF DIABETES ATLAS
   Jarasch A, 2018, DIABETOLOGE, V14, P486, DOI 10.1007/s11428-018-0384-1
   Lysenko A, 2016, BIODATA MIN, V9, DOI 10.1186/s13040-016-0102-8
   Needham M., 2019, GRAPH ALGORITHMS PRA
   Robinson, 2015, GRAPH DATABASES
   Simpson CM, 2020, DATABASE-OXFORD, DOI 10.1093/database/baaa110
   Stothers Jessica A M, 2020, AMIA Jt Summits Transl Sci Proc, V2020, P646
   Ulrich Hannes, 2018, Stud Health Technol Inform, V253, P55
   Vukotic A., 2015, NEO4J IN ACTION
   Wang L, 2020, COMPUT ASSIST SURG, V25, P29, DOI 10.1080/24699322.2020.1850866
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Yoon Byoung-Ha, 2017, Genomics & Informatics, V15, P19, DOI 10.5808/GI.2017.15.1.19
NR 17
TC 1
Z9 1
U1 1
U2 4
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1860-9716
EI 1860-9724
J9 DIABETOLOGE
JI Diabetologe
PD DEC
PY 2021
VL 17
IS 8
SI SI
BP 780
EP 787
DI 10.1007/s11428-021-00807-y
EA SEP 2021
PG 8
WC Endocrinology & Metabolism
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Endocrinology & Metabolism
GA XL7MN
UT WOS:000701631700002
OA Bronze
DA 2022-08-23
ER

PT J
AU Wang, YC
   Ge, XO
   Wang, B
   Kuo, CCJ
AF Wang, Yun-Cheng
   Ge, Xiou
   Wang, Bin
   Kuo, C-C Jay
TI KGBoost: A classification-based knowledge base completion method with
   negative sampling
SO PATTERN RECOGNITION LETTERS
LA English
DT Article
DE Knowledge base completion; Negative sampling; Binary classification;
   XGBoost Classifiers
AB Knowledge base completion is formulated as a binary classification problem in this work, where an XGBoost binary classifier is trained for each relation using relevant links in knowledge graphs (KGs). The new method, named KGBoost, adopts a modularized design and attempts to find hard negative samples so as to train a powerful classifier for missing link prediction. We conduct experiments on multiple benchmark datasets and demonstrate that KGBoost outperforms state-of-the-art methods across most datasets. Furthermore, as compared with models trained by end-to-end optimization, KGBoost works well under the low-dimensional setting so as to allow a smaller model size. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Wang, Yun-Cheng; Ge, Xiou; Kuo, C-C Jay] Univ Southern Calif, Los Angeles, CA 90007 USA.
   [Wang, Bin] Natl Univ Singapore, Singapore, Singapore.
RP Wang, YC (通讯作者)，Univ Southern Calif, Los Angeles, CA 90007 USA.
EM yunchenw@usc.edu
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, V26
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Cai L., 2018, P 2018 C N AM CHAPTE, V1
   Carlson A., ARCHITECTURE NEVER E
   Chen T., 2016, KDD16 P 22 ACM, P785, DOI DOI 10.1145/2939672.2939785
   Cochinwala M, 2001, INFORM SCIENCES, V137, P1, DOI 10.1016/S0020-0255(00)00070-0
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dong XL, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P601, DOI 10.1145/2623330.2623623
   Furnkranz J, 2002, LECT NOTES ARTIF INT, V2430, P97
   Ge X, 2022, PATTERN RECOGN LETT
   Huang ZC, 2018, LECT NOTES COMPUT SC, V10987, P400, DOI 10.1007/978-3-319-96890-2_33
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Shang C, 2019, AAAI CONF ARTIF INTE, P3060
   Sun Z., 2019, 7 INT C LEARN REPR
   Tang Y, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P2713
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vashishth S., P AAAI C ARTIFICIAL
   Wang PF, 2018, AAAI CONF ARTIF INTE, P2005
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Yang B., 2014, ABS14126575 CORR
   Zhang S, 2019, ADV NEUR IN, V32
NR 27
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8655
EI 1872-7344
J9 PATTERN RECOGN LETT
JI Pattern Recognit. Lett.
PD MAY
PY 2022
VL 157
BP 104
EP 111
DI 10.1016/j.patrec.2022.04.001
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1X5EE
UT WOS:000807476100001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Yang, YF
   Qu, GF
   Hua, LL
AF Yang, Yunfei
   Qu, Guifei
   Hua, Lianlian
TI Research Status, Hotspots, and Evolution Trend of Decision-Making in
   Marine Management Using VOSviewer and CiteSpace
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID SUPPORT-SYSTEM; BIBLIOMETRIC ANALYSIS; AUSTRALIAN COASTAL; KNOWLEDGE
   EXCHANGE; VISUALIZATION; VALUATION; LESSONS; MAKERS; RISK
AB Clarifying the research status, hotspots, and evolution trend of the field of research on decision-making in marine management has certain reference significance for the scholars and decision makers in the field of decision-making in marine management at the theoretical and practical levels. In view of this, this study used 443 pieces of literature retrieved from the Web of Science from 2002 to 2021 as the data source. It adopted the methods of statistical analysis and bibliometric analysis to conduct knowledge mapping visualization analysis in the field of research on decision-making in marine management. Theresults show that (1) there is a high correlation between the level of economic development among countries/regions and the number of publications, and the geographical distribution is closely related to the intensity of research cooperation; (2) the research organizations show the characteristics of the diversified distribution. Moreover, although there is no noticeable difference in scientific research output among research organizations, influential research organizations such as NOAA, The Nature Conservancy, and so forth have emerged; (3) the author groups as a whole do not show a high cooperation intensity, but there are also scholars with high cooperation intensity within the internal group and other research groups; (4) the research hotspots distribution in the field of decision-making in marine management is relatively extensive and belongs to a multidimensional and multiperspective systematic research. Furthermore, the research focuses on subdivision research hotspots mainly involving ecosystem services, knowledge exchange, decision analysis, multicriteria decision, decision support, fisheries management, specific mathematical models and theories, etc; (5) from the perspective of the evolution trend, it can also be found that different clusters show the evolution trend of overlapping common features and individual features.
C1 [Yang, Yunfei] Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China.
   [Yang, Yunfei; Qu, Guifei] Hebei Univ Engn, Sch Management Engn & Business, Handan 056038, Peoples R China.
   [Hua, Lianlian] Inner Mongolia Univ Technol, Sch Management, Hohhot 010051, Peoples R China.
RP Hua, LL (通讯作者)，Inner Mongolia Univ Technol, Sch Management, Hohhot 010051, Peoples R China.
EM hualianlian2010@imut.edu.cn
OI Yang, Yunfei/0000-0002-0703-4241
FU Annual Fund Project of Social Science Research of Colleges and
   Universities in Hebei Province [BJ2020068]
FX This study was supported by Annual Fund Project of Social Science
   Research of Colleges and Universities in Hebei Province (BJ2020068).
CR Andalecio MN, 2011, SUSTAINABLE AGRICULTURE, VOL 2, P251, DOI 10.1007/978-94-007-0394-0_14
   Bailey JL, 2018, J ENVIRON STUD SCI, V8, P189, DOI 10.1007/s13412-018-0478-5
   Brabo L, 2022, MAR POLLUT BULL, V174, DOI 10.1016/j.marpolbul.2021.113310
   Bradie JN, 2021, J APPL ECOL, V58, P587, DOI 10.1111/1365-2664.13822
   Bremer LL, 2015, ENVIRON MANAGE, V55, P884, DOI 10.1007/s00267-014-0426-4
   Cardwell E, 2015, GEOFORUM, V64, P157, DOI 10.1016/j.geoforum.2015.06.008
   Cvitanovic C, 2015, OCEAN COAST MANAGE, V112, P25, DOI 10.1016/j.ocecoaman.2015.05.002
   Cvitanovic C, 2015, MAR POLICY, V52, P38, DOI 10.1016/j.marpol.2014.10.026
   Cvitanovic C, 2014, ECOL SOC, V19, DOI 10.5751/ES-07019-190433
   Dagnino A, 2014, SCI TOTAL ENVIRON, V466, P119, DOI 10.1016/j.scitotenv.2013.06.084
   Dedman S, 2017, ICES J MAR SCI, V74, P576, DOI 10.1093/icesjms/fsw147
   Ding X, 2020, ELECTRON COMMER RES, DOI 10.1007/s10660-020-09410-7
   Du YW, 2021, EXPERT SYST APPL, V167, DOI 10.1016/j.eswa.2020.113871
   Du YW, 2021, INFORM SCIENCES, V547, P1201, DOI 10.1016/j.ins.2020.07.072
   Dutra LXC, 2015, OCEAN COAST MANAGE, V116, P382, DOI 10.1016/j.ocecoaman.2015.08.011
   Avelino JE, 2019, OCEAN COAST MANAGE, V181, DOI 10.1016/j.ocecoaman.2019.104891
   Guo XR, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su132313234
   Guo YM, 2021, FUTURE GENER COMP SY, V116, P316, DOI 10.1016/j.future.2020.10.023
   Havard L, 2015, OCEAN COAST MANAGE, V116, P116, DOI 10.1016/j.ocecoaman.2015.06.017
   Huang L, 2020, J CLEAN PROD, V252, DOI 10.1016/j.jclepro.2019.119908
   Huang LY, 2021, J PAIN RES, V14, P3773, DOI 10.2147/JPR.S340961
   Kao SL, 2020, TRANSPORT J, V59, P335, DOI 10.5325/transportationj.59.4.0335
   Karnauskaite D, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11113175
   Labianca C, 2020, ENVIRON ENG MANAG J, V19, P1891
   Lane DE, 2007, INT SER OPER RES MAN, V99, P237, DOI 10.1007/978-0-387-71815-6_13
   Lewison RL, 2015, BIOSCIENCE, V65, P486, DOI 10.1093/biosci/biv018
   Li T, 2021, REMOTE SENS-BASEL, V13, DOI 10.3390/rs13071279
   Li YN, 2021, CHEMOSPHERE, V263, DOI 10.1016/j.chemosphere.2020.128238
   Liang CC, 2018, SAGE OPEN MED, V6, DOI 10.1177/2050312118800199
   Lin MW, 2021, INT J INTELL COMPUT, V14, P104, DOI 10.1108/IJICC-06-2020-0067
   Liu H, 2020, FUEL, V262, DOI 10.1016/j.fuel.2019.116598
   Marre JB, 2016, J ENVIRON MANAGE, V178, P52, DOI 10.1016/j.jenvman.2016.04.014
   Marre JB, 2015, MAR POLICY, V56, P117, DOI 10.1016/j.marpol.2015.02.011
   McKenzie E, 2014, ENVIRON PLANN C, V32, P320, DOI 10.1068/c12292j
   Mohammadi M, 2020, OMEGA-INT J MANAGE S, V96, DOI 10.1016/j.omega.2019.06.001
   Naseer S, 2022, SOFT COMPUT, V26, P2489, DOI 10.1007/s00500-021-06507-5
   Nygard H, 2020, ENVIRON MANAGE, V66, P1024, DOI 10.1007/s00267-020-01356-8
   Pang CF, 2017, INTEGR ENVIRON ASSES, V13, P631, DOI 10.1002/ieam.1882
   Patel H, 2011, INT REGIONAL SCI REV, V34, P191, DOI 10.1177/0160017610389329
   Patrick WS, 2008, BIOL CONSERV, V141, P2906, DOI 10.1016/j.biocon.2008.08.014
   Pentz B, 2017, OCEAN COAST MANAGE, V145, P44, DOI 10.1016/j.ocecoaman.2017.05.007
   Pittman SJ, 2018, ECOL INFORM, V47, P26, DOI 10.1016/j.ecoinf.2017.09.008
   Ponton-Cevallos JF, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12176931
   Qi SJ, 2022, APPL RES QUAL LIFE, V17, P421, DOI 10.1007/s11482-020-09878-3
   Ren MN, 2021, INNOV FOOD SCI EMERG, V74, DOI 10.1016/j.ifset.2021.102823
   Rowden AA, 2019, OCEAN COAST MANAGE, V170, P1, DOI 10.1016/j.ocecoaman.2018.12.033
   Schoning L, 2020, OCEAN DEV INT LAW, V51, P95, DOI 10.1080/00908320.2019.1655619
   Sutrisno D, 2018, INT J DIGIT EARTH, V11, P863, DOI 10.1080/17538947.2017.1363825
   Syme GJ, 2012, OCEAN COAST MANAGE, V69, P92, DOI 10.1016/j.ocecoaman.2012.08.012
   Tan WJ, 2018, OCEAN COAST MANAGE, V153, P131, DOI 10.1016/j.ocecoaman.2017.12.012
   Tanriverdi G, 2020, J AIR TRANSP MANAG, V89, DOI 10.1016/j.jairtraman.2020.101916
   Tao ZB, 2020, ANN TRANSL MED, V8, DOI 10.21037/atm.2020.04.26
   Voyer M, 2021, ONE EARTH, V4, P1016, DOI 10.1016/j.oneear.2021.06.004
   Wang BJ, 2021, ECOL INDIC, V125, DOI 10.1016/j.ecolind.2021.107449
   Wang CY, 2017, OCEAN COAST MANAGE, V139, P42, DOI 10.1016/j.ocecoaman.2017.01.013
   Wang XX, 2021, INFORM SCIENCES, V547, P328, DOI 10.1016/j.ins.2020.08.036
   Wang ZY, 2020, ISPRS INT J GEO-INF, V9, DOI 10.3390/ijgi9110632
   Winther JG, 2020, NAT ECOL EVOL, V4, P1451, DOI 10.1038/s41559-020-1259-6
   Wu B, 2021, MARIT POLICY MANAG, V48, P299, DOI 10.1080/03088839.2020.1791994
   Xie J, 2016, ISPRS INT J GEO-INF, V5, DOI 10.3390/ijgi5080140
   Xie KF, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17176256
   Ye N, 2020, J CLEAN PROD, V272, DOI 10.1016/j.jclepro.2020.122679
   Ye XD, 2019, OCEAN COAST MANAGE, V172, P128, DOI 10.1016/j.ocecoaman.2019.02.003
   Yu SL, 2022, CHINESE GEOGR SCI, V32, P189, DOI 10.1007/s11769-022-1265-5
   Zhang CY, 2020, TOUR MANAG PERSPECT, V35, DOI 10.1016/j.tmp.2020.100715
   Zhang CH, 2021, EXPERT SYST APPL, V171, DOI 10.1016/j.eswa.2021.114586
   Zhou W, 2019, ECON RES-EKON ISTRAZ, V32, P3457, DOI 10.1080/1331677X.2019.1660202
   Zou X, 2018, ACCIDENT ANAL PREV, V118, P131, DOI 10.1016/j.aap.2018.06.010
   Zuanazzi NR, 2020, CHEMOSPHERE, V241, DOI 10.1016/j.chemosphere.2019.125016
NR 69
TC 0
Z9 0
U1 15
U2 15
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1024-123X
EI 1563-5147
J9 MATH PROBL ENG
JI Math. Probl. Eng.
PD APR 25
PY 2022
VL 2022
AR 8283417
DI 10.1155/2022/8283417
PG 15
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Mathematics
GA 1F4IO
UT WOS:000795132600002
OA gold
DA 2022-08-23
ER

PT J
AU Cheng, P
   Tang, HT
   Dong, Y
   Liu, K
   Jiang, P
   Liu, YL
AF Cheng, Peng
   Tang, Houtian
   Dong, Yue
   Liu, Ke
   Jiang, Ping
   Liu, Yaolin
TI Knowledge Mapping of Research on Land Use Change and Food Security: A
   Visual Analysis Using CiteSpace and VOSviewer
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE land use change; food security; visual analysis; CtieSpace; VOSviewer;
   progress and frontier
ID CLIMATE-CHANGE; AGRICULTURAL LAND; GRAIN PRODUCTION;
   ENVIRONMENTAL-CHANGE; CROP PRODUCTION; URBAN SPRAWL; CHINA; IMPACT;
   INTENSIFICATION; ADAPTATION
AB Many scholars have conducted in-depth research on the theme of land use change and food security, and formed fruitful research results, but there is a lack of quantitative analysis and comprehensive evaluation of research achievements. Therefore, based on the relevant literature on the theme of land use change and food security in the core collection of the Web of Science (WOS) database, this paper takes the advantage of CiteSpace and VOSviewer bibliometric software to draw the cooperative network and keyword cooccurrence map to analyze the research progress and frontier. The results reveal that: (1) The research started in 1999 and can be divided into three stages: initial research, rapid development, and a stable in-depth stage. This topic has increasingly become a research hotspot in the academic community. (2) The distribution of research institutions is concentrated and forms a small cluster, and the research networks between developed and developing countries have been established, and developed countries are in the core position, but the cooperation network is not prominent. (3) The research content is becoming increasingly organized and systematic, and the research hot topics are divided into seven aspects. (4) The research area of the subject covers multiple levels, such as global, national, and specific natural geographical regions, and has formed a research system of geographic information technology and satellite remote sensing technology. It also presents the trend of cross integration with economics, land management and soil science. In the future, theoretical innovation still needs to be strengthened, and we should strengthen the research on the impact of agricultural chemical fertilizers on food security and study the impact of urban expansion on land use change.
C1 [Cheng, Peng; Dong, Yue; Jiang, Ping; Liu, Yaolin] Wuhan Univ, Coll Resource & Environm Sci, Wuhan 430079, Peoples R China.
   [Tang, Houtian] Cent South Univ, Sch Publ Adm, Changsha 410083, Peoples R China.
   [Liu, Ke] Huazhong Agr Univ, Grad Sch, Wuhan, Peoples R China.
   [Jiang, Ping; Liu, Yaolin] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China.
RP Jiang, P (通讯作者)，Wuhan Univ, Coll Resource & Environm Sci, Wuhan 430079, Peoples R China.; Jiang, P (通讯作者)，Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China.
EM chengpeng1141@whu.edu.cn; tanghoutian@csu.edu.cn; dongyue@whu.edu.cn;
   liuke_1996@163.com; longkangkang@whu.edu.cn; 00007101@whu.edu.cn
OI Cheng, Peng/0000-0002-6498-1640
FU Major Projects of National Social Science Foundation [20ZDA086]
FX FundingThis research was funded by the Major Projects of National Social
   Science Foundation (20ZDA086).
CR Abati R, 2021, ENVIRON SCI POLLUT R, V28, P32282, DOI 10.1007/s11356-021-14224-7
   Al Masud M, 2020, LAND USE POLICY, V92, DOI 10.1016/j.landusepol.2019.104443
   Singirankabo UA, 2020, LAND-BASEL, V9, DOI 10.3390/land9050138
   Alexander P, 2015, GLOBAL ENVIRON CHANG, V35, P138, DOI 10.1016/j.gloenvcha.2015.08.011
   Ali M, 2021, J CLEAN PROD, V314, DOI 10.1016/j.jclepro.2021.127904
   Djurfeldt AA, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12208304
   Beltran-Tolosa LM, 2020, MITIG ADAPT STRAT GL, V25, P1103, DOI 10.1007/s11027-020-09923-4
   Bonye SZ, 2021, INT J URBAN SUSTAIN, V13, P383, DOI 10.1080/19463138.2021.1915790
   Brinkman M, 2020, BIOMASS BIOENERG, V141, DOI 10.1016/j.biombioe.2020.105695
   Carrino L, 2020, AGRONOMY-BASEL, V10, DOI 10.3390/agronomy10111690
   Chang JF, 2021, NAT FOOD, V2, P700, DOI 10.1038/s43016-021-00366-x
   Chen CM, 2008, DATA KNOWL ENG, V67, P234, DOI 10.1016/j.datak.2008.05.004
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen CM, 2009, J INFORMETR, V3, P191, DOI 10.1016/j.joi.2009.03.004
   Chen C, 2020, LAND USE POLICY, V99, DOI 10.1016/j.landusepol.2020.104870
   Cheng C, 2020, J CLEAN PROD, V256, DOI 10.1016/j.jclepro.2020.120692
   Chigbu UE, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16081354
   Colares GS, 2020, SCI TOTAL ENVIRON, V714, DOI 10.1016/j.scitotenv.2020.136776
   Daioglou V, 2019, GLOBAL ENVIRON CHANG, V54, P88, DOI 10.1016/j.gloenvcha.2018.11.012
   Dax T, 2021, LAND-BASEL, V10, DOI 10.3390/land10060591
   Domingo A, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18094404
   Donthu N, 2020, J BUS RES, V109, P1, DOI 10.1016/j.jbusres.2019.10.039
   Dunning RJ, 2021, LAND USE POLICY, V105, DOI 10.1016/j.landusepol.2021.105434
   Edmondson JL, 2014, J APPL ECOL, V51, P880, DOI 10.1111/1365-2664.12254
   Ericksen PJ, 2008, GLOBAL ENVIRON CHANG, V18, P234, DOI 10.1016/j.gloenvcha.2007.09.002
   Fami HS, 2021, IND MARKET MANAG, V97, P71, DOI 10.1016/j.indmarman.2021.06.016
   Fan JC, 2020, FRONT PUBLIC HEALTH, V8, DOI 10.3389/fpubh.2020.00477
   Fischer G, 2021, INT J AGR SUSTAIN, V19, P403, DOI 10.1080/14735903.2020.1791425
   Gao X, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18168798
   Garnett T, 2009, ENVIRON SCI POLICY, V12, P491, DOI 10.1016/j.envsci.2009.01.006
   Ge DZ, 2018, LAND USE POLICY, V70, P94, DOI 10.1016/j.landusepol.2017.10.010
   Godfray HCJ, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2012.0273
   Gonzalez-Roglich M, 2019, ENVIRON SCI POLICY, V93, P34, DOI 10.1016/j.envsci.2018.12.019
   Guo YH, 2021, ECOL INDIC, V120, DOI 10.1016/j.ecolind.2020.106935
   Harris E, 2018, SCI TOTAL ENVIRON, V628-629, P997, DOI 10.1016/j.scitotenv.2018.02.119
   Hasegawa T, 2020, CLIMATIC CHANGE, V163, P1587, DOI 10.1007/s10584-020-02838-8
   Hasegawa T, 2018, NAT CLIM CHANGE, V8, P699, DOI 10.1038/s41558-018-0230-x
   Hasegawa T, 2015, ENVIRON SCI TECHNOL, V49, P7245, DOI 10.1021/es5051748
   He JH, 2013, APPL GEOGR, V37, P127, DOI 10.1016/j.apgeog.2012.11.005
   Huang WH, 2021, ECOSYST SERV, V50, DOI 10.1016/j.ecoser.2021.101309
   Ickowitz A, 2019, GLOB FOOD SECUR-AGR, V20, P9, DOI 10.1016/j.gfs.2018.11.002
   Jabbar A, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17249258
   Jalilov SM, 2016, J HYDROL, V539, P648, DOI 10.1016/j.jhydrol.2016.05.071
   Ji B, 2021, CHEMOSPHERE, V262, DOI 10.1016/j.chemosphere.2020.128366
   Jiang LG, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0252187
   Jiang YW, 2019, CURR ISSUES TOUR, V22, P1925, DOI 10.1080/13683500.2017.1408574
   Khan S, 2009, AGR WATER MANAGE, V96, P349, DOI 10.1016/j.agwat.2008.09.022
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   Kong XS, 2021, RESOUR CONSERV RECY, V174, DOI 10.1016/j.resconrec.2021.105770
   Krishnamurthy PK, 2014, GLOBAL ENVIRON CHANG, V25, P121, DOI 10.1016/j.gloenvcha.2013.11.004
   Lazaro LLB, 2021, J CLEAN PROD, V303, DOI 10.1016/j.jclepro.2021.127102
   Le Page Y, 2013, ENVIRON RES LETT, V8, DOI 10.1088/1748-9326/8/1/015018
   Li Q, 2012, CATENA, V99, P9, DOI 10.1016/j.catena.2012.07.005
   Li YY, 2018, LAND DEGRAD DEV, V29, P1652, DOI 10.1002/ldr.2929
   Liao HC, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10010166
   Liu XQ, 2021, SCI TOTAL ENVIRON, V755, DOI 10.1016/j.scitotenv.2020.142415
   Liu YS, 2021, LAND USE POLICY, V109, DOI 10.1016/j.landusepol.2021.105699
   Liu YQ, 2019, ECOL INDIC, V96, P23, DOI 10.1016/j.ecolind.2018.01.029
   Long HL, 2021, LAND-BASEL, V10, DOI 10.3390/land10090903
   Long HL, 2018, J ENVIRON MANAGE, V209, P440, DOI 10.1016/j.jenvman.2017.12.047
   Lu D, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16224329
   Galeana-Pizana JM, 2018, LAND USE POLICY, V76, P442, DOI 10.1016/j.landusepol.2018.02.022
   Mertz O, 2009, ENVIRON MANAGE, V43, P804, DOI 10.1007/s00267-008-9197-0
   Moore N, 2012, CLIMATIC CHANGE, V110, P823, DOI 10.1007/s10584-011-0116-7
   Mulwa CK, 2021, AGR ECON-BLACKWELL, V52, P329, DOI 10.1111/agec.12621
   Murdiyarso D, 2000, ENVIRON MONIT ASSESS, V61, P123, DOI 10.1023/A:1006326404156
   Mutiibwa D, 2018, COMPUT ELECTRON AGR, V154, P54, DOI 10.1016/j.compag.2018.08.026
   Mwambo FM, 2020, LAND USE POLICY, V95, DOI 10.1016/j.landusepol.2020.104490
   Nobre CA, 2016, P NATL ACAD SCI USA, V113, P10759, DOI 10.1073/pnas.1605516113
   Pan T, 2019, SCI TOTAL ENVIRON, V659, P314, DOI 10.1016/j.scitotenv.2018.12.345
   Paoloni L, 2014, LAW DEV REV, V7, P369, DOI 10.1515/ldr-2014-0011
   Popp A, 2017, GLOBAL ENVIRON CHANG, V42, P331, DOI 10.1016/j.gloenvcha.2016.10.002
   Pravalie R, 2021, ENVIRON RES, V194, DOI 10.1016/j.envres.2020.110697
   Price Derek de S., 1963, LITTLE SCI BIG SCI
   Prokop P, 2020, REMOTE SENS APPL, V20, DOI 10.1016/j.rsase.2020.100432
   Qi XX, 2019, HABITAT INT, V94, DOI 10.1016/j.habitatint.2019.102064
   Qiu TW, 2020, LAND USE POLICY, V90, DOI 10.1016/j.landusepol.2019.104311
   Ranasinghe TKGP, 2020, J NATL SCI FOUND SRI, V48, P379, DOI 10.4038/jnsfsr.v48i4.8814
   Ranjan R, 2021, LAND USE POLICY, V108, DOI 10.1016/j.landusepol.2021.105595
   Ren YJ, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12030453
   Renzaho AMN, 2017, RENEW SUST ENERG REV, V78, P503, DOI 10.1016/j.rser.2017.04.072
   Russo M, 2021, TECHNOL FORECAST SOC, V168, DOI 10.1016/j.techfore.2021.120708
   Saddique Q, 2020, EUR J AGRON, V119, DOI 10.1016/j.eja.2020.126113
   Santisteban JI, 2021, PALAEOGEOGR PALAEOCL, V562, DOI 10.1016/j.palaeo.2020.110153
   Schiefer J, 2016, AGR ECOSYST ENVIRON, V230, P283, DOI 10.1016/j.agee.2016.06.021
   Schlindwein SL, 2021, LAND USE POLICY, V107, DOI 10.1016/j.landusepol.2021.105519
   Seddon AWR, 2016, NATURE, V531, P229, DOI 10.1038/nature16986
   Serra-Majem L, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17238758
   Song XQ, 2012, J GEOGR SCI, V22, P1061, DOI 10.1007/s11442-012-0983-5
   Sporton D, 1999, J S AFR STUD, V25, P441, DOI 10.1080/030570799108605
   Struik PC, 2017, AGRON SUSTAIN DEV, V37, DOI 10.1007/s13593-017-0445-7
   Therville C, 2020, SUSTAIN SCI, V15, P1435, DOI 10.1007/s11625-020-00821-x
   Tian YS, 2009, APPL ENERG, V86, pS77, DOI 10.1016/j.apenergy.2009.06.007
   UNDESA, 2020, WORLD FAM PLANN
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Verburg PH, 2013, CURR OPIN ENV SUST, V5, P494, DOI 10.1016/j.cosust.2013.07.003
   Verburg PH, 2004, ENVIRON PLANN B, V31, P125, DOI 10.1068/b307
   Verburg PH, 2000, AGR ECOSYST ENVIRON, V82, P333, DOI 10.1016/S0167-8809(00)00236-X
   VLEK PLG, 1990, FERT RES, V26, P327, DOI 10.1007/BF01048771
   Wang C, 2019, ECON MODEL, V79, P186, DOI 10.1016/j.econmod.2018.10.012
   Wang JY, 2018, LAND USE POLICY, V74, P204, DOI 10.1016/j.landusepol.2017.11.037
   Wang LY, 2021, J ENVIRON MANAGE, V285, DOI 10.1016/j.jenvman.2021.112054
   Wang SJ, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12010247
   Wang XZ, 2021, PLANTS-BASEL, V10, DOI 10.3390/plants10051027
   Wang X, 2020, SCI TOTAL ENVIRON, V716, DOI 10.1016/j.scitotenv.2020.137119
   Wezel A, 2015, AGRON SUSTAIN DEV, V35, P1283, DOI 10.1007/s13593-015-0333-y
   Wicker RJ, 2021, CHEM ENG J, V415, DOI 10.1016/j.cej.2021.128932
   Wilhelm JA, 2018, RENEW AGR FOOD SYST, V33, P481, DOI 10.1017/S1742170517000205
   Willy DK, 2019, LAND USE POLICY, V81, P100, DOI [10.1016/j.landusepol.2018.10.046, 10.1064/j.landusepol.2018.10.046]
   Wilts R, 2021, CLIM RISK MANAG, V33, DOI 10.1016/j.crm.2021.100330
   Yan HM, 2009, GLOBAL PLANET CHANGE, V67, P13, DOI 10.1016/j.gloplacha.2008.12.012
   Yawson DO, 2021, LAND USE POLICY, V101, DOI 10.1016/j.landusepol.2020.105132
   Yawson DO, 2017, LAND-BASEL, V6, DOI 10.3390/land6040074
   Youssef A, 2020, J URBAN PLAN DEV, V146, DOI 10.1061/(ASCE)UP.1943-5444.0000623
   Zhang Boya, 2018, Biodiversity Science, V26, P1243, DOI 10.17520/biods.2018117
   Zhang Y, 2020, COMPLEXITY, V2020, DOI 10.1155/2020/6139158
   Zhong TY, 2021, LAND-BASEL, V10, DOI 10.3390/land10101090
   Zhou Y, 2020, LAND USE POLICY, V91, DOI 10.1016/j.landusepol.2019.104330
   Zhou Y, 2020, LAND USE POLICY, V91, DOI 10.1016/j.landusepol.2019.104379
   Zimmermann A., 2018, TRADE FOOD SECURITY
NR 121
TC 5
Z9 5
U1 68
U2 84
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD DEC
PY 2021
VL 18
IS 24
AR 13065
DI 10.3390/ijerph182413065
PG 22
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA YA2XU
UT WOS:000738203200001
PM 34948674
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Gao, MZ
   Zhang, H
   Wang, CL
   Mou, XY
   Zhu, QJ
   Wang, JQ
   Gao, DM
AF Gao, Mingzhou
   Zhang, Hao
   Wang, Changlin
   Mou, Xiangyu
   Zhu, Qingjun
   Wang, Jieqiong
   Gao, Dongmei
TI Top 100 Cited Papers on Premenstrual Syndrome/Premenstrual Dysphoric
   Disorder: A Bibliometric Study
SO FRONTIERS IN PSYCHIATRY
LA English
DT Review
DE bibliometric analysis; citations; premenstrual syndrome; trends; PMDD
ID BIPOLAR DISORDER; MENSTRUAL-CYCLE; PREVALENCE; WOMEN; PREDICTORS;
   MANAGEMENT; UNIVERSITY; EFFICACY; BURDEN; SAMPLE
AB BackgroundPremenstrual syndrome/premenstrual dysphoric disorder is a serious condition affecting women worldwide, causing clinically significant distress or interference. Therefore, solving these diseases has become the utmost concern worldwide, culminating in numerous studies. In this study, we performed bibliometric analysis on the 100 most cited papers with the aim of identifying research hot spots and trends in this field. MethodsWe screened the Science Citation Index Expanded (SCIE) of Web of Science (WOS) to identify the top 100 cited studies on PMS/PMDD. Next, we analyzed relevant literature from various journals, countries/regions, institutions, authors, and keywords. Finally, we used VOSviewer and Citespace software to generate knowledge maps and identify hot spots and trends. ResultsThe top 100 highly cited studies were published in 55 journals, between 1999 and 2017, across 24 countries/regions around the world. Most articles were published in Obstetrics and Gynecology, whereas Psych neuroendocrinology had the largest average number of citations per paper. The United States had the highest number of publications, followed by England, Canada, and Sweden. The top three institutions that published the highly cited literature were the University of Pennsylvania, Yale University, and National Institute of Mental Health (NIMH). Obstetrics, Gynecology, Psychiatry, and Reproductive Biology were the main research directions, whereas the top 10 Co-occurrence of Keywords included double-blind, fluoxetine, efficacy, prevalence, epidemiology, phase sertraline treatment, depression, progesterone, placebo, and placebo-controlled trial. Results from cluster analysis indicated that more comprehensive epidemiology and steroid pathogenesis have gradually become the hot spots and trends. ConclusionThese findings demonstrated that bibliometric analysis can intuitively and rapidly reveal the frontiers and hot spots of research in PMS/PMDD. Notably, epidemiology, steroid pathogenesis, GABAA receptor delta subunits, and double-blind placebo-controlled trials are potential areas of focus for future research.
C1 [Gao, Mingzhou; Zhu, Qingjun] Shandong Univ Tradit Chinese Med, Innovat Res Inst Tradit Chinese Med, Team Res & Innovat Focusing Emot Dis & Syndromes, Jinan, Peoples R China.
   [Zhang, Hao] Shandong Univ Tradit Chinese Med, Expt Ctr, Jinan, Peoples R China.
   [Wang, Changlin; Mou, Xiangyu; Gao, Dongmei] Shandong Univ Tradit Chinese Med, Coll Tradit Chinese Med, Jinan, Peoples R China.
   [Wang, Jieqiong] Shandong Univ Tradit Chinese Med, Sch Pharm, Jinan, Peoples R China.
RP Gao, DM (通讯作者)，Shandong Univ Tradit Chinese Med, Coll Tradit Chinese Med, Jinan, Peoples R China.
EM gcy_112@163.com
FU Key Project of Natural Science Foundation of Shandong Province
   [ZR2020ZD17]; National Natural Science Foundation of China [81001484,
   81473558]; Natural Science Foundation of Shandong Province
   [ZR202102270167]; Shandong medical and health science and technology
   development plan project [202105010467]; Jinan [2020GXRC002]
FX This study was supported by funds from the Key Project of Natural
   Science Foundation of Shandong Province (No. ZR2020ZD17), National
   Natural Science Foundation of China (Nos. 81001484 and 81473558),
   Natural Science Foundation of Shandong Province (No. ZR202102270167),
   Shandong medical and health science and technology development plan
   project (No. 202105010467) and 20 articles for colleges and universities
   funded project in Jinan (No. 2020GXRC002).
CR Ahmad P, 2021, PERIODONTOL 2000, V85, P237, DOI 10.1111/prd.12376
   Albsoul-Younes A, 2018, PERSPECT PSYCHIATR C, V54, P348, DOI 10.1111/ppc.12252
   Barki M, 2022, GENE, V809, DOI 10.1016/j.gene.2021.146021
   Bixo M, 2018, J NEUROENDOCRINOL, V30, DOI 10.1111/jne.12553
   Cerqueira RO, 2017, ARCH WOMEN MENT HLTH, V20, P713, DOI 10.1007/s00737-017-0791-0
   Cirillo PC, 2012, REV BRAS PSIQUIATR, V34, P467, DOI 10.1016/j.rbp.2012.04.010
   Cohen LS, 2002, J AFFECT DISORDERS, V70, P125, DOI 10.1016/S0165-0327(01)00458-X
   Di Florio A, 2018, DEPRESS ANXIETY, V35, P1168, DOI 10.1002/da.22827
   Di Giulio G, 2006, J PSYCHOSOM OBST GYN, V27, P201, DOI 10.1080/01674820600747269
   Dimmock PW, 2000, LANCET, V356, P1131, DOI 10.1016/S0140-6736(00)02754-9
   El Dahr Y, 2022, ARCH WOMEN MENT HLTH, V25, P345, DOI 10.1007/s00737-022-01220-0
   Eldeeb SM, 2021, AFR J PRIM HEALTH CA, V13, DOI 10.4102/phcfm.v13i1.2924
   Epperson CN, 2012, AM J PSYCHIAT, V169, P465, DOI 10.1176/appi.ajp.2012.11081302
   Epperson CN, 2002, ARCH GEN PSYCHIAT, V59, P851, DOI 10.1001/archpsyc.59.9.851
   Gao MZ, 2021, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.596128
   Gehlert S, 2009, PSYCHOL MED, V39, P129, DOI 10.1017/S003329170800322X
   Golding JM, 2000, J PSYCHOSOM OBST GYN, V21, P69, DOI 10.3109/01674820009075612
   Halbreich U, 2003, PSYCHONEUROENDOCRINO, V28, P1, DOI 10.1016/S0306-4530(03)00098-2
   Halbreich U, 2007, GYNECOL ENDOCRINOL, V23, P123, DOI 10.1080/09513590601167969
   Halbreich U, 2006, CNS DRUGS, V20, P523, DOI 10.2165/00023210-200620070-00001
   Hantsoo L, 2020, NEUROBIOL STRESS, V12, DOI 10.1016/j.ynstr.2020.100213
   Hofmeister S, 2016, AM FAM PHYSICIAN, V94, P236
   Kenda M, 2021, MOLECULES, V26, DOI 10.3390/molecules26247421
   Liu YH, 2021, FRONT ONCOL, V11, DOI 10.3389/fonc.2021.795568
   Lu DH, 2022, JAMA NETW OPEN, V5, DOI 10.1001/jamanetworkopen.2022.1256
   Mishra S., 2022, PREMENSTRUAL DYSPHOR
   MORTON JH, 1950, AM J OBSTET GYNECOL, V60, P343, DOI 10.1016/0002-9378(50)90475-3
   National Institute of Diabetes and Digestive and Kidney Diseases, 2012, LIVERTOX CLIN RES IN
   O'Brien PMS, 1999, AM J OBSTET GYNECOL, V180, P18, DOI 10.1016/S0002-9378(99)70142-0
   Pereira D, 2022, ARCH WOMEN MENT HLTH, V25, P61, DOI 10.1007/s00737-021-01177-6
   Potter J, 2009, J WOMENS HEALTH, V18, P31, DOI 10.1089/jwh.2008.0932
   Rezende APR, 2022, REV BRAS GINECOL OBS, V44, P133, DOI 10.1055/s-0041-1741456
   Schmidt PJ, 2017, AM J PSYCHIAT, V174, P980, DOI 10.1176/appi.ajp.2017.16101113
   Sepede G, 2020, NEUROPSYCH DIS TREAT, V16, P415, DOI 10.2147/NDT.S202881
   Slyepchenko A, 2017, ACTA PSYCHIAT SCAND, V136, P473, DOI 10.1111/acps.12797
   Slyepchenko A, 2021, FRONT PSYCHIATRY, V12, DOI 10.3389/fpsyt.2021.719241
   Smith SS, 2007, PHARMACOL THERAPEUT, V116, P58, DOI 10.1016/j.pharmthera.2007.03.008
   Sveindottir H, 2000, ACTA OBSTET GYN SCAN, V79, P405, DOI 10.1034/j.1600-0412.2000.079005405.x
   Takeda T, 2006, ARCH WOMEN MENT HLTH, V9, P209, DOI 10.1007/s00737-006-0137-9
   Timby E, 2016, PSYCHOPHARMACOLOGY, V233, P2109, DOI 10.1007/s00213-016-4258-1
   Tolossa FW, 2014, BMC WOMENS HEALTH, V14, DOI 10.1186/1472-6874-14-52
   Tschudin S, 2010, ARCH WOMEN MENT HLTH, V13, P485, DOI 10.1007/s00737-010-0165-3
   Ullah A, 2020, FRONT PSYCHIATRY, V11, DOI 10.3389/fpsyt.2020.00572
   Wikman A, 2022, BMC WOMENS HEALTH, V22, DOI 10.1186/s12905-022-01612-5
   Wittchen HU, 2002, PSYCHOL MED, V32, P119, DOI 10.1017/S0033291701004925
   Yan HH, 2021, J AFFECT DISORDERS, V295, P339, DOI 10.1016/j.jad.2021.08.082
   Yang M, 2008, J WOMENS HEALTH, V17, P113, DOI 10.1089/jwh.2007.0417
   Yang Q, 2022, BMC MED, V20, DOI 10.1186/s12916-022-02275-7
   Yonkers KA, 2005, OBSTET GYNECOL, V106, P492, DOI 10.1097/01.AOG.0000175834.77215.2e
   Yonkers KA, 2018, AM J OBSTET GYNECOL, V218, P68, DOI 10.1016/j.ajog.2017.05.045
   Younes Y, 2021, BMC PSYCHIATRY, V21, DOI 10.1186/s12888-021-03567-7
NR 51
TC 0
Z9 0
U1 1
U2 1
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-0640
J9 FRONT PSYCHIATRY
JI Front. Psychiatry
PD JUL 14
PY 2022
VL 13
AR 936009
DI 10.3389/fpsyt.2022.936009
PG 15
WC Psychiatry
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Psychiatry
GA 3K3IK
UT WOS:000833972900001
PM 35911247
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Mitchell, SM
   Seamans, CB
AF Mitchell, Susan M.
   Seamans, Carolyn B.
TI Could removal of project-level knowledge flow obstacles contribute to
   software process improvement? A study of software engineer perceptions
SO INFORMATION AND SOFTWARE TECHNOLOGY
LA English
DT Article
DE Software process; Software process improvement; SPI; Knowledge
   management; Knowledge flow (K-flow); Software engineering
ID MANAGEMENT
AB Context: Software process improvement (SPI) is one type of innovation often formulated to address problems such as uncontrollable costs, schedule overruns, and poor end product quality. This study investigates SPI through the application of knowledge management (MM) at the software project level, viewing MM, when applied in software development, as complementary to SPI.
   Objective: This study advances the use of MM in SPI by investigating impediments to the use and flow of knowledge within an individual software development project. Our proposition is that the removal of obstacles to project-level knowledge flow (K-flow) will enhance SPI. We investigate this proposition by exploring the problems associated with project K-flows as seen by software project team members.
   Method: We conducted a descriptive case study of an industrial software development project, in which we collected data concerning project knowledge sources and K-flows using semi-structured interviews. Using this data, we constructed a diagnostic project knowledge map (K-map). The K-map was analyzed to identify K-flow obstacles and potential solutions. Questionnaires based on these obstacles and solutions were formulated to probe software engineers' perceptions of the effect of the solutions on SPI.
   Results: Findings from participant questionnaires reveal that software engineers perceive that the removal or mitigation of project-level K-flow obstacles generally reduces the time to perform their work, helps them to meet their deadlines, and improves their work quality, thus resulting in SPI.
   Conclusions: This study provides support for the usefulness of project-level K-flow obstacle removal for SPI. It provides a unique project-level perspective, using input from the project's software engineers. It also explains and supports the use of K-mapping for the identification of project-level K-flow obstacles. With this approach, practitioners gain insight into SPI in "real time" as a project is executed. These insights may help to enhance their current and future SPI efforts. (C) 2015 Elsevier B.V. All rights reserved.
C1 [Mitchell, Susan M.; Seamans, Carolyn B.] Univ Maryland Baltimore Cty, Dept Informat Syst, 1000 Hilltop Circle, Baltimore, MD 21250 USA.
RP Mitchell, SM (通讯作者)，Univ Maryland Baltimore Cty, Dept Informat Syst, 1000 Hilltop Circle, Baltimore, MD 21250 USA.
EM susan.mitchell@comcast.net; cseaman@umbc.edu
CR [Anonymous], 2013, TOT QUAL MAN KEY BUS
   Arent J., 2000, 33 HAW INT C SYST SC
   BASILI VR, 1994, ENCY SOFTWARE ENG, V1, P469, DOI DOI 10.1002/0471028959.SOF110
   Bjornson FO, 2008, INFORM SOFTWARE TECH, V50, P1055, DOI 10.1016/j.infsof.2008.03.006
   Chrissis M. B., 2011, CMMI GUIDELINES PROC
   Cockburn A., 2006, AGILE SOFTWARE DEV C
   Curtis B., 2010, PEOPLE CMM FRAMEWORK
   Davenport TH., 1998, WORKING KNOWLEDGE OR
   DEBENHAM J, 1994, ROBOT CIM-INT MANUF, V11, P201, DOI 10.1016/0736-5845(94)90035-3
   Denzin N.K., 2017, RES ACT THEORETICAL
   EHMS K, 2002, HOLISTIC DEV KNOWLED
   Eppler M.J., 2006, INFORM VISUAL, V5, P202, DOI DOI 10.1057/PALGRAVE.IVS.9500131
   Hansen B, 2004, INT J INFORM MANAGE, V24, P457, DOI 10.1016/j.ijinfomgt.2004.08.007
   Hansen BH, 2004, LECT NOTES COMPUT SC, V3281, P126
   Ishikawa K, 1986, GUIDE QUALITY CONTRO
   LANZARA GF, 1985, INFORM MANAGE, V8, P3, DOI 10.1016/0378-7206(85)90065-5
   Liebowitz J., 2008, MAKING CENTS OIL KNO
   Lincoln Y.S., 1985, NATURALISTIC INQUIRY
   Long J.O., 2012, ITIL 2011 GLANCE
   Merriam S.B., 2015, QUALITATIVE RES GUID
   Mitchell S.M., 2012, SOFTWARE PROCESS IMP
   Mitchell SM, 2012, PROC INT CONF SOFTW, P1265, DOI 10.1109/ICSE.2012.6227103
   Mitchell SM, 2011, INT SYMP EMP SOFTWAR, P347, DOI 10.1109/ESEM.2011.45
   Monk A, 1998, INTERACTIONS, V5, P21, DOI DOI 10.1145/274430.274434
   Nissen M.E., 2006, HARNESSING KNOWLEDGE
   NISSEN ME, 2002, COMMUNICATIONS ASS I, V8, P251
   Nonaka I., 1995, KNOWLEDGE CREATING C
   Petrash G., 1996, KNOWL BAS LEAD C
   Platt L.E., 1996, COMPETING KNOWLEDG S
   Rodriguez-Elias OM, 2009, INT J SOFTW ENG KNOW, V19, P185, DOI 10.1142/S0218194009004155
   Royce W., 1970, IEEE WESCON LOS ANG
   Salo O, 2005, LECT NOTES ARTIF INT, V3782, P106
   Sommerville I., 2015, SOFTWARE ENG
   Sy T., 2004, Journal of Management Development, V23, P437, DOI 10.1108/02621710410537056
   Taylor SJ, 2015, INTRO QUALITATIVE RE
   Wiig KM, 1997, EXPERT SYST APPL, V13, P1, DOI 10.1016/S0957-4174(97)00018-3
   Yin K.R., 2013, CASE STUDY RES DESIG
NR 37
TC 12
Z9 12
U1 3
U2 10
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0950-5849
EI 1873-6025
J9 INFORM SOFTWARE TECH
JI Inf. Softw. Technol.
PD APR
PY 2016
VL 72
BP 151
EP 170
DI 10.1016/j.infsof.2015.12.007
PG 20
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DX9HP
UT WOS:000384704500010
DA 2022-08-23
ER

PT J
AU Wang, YC
   Zhao, FK
   Liu, Q
   Yu, ZY
   Wang, J
   Zhang, JS
AF Wang, Yu-Chi
   Zhao, Fang-Kun
   Liu, Qian
   Yu, Zi-Yan
   Wang, Jing
   Zhang, Jin-Song
TI Bibliometric analysis and mapping knowledge domain of pterygium:
   2000-2019
SO INTERNATIONAL JOURNAL OF OPHTHALMOLOGY
LA English
DT Article
DE pterygium; bibliometric analysis; mapping knowledge domain; VOSviewer
ID LIMBAL-CONJUNCTIVAL AUTOGRAFT; ENDOTHELIAL GROWTH-FACTOR; AMNIOTIC
   MEMBRANE; MITOMYCIN-C; DRY EYE; TRANSPLANTATION; EXPRESSION;
   FIBROBLASTS; OVEREXPRESSION; EXCISION
AB AIM: To track the knowledge structure, topics in focus, and trends in emerging research in pterygium in the past 20y.
   METHODS: Base on the Web of Science Core Collection (WoSCC), studies related to pterygium in the past 20y from 2000-2019 have been included. With the help of VOSviewer software, a knowledge map was constructed and the distribution of countries, institutions, journals, and authors in the field of pterygium noted. Meanwhile, using cocitation analysis of references and co-occurrence analysis of keywords, we identified basis and hotspots, thereby obtaining an overview of this field.
   RESULTS: The search retrieved 1516 publications from WoSCC on pterygium published between 2000 and 2019. In the past two decades, the annual number of publications is on the rise and fluctuated a little. Most productive institutions are from Singapore but the most prolific and active country is the United States. Journal Cornea published the most articles and Coroneo MT contributed the most publications on pterygium. From cooccurrence analysis, the keywords formed 3 clusters: 1) surgical therapeutic techniques and adjuvant of pterygium, and 3) epidemiology, and etiology of pterygium formation. These three clusters were consistent with the clustering in co-citation analysis, in which Cluster 1 contained the most references (74 publications, 47.74%), Cluster 2 contained 53 publications, accounting for 34.19%, and Cluster 3 focused on epidemiology with 18.06% of total 155 cocitation publications.
   CONCLUSION: This study demonstrates that the research of pterygium is gradually attracting the attention of scholars and researchers. The interaction between authors, institutions, and countries is lack of. Even though, the research hotspot, distribution, and research status in pterygium in this study could provide valuable information for scholars and researchers.
C1 [Wang, Yu-Chi; Zhao, Fang-Kun; Liu, Qian; Yu, Zi-Yan; Zhang, Jin-Song] China Med Univ, Eye Hosp, Affiliated Hosp 4, Dept Ophthalmol,Key Lab Lens Liaoning Prov, 11 Xinhua Rd, Shenyang 110000, Liaoning, Peoples R China.
   [Wang, Jing; Zhang, Jin-Song] Shenyang Aier Excellent Eye Hosp, Cataract Ctr, Shenyang 110000, Liaoning, Peoples R China.
   [Wang, Jing; Zhang, Jin-Song] Cent South Univ, Aier Sch Ophthalmol, Changsha 410000, Hunan, Peoples R China.
RP Wang, J; Zhang, JS (通讯作者)，China Med Univ, Eye Hosp, Affiliated Hosp 4, Dept Ophthalmol,Key Lab Lens Liaoning Prov, 11 Xinhua Rd, Shenyang 110000, Liaoning, Peoples R China.
EM wangjing@cmu.edu.cn; cmu4h-zjs@126.com
FU National Natural Science Foundation of China [81870644]
FX Supported by the National Natural Science Foundation of China
   (No.81870644).
CR Amano Tatsuya, 2016, PLoS Biol, V14, pe2000933, DOI 10.1371/journal.pbio.2000933
   Aspiotis M, 2007, EYE, V21, P1095, DOI 10.1038/sj.eye.6702495
   Bikbov MM, 2019, AM J OPHTHALMOL, V205, P27, DOI 10.1016/j.ajo.2019.02.031
   Boudry C, 2018, OCUL SURF, V16, P173, DOI 10.1016/j.jtos.2017.10.002
   Chalkia AK, 2019, INT J ONCOL, V54, P1503, DOI 10.3892/ijo.2019.4755
   CHEN PP, 1995, AM J OPHTHALMOL, V120, P151, DOI 10.1016/S0002-9394(14)72602-9
   Chen YH, 2020, PEERJ, V8, DOI 10.7717/peerj.9056
   Chui J, 2011, AM J PATHOL, V178, P817, DOI 10.1016/j.ajpath.2010.10.037
   Clearfield E, 2017, AM J OPHTHALMOL, V182, P8, DOI 10.1016/j.ajo.2017.07.004
   Cui YH, 2020, J CELL PHYSIOL, V235, P5130, DOI 10.1002/jcp.29387
   Di Girolamo N, 2004, PROG RETIN EYE RES, V23, P195, DOI 10.1016/j.preteyeres.2004.02.002
   Di Girolamo N, 2002, INVEST OPHTH VIS SCI, V43, P3430
   Martins TGD, 2016, INT J OPHTHALMOL-CHI, V9, P465, DOI 10.18240/ijo.2016.03.25
   Dushku N, 1997, CURR EYE RES, V16, P1179, DOI 10.1076/ceyr.16.12.1179.5036
   Elgouhary SM, 2020, INT OPHTHALMOL, V40, P2593, DOI 10.1007/s10792-020-01440-2
   Engelsvold DH, 2013, EXP EYE RES, V115, P189, DOI 10.1016/j.exer.2013.07.003
   Garg P, 2019, INDIAN J OPHTHALMOL, V67, P1036, DOI 10.4103/ijo.IJO_1921_18
   Ghoz N, 2019, EYE, V33, P1776, DOI 10.1038/s41433-019-0493-0
   Gris O, 2000, OPHTHALMOLOGY, V107, P270, DOI 10.1016/S0161-6420(99)00041-X
   Gumus K, 2014, BRIT J OPHTHALMOL, V98, P796, DOI 10.1136/bjophthalmol-2012-301944
   Hernandez-Bogantes E, 2015, BRIT J OPHTHALMOL, V99, P1598, DOI 10.1136/bjophthalmol-2015-306857
   Josifovska N, 2017, CONTACT LENS ANTERIO, V40, P283, DOI 10.1016/j.clae.2017.04.002
   Kam KW, 2019, GRAEF ARCH CLIN EXP, V257, P2683, DOI 10.1007/s00417-019-04499-5
   Kim KW, 2017, EXP EYE RES, V164, P74, DOI 10.1016/j.exer.2017.08.008
   Kim KW, 2016, EXP EYE RES, V142, P32, DOI 10.1016/j.exer.2015.01.010
   Lee SB, 2000, CURR EYE RES, V20, P325, DOI 10.1076/0271-3683(200004)20:4;1-5;FT325
   Li XM, 2018, CELL PHYSIOL BIOCHEM, V47, P1630, DOI 10.1159/000490982
   Meng QL, 2017, INVEST OPHTH VIS SCI, V58, P4514, DOI 10.1167/iovs.17-21974
   Notara M, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-30021-8
   Ozer A, 2009, OPHTHALMOLOGICA, V223, P269, DOI 10.1159/000210444
   Ozturk BT, 2017, EYE, V31, P491, DOI 10.1038/eye.2016.254
   Rahman I, 2009, EYE, V23, P1954, DOI 10.1038/eye.2008.410
   Rezvan F, 2018, SURV OPHTHALMOL, V63, P719, DOI 10.1016/j.survophthal.2018.03.001
   Rim TH, 2017, BMJ OPEN, V7, DOI 10.1136/bmjopen-2017-017014
   Romano V, 2016, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD011308.pub2
   Schargus M, 2018, OCUL SURF, V16, P180, DOI 10.1016/j.jtos.2017.09.006
   Tsai RJ, 2000, NEW ENGL J MED, V343, P86, DOI 10.1056/NEJM200007133430202
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Viso E, 2011, EYE, V25, P350, DOI 10.1038/eye.2010.204
   Wu HP, 2017, SCI REP-UK, V7, DOI 10.1038/srep45115
   Wu ML, 2017, BIOMED PHARMACOTHER, V93, P609, DOI 10.1016/j.biopha.2017.06.090
   Yang QC, 2019, J CELL BIOCHEM, V120, P4203, DOI 10.1002/jcb.27707
   Yoshitomi F, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-35121-z
   Young AL, 2013, OPHTHALMOLOGY, V120, P2390, DOI 10.1016/j.ophtha.2013.05.033
   Zeleznik D, 2017, J ADV NURS, V73, P2407, DOI 10.1111/jan.13296
   Zhao FK, 2019, PEERJ, V7, DOI 10.7717/peerj.7603
   Zhao FK, 2019, CORNEA, V38, P847, DOI 10.1097/ICO.0000000000001984
   Zou X, 2018, ACCIDENT ANAL PREV, V118, P131, DOI 10.1016/j.aap.2018.06.010
NR 48
TC 2
Z9 2
U1 2
U2 12
PU IJO PRESS
PI XI AN
PA NO 269 YOUYI EAST RD, XI AN, 710054, PEOPLES R CHINA
SN 2222-3959
EI 2227-4898
J9 INT J OPHTHALMOL-CHI
JI Int. J. Ophthalmol.
PD JUN 18
PY 2021
VL 14
IS 6
BP 903
EP 914
DI 10.18240/ijo.2021.06.17
PG 12
WC Ophthalmology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Ophthalmology
GA SM1RO
UT WOS:000657389200017
PM 34150547
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Guo, MG
   Sosa, DN
   Altman, RB
AF Guo, Margaret G.
   Sosa, Daniel N.
   Altman, Russ B.
TI Challenges and opportunities in network-based solutions for biological
   questions
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Article
DE networks; knowledge graphs; embeddings; interpretability; biological
   validation
ID REGULATORY NETWORKS; INFERENCE; MODEL; INTERACTOME; DISEASE
AB Network biology is useful for modeling complex biological phenomena; it has attracted attention with the advent of novel graph-based machine learning methods. However, biological applications of network methods often suffer from inadequate follow-up. In this perspective, we discuss obstacles for contemporary network approaches-particularly focusing on challenges representing biological concepts, applying machine learning methods, and interpreting and validating computational findings about biology-in an effort to catalyze actionable biological discovery.
C1 [Guo, Margaret G.; Sosa, Daniel N.] Stanford Univ, Biomed Informat, Stanford, CA 94305 USA.
   [Altman, Russ B.] Stanford Univ, Bioengn Genet Med Biomed Data Sci & Courtesy Comp, Stanford, CA 94305 USA.
RP Altman, RB (通讯作者)，Stanford Univ, Dept Bioengn, Stanford, CA 94305 USA.; Altman, RB (通讯作者)，Stanford Univ, Dept Genet, Stanford, CA 94305 USA.
EM russ.altman@stanford.edu
OI Altman, Russ/0000-0003-3859-2905; Guo, Margaret/0000-0001-7713-3846;
   Sosa, Daniel/0000-0001-7184-8736
FU National Institutes of Health [GM102365, LM007033, GM007365]
FX This work was supported by the National Institutes of Health (grant
   numbers GM102365, LM007033 and GM007365).
CR Aghamirzaie D, 2016, BMC GENOMICS, V17, DOI 10.1186/s12864-016-3172-6
   Aguet F, 2017, NATURE, V550, P204, DOI 10.1038/nature24277
   Albert R, 2007, PLANT CELL, V19, P3327, DOI 10.1105/tpc.107.054700
   Bader G, 2013, PATHGUIDE PATHWAY RE
   Bader GD, 2006, NUCLEIC ACIDS RES, V34, pD504, DOI 10.1093/nar/gkj126
   Beltrao P, 2010, CELL, V141, P739, DOI 10.1016/j.cell.2010.05.019
   Brasoveanu A, 2020, CEUR WORKSH P 33 C N, P2657
   Carrera J, 2015, TRENDS CELL BIOL, V25, P719, DOI 10.1016/j.tcb.2015.09.004
   Chai LE, 2014, COMPUT BIOL MED, V48, P55, DOI 10.1016/j.compbiomed.2014.02.011
   Chen L, 2018, FRONT PHYSIOL, V9, DOI 10.3389/fphys.2018.01328
   Covert MW, 2004, NATURE, V429, P92, DOI 10.1038/nature02456
   Das R., 2018, ICLR
   Davis CA, 2018, NUCLEIC ACIDS RES, V46, pD794, DOI 10.1093/nar/gkx1081
   Decalf J, 2019, J PATHOL, V247, P650, DOI 10.1002/path.5223
   Friedman N, 2000, J COMPUT BIOL, V7, P601, DOI 10.1089/106652700750050961
   Gebert J, 2007, EUR J OPER RES, V181, P1148, DOI 10.1016/j.ejor.2005.11.044
   Gillis J, 2014, J PROTEOMICS, V100, P44, DOI 10.1016/j.jprot.2014.01.020
   Greene CS, 2015, NAT GENET, V47, P569, DOI 10.1038/ng.3259
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Guven-Maiorov E, 2017, PLOS COMPUT BIOL, V13, DOI 10.1371/journal.pcbi.1005579
   Hamilton W.L., 2017, ARXIV170905584
   Hecker M, 2009, BIOSYSTEMS, V96, P86, DOI 10.1016/j.biosystems.2008.12.004
   Ideker T, 2017, PLOS COMPUT BIOL, V13, DOI 10.1371/journal.pcbi.1005771
   JK H, 2018, CELL SYST, V6
   Karr JR, 2012, CELL, V150, P389, DOI 10.1016/j.cell.2012.05.044
   KAUFFMAN SA, 1969, J THEOR BIOL, V22, P437, DOI 10.1016/0022-5193(69)90015-0
   Klein C, 2012, BRIEF FUNCT GENOMICS, V11, P420, DOI 10.1093/bfgp/els030
   Kohler S, 2008, AM J HUM GENET, V82, P949, DOI 10.1016/j.ajhg.2008.02.013
   Koutrouli M, 2020, FRONT BIOENG BIOTECH, V8, DOI 10.3389/fbioe.2020.00034
   Lee TI, 2002, SCIENCE, V298, P799, DOI 10.1126/science.1075090
   Lei XJ, 2016, INFORM SCIENCES, V329, P303, DOI 10.1016/j.ins.2015.09.028
   Li MZ, 2021, AAAI CONF ARTIF INTE, V35, P4189
   Lipton ZC, 2018, COMMUN ACM, V61, P36, DOI 10.1145/3233231
   Liu C, 2020, PHYS REP, V846, P1, DOI 10.1016/j.physrep.2019.12.004
   Maulik U, 2017, SCI REP-UK, V7, P1
   Mehla Jitender, 2015, Cold Spring Harb Protoc, V2015, P425, DOI 10.1101/pdb.top083345
   Menche J, 2015, SCIENCE, V347, DOI 10.1126/science.1257601
   NELDER JA, 1986, J R STAT SOC A STAT, V149, P109, DOI 10.2307/2981525
   Nelson W, 2019, FRONT GENET, V10, DOI 10.3389/fgene.2019.00381
   Niepert M, 2016, 33 INT C MACH LEARN, P4
   Pavlopoulos GA, 2011, BIODATA MIN, V4, DOI 10.1186/1756-0381-4-10
   Percha B, 2018, BIOINFORMATICS, V34, P2614, DOI 10.1093/bioinformatics/bty114
   Sang ST, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2167-5
   Schrynemackers Marie, 2013, Frontiers in Genetics, V4, P262, DOI 10.3389/fgene.2013.00262
   Schuster S, 2000, NAT BIOTECHNOL, V18, P326, DOI 10.1038/73786
   Schwab JD, 2020, COMPUT STRUCT BIOTEC, V18, P571, DOI 10.1016/j.csbj.2020.03.001
   Secrier M, 2014, BRIEF BIOINFORM, V15, P771, DOI 10.1093/bib/bbt021
   Sirota M, 2011, SCI TRANSL MED, V3, DOI 10.1126/scitranslmed.3001318
   Skinnider MA, 2018, PLOS COMPUT BIOL, V14, DOI 10.1371/journal.pcbi.1006474
   Song L, 2012, BMC BIOINFORMATICS, V13, DOI 10.1186/1471-2105-13-328
   Sosa DN, 2020, PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, P463
   Szklarczyk D, 2019, NUCLEIC ACIDS RES, V47, pD607, DOI 10.1093/nar/gky1131
   Tabe-Bordbar S, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-24937-4
   Tagkopoulos I, 2008, SCIENCE, V320, P1313, DOI 10.1126/science.1154456
   Teichmann SA, 2004, NAT GENET, V36, P492, DOI 10.1038/ng1340
   Terzer M, 2009, WIRES SYST BIOL MED, V1, P285, DOI 10.1002/wsbm.37
   Thorn Caroline F, 2013, Methods Mol Biol, V1015, P311, DOI 10.1007/978-1-62703-435-7_20
   Tomczak Katarzyna, 2015, Contemp Oncol (Pozn), V19, pA68, DOI 10.5114/wo.2014.47136
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Vidal M, 2011, CELL, V144, P986, DOI 10.1016/j.cell.2011.02.016
   Vohradsky J, 2001, FASEB J, V15, P846, DOI 10.1096/fj.00-0361com
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   West DB., 2012, VACCINE, V43, P1
   Yan JH, 2018, FRONT COMPUT SCI-CHI, V12, P55, DOI 10.1007/s11704-016-5228-9
   Zhao M, 2021, BRIEF BIOINFORM, V2021, P1
NR 65
TC 2
Z9 2
U1 3
U2 3
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1467-5463
EI 1477-4054
J9 BRIEF BIOINFORM
JI Brief. Bioinform.
PD JAN 17
PY 2022
VL 23
IS 1
AR bbab437
DI 10.1093/bib/bbab437
PG 4
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA ZK5AS
UT WOS:000763000800082
PM 34849568
OA Green Published, hybrid
DA 2022-08-23
ER

EF